%0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e59217 %T Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative %A Mandel,Hannah L %A Shah,Shruti N %A Bailey,L Charles %A Carton,Thomas %A Chen,Yu %A Esquenazi-Karonika,Shari %A Haendel,Melissa %A Hornig,Mady %A Kaushal,Rainu %A Oliveira,Carlos R %A Perlowski,Alice A %A Pfaff,Emily %A Rao,Suchitra %A Razzaghi,Hanieh %A Seibert,Elle %A Thomas,Gelise L %A Weiner,Mark G %A Thorpe,Lorna E %A Divers,Jasmin %A , %+ Department of Population Health, New York University Grossman School of Medicine, 180 Madison Avenue, New York, NY, 10016, United States, 1 732 314 1595, Hannah.Mandel@nyulangone.org %K COVID-19 %K SARS-CoV-2 %K Long COVID, post-acute COVID-19 syndrome %K electronic health records %K machine learning %K public health surveillance %K post-infection syndrome %K medical informatics %K electronic medical record %K electronic health record network %K electronic health record data %K clinical research network %K clinical data research network %K common data model %K digital health %K infection %K respiratory %K infectious %K epidemiological %K pandemic %D 2025 %7 5.3.2025 %9 Viewpoint %J J Med Internet Res %G English %X The benefits and challenges of electronic health records (EHRs) as data sources for clinical and epidemiologic research have been well described. However, several factors are important to consider when using EHR data to study novel, emerging, and multifaceted conditions such as postacute sequelae of SARS-CoV-2 infection or long COVID. In this article, we present opportunities and challenges of using EHR data to improve our understanding of long COVID, based on lessons learned from the National Institutes of Health (NIH)–funded RECOVER (REsearching COVID to Enhance Recovery) Initiative, and suggest steps to maximize the usefulness of EHR data when performing long COVID research. %M 40053748 %R 10.2196/59217 %U https://www.jmir.org/2025/1/e59217 %U https://doi.org/10.2196/59217 %U http://www.ncbi.nlm.nih.gov/pubmed/40053748 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e57457 %T Availability and Key Characteristics of National Early Warning Systems for Emerging Profiles of Antimicrobial Resistance in High-Income Countries: Systematic Review %A Iera,Jessica %A Isonne,Claudia %A Seghieri,Chiara %A Tavoschi,Lara %A Ceparano,Mariateresa %A Sciurti,Antonio %A D'Alisera,Alessia %A Sane Schepisi,Monica %A Migliara,Giuseppe %A Marzuillo,Carolina %A Villari,Paolo %A D'Ancona,Fortunato %A Baccolini,Valentina %K early warning system %K surveillance %K emerging AMR %K high-income countries %K antimicrobial resistance %D 2025 %7 15.1.2025 %9 %J JMIR Public Health Surveill %G English %X Background: The World Health Organization (WHO) recently advocated an urgent need for implementing national surveillance systems for the timely detection and reporting of emerging antimicrobial resistance (AMR). However, public information on the existing national early warning systems (EWSs) is often incomplete, and a comprehensive overview on this topic is currently lacking. Objective: This review aimed to map the availability of EWSs for emerging AMR in high-income countries and describe their main characteristics. Methods: A systematic review was performed on bibliographic databases, and a targeted search was conducted on national websites. Any article, report, or web page describing national EWSs in high-income countries was eligible for inclusion. EWSs were identified considering the emerging AMR-reporting WHO framework. Results: We identified 7 national EWSs from 72 high-income countries: 2 in the East Asia and Pacific Region (Australia and Japan), 3 in Europe and Central Asia (France, Sweden, and the United Kingdom), and 2 in North America (the United States and Canada). The systems were established quite recently; in most cases, they covered both community and hospital settings, but their main characteristics varied widely across countries in terms of the organization and microorganisms under surveillance, with also different definitions of emerging AMR and alert functioning. A formal system assessment was available only in Australia. Conclusions: A broader implementation and investment of national surveillance systems for the early detection of emerging AMR are still needed to establish EWSs in countries and regions lacking such capabilities. More standardized data collection and reporting are also advisable to improve cooperation on a global scale. Further research is required to provide an in-depth analysis of EWSs, as this study is limited to publicly available data in high-income countries. %R 10.2196/57457 %U https://publichealth.jmir.org/2025/1/e57457 %U https://doi.org/10.2196/57457 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 17 %N %P e56495 %T Nowcasting to Monitor Real-Time Mpox Trends During the 2022 Outbreak in New York City: Evaluation Using Reportable Disease Data Stratified by Race or Ethnicity %A Rohrer,Rebecca %A Wilson,Allegra %A Baumgartner,Jennifer %A Burton,Nicole %A Ortiz,Ray R %A Dorsinville,Alan %A Jones,Lucretia E %A Greene,Sharon K %K data quality %K epidemiology %K forecasting %K infectious disease %K morbidity and mortality trends %K mpox %K nowcasting %K public health practice %K surveillance %D 2025 %7 14.1.2025 %9 %J Online J Public Health Inform %G English %X Background: Applying nowcasting methods to partially accrued reportable disease data can help policymakers interpret recent epidemic trends despite data lags and quickly identify and remediate health inequities. During the 2022 mpox outbreak in New York City, we applied Nowcasting by Bayesian Smoothing (NobBS) to estimate recent cases, citywide and stratified by race or ethnicity (Black or African American, Hispanic or Latino, and White). However, in real time, it was unclear if the estimates were accurate. Objective: We evaluated the accuracy of estimated mpox case counts across a range of NobBS implementation options. Methods: We evaluated NobBS performance for New York City residents with a confirmed or probable mpox diagnosis or illness onset from July 8 through September 30, 2022, as compared with fully accrued cases. We used the exponentiated average log score (average score) to compare moving window lengths, stratifying or not by race or ethnicity, diagnosis and onset dates, and daily and weekly aggregation. Results: During the study period, 3305 New York City residents were diagnosed with mpox (median 4, IQR 3-5 days from diagnosis to diagnosis report). Of these, 812 (25%) had missing onset dates, and of these, 230 (28%) had unknown race or ethnicity. The median lag in days from onset to onset report was 10 (IQR 7-14). For daily hindcasts by diagnosis date, the average score was 0.27 for the 14-day moving window used in real time. Average scores improved (increased) with longer moving windows (maximum: 0.47 for 49-day window). Stratifying by race or ethnicity improved performance, with an overall average score of 0.38 for the 14-day moving window (maximum: 0.57 for 49 day-window). Hindcasts for White patients performed best, with average scores of 0.45 for the 14-day window and 0.75 for the 49-day window. For unstratified, daily hindcasts by onset date, the average score ranged from 0.16 for the 42-day window to 0.30 for the 14-day window. Performance was not improved by weekly aggregation. Hindcasts underestimated diagnoses in early August after the epidemic peaked, then overestimated diagnoses in late August as the epidemic waned. Estimates were most accurate during September when cases were low and stable. Conclusions: Performance was better when hindcasting by diagnosis date than by onset date, consistent with shorter lags and higher completeness for diagnoses. For daily hindcasts by diagnosis date, longer moving windows performed better, but direct comparisons are limited because longer windows could only be assessed after case counts in this outbreak had stabilized. Stratification by race or ethnicity improved performance and identified differences in epidemic trends across patient groups. Contributors to differences in performance across strata might include differences in case volume, epidemic trends, delay distributions, and interview success rates. Health departments need reliable nowcasting and rapid evaluation tools, particularly to promote health equity by ensuring accurate estimates within all strata. %R 10.2196/56495 %U https://ojphi.jmir.org/2025/1/e56495 %U https://doi.org/10.2196/56495 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e60022 %T Strategies to Increase Response Rate and Reduce Nonresponse Bias in Population Health Research: Analysis of a Series of Randomized Controlled Experiments during a Large COVID-19 Study %A Atchison,Christina J %A Gilby,Nicholas %A Pantelidou,Galini %A Clemens,Sam %A Pickering,Kevin %A Chadeau-Hyam,Marc %A Ashby,Deborah %A Barclay,Wendy S %A Cooke,Graham S %A Darzi,Ara %A Riley,Steven %A Donnelly,Christl A %A Ward,Helen %A Elliott,Paul %K study recruitment %K response rate %K population-based research %K COVID-19 %K SARS-CoV-2 %K web-based questionnaires %D 2025 %7 9.1.2025 %9 %J JMIR Public Health Surveill %G English %X Background: High response rates are needed in population-based studies, as nonresponse reduces effective sample size and bias affects accuracy and decreases the generalizability of the study findings. Objective: We tested different strategies to improve response rate and reduce nonresponse bias in a national population–based COVID-19 surveillance program in England, United Kingdom. Methods: Over 19 rounds, a random sample of individuals aged 5 years and older from the general population in England were invited by mail to complete a web-based questionnaire and return a swab for SARS-CoV-2 testing. We carried out several nested randomized controlled experiments to measure the impact on response rates of different interventions, including (1) variations in invitation and reminder letters and SMS text messages and (2) the offer of a conditional monetary incentive to return a swab, reporting absolute changes in response and relative response rate (95% CIs). Results: Monetary incentives increased the response rate (completed swabs returned as a proportion of the number of individuals invited) across all age groups, sex at birth, and area deprivation with the biggest increase among the lowest responders, namely teenagers and young adults and those living in more deprived areas. With no monetary incentive, the response rate was 3.4% in participants aged 18‐22 years, increasing to 8.1% with a £10 (US $12.5) incentive, 11.9% with £20 (US $25.0), and 18.2% with £30 (US $37.5) (relative response rate 2.4 [95% CI 2.0-2.9], 3.5 [95% CI 3.0-4.2], and 5.4 [95% CI 4.4-6.7], respectively). Nonmonetary strategies had a modest, if any, impact on response rate. The largest effect was observed for sending an additional swab reminder (SMS text message or email). For example, those receiving an additional SMS text message were more likely to return a completed swab compared to those receiving the standard email-SMS approach, 73.3% versus 70.2%: percentage difference 3.1% (95% CI 2.2%-4.0%). Conclusions: Conditional monetary incentives improved response rates to a web-based survey, which required the return of a swab test, particularly for younger age groups. Used in a selective way, incentives may be an effective strategy for improving sample response and representativeness in population-based studies. %R 10.2196/60022 %U https://publichealth.jmir.org/2025/1/e60022 %U https://doi.org/10.2196/60022 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e55376 %T Using Laboratory Test Results for Surveillance During a New Outbreak of Acute Hepatitis in 3-Week- to 5-Year-Old Children in the United Kingdom, the Netherlands, Ireland, and Curaçao: Observational Cohort Study %A Swets,Maaike C %A Kerr,Steven R %A MacKenna,Brian %A Fisher,Louis %A van Wijnen,Merel %A Brandwagt,Diederik %A Schenk,Paul W %A Fraaij,Pieter %A Visser,Leonardus G %A Bacon,Sebastian %A Mehrkar,Amir %A Nichol,Alistair %A Twomey,Patrick %A Matthews,Philippa C %A , %A Semple,Malcolm G %A Groeneveld,Geert H %A Goldacre,Ben %A Jones,Iain %A Baillie,J Kenneth %K pediatric hepatitis %K disease surveillance %K outbreak detection %K pandemic preparedness %K acute hepatitis %K children %K data analytics %K hospital %K laboratory %K all age groups %K pre-pandemic %K United Kingdom %K Netherlands %K Ireland Curacao %K single center %K federated analytics %K pandemic surveillance %K outbreaks %K public health %D 2024 %7 23.12.2024 %9 %J JMIR Public Health Surveill %G English %X Background: In March 2022, a concerning rise in cases of unexplained pediatric hepatitis was reported in multiple countries. Cases were defined as acute hepatitis with serum transaminases >500 U/L (aspartate transaminase [AST] or alanine transaminase [ALT]) in children aged 16 years or younger. We explored a simple federated data analytics method to search for evidence of unreported cases using routinely held data. We conducted a pragmatic survey to analyze changes in the proportion of hospitalized children with elevated AST or ALT over time. In addition, we studied the feasibility of using routinely collected clinical laboratory results to detect or follow-up the outbreak of an infectious disease. Objective: We explored a simple federated data analytics method to search for evidence of unreported cases using routinely held data. Methods: We provided hospitals with a simple computational tool to enable laboratories to share nondisclosive summary-level data. Summary statistics for AST and ALT measurements were collected from the last 10 years across all age groups. Measurements were considered elevated if ALT or AST was >200 U/L. The rate of elevated AST or ALT test for 3-week- to 5-year-olds was compared between a period of interest in which cases of hepatitis were reported (December 1, 2021, to August 31, 2022) and a prepandemic baseline period (January 1, 2012, to December 31, 2019). We calculated a z score, which measures the extent to which the rate for elevated ALT or AST was higher or lower in the period of interest compared to a baseline period, for the 3-week- to 5-year-olds. Results: Our approach of sharing a simple software tool for local use enabled rapid, federated data analysis. A total of 34 hospitals in the United Kingdom, the Netherlands, Ireland, and Curaçao were asked to contribute summary data, and 30 (88%) submitted their data. For all locations combined, the rate of elevated AST or ALT measurements in the period of interest was not elevated (z score=−0.46; P=.64). Results from individual regions were discordant, with a higher rate of elevated AST or ALT values in the Netherlands (z score=4.48; P<.001), driven by results from a single center in Utrecht. We did not observe any clear indication of changes in primary care activity or test results in the same period. Conclusions: Hospital laboratories collect large amounts of data on a daily basis that can potentially be of use for disease surveillance, but these are currently not optimally used. Federated analytics using nondisclosive, summary-level laboratory data sharing was successful, safe, and efficient. The approach holds potential as a tool for pandemic surveillance in future outbreaks. Our findings do not indicate the presence of a broader outbreak of mild hepatitis cases among young children, although there was an increase in elevated AST or ALT values locally in the Netherlands. %R 10.2196/55376 %U https://publichealth.jmir.org/2024/1/e55376 %U https://doi.org/10.2196/55376 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 16 %N %P e60140 %T In the Shadow of Medicine: The Glaring Absence of Occurrence Records of Human-Hosted Biodiversity %A Poncet,Rémy %A Gargominy,Olivier %K human microbiome %K bacterial occurrence data %K public health %K one health %K biodiversity data gap %K medical data integration %K medical data %K microbiome %K bacterial %K bacteria %K biodiversity %K disease prevention %K pathogens %K user-friendly %K bacterial pathogens %D 2024 %7 9.12.2024 %9 %J Online J Public Health Inform %G English %X Microbial diversity is vast, with bacteria playing a crucial role in human health. However, occurrence records (location, date, observer, and host interaction of human-associated bacteria) remain scarce. This lack of information hinders our understanding of human-microbe relationships and disease prevention. In this study, we show that existing solutions such as France’s Système d’Information sur le Patrimoine Naturel framework, can be used to efficiently collect and manage occurrence data on human-associated bacteria. This user-friendly system allows medical personnel to easily share and access data on bacterial pathogens. By adopting similar national infrastructures and treating human-associated bacteria as biodiversity data, we can significantly improve public health management and research, and our understanding of the One Health concept, which emphasizes the interconnectedness of human, animal, and environmental health. %R 10.2196/60140 %U https://ojphi.jmir.org/2024/1/e60140 %U https://doi.org/10.2196/60140 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e56926 %T SARS-CoV-2 Infection Risk by Vaccine Doses and Prior Infections Over 24 Months: ProHEpiC-19 Longitudinal Study %A Torán-Monserrat,Pere %A Lamonja-Vicente,Noemí %A Costa-Garrido,Anna %A Carrasco-Ribelles,Lucía A %A Quirant,Bibiana %A Boigues,Marc %A Molina,Xaviera %A Chacón,Carla %A Dacosta-Aguayo,Rosalia %A Arméstar,Fernando %A Martínez Cáceres,Eva María %A Prado,Julia G %A Violán,Concepción %A , %K SARS-CoV-2 %K COVID-19 %K health care workers %K cohort %K extended Cox models %K coronavirus %K epidemiology %K epidemiological %K risks %K infectious %K respiratory %K longitudinal %K vaccines %K vaccination %K vaccinated %D 2024 %7 22.11.2024 %9 %J JMIR Public Health Surveill %G English %X Background: As the vaccination campaign against COVID-19 progresses, it becomes crucial to comprehend the lasting effects of vaccination on safeguarding against new infections or reinfections. Objective: This study aimed to assess the risk of new SARS-CoV-2 infections based on the number of vaccine doses, prior infections, and other clinical characteristics. Methods: We defined a cohort of 800 health care workers in a 24-month study (March 2020 to December 2022) in northern Barcelona to determine new infections by SARS-CoV-2. We used extended Cox models, specifically Andersen-Gill (AG) and Prentice-Williams-Peterson, and we examined the risk of new infections. The AG model incorporated variables such as sex, age, job title, number of chronic conditions, vaccine doses, and prior infections. Additionally, 2 Prentice-Williams-Peterson models were adjusted, one for those individuals with no or 1 infection and another for those with 2 or 3 infections, both with the same covariates as the AG model. Results: The 800 participants (n=605, 75.6% women) received 1, 2, 3, and 4 doses of the vaccine. Compared to those who were unvaccinated, the number of vaccine doses significantly reduced (P<.001) the risk of infection by 66%, 81%, 89%, and 99%, respectively. Unit increase in the number of prior infections reduced the risk of infection by 75% (P<.001). When separating individuals by number of previous infections, risk was significantly reduced for those with no or 1 infection by 61% (P=.02), and by 88%, 93%, and 99% (P<.001) with 1, 2, 3, or 4 doses, respectively. In contrast, for those with 2 or 3 previous infections, the reduction was only significant with the fourth dose, at 98% (P<.001). The number of chronic diseases only increased the risk by 28%‐31% (P<.001) for individuals with 0‐1 previous infections. Conclusions: The study suggests that both prior infections and vaccination status significantly contribute to SARS-CoV-2 immunity, supporting vaccine effectiveness in reducing risk of reinfection for up to 24 months after follow-up from the onset of the pandemic. These insights contribute to our understanding of long-term immunity dynamics and inform strategies for mitigating the impact of COVID-19. Trial Registration: ClinicalTrials.gov NCT04885478; http://clinicaltrials.gov/ct2/show/NCT04885478 %R 10.2196/56926 %U https://publichealth.jmir.org/2024/1/e56926 %U https://doi.org/10.2196/56926 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 13 %N %P e57265 %T Knowledge, Attitudes, and Behaviors Toward Salt Consumption and Its Association With 24-Hour Urinary Sodium and Potassium Excretion in Adults Living in Mexico City: Cross-Sectional Study %A Gutiérrez-Salmeán,Gabriela %A Miranda-Alatriste,Paola Vanessa %A Benítez-Alday,Patricio %A Orozco-Rivera,Luis Enrique %A Islas-Vargas,Nurit %A Espinosa-Cuevas,Ángeles %A Correa-Rotter,Ricardo %A Colin-Ramirez,Eloisa %+ Centro de Investigación en Ciencias de la Salud, Facultad de Ciencias de la Salud, Universidad Anáhuac México, Avenida Universidad Anáhuac 46,, Lomas Anáhuac, Huixquilucan, Estado de México, 52786, Mexico, 52 55 5627 0210, eloisa.colinr@incmnsz.mx %K beliefs %K attitudes %K hypertension %K knowledge %K salt consumption %K sodium intake %K potassium intake %K Mexico %D 2024 %7 18.11.2024 %9 Original Paper %J Interact J Med Res %G English %X Background: The World Health Organization recommends a daily sodium intake of less than 2000 mg for adults; however, the Mexican population, like many others globally, consumes more sodium than this recommended amount. Excessive sodium intake is often accompanied by inadequate potassium intake. The association between knowledge, attitudes, and behaviors (KAB) and actual sodium intake has yielded mixed results across various populations. In Mexico, however, salt/sodium-related KAB and its relationship with sodium and potassium intake have not been evaluated. Objective: This study primarily aims to describe salt/sodium-related KAB in a Mexican population and, secondarily, to explore the association between KAB and 24-hour urinary sodium and potassium excretion. Methods: We conducted a cross-sectional study in an adult population from Mexico City and the surrounding metropolitan area. Self-reported KAB related to salt/sodium intake was assessed using a survey developed by the Pan American Health Organization. Anthropometric measurements were taken, and 24-hour urinary sodium and potassium excretion levels were determined. Descriptive statistics were stratified by sex and presented as means (SD) or median (25th-75th percentiles) for continuous variables, and as absolute and relative frequencies for categorical variables. The associations between KAB and sodium and potassium excretion were assessed using analysis of covariance, adjusting for age, sex, BMI, and daily energy intake as covariates, with the Šidák correction applied for multiple comparisons. Results: Overall, 232 participants were recruited (women, n=184, 79.3%). The mean urinary sodium and potassium excretion were estimated to be 2582.5 and 1493.5 mg/day, respectively. A higher proportion of men did not know the amount of sodium they consumed compared with women (12/48, 25%, vs 15/184, 8.2%, P=.01). More women reported knowing that there is a recommended amount for daily sodium intake than men (46/184, 25%, vs 10/48, 20.8%, P=.02). Additionally, more than half of men (30/48, 62.5%) reported never or rarely reading food labels, compared with women (96/184, 52.1%, P=.04). Better salt/sodium-related KAB was associated with higher adjusted mean sodium and potassium excretion. For example, mean sodium excretion was 3011.5 (95% CI 2640.1-3382.9) mg/day among participants who reported knowing the difference between salt and sodium, compared with 2592.8 (95% CI 2417.2-2768.3) mg/day in those who reported not knowing this difference (P=.049). Similarly, potassium excretion was 1864.9 (95% CI 1669.6-2060.3) mg/day for those who knew the difference, compared with 1512.5 (95% CI 1420.1-1604.8) mg/day for those who did not (P=.002). Additionally, higher urinary sodium excretion was observed among participants who reported consuming too much sodium (3216.0 mg/day, 95% CI 2867.1-3565.0 mg/day) compared with those who claimed to eat just the right amount (2584.3 mg/day, 95% CI 2384.9-2783.7 mg/day, P=.01). Conclusions: Salt/sodium-related KAB was poor in this study sample. Moreover, KAB had a greater impact on potassium excretion than on sodium excretion, highlighting the need for more strategies to improve KAB related to salt/sodium intake. Additionally, it is important to consider other strategies aimed at modifying the sodium content of foods. %M 39556832 %R 10.2196/57265 %U https://www.i-jmr.org/2024/1/e57265 %U https://doi.org/10.2196/57265 %U http://www.ncbi.nlm.nih.gov/pubmed/39556832 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e58176 %T Crowdsourcing Adverse Events Associated With Monoclonal Antibodies Targeting Calcitonin Gene–Related Peptide Signaling for Migraine Prevention: Natural Language Processing Analysis of Social Media %A Zhang,Pengfei %A Kamitaki,Brad K %A Do,Thien Phu %+ Department of Neurology, Rutgers-Robert Wood Johnson Medical School, 125 Paterson Street, Suite 6200, New Brunswick, NJ, 08901, United States, 1 7322357729, pz124@rwjms.rutgers.edu %K internet %K patient reported outcome %K headache %K health information %K Reddit %K registry %K monoclonal antibody %K crowdsourcing %K postmarketing %K safety %K surveillance %K migraine %K preventives %K prevention %K self-reported %K calcitonin gene–related peptide %K calcitonin %K therapeutics %K social media %K medication-related %K posts %K propranolol %K topiramate %K erenumab %K fremanezumab %K cross-sectional %K surveys %D 2024 %7 8.11.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Clinical trials demonstrate the efficacy and tolerability of medications targeting calcitonin gene–related peptide (CGRP) signaling for migraine prevention. However, these trials may not accurately reflect the real-world experiences of more diverse and heterogeneous patient populations, who often have higher disease burden and more comorbidities. Therefore, postmarketing safety surveillance is warranted. Regulatory organizations encourage marketing authorization holders to screen digital media for suspected adverse reactions, applying the same requirements as for spontaneous reports. Real-world data from social media platforms constitute a potential venue to capture diverse patient experiences and help detect treatment-related adverse events. However, while social media holds promise for this purpose, its use in pharmacovigilance is still in its early stages. Computational linguistics, which involves the automatic manipulation and quantitative analysis of oral or written language, offers a potential method for exploring this content. Objective: This study aims to characterize adverse events related to monoclonal antibodies targeting CGRP signaling on Reddit, a large online social media forum, by using computational linguistics. Methods: We examined differences in word frequencies from medication-related posts on the Reddit subforum r/Migraine over a 10-year period (2010-2020) using computational linguistics. The study had 2 phases: a validation phase and an application phase. In the validation phase, we compared posts about propranolol and topiramate, as well as posts about each medication against randomly selected posts, to identify known and expected adverse events. In the application phase, we analyzed posts discussing 2 monoclonal antibodies targeting CGRP signaling—erenumab and fremanezumab—to identify potential adverse events for these medications. Results: From 22,467 Reddit r/Migraine posts, we extracted 402 (2%) propranolol posts, 1423 (6.33%) topiramate posts, 468 (2.08%) erenumab posts, and 73 (0.32%) fremanezumab posts. Comparing topiramate against propranolol identified several expected adverse events, for example, “appetite,” “weight,” “taste,” “foggy,” “forgetful,” and “dizziness.” Comparing erenumab against a random selection of terms identified “constipation” as a recurring keyword. Comparing erenumab against fremanezumab identified “constipation,” “depression,” “vomiting,” and “muscle” as keywords. No adverse events were identified for fremanezumab. Conclusions: The validation phase of our study accurately identified common adverse events for oral migraine preventive medications. For example, typical adverse events such as “appetite” and “dizziness” were mentioned in posts about topiramate. When we applied this methodology to monoclonal antibodies targeting CGRP or its receptor—fremanezumab and erenumab, respectively—we found no definite adverse events for fremanezumab. However, notable flagged words for erenumab included “constipation,” “depression,” and “vomiting.” In conclusion, computational linguistics applied to social media may help identify potential adverse events for novel therapeutics. While social media data show promise for pharmacovigilance, further work is needed to improve its reliability and usability. %M 39515814 %R 10.2196/58176 %U https://formative.jmir.org/2024/1/e58176 %U https://doi.org/10.2196/58176 %U http://www.ncbi.nlm.nih.gov/pubmed/39515814 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e58732 %T Predictive Models for Sustained, Uncontrolled Hypertension and Hypertensive Crisis Based on Electronic Health Record Data: Algorithm Development and Validation %A Nguyen,Hieu Minh %A Anderson,William %A Chou,Shih-Hsiung %A McWilliams,Andrew %A Zhao,Jing %A Pajewski,Nicholas %A Taylor,Yhenneko %K machine learning %K risk prediction %K predictive model %K decision support %K blood pressure %K cardiovascular %K electronic health record %D 2024 %7 28.10.2024 %9 %J JMIR Med Inform %G English %X Background: Assessing disease progression among patients with uncontrolled hypertension is important for identifying opportunities for intervention. Objective: We aim to develop and validate 2 models, one to predict sustained, uncontrolled hypertension (≥2 blood pressure [BP] readings ≥140/90 mm Hg or ≥1 BP reading ≥180/120 mm Hg) and one to predict hypertensive crisis (≥1 BP reading ≥180/120 mm Hg) within 1 year of an index visit (outpatient or ambulatory encounter in which an uncontrolled BP reading was recorded). Methods: Data from 142,897 patients with uncontrolled hypertension within Atrium Health Greater Charlotte in 2018 were used. Electronic health record–based predictors were based on the 1-year period before a patient’s index visit. The dataset was randomly split (80:20) into a training set and a validation set. In total, 4 machine learning frameworks were considered: L2-regularized logistic regression, multilayer perceptron, gradient boosting machines, and random forest. Model selection was performed with 10-fold cross-validation. The final models were assessed on discrimination (C-statistic), calibration (eg, integrated calibration index), and net benefit (with decision curve analysis). Additionally, internal-external cross-validation was performed at the county level to assess performance with new populations and summarized using random-effect meta-analyses. Results: In internal validation, the C-statistic and integrated calibration index were 0.72 (95% CI 0.71‐0.72) and 0.015 (95% CI 0.012‐0.020) for the sustained, uncontrolled hypertension model, and 0.81 (95% CI 0.79‐0.82) and 0.009 (95% CI 0.007‐0.011) for the hypertensive crisis model. The models had higher net benefit than the default policies (ie, treat-all and treat-none) across different decision thresholds. In internal-external cross-validation, the pooled performance was consistent with internal validation results; in particular, the pooled C-statistics were 0.70 (95% CI 0.69‐0.71) and 0.79 (95% CI 0.78‐0.81) for the sustained, uncontrolled hypertension model and hypertensive crisis model, respectively. Conclusions: An electronic health record–based model predicted hypertensive crisis reasonably well in internal and internal-external validations. The model can potentially be used to support population health surveillance and hypertension management. Further studies are needed to improve the ability to predict sustained, uncontrolled hypertension. %R 10.2196/58732 %U https://medinform.jmir.org/2024/1/e58732 %U https://doi.org/10.2196/58732 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e53711 %T An Ontology to Bridge the Clinical Management of Patients and Public Health Responses for Strengthening Infectious Disease Surveillance: Design Science Study %A Lim,Sachiko %A Johannesson,Paul %+ Department of Computer and Systems Sciences, Stockholm University, Nodhuset, Borgarfjordsgatan 12, Kista, SE-164 07, Sweden, 46 0760968462, sachiko@dsv.su.se %K infectious disease %K ontology %K IoT %K infectious disease surveillance %K patient monitoring %K infectious disease management %K risk analysis %K early warning %K data integration %K semantic interoperability %K public health %D 2024 %7 26.9.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Novel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is ontology. Objective: This study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients, and disparate heterogeneous infectious disease surveillance. Methods: The ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, ease of extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals. Results: We designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring, clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information. Conclusions: The development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance, and epidemic risk analysis. The novelty and uniqueness of the ontology lie in building a bridge to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems, providing actionable insights to support public health organizations and practitioners in making informed decisions in a timely manner. %M 39325530 %R 10.2196/53711 %U https://formative.jmir.org/2024/1/e53711 %U https://doi.org/10.2196/53711 %U http://www.ncbi.nlm.nih.gov/pubmed/39325530 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e54503 %T Assessment of the Effective Sensitivity of SARS-CoV-2 Sample Pooling Based on a Large-Scale Screening Experience: Retrospective Analysis %A Cabrera Alvargonzalez,Jorge J %A Larrañaga,Ana %A Martinez,Javier %A Pérez Castro,Sonia %A Rey Cao,Sonia %A Daviña Nuñez,Carlos %A Del Campo Pérez,Víctor %A Duran Parrondo,Carmen %A Suarez Luque,Silvia %A González Alonso,Elena %A Silva Tojo,Alfredo José %A Porteiro,Jacobo %A Regueiro,Benito %+ Microbiology Department, Complexo Hospitalario Universitario de Vigo, Servicio Galego de Saude, Estrada de Clara Campoamor, 341, Vigo, 36312, Spain, 34 986811111, jorge.julio.cabrera.alvargonzalez@sergas.es %K pooling %K sensitivity %K SARS-CoV-2 %K PCR %K saliva %K screening %K surveillance %K COVID-19 %K nonsymptomatic %K transmission control %D 2024 %7 24.9.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The development of new large-scale saliva pooling detection strategies can significantly enhance testing capacity and frequency for asymptomatic individuals, which is crucial for containing SARS-CoV-2. Objective: This study aims to implement and scale-up a SARS-CoV-2 screening method using pooled saliva samples to control the virus in critical areas and assess its effectiveness in detecting asymptomatic infections. Methods: Between August 2020 and February 2022, our laboratory received a total of 928,357 samples. Participants collected at least 1 mL of saliva using a self-sampling kit and registered their samples via a smartphone app. All samples were directly processed using AutoMate 2550 for preanalytical steps and then transferred to Microlab STAR, managed with the HAMILTON Pooling software for pooling. The standard pool preset size was 20 samples but was adjusted to 5 when the prevalence exceeded 2% in any group. Real-time polymerase chain reaction (RT-PCR) was conducted using the Allplex SARS-CoV-2 Assay until July 2021, followed by the Allplex SARS-CoV-2 FluA/FluB/RSV assay for the remainder of the study period. Results: Of the 928,357 samples received, 887,926 (95.64%) were fully processed into 56,126 pools. Of these pools, 4863 tested positive, detecting 5720 asymptomatic infections. This allowed for a comprehensive analysis of pooling’s impact on RT-PCR sensitivity and false-negative rate (FNR), including data on positive samples per pool (PPP). We defined Ctref as the minimum cycle threshold (Ct) of each data set from a sample or pool and compared these Ctref results from pooled samples with those of the individual tests (ΔCtP). We then examined their deviation from the expected offset due to dilution [ΔΔCtP = ΔCtP – log2]. In this work, the ΔCtP and ΔΔCtP were 2.23 versus 3.33 and –0.89 versus 0.23, respectively, comparing global results with results for pools with 1 positive sample per pool. Therefore, depending on the number of genes used in the test and the size of the pool, we can evaluate the FNR and effective sensitivity (1 – FNR) of the test configuration. In our scenario, with a maximum of 20 samples per pool and 3 target genes, statistical observations indicated an effective sensitivity exceeding 99%. From an economic perspective, the focus is on pooling efficiency, measured by the effective number of persons that can be tested with 1 test, referred to as persons per test (PPT). In this study, the global PPT was 8.66, reflecting savings of over 20 million euros (US $22 million) based on our reagent prices. Conclusions: Our results demonstrate that, as expected, pooling reduces the sensitivity of RT-PCR. However, with the appropriate pool size and the use of multiple target genes, effective sensitivity can remain above 99%. Saliva pooling may be a valuable tool for screening and surveillance in asymptomatic individuals and can aid in controlling SARS-CoV-2 transmission. Further studies are needed to assess the effectiveness of these strategies for SARS-CoV-2 and their application to other microorganisms or biomarkers detected by PCR. %M 39316785 %R 10.2196/54503 %U https://publichealth.jmir.org/2024/1/e54503 %U https://doi.org/10.2196/54503 %U http://www.ncbi.nlm.nih.gov/pubmed/39316785 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e56571 %T Lyme Disease Under-Ascertainment During the COVID-19 Pandemic in the United States: Retrospective Study %A Jones,Brie S %A DeWitt,Michael E %A Wenner,Jennifer J %A Sanders,John W %K surveillance %K ascertainment %K Lyme diseases %K vector-borne diseases %K vector-borne disease %K vector-borne pathogens %K public health %K Lyme disease %K United States %K North Carolina %K COVID-19 %K pandemic %K hospital %K hospitals %K clinic-based %K surveillance program %K geospatial model %K spatiotemporal %D 2024 %7 12.9.2024 %9 %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic resulted in a massive disruption in access to care and thus passive, hospital- and clinic-based surveillance programs. In 2020, the reported cases of Lyme disease were the lowest both across the United States and North Carolina in recent years. During this period, human contact patterns began to shift with higher rates of greenspace utilization and outdoor activities, putting more people into contact with potential vectors and associated vector-borne diseases. Lyme disease reporting relies on passive surveillance systems, which were likely disrupted by changes in health care–seeking behavior during the pandemic. Objective: This study aimed to quantify the likely under-ascertainment of cases of Lyme disease during the COVID-19 pandemic in the United States and North Carolina. Methods: We fitted publicly available, reported Lyme disease cases for both the United States and North Carolina prior to the year 2020 to predict the number of anticipated Lyme disease cases in the absence of the pandemic using a Bayesian modeling approach. We then compared the ratio of reported cases divided by the predicted cases to quantify the number of likely under-ascertained cases. We then fitted geospatial models to further quantify the spatial distribution of the likely under-ascertained cases and characterize spatial dynamics at local scales. Results: Reported cases of Lyme Disease were lower in 2020 in both the United States and North Carolina than prior years. Our findings suggest that roughly 14,200 cases may have gone undetected given historical trends prior to the pandemic. Furthermore, we estimate that only 40% to 80% of Lyme diseases cases were detected in North Carolina between August 2020 and February 2021, the peak months of the COVID-19 pandemic in both the United States and North Carolina, with prior ascertainment rates returning to normal levels after this period. Our models suggest both strong temporal effects with higher numbers of cases reported in the summer months as well as strong geographic effects. Conclusions: Ascertainment rates of Lyme disease were highly variable during the pandemic period both at national and subnational scales. Our findings suggest that there may have been a substantial number of unreported Lyme disease cases despite an apparent increase in greenspace utilization. The use of counterfactual modeling using spatial and historical trends can provide insight into the likely numbers of missed cases. Variable ascertainment of cases has implications for passive surveillance programs, especially in the trending of disease morbidity and outbreak detection, suggesting that other methods may be appropriate for outbreak detection during disturbances to these passive surveillance systems. %R 10.2196/56571 %U https://publichealth.jmir.org/2024/1/e56571 %U https://doi.org/10.2196/56571 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9730 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveThe aim of this study is to present the syndromic groups that will be routinely monitored for the reactive mortality surveillance based on free-text medical causes of death.IntroductionIn 2004, Santé publique France, the French Public Health Agency set up a reactive all-cause mortality surveillance based on the administrative part of the death certificate, in the final objectives 1/ to detect unexpected or usual variations in mortality and 2/ to provide a first evaluation of mortality impact of events.In 2007, an Electronic Death Registration System (EDRS) was implemented, enabling electronic transmission of the medical causes of death to the agency in real-time. To date, 12% of the mortality is registered electronically. A pilot study demonstrated that these data were valuable for a reactive mortality surveillance system based on causes of death [1].A strategy has thus been developed for the analysis in routine of the medical causes of death with the objectives of early detection of expected and unexpected outbreaks and reactive evaluation of their impact. This system will allow approaching the cause accountability when an excess death will be observed.MethodsMortality syndromic groups (MSG) were defined as clusters of medical causes of death (pathologies, syndromes or symptoms) that meet the objectives of the surveillance system. The causes of death are available reactively in free-text (words, terms, expressions) and with a delay of 6 to 24 months in ICD10 codes format.We explored multiple biomedical classifications such as the Mesh, SNOMED, UMLS or ICD10 to learn from their various ways to classify diseases. Based on ICD10, we defined MSGs by a list of ICD10 codes, each codes belonging to a unique MSGs. Each MSG definition was then discussed in working group including medical and epidemiological experts.Additionally, we used a dictionary (provided by the Epidemiology Center on Causes of Death (Inserm-CépiDc)) of each term/expression found in the death certificates since the early 2000 to enrich variety of expression of each MSG.We classified causes of death into MSGs from E-death certificates from 2012 to 2016: 1/ using the ICD10 codes assigned by Inserm-CépiDc based on rules defined by WHO in order to produce the national mortality statistics and 2/ using a linear Support Vector Machine (SVM) method to classify free-text causes of death. Then we compared the fluctuations of the weekly numbers of each MSG built by using both classification methods (ICD10 codes and the SVM classification) [2].ResultsA list of a hundred MSGs was defined, divided into 20 topics (Respiratory conditions, Digestive conditions, Infectious conditions, Cardio and Cerebrovascular conditions, General symptoms…). 60 MSGs were dedicated to alert and detection of both expected seasonal epidemics (12 MSGs) and unexpected events (42 MSGs). They contain unspecific or acute pathologies and symptoms. 40 MSG included medical causes of death related to chronic diseases and medical history.The list of established MSGs was composed of:- MSGs for detection of expected seasonal events such as: “Influenza”, “Low acute respiratory infection”, “Gastroenteritis”, “Chikungunya”, “Heat related death”, “Dehydration”…- MSGs for detection of the impact of unexpected events such as: “Epilepsy”, “Choc”, “Coma”, “Unspecified fever”, “Headache”, “Suicide”, “Drugs/opioids poisoning”…- MSGs for Chronic diseases and Medical history: “Chronic digestive diseases”, “Chronic endocrine diseases”, “Genitourinary chronic diseases”, “History of diseases”…The weekly number of MSGs built using SVM classification was close and highly correlated to the weekly number of MSGs built using ICD10 codes (Figure 1). Seasonality and peaks were visible using both classifications. For instance, the increase of the MSG “Influenza” occurred during winter months which are known to be the circulating months of the influenza virus (Figure 1, left) [3].For unusual and rare events such as death due to burns, we observed that the weekly numbers of MSG “Burns” were also similar using both methods. We observed (Figure 1, right) that the outbreak that occurred in September 2016 related to a major bus accident was found using ICD10 codes or SVM classification.ConclusionsThe use of free-text causes of death for reactive mortality surveillance requires the development of a strategy for the analysis of these data. Defining MSGs was essential for the implementation of automatic classification methods of the death certificates in routine.The dynamic of MSGs using ICD10 codes or SVM classification were comparable. However, the use of ICD10 codes for reactive mortality surveillance is not an option due to the delay of availability of the codes. The uses of machine learning methods, thus, enable to harness free-text causes of death for the reactive mortality surveillance with an objective of detection and early impact assessment.References1. Lassalle M, Caserio-Schönemann C, Gallay A, Rey G, Fouillet A: Pertinence of electronic death certificates for real-time surveillance and alert, France, 2012–2014. Public Health 2017, 143:85-93.[1]2. Baghdadi Y, Bourrée A, Robert A, Rey G, Gallay A, Zweigenbaum P, Grouin C, Fouillet A: Automatic classification of medical causes from free-text death certificates for reactive mortality surveillance in France. Int J Med Inf 2018, Under review.[2]3. Bedford T, Riley S, Barr IG, Broor S, Chadha M, Cox NJ, Daniels RS, Gunasekaran CP, Hurt AC, Kelso A et al: Global circulation patterns of seasonal influenza viruses vary with antigenic drift. Nature 2015, 523(7559):217-220.[3] %R 10.5210/ojphi.v11i1.9730 %U %U https://doi.org/10.5210/ojphi.v11i1.9730 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9735 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveLaboratory of the Ministry of Agriculture (LMA) conducts Anthrax diagnostics using Bacteriology and Molecular Biology Methods: Isolated cultures through the classical bacteriology methods are always confirmed by Molecular Biology assay (PCR).In the study the samples were screened for the presence of B. anthracis via two concurrent approaches to compare classical methods and a novel PCR method. Before the TAP-7 project, PCR was only used to confirm the identity of cultures isolated by the Bacteriology. New SOPs and algorythm was created for better laboratory diagnostic.IntroductionBacillus anthracis, the etiologic agent of anthrax, is a member of a highly diverse group of endospore-forming bacteria. Bacillus anthracis spores are typically found in soil, from which they may spread via contaminated dust, water, and materials of plant and animal origin. Although anthrax is primarily a disease of herbivores, humans may contract anthrax directly or indirectly from animals.Laboratory of the Ministry of Agriculture (LMA) conducts Anthrax diagnostics using Bacteriology and Molecular Biology Methods: Isolated cultures through the classical bacteriology methods are always confirmed by Molecular Biology assay (PCR).In 2014, within Tap7 project ,Identification and Mapping of Anthrax foci in Georgia’’ Anthrax suspected soil samples were tested using two lab diagnostic methods and they were compared to each other.MethodsAnthrax suspected samples were tested by two methods - classical method and new method.Classical method included isolation of bacterium from soil samples using standard bacteriology tests and then PCR confirmed its identity. New method was initial PCR testing of soil samples.302 soil samples were tested by classical method.At the same time, approximately 10% (32 samples) of the already mentioned 302 soil samples were also tested by initial PCR.Results24 cultures isolated through bacteriology tests (Gram staining; lysis by gamma phage; motility testing; detection of polyDglutamic acid capsule by direct fluorescent antibody (DFA) were confirmed by PCR.Out of the above mentioned 32-suspected samples, 11 were confirmed positive using the classical methods, versus 9 confirmed positive using the direct PCR approach. Two bacteriologically positive samples appeared negative by the direct PCR method, i.e. only two samples did not match.ConclusionsThe samples were screened for the presence of B. anthracis via two concurrent approaches to compare classical methods and a novel PCR method. Before the TAP-7 project, PCR was only used to confirm the identity of cultures isolated by the Bacteriology.The purpose of the investigation of the new method was to identify if a less labor-intensive process with fewer points of operator manipulation was as efficacious as the classical method of bacteriology followed by PCR analysis of suspected samples.Despise the limited sampling and the little difference in the efficacy of the two methods, classical method stays prior to new one. %R 10.5210/ojphi.v11i1.9735 %U %U https://doi.org/10.5210/ojphi.v11i1.9735 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9736 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo describe the strategy and process used by the Florida Department of Health (FDOH) Bureau of Epidemiology to onboard emergency medical services (EMS) data into FDOH’s syndromic surveillance system, the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL).IntroductionSyndromic surveillance has become an integral component of public health surveillance efforts within the state of Florida. The near real-time nature of these data are critical during events such as the Zika virus outbreak in Florida in 2016 and in the aftermath of Hurricane Irma in 2017. Additionally, syndromic surveillance data are utilized to support daily reportable disease detection and other surveillance efforts. Although syndromic systems typically utilize emergency department (ED) visit data, ESSENCE-FL also includes data from non-traditional sources: urgent care center visit data, mortality data, reportable disease data, and Florida Poison Information Center Network (FPICN) data. Inclusion of these data sources within the same system enables the broad accessibility of the data to more than 400 users statewide, and allows for rapid visualization of multiple data sources in order to address public health needs. Currently, the ESSENCE-FL team is actively working to incorporate EMS data into ESSENCE-FL to further increase public health surveillance capacity and data visualization.MethodsThe ESSENCE-FL team worked collaboratively with various public health program stakeholders to bring EMS data, aggregated by the FDOH Bureau of Emergency Medical Oversight Emergency Medical Services Tracking and Reporting System (EMSTARS) team, into ESSENCE-FL. The ESSENCE-FL team met with the EMSTARS team to discuss use cases, demonstrate both systems, and to obtain project buy-in and support. Initial project meetings included review of ESSENCE-FL system support, user types (roles and access), as well as data security and compliance. An overall project timeline was established, and deliverables were added into system support contracts. Multiple stakeholders, across disciplines representing each key use case, reviewed the Florida version of the National Emergency Medical Services Information System (NEMSIS) version 3.4 data dictionary to identify program-specific data element needs. An element scoring spreadsheet was returned to the ESSENCE-FL team. These scores were aggregated and discordant scores were reviewed by the ESSENCE-FL team. A one-month extract of EMS data was reviewed to assess variable completeness and relevance. Monthly team meetings facilitated the final decisions on the data elements by leveraging lessons learned through onboarding other data sources, findings from the analysis of the one-month extract, stakeholder comments, and advice from other states known to be leveraging EMS data for public health surveillance.ResultsThrough a collaborative and broad approach with partners, the ESSENCE-FL team attained stakeholder buy-in and identified 81 data elements to be included in the EMS feed to ESSENCE-FL. The final list of data elements was determined to best support health surveillance of this population prior to presenting to the ED. The inclusion of the EMS data in ESSENCE-FL will increase the epidemiologic characterization and analysis of the opioid epidemic in Florida. Additional key use cases identified during this project included enhanced injury surveillance, enhanced occupational health surveillance, and characterization of potential differences between EMS and ED visits.ConclusionsThis comprehensive approach can be used by other jurisdictions considering adding EMS data to their syndromic surveillance systems. When considering onboarding a new data source into a surveillance system, it is important to work closely with stakeholders from disciplines representing each of the key use cases to broaden buy-in and support for the project. Through employing this comprehensive approach, syndromic surveillance systems can be better developed to include data that are widely utilizable to many different stakeholders in the public health community. %R 10.5210/ojphi.v11i1.9736 %U %U https://doi.org/10.5210/ojphi.v11i1.9736 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9737 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveSchmallenberg virus (SBV) is an orthobunyavirus that primarily infects domestic and wild ruminants and causes symptoms such as transient fever, diarrhea, reduced milk production, congenital malformations and abortion. The first virus was identified in 2011 at the onset of a major outbreak in Europe (Germany, Hungary, and France).IntroductionIn 2012 - 2017 in Azerbaijan there was an unexpected increase of abortions in cattle and sheep that was unrelated to brucellosis or chlamydia infection. The first confirmed case of Schmallenberg disease was received from Beylagan district of Azerbaijan in October 2012. The import of cattle from Europe to Azerbaijan has commenced in 2012. Therefore, the surveillance study was launched to determine spread of infection among cattle and sheep and to monitor the situation in the country.MethodsState Veterinary Control Service notified 42 Regional Veterinary Offices of Azerbaijan to commence the monitoring of Schmallenberg disease. Blood samples were collected from sheep, and cattle and biopsies of heads or necks from aborted fetuses were sampled too.. The collected samples were tested in the Republican Veterinary Laboratory. ELISA was used to investigate the presence of specific antibodies against Schmallenberg virus in the blood samples using IDEXX Schmallenberg Ab Test Kit. The commercially available real-time PCR kits (VetMAX™ Schmallenberg Virus Kit) were applied to test the biopsy samples. Both tests were recommended by the World Organization for Animal Health.ResultsTotal, 40,257 blood samples were collected from suspicious cattle and sheep. 671 biopsies samples were taken from fetuses. 4,281 cattle and 999 sheep with antibodies against SBVwere detected. The PCR results showed that the 77 biopsies samples were positive for SBV. The highest numbers of seropositive animals were found in Ganja, Aghdash, Barda, and Baku.ConclusionsThis biosurveillance study determined SBV in the samples of cattle and sheep in Azerbaijan, therefore, it is important to carry out annual seromonitoring and start the vaccination program. It is essential to check the passport of imported cattle, which has the disease history and seroprevalence of SBV.ReferencesLaloy, E., Breard, E., Sailleau, C., Viarouge, C., Desprat, A., Zientara, S., Klein, F., Hars, J., Rossi, S., 2014. Schmallenberg virus infection among red deer, France, 2010-2012. Emerg. Infect. Dis. 20, 131–134. https://doi.org/10.3201/eid2001.130411Larska, M., Krzysiak, M.K., Kesik-Maliszewska, J., Rola, J., 2014. Cross-sectional study of Schmallenberg virus seroprevalence in wild ruminants in Poland at the end of the vector season of 2013. BMC Vet. Res. 10, 967. https://doi.org/10.1186/s12917-014-0307-3 %R 10.5210/ojphi.v11i1.9737 %U %U https://doi.org/10.5210/ojphi.v11i1.9737 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9738 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveQ fever is poorly understood in Georgia and its prevalence is largely underestimated in both humans and animals.One of the main goal of the project was shedding study in domestic animals – isolation of C. burnetii from suspected seropositive animal blood, milk samples.IntroductionQ fever is a zoonotic bacterial disease resulting from infection by Coxiella burnetii. Domestic ruminants (cattle, sheep, and goats) are considered the main reservoir for the pathogen, which can also infect humans. Q fever is poorly understood in Georgia and its prevalence is largely underestimated in both humans and animals.In Georgia Q fever laboratory diagnostic was started and implemented at the Laboratory of the Ministry of Georgia (LMA) within GG20 ,,Prevalence, Epidemiological Surveillance, and Laboratory Analysis of Coxiella burnetii in Georgia’’.MethodsLMA conducted Coxiella burnetii shedding evaluation in three specific farms from Kvemo Kartli (Tsalka, Dmanisi) and Mtskheta-Mtianeti (Dusheti). Seropositive cattle and small ruminants were sampled per week. Sampling lasted 7 weeks and totally 581 samples samples (blood, milk and swab) were tested. Testing were conducted in a BSL3 laboratory under BSL3 working conditions. ACCM medium was used (2XACCm-2 acidified Citrate Cysteine Medium PH-4.75G N NaOH). The samples were incubated at 37°C using CO2.ResultsAs a result of the study, one culture was bacteriologically isolated from seropositive cattle milk sample ( the sample was taken on the third week of the study in Beshtasheni farm, Tslka, Kvemo Kartli) and confirmed by Molecular biology (PCR).ConclusionsThe study confirmed Q fever existence in Georgia. Traditionally considered an obligate intracellular agent, the requirement to be grown in tissue culture cells, embryonated eggs, or animal hosts has made it difficult to isolate C. Burnetii strains. Within the study one culture was isolated from the seropositive animal milk sample that was collected in the third week of the study. shedding of Coxiella burnetii in milk by infected cows appeared to be the most frequent positive sample for the bacterium. %R 10.5210/ojphi.v11i1.9738 %U %U https://doi.org/10.5210/ojphi.v11i1.9738 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9739 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveIn the presented study, we examined the impact of school holidays (Autumn, Winter, Summer, and Spring Breaks) and social events (Super Bowl, NBA Finals, World Series, and Black Friday) for five age groups (<4, 5-24, 25-44, 45-64, >65 years) on four health outcomes of influenza (total tested, all influenza positives, positives for influenza A, and B) in Milwaukee, WI, in 2004-2009 using routine surveillance.IntroductionInfluenza viral infection is contentious, has a short incubation period, yet preventable if multiple barriers are employed. At some extend school holidays and travel restrictions serve as a socially accepted control measure1,2. A study of a spatiotemporal spread of influenza among school-aged children in Belgium illustrated that changes in mixing patterns are responsible for altering disease seasonality3. Stochastic numerical simulations suggested that weekends and holidays can delay disease seasonal peaks, mitigate the spread of infection, and slow down the epidemic by periodically dampening transmission. While Christmas holidays had the largest impact on transmission, other school breaks may also help in reducing an epidemic size. Contrary to events reducing social mixing, sporting events and mass gatherings facilitate the spread of infections4. A study on county-level vital statistics of the US from 1974-2009 showed that Super Bowl social mixing affects influenza dissemination by decreasing mortality rates in older adults in Bowl-participating counties. The effect is most pronounced for highly virulent influenza strains and when the Super Bowl occurs closer to the influenza seasonal peak. Simulation studies exploring how social mixing affects influenza spread5 demonstrated that impact of the public gathering on prevalence of influenza depends on time proximity to epidemic peak. While the effects of holidays and social events on seasonal influenza have been explored in surveillance time series and agent-based modeling studies, the understanding of the differential effects across age groups is incomplete.MethodsThe City of Milwaukee Health Department Laboratory (MHDL), Wisconsin routinely collect tests from residents of metropolitan areas and vicinities of the Marquette University (MU). We obtained weekly counts of total tested, all influenza positives, positives for influenza A and B, from MHDL between 5/16/04-3/7/09 (before the surge of tests associated with “swine flu”). Cases for <1 and 1-4 age groups were combined. Meteorological data are routinely collected by a monitoring station at the General Mitchell International airport located 7.5 miles from Milwaukee. Daily dewpoint values representing the perceived ambient temperature corrected for the air moisture content were downloaded from the open source website6 and aggregated to weekly averages with Sunday designating the beginning of each week. School holidays were obtained from academic calendars on the MU website with holiday weeks defined as having one or more school holiday observed.7 Selected social events were retrieved from a public website.8 As part of exploratory analysis, average cases per week (c/w) for each outcome for school holiday and non-holiday weeks were compared using a non-parametric the Mann–Whitney U-test. We analyzed the association between weekly cases and holiday effects using negative binomial regression with sets of indicator variables for non-overlapping school holidays and social events and with adjustments for weather fluctuations with harmonic terms (Model 1). Results are presented as Relative Risk (RR) estimates along with their confidence intervals (95%CI). Further analyses examined seasonal signatures (lead-lag structures) using a segmented regression approach for weekly counts and rates 5 academic weeks (aw) before, 2-6 weeks during, and 5 weeks after select holidays (Model 2).ResultsOver 251 study weeks, 2282 tests were submitted, out of which 1098 cases were from 5-24 y.o. age group. 477 (21%) tests we positive, with 399 (84%) cases of influenza A (73 tests were not subtyped) and 78 (16%) cases of influenza B. Figure 1 shows the time series of weekly counts of influenza tests and percent positives with superimposed information on school holiday occurrences. Overall, during 135 weeks of the school period the average number of tests was two times higher as compared to those during 116 holiday weeks (11.9±10.3 vs 5.8±6.5 c/w, p<0.001). Similarly, the average weekly number of positive tests was higher in non-holiday than during holiday periods (2.9±5.7 vs 0.7±2.6 c/w, p<0.001). The reduction in tests during holidays was confirmed by the regression model (RR=0.71; 95%CI=[0.60-0.86]). The reduction in weekly tests was most pronounced during the Winter Break (15-19 aw) for all age groups (4.8±3.0 c/w, p<0.001; RR=0.3; 95%CI=[0.23-0.41]) and especially for school-aged children, young adults and adults (RR=0.14; 95%CI=[0.09-0.22] and RR=0.32; 95%CI=[0.16-0.62] for 5-24 and 25-44 age groups, respectively). In contrast, during the Spring Break (27-30 aw) the number of tests has almost doubled (20.4±10.4 c/w; p<0.001) as compared to the school period, with the most noticeable increase in 5-24 and 25-44 age groups. Spring Break differential effects were primarily due to later peaks in influenza B shown by segmented regression results in Figure 2. The seasonal increase in weekly rates is the steepest after the winter holidays. The effects of the selected sporting and social events were inconclusive.ConclusionsThe differential effects of calendar events on seasonal influenza can be detected by routine surveillance and further explored with respect to lead-lag structures. We recommend incorporating location-specific calendar effects in influenza near-term forecasting models tailored to susceptible age groups to better predict and assess targeted intervention measures.References1. Jackson C, et al. (2016). The relationship between school holidays and transmission of influenza in England and wales. Am Journal of Epidemiology. 184(9), 644-51.2. Chu Y, et al. (2017). Effects of school breaks on influenza-like illness incidence in a temperate Chinese region: an ecological study from 2008 to 2015. BMJ. 7(3), e013159.3. Luca G, et al. (2018) The impact of regular school closure on seasonal influenza epidemics: a data-driven spatial transmission model for Belgium. BMC Infect Dis. 18(1): 29.4. Stoecker C, et al. (2016) Success Is Something to Sneeze At: Influenza Mortality in Cities that Participate in the Super Bowl. Am Journal of Health Econ. 2(1):125-43.5. Shi P, et al. (2010) The impact of mass gatherings and holiday traveling on the course of an influenza pandemic: a computational model. BMC Public Health. 10: 778.6. www.wunderground.com.7. www.marquette.edu/mucentral/registrar/ArchivedAcademicCalendars.shtml.8. www.timeanddate.com. %R 10.5210/ojphi.v11i1.9739 %U %U https://doi.org/10.5210/ojphi.v11i1.9739 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9740 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo compare prevalence estimates obtained by the ADDM cerebral palsy surveillance method to other administrative or diagnostic indications of cerebral palsy.IntroductionCerebral Palsy (CP) is the most common cause of motor disability in children. CP registries often rely on administrative data such as CP diagnoses or International Classification of Diseases (ICD) codes indicative of CP. However, little is known about the validity of these indicators. We calculated sensitivity, specificity, positive and negative predictive values of CP ICD-9 codes and CP diagnoses compared to a “gold standard” CP classification based on detailed medical and education record review.MethodsThis sample includes 50,332 8-year-olds living in four US sites (32 counties in Alabama, 5 counties in Georgia, 10 counties in Wisconsin, and 5 counties in Missouri) in 2006, 2008, and 2010. The Autism and Developmental Disabilities Monitoring (ADDM) Network reviewed medical and education records for these children as part of the US Centers for Disease Control and Prevention population-based surveillance of developmental disabilities. All of these children received special education services or were assigned one or more ICD-9 codes associated with a variety of developmental disabilities by community medical providers. Medical and education records were reviewed by trained staff; if the records contained CP diagnoses or motor findings indicative of CP, detailed clinical information was abstracted for additional review by trained clinicians who determined whether the child met the CP case definition based on all information available. Abstracted records were also reviewed for evidence of known motor disorders or genetic conditions that disqualified a child from being a CP case, such as inborn error of metabolism or muscular dystrophy. Trained clinicians reviewed and excluded children with confirmed disqualifying conditions.We calculated CP prevalence, sensitivity, specificity, and positive and negative predictive values for three different methods used to identify cases, using the ADDM surveillance case identification as the gold standard. These methods include: 1) ICD-9 codes for CP (342–344); 2) a CP diagnosis written in the medical or education records, excluding children with disqualifying conditions, and 3) both ICD-9 codes (342–344) and a CP diagnosis written in the medical or education records, excluding children with disqualifying conditions. In an attempt to avoid requiring record review for method 1, we considered using ICD-9 codes for disqualifying conditions. However, we found that ICD codes for these conditions did not correlate well with disqualifying conditions identified in medical record reviews; therefore disqualifying conditions were not considered for method 1. Methods 2 and 3 did require review of medical records for disqualifying conditions and for a written CP diagnosis, but overall were less extensive than traditional ADDM surveillance methods.In order to determine the impact of different classification criteria on how and which children are captured by surveillance methods, we compared demographic and other characteristics of all children who met the ADDM surveillance case definition. We compared children who would and would not be classified as CP cases using method 3.ResultsOut of the total 50,332 children, 1294 met the ADDM surveillance case definition, 2201 had CP ICD codes (method 1), 1502 had a written CP diagnosis and no disqualifying conditions (method 2), and 1345 had both CP ICD codes and a written diagnosis and no disqualifying conditions (method 3). Each study year, between 32—48% of abstracted children were excluded due to disqualifying conditions found in medical records. The ADDM network gold standard CP prevalence was 3.3 per 1000 in 2006, 3.1 per 1000 in 2008, and 2.9 per 1000 in 2010.For method 1, sensitivity was 90.0%, specificity was 97.4%, positive predictive value was 51.6% and negative predictive value was 99.7%. Method 1 prevalence estimates were 5.3 per 1000 in 2006, 4.6 per 1000 in 2008, and 4.6 per 1000 in 2010. For method 2, sensitivity was 98.1%, specificity was 88.4%, PPV was 84.5% and NPV was 98.4% compared to the ADDM Network definition. Method 2 estimated prevalence was 3.9 per 1000 for 2006, 3.6 per 1000 for 2008, and 3.2 per 1000 for 2010. For method 3, sensitivity was 89.6%, specificity was 99.5%, PPV was 84.3% and NPV was 99.7%. Method 3 estimated prevalence was 3.5 per 1000 for 2006, 3.2 per 1000 for 2008, and 2.8 per 1000 for 2010.Using Pearson’s Chi-Square tests, we compared demographic and other characteristics of ADDM Network CP case children who also met method 3 case definition (n = 1134) and children who met the ADDM Network CP definition but not method 3 case definition (n = 160). Demographic information was not different between these children. ADDM Network CP case children who did not meet method 3 criteria were significantly less likely to require a wheelchair for mobility than children who met method 3 criteria (4.4% versus 27.4%, p < .05).ConclusionsRelying on ICD-9 codes without excluding disqualifying conditions to identify CP cases (method 1) resulted in high sensitivity (90%), but low positive predictive value as well as an overestimated CP prevalence when compared with the ADDM Network method. Use of a written diagnosis and excluding disqualifying conditions (method 2) resulted in very high sensitivity (98%), with fewer false positives but overestimated CP prevalence compared to the ADDM estimate. In contrast, using both CP ICD codes and a written CP diagnosis and excluding disqualifying conditions (method 3) yielded prevalence estimates similar to ADDM Network CP estimates; this approach also had high sensitivity, specificity, and PPV. Methods 2 and 3 still require manual record review, unlike method 1. For method 2, reviewers would need to review all records for CP and disqualifying conditions. Method 3 only requires review of records with CP ICD codes, comprising 4% of all records currently reviewed. Method 3 would fail to capture children without both a written diagnosis and ICD codes; and this approach may be less sensitive for detecting CP among children with less severe motor impairment than using the gold standard.Using ICD codes and written CP diagnoses contained in medical and education records combined with a limited medical record review to identify disqualifying conditions could lower operational costs of CP surveillance while preserving accurate prevalence estimates compared with the more labor-intensive processes currently used. Further evaluation is needed to determine if improvements in efficiency are worth potential trade-offs in the data collected by the system. Of particular importance is whether the approach could capture all the necessary indicators that are important to stakeholders. Additional analyses would also need to evaluate whether the surveillance methods affect other findings, such as previously observed disparities, co-occurring conditions, or CP severity. %R 10.5210/ojphi.v11i1.9740 %U %U https://doi.org/10.5210/ojphi.v11i1.9740 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9741 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo determine the merits of different surveillance methods for cluster detection, in particular when used in conjuction with small area data. This will be investigated using a simulated framework. This is with a view to support further surviellance work using real small area data.IntroductionHealth surveillance is well established for infectious diseases, but less so for non-communicable diseases. When spatio-temporal methods are used, selection often appears to be driven by arbitrary criteria, rather than optimal detection capabilities. Our aim is to use a theoretical simulation framework with known spatio-temporal clusters to investigate the sensitivity and specificity of several traditional (e.g. SatScan and Cusum) and Bayesian (incl. BaySTDetect and Dcluster) statistical methods for spatio-temporal cluster detection of non-communicable disease.MethodsCount data were generated using various random effects (RE). A subset of areas was randomly given an increased relative risk (RR) to simulate disease clusters. Simulations were conducted in R using a grid of 625 areas. We used 12 times= nteps within a hierarchical Poisson model. Multiple values of model parameters, including REs and the RR within clusters, were then tested. The range of RE (values) was derived from real-world data from England on common and rare diseases. RR ranging between 1.2 and 1.8 were tested to reflect both low and high exposures to pollutants and other risk factors. ROC analysis, based on 50 simulations, was used to assess the performance of each statistical method for each combination of parameter values.ResultsOur ROC analysis suggested that SaTScan usually had the highest specificity at low sensitivities (<0.5), although its maximum sensitivity was often lower than when using the Bayesian methods. In scenarios where the RR within clusters was lower, all methods had less sensitivity at a given specificity. Cusum usually performed quite similarly to SatScan, while the two Bayesian methods considered often misidentified a high proportion of disease clusters. P-values generated by SaTScan need to be considered with caution as they did not relate closely with the sensitivity or specificity of the ROC curves from our simulations.ConclusionsReal-world investigations of spatio-temporal signals (e.g. disease clusters) are often complex and time consuming. Identifying the best method to reduce the risks of identifying false positives and of missing real clusters is therefore essential. Despite the inherent constraints of theoretical simulations, such a framework allows to objectively assess the performance of different methods. Overall, our simulation framework suggested that SatScan would usually be the easiest, most user-friendly and best performing space-time methods for non-communicable disease surveillance. %R 10.5210/ojphi.v11i1.9741 %U %U https://doi.org/10.5210/ojphi.v11i1.9741 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9742 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveEpidemiologists will understand the differences between syndromic and discharge emergency department data sources, the strengths and limitations of each data source, and how each of these different emergency department data sources can be best applied to inform a public health response to the opioid overdose epidemic.IntroductionTimely and accurate measurement of overdose morbidity using emergency department (ED) data is necessary to inform an effective public health response given the dynamic nature of opioid overdose epidemic in the United States. However, from jurisdiction to jurisdiction, differing sources and types of ED data vary in their quality and comprehensiveness. Many jurisdictions collect timely emergency department data through syndromic surveillance (SyS) systems, while others may have access to more complete, but slower emergency department discharge datasets. State and local epidemiologists must make decisions regarding which datasets to use and how to best operationalize, interpret, and present overdose morbidity using ED data. These choices may affect the number, timeliness, and accuracy of the cases identified.MethodsCDC partnered with 45 states and the District of Columbia to combat the worsening opioid overdose epidemic through three cooperative agreements: Prevention for States (PFS), Data Driven Prevention Initiative (DDPI), and Enhanced State Opioid Overdose Surveillance (ESOOS). To support funded jurisdictions in monitoring non-fatal opioid overdoses, CDC developed two different sets of indicator guidance for measuring non-fatal opioid overdoses using ED data, with each focusing on different ED data sources (SyS and discharge). We report on the following attributes for each type of ED data source1,2: 1) timeliness; 2) data quality (e.g., percent completeness by field); 3) validity; and 4) representativeness (e.g., percent of facilities included).ResultsWhen comparing timeliness across data sources, SyS data has clear advantages, with many jurisdictions receiving data within 24 hours of an event. For discharge data, timeliness is more variable with some jurisdictions receiving data within weeks while others wait over 1.5 years before receiving a complete discharge dataset. Data quality and completeness tends to be stronger in discharge datasets as facilities are required to submit complete discharge records with valid ICD-10-CM codes in order to be reimbursed by payers. By contrast, for SyS data systems, participating facilities may not consistently submit data for all possible fields, including diagnosis. Validity is dependent on the data source as well as the case definition or syndrome definition used; with this in mind, SyS data overdose indicators are designed to have high sensitivity, with less attention to specificity. Discharge data overdose indicators are designed to have a high positive predictive value, while sensitivity and specificity are both important considerations. Discharge datasets often include records for 100% of ED visits from all nonfederal, acute care-affiliated facilities in a state included. By contrast, representativeness of facilities in SyS data systems varies widely across states with some states having less than 50% of facilities reporting.ConclusionsCDC funded partners share overdose morbidity data with CDC using either ED SyS data, ED discharge data, or both. CDC indicator guidance for ED discharge data is designed for states to track changes in health outcomes over time for descriptive, performance monitoring, and evaluation purposes and to create rates that are more comparable across injury category, time, and place. Considering these objectives, CDC placed a higher priority on data quality, validity (i.e., positive predictive value), and representativeness, all of which are stronger attributes of discharge data. CDC’s indicator guidance for ED SyS data is designed for states to rapidly identify changes in nonfatal overdoses and to identify areas within a particular state that are experiencing rapid change in the frequency or types of overdose events. When considering these needs, CDC prioritized timeliness and validity in terms of sensitivity, both of which are stronger attributes of SyS data. SyS and discharge ED data each lend themselves to different informational applications and interpretations based on the strengths and limitations of each dataset. An effective, informed public health response to the opioid overdose epidemic requires continued investment in public health surveillance infrastructure, careful consideration of the needs of the data user, and transparency regarding the unique strengths and limitations of each dataset.References1. Pencheon, D. (2006). Oxford handbook of public health practice. 2nd ed. Oxford: Oxford University Press.2. Centers for Disease Control and Prevention (CDC) Evaluation Working Group on Public Health Surveillance Systems for Early Detection of Outbreaks. (May 7, 2004). Framework for Evaluating Public Health Surveillance Systems for Early Detection of Outbreaks. MMWR. Morbidity and Mortality Weekly Reports. Retrieved from: https://www.cdc.gov/mmwr/preview/mmwrhtml/rr5305a1.htm %R 10.5210/ojphi.v11i1.9742 %U %U https://doi.org/10.5210/ojphi.v11i1.9742 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9743 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo develop specimen pooling algorithms that reduce the number of tests needed to test individuals for infectious diseases with multiplex assays.IntroductionAn essential tool for infectious disease surveillance is to have a timely and cost-effective testing method. For this purpose, laboratories frequently use specimen pooling to assay high volumes of clinical specimens. The simplest pooling algorithm employs a two-stage process. In the first stage, a set number of specimens are amalgamated to form a “group” that is tested as if it were one specimen. If this group tests negatively, all individuals within the group are declared disease free. If this group tests positively, a second stage is implemented with retests performed on each individual. This testing algorithm is repeated across all individuals that need to be tested. In comparison to testing each individual specimen, large reductions in the number of tests occur when overall disease prevalence is small because most groups will test negatively.Most pooling algorithms have been developed in the context of single-disease assays. New pooling algorithms are developed in the context of multiplex (multiple-disease) assays applied over two or three hierarchical stages. Individual risk information can be employed by these algorithms to increase testing efficiency.MethodsMonte Carlo simulations are used to emulate pooling and testing processes. These simulations are based on retrospective chlamydia and gonorrhea testing data collected over a two-year period in Idaho, Iowa, and Oregon. For each simulation, the number of tests and measures of accuracy are recorded. All tests were originally performed by the Aptima Combo 2 Assay. Sensitivities and specificities for this assay are included in the simulation process.The R statistical software package is used to perform all simulations. For reproducibility of the research, programs are made available at www.chrisbilder.com/grouptesting to implement the simulations.ResultsReductions in the number of tests were obtained for all states when compared to individual specimen testing. For example, the pooling of Idaho female specimens without taking into account individual risk information resulted in a 47% and a 51% reduction in tests when using two and three stages, respectively. With the addition of individual risk information, further reductions in tests occurred. For example, the pooling of Idaho female specimens resulted in an additional 5% reduction of tests when compared directly to not using individual risk information. These reductions in tests were found to be related to the type of risk information available and the variability in risk levels. For example, males were found to have much more variability than females. For Idaho, this resulted in a 15% further reduction in tests than when not using the risk information.ConclusionsSignificant reductions in the number of tests occur through pooling. These reductions are the most significant when individual risk information is taken into account by the pooling algorithm. %R 10.5210/ojphi.v11i1.9743 %U %U https://doi.org/10.5210/ojphi.v11i1.9743 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9744 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo utilize clinical data in Electronic Health Records (EHRs) to develop chronic disease phenotypes appropriate for conducting population health surveillance.IntroductionChronic diseases, including hypertension, type 2 diabetes mellitus (diabetes), obesity, and hyperlipidemia, are some of the leading causes of morbidity and mortality in the United States. Monitoring disease prevalence guides public health programs and policies that help prevent this burden. EHRs can supplement traditional sources of chronic disease surveillance, such as health surveys and administrative claims datasets, by offering near real-time data, large sample sizes, and a rich source of clinical data. However, few studies have provided clear, consistent EHR phenotypes that were developed to inform population health surveillance.MethodsRetrospective EHR data were obtained for patients seen at New York University Langone Health in 2017 (n=1,397,446). To better estimate chronic disease burden among New York City (NYC) adults, the patient population was limited to NYC residents aged 20 or older, who were seen in the ambulatory primary care setting (n=153,653). Rule-based algorithms for identifying patients with hypertension, statin-eligibility, diabetes, and obesity were developed based on a combination of diagnostic codes, lab results or vitals, and relevant prescriptions. We compared the performance of our metric definitions to selected phenotypes from the literature using percent agreement and Cohen’s kappa. Patients with discordant disease classifications between the two sets of definitions were analyzed through natural language processing (NLP) on the patients’ 2017 medical notes using a support vector machine model. Statin-eligibility is a novel phenotype and therefore did not have a comparable definition in the literature. Sensitivity analyses were conducted to determine how disease burden changed under alternative rules for each metric.ResultsOf 153,653 adult ambulatory care patients in 2017, an estimated 53.7% had hypertension, 12.4% had diabetes, 27.8% were obese, and 30.0% were statin-eligible under our proposed definitions. The estimated prevalence of hypertension increased from 28.1% to 53.7% when diagnostic codes were supplemented with blood pressure measurements and anti-hypertensive medications, while the estimated prevalence of diabetes increased less than one percentage point with inclusion of diabetes-related medications and elevated A1C measurements. There was high agreement between our obesity (94.5% agreement, k=0.86) and diabetes (96.2% agreement, k=0.81) definitions and selected definitions from the literature and moderate agreement between the hypertension definitions (74.8% agreement, k=0.41). NLP classification of discordant cases had greater alignment with the classification results of our definitions for both hypertension (78.0% agreement) and diabetes (71.2% agreement) but did not show strong agreement with either obesity algorithm. Sensitivity analyses did not have large impacts on prevalence estimates for any of the indicators, with all estimates within two percentage points of the final algorithms.ConclusionsOur proposed rule-based phenotypes using prescriptions, labs, and vitals improved ascertainment of conditions beyond diagnostic codes and were robust to modifications per sensitivity analyses. Results from our algorithms were highly consistent with standard phenotypes from the literature and may improve case capture for surveillance purposes. These algorithms can be replicated across diverse EHR networks and can be weighted to generate population prevalence estimates. %R 10.5210/ojphi.v11i1.9744 %U %U https://doi.org/10.5210/ojphi.v11i1.9744 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9745 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo share progress on a custom spell-checker for emergency department chief complaint free-text data and demonstrate a spell-checker validation Shiny application.IntroductionEmergency department (ED) syndromic surveillance relies on a chief complaint, which is often a free-text field, and may contain misspelled words, syntactic errors, and healthcare-specific and/or facility-specific abbreviations. Cleaning of the chief complaint field may improve syndrome capture sensitivity and reduce misclassification of syndromes. We are building a spell-checker, customized with language found in ED corpora, as our first step in cleaning our chief complaint field. This exercise would elucidate the value of pre-processing text and would lend itself to future work using natural language processing (NLP) techniques, such as topic modeling. Such a tool could be extensible to other datasets that contain free-text fields, including electronic reportable disease lab and case reporting.MethodsChief complaints may contain words that are incorrect if they are misspelled (e.g.,“patient has herpertension”), or, if the word yields a syntactically incorrect phrase (e.g., the word “huts” in the phrase: “my toe huts”).We are developing a spell-checker tool for chief complaint text using the R and Python programming languages. The first stage in the development of the spell-checker is the identifying and handling of misspellings; future work will address syntactic errors. Known abbreviations are identified using regular expressions, and unknown abbreviations are addressed by the spell-checker. The spell checker performs 4 steps on chief complaint data: identification of misspellings, generation of a substitute candidate word list, word sense disambiguation to identify replacement word, and replacement of the misspelled word, based on methods found in the literature.[1] As the spell-checker requires a dictionary of correctly spelled, healthcare-specific terms including all terms that would appear in an ED corpus, we used vocabularies from the Unified Medical Language System, ED-specific terminology, and domain expert user input. Dictionary construction, misspelling identification algorithms, and word list generation algorithms are in the development stage.Simultaneously, we are building an R Shiny interactive web application for syndromic surveillance analysts to manually correct a subset of misspelled words, which we will use to validate and evaluate the performance of the spell-checker tool.[1] Tolentino HD, Matters MD, Walop W, et al. A UMLS-based spell checker for natural language processing in vaccine safety. BMC Medical Informatics and Decision Making. 2007;7(1). doi:10.1186/1472-6947-7-3.ResultsProject still in development phase.ConclusionsThe audience will learn about important considerations for developing a spell-checker, including those for data structure of a dictionary and algorithms for identification of misplaced words and identification of candidate replacement words. We will demonstrate our word list generation algorithm and the Shiny application which uses these words for spell-checker validation. We will share relevant code; after our presentation, audience members should able to apply code and lessons to their own projects and/or to collaborate with the NYC Department of Health and Mental Hygiene. %R 10.5210/ojphi.v11i1.9745 %U %U https://doi.org/10.5210/ojphi.v11i1.9745 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9746 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo develop and implement a classifcation algorithm to identify likely acute opioid overdoses from text fields in emergency medical services (EMS) records.IntroductionOpioid overdoses have emerged within the last five to ten years to be a major public health concern. The high potential for fatal events, disease transmission, and addiction all contribute to negative outcomes. However, what is currently known about opioid use and overdose is generally gathered from emergency room data, public surveys, and mortality data. In addition, opioid overdoses are a non-reportable condition. As a result, state/national standardized procedures for surveillance or reporting have not been developed, and local government monitoring is frequently not specific enough to capture and track all opioid overdoses. Lastly, traditional means of data collection for conditions such as heart disease through hospital networks or insurance companies are not necessarily applicable to opioid overdoses, due to the often short disease course of addiction and lack of consistent health care visits. Overdose patients are also reluctant to follow-up or provide contact information due to law enforcement or personal reasons. Furthermore, collected data related to overdoses several months or years after the fact are useless in terms of short-term outreach. Therefore, given the potentially brief timeline of addiction or use to negative outcome, the current project set to create a near real-time surveillance and treatment/outreach system for opioid overdoses using an already existing EMS data collection framework.MethodsMarin County Department of Health and Human Services EMS data (2015-2017) was used for development of the system. The pool of data for model development and evaluation consisted of 15,000 EMS records randomly selected from 2015, 2016, and 2017. Each record was manually classified in a binary manner with the criteria of “more likely than not opioid related”, using only selected text fields. The event did not need to be exclusively opioid related, nor did opioids have to be the primary cause for the EMS call. 2,000 records were selected for review by the medical director for Marin County EMS, with a Cohen’s kappa coefficient of approximately 0.94. Overall, the proportion of opioid overdoses was less than 0.01 amongst the 15,000 records. An enriched data set of 80 randomly selected overdoses and 320 randomly selected non-overdoses was created for the purposes of feature engineering. These 400 records were excluded for further use in model training and testing. Within the enriched set, the descriptive text fields were tokenized based on the hypothesis that opioid overdoses and non-overdoses are separable based on the content of the descriptive fields. Each field was tokenized as words, bigrams (pairs of consecutive words), and trigrams (triplets of consecutive words). The frequencies of each token as a percentage of overall words were calculated separately for opioid overdoses and non-overdoses. Structured fields used in the analysis were not tokenized prior to frequency calculations. The frequencies for each token/phrase were then compared across opioid overdoses status with a proportion test for equality at an alpha of 0.05 with a Bonferroni correction for multiple comparisons. The tokens/phrases that were statistically significantly more likely to be present in opioid overdoses were assigned to a quintile based on their p-value, with smallest p-values assigned five, and largest p-values assigned one. Tokens/phrases statistically significantly more likely to be present in non-overdoses were scored in the same manner, with the smallest p-value assigned negative five, and the largest p-value negative one. The tokens/phrases that were statistically different across opioid overdose status were stored along with their quintile scores in dictionaries that were kept for future modeling use. From the initial 15,000 classified records, excluding the 400 used for the enriched data set, 10,000 records were randomly selected for model training and development. Each record had their text fields tokenized into words, bigrams, and trigrams, and each was compared with the corresponding dictionary. If a token was present in the entry and also in the dictionary, that token’s quintile score was assigned to the record, with multiple tokens being summed to produce a score for each field-token option. The final created feature was the count of opioid specific terms such as “heroin”, “fentanyl”, “narcan”, etc. within the main narrative field. The intent was to create a variety of numerical features that were indicative of presence of tokens/phrases that were positively associated with opioid overdoses such that higher scores were more associated. Several models including support vector machines, neural nets, gradient boosted machines, and logistic regression were tested via 10-fold cross validation, with logistic regression yielding the best error rates and lowest computational costs. Although all models resulted in a sensitivity greater than 85 percent, logistic regression was by far the best in terms of false positive rate. The coefficients for the logistic regression model were selected from the eight created features along with patient sex and patient age by best subsets selection via Akaike information criterion (AIC), and the probability threshold for classification was selected via optimizing the receiver operating curve (ROC).ResultsFollowing the variable selection and threshold optimization for logistic regression, the sensitivity and specificity of the model were between 90 percent and 95 percent. However, given the large number of records fed through the algorithm either each week for ''real-time'' surveillance and treatment/outreach, or for larger retrospective data sets, improving specificity is crucial to reduce the number of false positives. Additionally, given that a public health treatment/outreach staff has a finite amount of time and resources, limiting false positives will allow them to focus on the true cases. Further model improvements were made with a series of binary filters that allowed for overall sensitivity/specificity improvements as well as ensuring that the records sent for outreach are appropriate for outreach. The application of the filters pushed the classification sensitivity and specificity to greater than 99 percent. Further, the filters removed cases inappropriate for outreach at greater than 90 percent efficiency.ConclusionsThe algorithm was able to classify opioid overdoses in EMS data with a sensitivity and specificity greater than 99 percent. It was implemented into a viable public health treatment/outreach system through the Marin County Department of Health and Human Services in May 2018, and has identified approximately 50 overdoses for outreach as of September, 2018. It is possible, using minimal computational power and infrastructure to develop a fully realized surveillance system through EMS data for nearly any size public health entity. Additionally, the framework allows for flexibility such that the system can be tailored for specific clinical or surveillance needs - there is no ''black box'' component. Lastly, the application of this methodology to other diseases/conditions is possible and has already been done using the same data for both sepsis and falls in older adults.References1) R Core Team. 2018. R: A Language and Environment for Statistical Computing. Available: https://www.r-project.org/.2) RStudio Team. 2018. RStudio: Integrated Development Environment for R. Available: http://www.rstudio.com/. %R 10.5210/ojphi.v11i1.9746 %U %U https://doi.org/10.5210/ojphi.v11i1.9746 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9747 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveThe presentation describes the results of the daily monitoring of health indicators conducted by the French public health agency during the major floods and the cold wave that occurred in January 2018 in France, in order to early identify potential impact of those climatic events on the population.IntroductionThe Seine River rises at the north-East of France and flows through Paris before emptying into the English Channel. On January 2018 (from 22th January to 11th February, Weeks 4 to 6), major floods occurred in the Basin of Seine River, after an important rainy period. This period was also marked by the occurrence on the same area of a first cold wave on Week 6 (from 5th to 7th February), including heavy snowfall and ice conditions from 9th to 10th February. A second similar cold wave occured from 28th February and 1st March.Floods of all magnitude are known to have potential health impacts on population [1], both at short, medium and long term both on physical (injuries, diarrhoeal disease, Carbon Monoxyde poisoning, vector-borne disease) and mental health. Extreme cold weather have also the potential to further impact on human health through direct exposure to lower temperatures, and associated adverse conditions, such as snow and ice [2]. Such situations may be particularly associated to direct impact like hypothermia, frostbite and selected bone/joint injuries).MethodsSince 2004, the French Public Health Agency (Santé publique France) set up a national syndromic surveillance system SurSaUD, enabling to ensure morbidity and mortality surveillance [3]. In 2018, morbidity data were daily collected from a network involving about 700 emergency departments (ED) and 58 emergency general practitioners’ associations SOS Médecins. 92% of the national ED attendances and 95% of national SOS Médecins visits are caught by the system.Both demographic (age and gender), administrative (date and location of consultation, transport) and medical information (chief complaint, medical diagnosis using ICD10 codes in ED and specific thesauri in SOS Médecins associations, severity, hospitalization after discharge) are recorded for each patient.The daily and weekly evolution of the number of all-cause ED attendances and SOS Médecins consultations during the flooding period were compared to the evolution on the two previous years. The number of hospitalisations after ED discharge was also monitored. The immediate health impact of floods and cold waves was assessed by monitoring eight syndromic indicators: gastroenteritis, carbon monoxide poisoning, burnt, stress, faintness, drowning, injuries and hypothermia.Analyses were performed by age group (<15 years, 15-64 years, more than 65 years) and at different geographical levels (national, Paris region and districts located in the Basin of Seine River).ResultsIn 2018, syndromic surveillance did not show any major impact on all-cause ED attendances and SOS Médecins consultations from week 4 to week 6, neither in Paris area nor in other areas along the Seine River. The recorded numbers were comparable to the two precedent years in all age groups.A decrease of the all-cause ED attendances was observed during the 1st day with ice conditions in Normandy and Paris, mainly in children and adults aged 15-64 years.During week 6 in Paris area, an increase of ED attendances was observed for injuries (+4% compared to the past weeks – figure 1) and to a lesser extent for hypothermia and frostbite (16 attendances compared to less than 9 for the past weeks). Similar increase in injuries were observed in Normandy during the second cold wave (Figure 1).ConclusionsDuring the flood episode, the rising water level was slow with foreseeable evolution, compared to other sudden flood events occurring in south of France in 2010 due to violent thunderstorms. This progressive evolution allows French authority to deploy wide specific organization in order to mitigate impact on concerned populations. That may explain the absence impact observed in ED at regional and national levels during the flood disaster. The evolution of injuries during 2018 episode is attributable to the cold wave that occurred simultaneously.As the French syndromic surveillance system is implemented on the whole territory and collects emergency data routinely since several years, it constitutes a reactive tool to assess the potential public health impact of both sudden and predictable disasters. It can either contribute to adapt management action or reassure decision makers if no major impact is observed.References[1] Ahern M, Kovats S. The health impacts of floods. In: Few R, Matthies F, eds. Flood hazards and health: responding to present and future risks. London, Earthscan, 2006:28–53.[2] Hughes H, Morbey R, Hughes T. et al. Using an Emergency Department Syndromic Surveillance System to investigate the impact of extreme cold weather events Public Health. 2014 Jul;128(7):628-35.[3] Caserio-Schönemann C, Bousquet V, Fouillet A, Henry V. The French syndromic surveillance system SurSaUD (R). Bull Epidémiol Hebd 2014;3-4:38-44. %R 10.5210/ojphi.v11i1.9747 %U %U https://doi.org/10.5210/ojphi.v11i1.9747 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9748 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo identify additional data elements in existing syndromic surveillance message feeds that can provide additional insight into public health concerns such as the influenza season.IntroductionSyndromic surveillance achieves timeliness by collecting prediagnostic data, such as emergency department chief complaints, from the start of healthcare interactions. The tradeoff is less precision than from diagnosis data, which takes longer to generate. As the use and sophistication of electronic health information systems increases, additional data that provide an intermediate balance of timeliness and precision are becoming available.Information about the procedures and treatments ordered for a patient can indicate what diagnoses are being considered. Procedure records can also be used to track the use of preventive measures such as vaccines that are also relevant to public health surveillance but not readily captured by typical syndromic data elements. Some procedures such as laboratory tests also provide results which can provide additional specificity about which diagnoses will be considered. If procedure and treatment orders and test results are included in existing syndromic surveillance feeds, additional specificity can be achieved with timeliness comparable to prediagnostic assessments.MethodsHL7 messages were collected for syndromic surveillance using EpiCenter software. They were retroactively scanned for PR1 procedure segments; procedure codes and descriptions were extracted when available. Influenza-related procedures were identified and classified as either a test for the virus or an administration of a vaccine. Classification was based on the procedure code when a standard code set was used and could be identified, otherwise it was based on the text description of the procedure.Messages were also scanned for the presence of ‘influenza’ in text fields. Influenza test results were identified first by selecting messages with ‘influenza’ in an OBX segment and then further refining based on the test code and description.ResultsA total of 443,074,748 messages from 2,577 healthcare facilities received between July 1, 2017 and August 31, 2018 were scanned for procedure information. Procedure codes were present in 39,142,670 messages from 287 facilities. The most common procedures included blood glucose measurements and other diabetes maintenance activities, incentive spirometry, blood count and metabolic panels, safety observation, and vital signs.Of those, 995,754 messages from 142 facilities contained influenza-related procedure codes for 106,610 visits. 14,672 visits from 62 facilities had one of 48 vaccine procedure codes, and 91, 948 visits from 127 facilities had one of 66 test codes. Time series of both types of procedures showed a seasonal trend consistent with the influenza season. Figure 1 shows the daily counts of influenza test orders and vaccine administrations. Figure 2 breaks out the test orders by test type (antibody assay, antigen assay, PCR, or unspecified).Seven facilities sent a total of 58,182 messages containing influenza test results. These included both positive and negative results. These results distinguished between influenza A and influenza B. Figure 3 shows the daily counts of both positive and negative results by virus type; this also follows the expected seasonal pattern.ConclusionsSince procedure information was not specifically requested from healthcare facilities, the overall representation of procedure data elements was low. These initial results indicate that such data would be useful both as a supplement to syndromic surveillance activities and as a new data source for other surveillance activities such as vaccine uptake tracking. Given the frequency of procedures and treatments for chronic diseases such as diabetes and heart disease, these data may be relevant for understanding the prevalence of those conditions as well. Tests and treatments relevant to other public health concerns like opioid use disorder were also present, suggesting a wide range of potential applications.It is also possible to obtain and extract influenza test results from these syndromic surveillance messages. Both positive and negative results were present, providing information not just on the number of positive cases but also the rate of testing and rate of positive results. The pattern of testing and results also indicates that at least some facilities test for influenza throughout the season, contrary to some conventional wisdom about testing patterns. %R 10.5210/ojphi.v11i1.9748 %U %U https://doi.org/10.5210/ojphi.v11i1.9748 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9749 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo investigate epidemiological features and identify high relative risk space-time Intestinal infectious diseases clusters at the township level in Beijing city in order to provide the scientific evidence for making prevention and control measures.IntroductionIntestinal infectious diseases (IID) is a common cause of illness in the community and results in a high burden of consultations to general practice, mostly affecting the health of infants, preschool children, young adults and elderly people, especially those living in low income countries. According to the published study on the global burden of disease, intestinal infectious diseases were responsible for 221,300 deaths worldwide in 2013. The Chinese Ministry of Health has listed bacillary dysentery, amebic dysentery, typhoid fever and paratyphoid fever as notifiable Class-B communicable diseases and other infectious diarrhea as notifiable Class-C communicable diseases to be included in the surveillance system and reporting network since 2004. Many studies of IID in different regions have been published. However, the epidemiological characteristics and space-time patterns of individual-level IID cases in a major city such as Beijing are still unknown. We aim to analyze the epidemiology features and identify space-time clusters of Beijing IID at a fine spatial scale in this study.MethodsData collection. Data on IID cases in the 2008-2010 period were provided by Beijing Center for Disease Prevention and Control, China, including basic social-demographic information and clinical diagnosis (mainly including upper respiratory tract infection, indigestion, gastrointestinal disorders, bacillary dysentery, amebic dysentery, typhoid fever, paratyphoid fever and other infectious diarrhea). The demographic data for each township was calculated based on 2010 census data and the data published in the Beijing Statistical Yearbook.Epidemiological analysis. The home addresses from IID case records were matched to the geographic coordinates of the township level divisions. Age-gender incidence of IID (1/100,000) was defined as the number of IID cases in each age-gender group divided by the population size of that age-gender group. Total incidence was defined as the total number of IID cases divided by the average population size during the study period.Space-time analysis. Local spatial autocorrelation analysis based on Indicators of Spatial Association (LISA) was used to measure the spatial autocorrelation of IID incidence. The High-High and Low-Low townships suggested the clustering of similar values for IID incidence, whereas the Low-High and High-Low townships indicated spatial outliers. The spatial and space-time scan statistics combined the covariates (gender and age) method were used to reveal the space-time clusters of Beijing IID.ResultsEpidemiological features. A total of 561,199 individual-level IID cases were reported in Beijing in the period, in which 95 cases without the township information. 22.1% (124,025) of the cases were in the 0 to 4-year age group. Secondly 21.8% (122,345) were in the 50+-year age group. Next 13.17% were in the 25 to 29-year age group (73,931) and 11.9% were in the 20 to 24-year age group (66,787). Among the total IID cases, 307,920 were male, and 253,278 were female. The average male-to-female sex ratio was 1.22. Total IID incidence was 1003.54 /100,000 (1035.16 in 2008, 992.67 in 2009 and 985.30 in 2010). Total IID age-specific incidence in the 0 to 4-year age group (19,004.95) was the highest, followed by 3267.40 in the 25 to 29-year age group. The sex ratio of IID cases varied among the different age-gender groups. For the 50+-year age group, the incidence in female was higher than that in male. However, for the other age groups, the incidence in female was usually lower. The monthly distribution of IID cases exhibited significant seasonality and periodicity. The annual peaks in incidence mostly occurred between May and July. The annual number of IID cases was the lowest (183,326) in 2008 and the greatest (193,237) in 2010.Space-time Patterns. LISA analysis found that the borders between old city (Xicheng and Dongcheng) and urban districts (Haidian, Chaoyang, Shijingshan and Fengtai) showed the clear High-High positive spatial association for IID incidence. Rural areas (Yanqing, Huairou, Miyun and Pinggu) and outlying districts (the west of Mentougou and Fangshan, the southeast of Daxing and Tongzhou) showed the stable Low-Low positive spatial association for IID incidence. The townships showing Low-Low negative spatial association were mainly distributed in the urban-rural transition zones around the old city, while the High-Low spatial outliers mainly scattered in Xinggu county of Pinggu and Shahe town of Changping.Detected spatial scan clusters varied from year to year. The most likely clusters occurred in 15 townships around Chongwenmenwai of Dongcheng district (2008, Relative risk (RR) = 9.39, Log likelihood ratio (LLR) = 53927.93, P-value (P) < 0.001), Donghuamen and Qianmen of Dongcheng district (2009, RR = 35.01, LLR = 53286.52, P < 0.001), Donghuamen of Dongcheng district (2010, RR= 43.83, LLR = 62674.76, P < 0.001). The most likely space-time cluster (RR = 41.3, P < 0.001) was located in Donghuamen and Qianmen of Dongcheng district during the period from 2009/5/1 to 2010/10/31. The secondary space-time clusters (RR = 2.02, P < 0.001) were mainly scattered in the west part of Beijing including 133 townships during the period from 2010/6/1 to 2010/9/30.ConclusionsThe detected locations and space-time patterns of Beijing IID clusters are important for the local health officials to determine the source of the cluster to design effective prevention strategies and interventions against Beijing IID. The variations in Beijing IID epidemics over population, space, and time that were revealed by this study emphasize the need for more thorough research about the driving forces and risk factors (climate, geography, environment, and social-economic) that contribute to prevent and control Beijing IID outbreaks.ReferencesAbubakar I I et al. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet, 2015, 385(9963): 117-171.Ghoshal U C et al. The role of the microbiome and the use of probiotics in gastrointestinal disorders in adults in the Asia-Pacific region background and recommendations of a regional consensus meeting. Journal of gastroenterology and hepatology, 2018, 33(1): 57-69. %R 10.5210/ojphi.v11i1.9749 %U %U https://doi.org/10.5210/ojphi.v11i1.9749 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9750 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveOur presentation will explain current use, and barriers to use, of reproducible research practices in public health. We will also introduce a set of modules for researchers wishing to increase their use of reproducible research practices.IntroductionAn important goal of surveillance is to inform public health interventions that aim to reduce the burden of disease in the population. Ensuring accuracy of results is paramount to achieving this goal. However, science is currently facing a “reproducibility crisis” where researchers have found it difficult or impossible to reproduce study results. Organized and well-documented statistical source code that is publicly available could increase research reproducibility, especially for research relying on publicly available surveillance data like the BRFSS, NHANES, GSS, SEER, and others. As part of our overall goal to improve training around reproducible research practices, we surveyed public health data analysts to determine current practices and barriers to code sharing.MethodsWe conducted a cross-sectional web-based survey about code organization, documenting, storage, and sharing. We surveyed public health scientists who reported recently conducting statistical analyses for a report or manuscript. A total of 247 of 278 screened eligible to filled out the survey, and 209 answered every applicable question. We used traditional descriptive statistics and graphs to examine the survey data.ResultsMost participants reported using some promising coding practices, with 67% including a prolog to introduce the code and 85% including comments in statistical code to explain operations and analyses. Of 10 common code organization strategies (e.g., naming variables logically, using white space), most (82%) respondents reported employing at least three of the strategies and just under half (47%) reported using five or more. Over half of participants (59%) reported code was developed or checked by two or more people. Many participants also reported promising file management habits for data and code used in publications. Three-quarters (75%) had a variable dictionary to accompany the dataset used, 48% created clean versions of code files, and 64% created clean versions of data files at the time of publication. Forty three percent of participants reported that if they suddenly left their current position, it would not be easy for others to find their statistical code files. Public code sharing was much less common among participants with just 9% reporting sharing code publicly from a recent publication and 20% of those surveyed reported ever having shared code publicly.The top two barriers to using reproducible research practices were lack of training in reproducible research (n=108) and data privacy issues (n=105). Journals and funders not requiring reproducible practices were barriers selected by 94 and 84 participants, respectively. Few participants identified fear of errors being discovered (n=26) or a lack of workplace incentives (n=32) as barriers.ConclusionsMost participants were using some promising practices for organizing and formatting statistical code but few were sharing statistical code publicly. The second most frequently identified barrier to using reproduciible practices was data privacy, which could prohibit easily sharing a data source. With surveillance data often being publicly available, researchers working with surveillance data have overcome this top barrier without any change to current research practices. Researchers using surveillance data could greatly increase research reproduciblity by adopting promising practices for code formatting, like using logical variable names and limiting line length, and posting code in a public repository like GitHub.To overcome the top barrier to use of reproducible research practices, lack of training, we developed brief training modules on formatting, documenting, and sharing statistical code and data. As part of our presentation we will introduce and provide access to these online modules. The introduction will focus on the relevant modules for surveillance data users, which include statistical code formatting and statistical code sharing via GitHub.With fewer barriers to practicing reproducible research, public health researchers using surveillance data have the opportunity to be leaders in improving the adoption of reproducible research practices and subsequently improving the quality of research we rely on to improve public health. %R 10.5210/ojphi.v11i1.9750 %U %U https://doi.org/10.5210/ojphi.v11i1.9750 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9751 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveBy the end of this session, users will be able to describe the innovative and multilayered suppression rules that are applied to Missouri''s homegrown health data web query system. They will also be able to use the lessons learned and user feedback described in the session to facilitate discussions surrounding the application of suppression to their specific data systems.IntroductionIn Spring 2017, the Missouri Department of Health and Senior Services (MODHSS) launched the Missouri Public Health Information Management System (MOPHIMS) web-based health data platform. Missouri has supported a similar data system since the 1990s, allowing the public, local public health departments, and other stakeholders access to community level birth, death, and hospitalization data (among other datasets). The MOPHIMS system is composed of two separate pieces. Community Data Profiles are topic-, disease-, or demographic-specific reports that contain 15-10 indicators relevant to the report. Because these static reports are developed in-house a multilayered suppression rule is not required. The second piece of MOPHIMS, the Data MICAs, or Missouri Information for Community Assessent, can be used to create customized datasets that slice and dice up to a dozen demographic and system-specific variables to answer complex research questions.The MOPHIMS interface features, among other things, a new and innovative method for addressing confidentiality concerns through the suppression of health data. This pioneering approach integrates multi-level logic that uses inner and outer cell analytics, the use of exempt and conditionally exempt variables, and multiple levels of user access. Moving beyond a simple model of suppressing any values below a certain threshold, MOPHIMS takes a bold step in providing users exceptionally granular data while still protecting citizen privacy.MethodsIn order to implement this new suppression methodology, MODHSS worked with both internal information technology resources (OA-ITSD) and outside contractors to develop the suppression rules utilized in the Data MICAs. Before these meetings began, MODHSS analysts met weekly to determine the overall goals and frames for the rule, knowing that writing the code to implement the complicated and comprehensive vision would be a collaborative and iterative process. Because the MOPHIMS system is homegrown and this specific confidentiality process is not currently utilized (to our knowledge) elsewhere, all of those at the discussion table were required to be innovative, open to criticism, and willing to engage in extremely detailed explanations. A team of users from Missouri’s local public health departments provided feedback throughout this process.A basic description of the process flow that occurs before suppression is applied in MOPHIMS follows. To begin, de-identified record-level data are loaded into online analytical processing (OLAP) cubes and relational databases. No suppression is applied to these back end databases. The information is then aggregated for display on the front end screens of the Data MICAs based on customized user selections. Depending upon which level of access a user has logged in, suppression is then applied to the data output generated using these customized selections. Not only are the rules applied to data tables but also to the MOPHIMS data visualization tools, which include multiple types of charts and maps.ResultsIn addition to the rules themselves, MOPHIMS contains a mechanism that allows users to log in at different levels of access. Public and Registered user levels are free and available to all operators with a valid e-mail address. Partner level access is reserved for epidemiologists at the state and local level who are using the Data MICAs for program planning, evaluation, and grant writing. Because these individuals are required to adhere to the same data dissemination policies as those who create the MOPHIMS system, Partner level access turns off suppression in the MOPHIMS system. Values that would be suppressed at the Public or Registered user levels are shown in italicized, red font. A multi-level approval process is required for individuals to obtain Partner level access to MOPHIMS.ConclusionsMODHSS created an innovative suppression system that allows public health planners to access granular data through customizable queries without risking a confidentiality breach. Users have indicated this is highly preferable to a blanket suppression rule that hides any value under a certain threshold. Additionally, approved MOPHIMS users can view specially formatted values that would otherwise have been suppressed. The flexibility associated with creating a homegrown web query system has allowed the formation and implementation of this multilayered rule, which likely would not have been possible if using an off-the-shelf product. Data disseminators are encouraged to review current confidentiality and suppression rules to determine whether they might be modified to provide more granular data users while still protecting the privacy of citizens. %R 10.5210/ojphi.v11i1.9751 %U %U https://doi.org/10.5210/ojphi.v11i1.9751 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9752 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveUsing the information that we have available, our primary objective is to explore if there was any cross-correlation between pneumonia admissions and hospital influenza positivity. We then aim to develop a data driven approach to forecast pneumonia admissions using data from our hospital’s weekly surveillance. We also attempted using external sources of information such as national infectious diseases notifications and climate data to see if they were useful for our model.IntroductionInfluenza peaks around June and December in Singapore every year. Facing an ageing population, hospitals in Singapore have been constantly reaching maximum bed occupancy. The ability to be able to make early decisions during peak periods is important. Tan Tock Seng Hospital is the second largest adult acute care general hospital in Singapore. Pneumonia-related emergency department (ED) admissions are a huge burden to the hospital''s resources. The number of cases vary year on year as it depends on seasonal vaccine effectiveness and the population’s immunity to the circulating strain. While many pneumonia cases are of unknown origin, they tend to mirror the influenza seasons very closely.MethodsWe used data from epidemiological week (e-week) 1 of 2013 to e-week 34 of August 2017 to train our model, with the next 52 weeks (e-week of 35 of 2017 to e-week 34 of 2018 ) being used as validation cohort. Pneumonia and influenza data were obtained from our hospital’s weekly surveillance. National level acute upper respiratory illness (AURI) was obtained from Ministry of Health’s (MOH) weekly infectious diseases bulletin. Climate data were obtained from the National Environment Agency’s website. Daily rainfall, temperature and wind data from the S20 satellite station were used. Automatic autoregressive (A-ARIMA), non-seasonal and seasonal vector autoregressive models (VAR) were used to either analyse the univariate pneumonia trends or simultaneously model pneumonia, influenza, AURI notification and climatic data. Granger-causality tests were performed to check if these variables were causal of pneumonia admissions. As most of the seasonal variation are seen in older patients, stratified analysis were performed on those that were below and above 65 years old. Forecasts were calculated up to 3 weeks in advance. Mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) were used to validate the model performance. These performance metrics were applied on 3-week ahead forecasts comparing A-ARIMA, VAR, and seasonal-adjusted VAR.ResultsFigure 1 shows that both influenza and pneumonia admissions follow similar trends. We see that the number of influenza cases have reduced as compared to the previous years. The number hospital influenza cases and the number of AURI cases nationwide are strongly cross-correlated with pneumonia admissions. Granger-causality tests confirmed the directionality of the relationships (p <0.01). Climate factors do not strongly affect the number of pneumonia admissions. (Fig 2) Unsurprisingly, the A-ARIMA model showed that the 1-day forecasts were most accurate (MAE: 7.0; MAPE: 12.7; RMSE: 8.7 for elderly subgroup). However, the 3-day ahead forecasts were only slightly less precise (MAE: 7.2 ; MAPE: 13.2; RMSE: 9 for elderly subgroup). Testing for significant lags using the various information criteria suggested that a lag3 model should be used. The non-seasonal and seasonal VAR models showed that historical pneumonia admissions and influenza positivity was the best model. The MAPE for all 3 models hovered between 12-13%, with the A-ARIMA model performing slightly better. This is not surprising as the A-ARIMA takes the latest information at hand to derive the best model. Accounting for seasonality allowed better precision as compared to the non-seasonal VAR but was not better as compared to the A-ARIMA model.ConclusionsHospital surveillance data are the most useful for developing forecast models for hospital pneumonia admissions. Climate data were likely not to be useful as Singapore does not experience much variaton in weather throughout the year. Pneumonia peaks do not follow necessarily fall on the same week every season. Therefore, both the autoregressive and seasonal-adjusted vector autoregressive models can be useful complements to each other for forecasting pneumonia admissions. %R 10.5210/ojphi.v11i1.9752 %U %U https://doi.org/10.5210/ojphi.v11i1.9752 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9753 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo develop a set of clinical indicators of opioid overdose using Emergency Medical Services (EMS) records that included data from Computer Aided Dispatch (CAD), ProQA systems, Electronic Patient Care Reporting (ePCR) and Hospital Medical Records.IntroductionIn North America we experience the highest rate of drug related mortality in the world. In the US, overdose is now the leading cause of death among adults under 50. Each day more than 115 people in the United States die due to an opioid overdose. The opioid overdose national crisis is rapidly evolving due to changes in drug availability and the presence of adulterated fentanyl in some areas leading to a critical need for innovative methods to identify opioid overdoses for both surveillance and intervention purposes. As an effort to strengthen our understanding of the epidemic through surveillance of Emergency Medical Services (EMS) we have developed a set of clinical indicators that identify opioid overdose within the information provided by an Electronic Patient Care Reporting (ePCR), Computer Aided Dispatch (CAD), ProQA systems and Hospital Medical Records.MethodsWe initially created a set of EMS agency specific opioid overdose filters using FirstWatch® software as part of a public health research study. Following that initial development, we have built a generic set of opioid overdose identifiers. In the initial approach we used a Zoll Data System software for ePCR and TriTech Inform CAD to define 3 set of identifiers: (T1) captured calls in which naloxone was administered and a positive clinical response was documented, (T2) had the same criteria as T1 except there was no positive response to the administration of naloxone, and (T3) consisted of calls in which one or more drug-related keywords were present within the narrative of the ePCR. Because the initial analysis was conducted in the context of a single research study, we aimed to create a more generalizable set of identifiers of opioid overdose that would function across different EMS agencies, software, and data sources. In addition, we included variables provided by Hospital Medical Records to our filtering criteria to provide a more robust and complete set of opioid overdose clinical indicators.ResultsUtilizing the EMS data sources CAD, ProQA and ePCR as well as Hospital Medical Records we have developed a set of identifiers of opioid overdose. Utilizing FirstWatch® software analytics the following variables where coded into the software: 1. CAD Data.- Chief Complaint and Opioid Overdose Keyword search; 2. ProQA.- Protocols 6, 9, 23, 31 and 32; 3. ePCR.- Primary and Secondary Impressions, Chief Complaint, Intervention of Narcan (Naloxone) Administration, Vital Signs and Opioid Overdose Keyword search; 4. Medical Records.- Patient''s Admission and Discharge Diagnosis (Diagram 1). The clinical indicators obtained from this analysis where created to be utilized across different EMS specific software vendors for CAD, ProQA and ePCR systems. For the Medical Records variables a single software vendor was available to be integrated into the analysis. Nonetheless, as we used the International Statistical Classification of Diseases and Related Health Problems codes on their 10th revision (ICD-10) our determining variable codes could be generalized to other Hospital Record system if they would become available.ConclusionsCorrectly identifying an opioid overdose can a be a challenge. Its clinical features are non-specific and bystanders fear repercussions of disclosing the nature of the 911 call. Determining the correct number of opioid overdoses requires a tailored identification process. A combination of clinical determinants and incorporation of multiple EMS data sources appears to be feasible in determining opioid overdose related 911 calls.References1. The United Nations Office on Drugs and Crime (UNODC) ''2017 World Drug Report''.2. Hedegaard H, Warner M, Miniño AM. Drug overdose deaths in the United States, 1999–2016. NCHS Data Brief, no 294. Hyattsville, MD: National Center for Health Statistics. 2017.3. Multiple Cause of Death 1999–2016 on CDC Wide-ranging Online Data for Epidemiologic Research (CDC WONDER). Atlanta, GA: CDC, National Center for Health Statistics. 2017.4. CDC/NCHS, National Vital Statistics System, Mortality. CDC Wonder, Atlanta, GA: US Department of Health and Human Services, CDC; 2017. %R 10.5210/ojphi.v11i1.9753 %U %U https://doi.org/10.5210/ojphi.v11i1.9753 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9754 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveWe develop new spatial scan models that use individuals'' movement data, rather than a single location per individual, in order to identify areas with a high relative risk of infection by dengue disease.IntroductionTraditionally, surveillance systems for dengue and other infectious diseases locate each individual case by home address, aggregate these locations to small areas, and monitor the number of cases in each area over time. However, human mobility plays a key role in dengue transmission, especially due to the mosquito day-biting habit,1 and relying solely on individuals’ residential address as a proxy for dengue infection ignores a multitude of exposures that individuals are subjected to during their daily routines. Residence locations may be a poor indicator of the actual regions where humans and infected vectors tend to interact more, and hence, provide little information for dengue prevention. The increasing availability of geolocated data in online platforms such as Twitter offers a unique opportunity: in addition to identifying diseased individuals based on the textual content, we can also follow them in time and space as they move on the map and model their movement patterns. Comparing the observed mobility patterns for case and control individuals can provide relevant information to detect localized regions with higher risk of dengue infection. Incorporating the mobility of individuals into risk modeling requires the development of new spatial models that can cope with this type of data in a principled way and efficient algorithms to deal with the ever-growing amount of data. We propose new spatial scan models and exploit geo-located data from Twitter to detect geographic clusters of dengue infection risk.MethodsAs the spatial tracking of a large sample of infected and non-infected individuals is expensive and raises serious privacy issues, we instead analyze geo-located Twitter data (tweets), which is readily and publicly available. We identify “infected” individuals (cases) as those individuals who have at least one tweet classified as a current, personal experience with dengue. We note that, because of the incubation period and recovery time, infected Twitter users are likely to mention dengue in their tweets days after they are infected, and usually not at the location where the exposure (mosquito bite) occurred. Once we have identified cases and controls based on the textual content of the messages, we then compare the mobility patterns of the two groups. The key aspect of our method is that the input is a series of locations rather than a single location, such as the residence address, for each individual. The number of positions ni composing each mobility pattern can vary substantially between individuals i, and thus simple approaches like counting the total numbers of case and control tweets per location would be biased and inaccurate; moreover, individuals with larger numbers of tweets may be more likely to be identified as a case. Nevertheless, our assumption is that the entire mobility patterns will be informative of the riskier areas if we compare the spatial patterns from infected and non-infected individuals.We have developed two new spatial scan methods (unconditional and conditional spatial logistic models) which correctly account for the multiple, varying number of spatial locations per individual. Both models use the proportion of an individual’s tweets in each location as an estimate of the proportion of time spent in that location; the estimate is biased by individuals’ propensity to tweet in different locations, but is expected to capture the large amounts of time spent at frequently visited locations. Our unconditional model controls the variable contribution of each individual through a non-parametric estimation of the odds of being a case and has a semi-parametric logistic specification. When estimating the previous offset becomes a complex task, we propose a case-control matching strategy in the conditional model to control for the number of tweets ni. Based on the subset scan approach,3 we search for localized regions where the infection risk is substantially higher than in the rest of the map by maximizing a log-likelihood ratio statistic over subsets of the data.ResultsWe demonstrate the detection of high-risk clusters for dengue infection using Twitter data we collected in Brazil during the year of 2015, when a strong surge of dengue hit several cities. We apply our method to the cities with highest number of case individuals. There are many points of interest, such as hospitals and parks, inside the detected regions. As those places are non-residential, standard approaches would fail to consider them as potential infection places in the event of a spike in the number of cases. Figure 1 shows the detected regions in the city of Campinas, Brazil. Synthetic and real-world evaluation results demonstrate that our methods work better than either just mapping each individual to their most frequent location (which is a proxy for home address) and running a traditional spatial scan, or scanning using tweet volume as an input.ConclusionsIdentifying places where people have higher risk of being infected, rather than focusing on residential address locations, may be key to surveillance for vector-borne diseases such as malaria and dengue, allowing public health officials to focus mitigation actions. The stochasticity of location data is not appropriate for typical spatial cluster detection tools such as the traditional spatial scan statistic.2 Each user is represented by a different number of geographic points and the variability of these numbers is large; traditional approaches can be easily misled if not extended to account for this special structure. Dengue is just one of many infectious diseases with a well-known etiology but a huge number of uncertain and difficult to obtain parameters that quantify factors such as infected mosquito population, likelihood of being bitten by an infected mosquito, and human movement in the mosquito-infested areas. Our methods add to the set of tools that spatial epidemiologists have available to search for spatially localized risk clusters using readily available Twitter data. We expect that our method will also be useful to other public health surveillance problems where movement data can bring relevant information.References1. Stoddard, ST., et al. The role of human movement in the transmission of vector-borne pathogens. PLOS NTDS. 2009; 3 (7): 1–92. Kulldorff M. A spatial scan statistic. Commun Stat Theory Methods. 1997; 26(2): 1481-14963. Neill DB. Fast subset scan for spatial pattern detection. J. Royal Stat. Soc. B. 2012; 74(2): 337-360 %R 10.5210/ojphi.v11i1.9754 %U %U https://doi.org/10.5210/ojphi.v11i1.9754 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9756 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjeciveThe study aims to evaluate the potential impact of the revision of the thesaurus used by ED physicians to code medical diagnoses, on the syndromic indicators used daily to achieve the detection objective of the French syndromic surveillance system.IntroductionAs part of the French syndromic surveillance system SurSaUD®, the French Public Health Agency (Santé publique France) collects daily data from the emergency department (ED) network OSCOUR® [1]. The system aims to timely identify, follow and assess the health impact of unusual or seasonal events on emergency medical activity.Individual ED data contain demographic (age, gender, residence zip code), administrative (dates of attendances and discharge, ED, etc.) and medical information (chief complaint, main and associated medical diagnoses, severity). Medical diagnoses are encoded using the ICD10 classification. Then syndromic groups are built based on these ICD10 codes for ensuring syndromic surveillance in routine.Even if ICD10 is recommended on the national guidelines for coding ED attendances, this thesaurus offers a too large variety of codes. Particularly, it includes lots of diseases that may never be observed or confirmed in ED. This variety let selection of the appropriate codes difficult for physicians in a reactive use and could discourage them to code diagnoses.In order to encourage appropriate and reactive coding practice, we decided in 2017 to produce a new diagnoses thesaurus with a limited list of ICD10 codes. Then a committee of medical and epidemiological experts was created by the Federation of regional emergency observatories (FedORU), to propose an operational thesaurus that includes relevant codes for both ED in a daily routine practice and syndromic surveillance.MethodsThe committee has met 10 times since 2017. Since it would have been hard to work on the complete ICD10 list, the work was based on a more limited thesaurus already used by part of French ED. Only codes, which were pertinent regarding ED activity and interest for public health alert, have been considered. The main principles that have guided the selection were to 1) keep codes related to diagnoses that physicians are able to diagnose on a clinical basis or with rapid diagnostic tests, 2) remove diagnoses providing redundant information regarding other variables (such as circumstantial information) and 3) ensure that a substitution code was kept when a removed code was frequently used or was of interest for syndromic surveillance.Among the 86 syndromic groups defined on the basis of a list of ICD10 codes selected in the complete thesaurus, 34 are daily analyzed by Santé publique France for outbreak detection and early assessment of public health events. Those 34 syndromic groups have been recalculated by considering the revised thesaurus on a three-year period (from 2015 to 2017) at national level.In order to measure the potential impact of the revised thesaurus on the syndromic groups, we have considered three evaluation measures:1. the proportion of ICD10 codes deleted (removal rate) from the initial definition of each syndromic group, due to the limitation of the thesaurus (calculated for the 86 syndromic groups);2. the mean difference in the daily number of attendances between the initial and the new versions of each syndromic group (calculated for the 34 syndromic groups);3. the linear correlation coefficient between the daily numbers of attendances of the initial and the new version of each syndromic group, in order to assess if the daily fluctuations of the new syndromic group are similar to those of the initial syndromic group (calculated for the 34 syndromic groups).ResultsAmong the 86 syndromic groups, 75 (85%) have been impacted by the revised thesaurus, which implied codes removal. Among those 75 syndromic groups, the number of ICD10 codes included in their definition has been reduced by 71% on average. This removal rate varied between 17% and 100%. Syndromic groups including initially more than 100 codes have been the most concerned by a limitation of the number of ICD10 codes.Among the 34 syndromic groups daily analyzed for outbreak detection, 32 have been impacted by code removal with a mean removal rate of 68% (0%-97%). On average, 77% of daily attendances have been retained by the new version of syndromic groups, varying from 15% to 100%. Only 3 syndromic groups have kept less than 60% of attendances: Decrease of well-being (36%), Conjunctivitis (32%) and Hypothermia (15%).On average, the correlation coefficient has been of 0.96, varying from 0.57 to 1. The lowest values have been observed for the same three syndromic groups listed above: Decrease of well-being (0.57), Conjunctivitis (0.91) and Hypothermia (0.59). 18 among the 34 syndromic groups had a correlation coefficient higher than 0.99.ConclusionsThe study showed that most of the syndromic groups were impacted by the revised thesaurus, which resulted in a removal of about two thirds of the ICD10 codes usually considered in daily surveillance. However, more than three quarters of attendances were still retained in the new syndromic groups. This new thesaurus was conceived to rationalize the number of diagnoses codes but a substitution code was systematically proposed to replace removed codes.Those results highlighted that a large number of codes included in the complete ICD10 thesaurus were rarely used and that the most frequent codes were kept in the revised thesaurus version.However, this study showed that a few syndromic groups were strongly impacted by the revised thesaurus and can suffer of reduced performances to detect unusual variations. Based on those results, a second round of exploration of specific parts of the complete ICD10 thesaurus will be necessary to adapt either syndromic groups or the revised thesaurus.Even if the number of attendances may be reduced due to the removal of ICD10 codes, temporal variations remain similar for the majority of syndromic groups. Syndromic surveillance system does not aim to provide exhaustive quantification of attendances for a pathology, but aims to be able to detect expected or unusual public health variations.These evaluation results correspond to the worst-case scenario assuming that ED physicians will not modify their encoding habits by using the substitution codes but keep using their current thesaurus. However, we expect that this new and simplified version will facilitate diagnosis encoding task and lead toward a better diagnosis encoding rate. Once this new thesaurus will be widely used, we can expect a substantial improvement of the quality of ED medical data and then of syndromic surveillance results.Finally, this study enhances the importance that both data providers and epidemiologists in charge of syndromic surveillance work closely, in order to improve system in shared objectives.References[1] Fouillet A, Bousquet V, Pontais I, Gallay A, Caserio-Schönemann C. The French emergency department OSCOUR network: evaluation after a 10-year existence. Online J Public Health Inform. 2015; 7(1): e74. %R 10.5210/ojphi.v11i1.9756 %U %U https://doi.org/10.5210/ojphi.v11i1.9756 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9757 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo describe influenza laboratory testing and results in the Military Health System and how influenza laboratory results may be used in DoD Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE)IntroductionTimely influenza data can help public health decision-makers identify influenza outbreaks and respond with preventative measures. DoD ESSENCE has the unique advantage of ingesting multiple data sources from the Military Health System (MHS), including outpatient, inpatient, and emergency department (ED) medical encounter diagnosis codes and laboratory-confirmed influenza data, to aid in influenza outbreak monitoring. The Influenza-like Illness (ILI) syndrome definition includes ICD-9 or ICD-10 codes that may increase the number of false positive alerts. Laboratory-confirmed influenza data provides an increased positive predictive value (PPV). The gold standard for influenza testing is molecular assays or viral culture. However, the tests may take 3-10 days to result. Rapid influenza diagnostic tests (RIDTs) have a lower sensitivity, but the timeliness of receiving a result improves to within <15 minutes. We evaluate the utility of RIDTs for routine ILI surveillance.MethodsAdministrative medical encounters for ILI and influenza laboratory-confirmed data were analyzed from the MHS from June 2013 – September 2017 (Figure 1). The medical encounters and laboratory data include outpatient, inpatient, and ED data. The ILI syndrome case definition is a medical encounter during the study period with an ICD-9 or ICD-10 codes in any diagnostic position (ICD-9 codes = 79.99, 382.9, 460, 461.9, 465.8, 465.9, 466.0, 486, 487.0, 487.1, 487.8, 488, 490, 780.6, or 786.2; ICD-10 codes = B97.89, H66.9, J00, J01.9, J06.9, J09, J09.X, J10, J10.0, J10.1, J10.2, J10.8, J11, J11.0, J11.1, J11.2, J11.8, J12.89, J12.9, J18, J20.9, J40, R05, R50.9). The ILI dataset was limited to care provided in the MHS as laboratory data is only available for direct care. We describe influenza laboratory testing practices in the MHS. We aggregated the ILI encounters and RIDT positive results into daily counts and generated a weekly Pearson’s correlation.ResultsInfluenza tests are ordered throughout the year; the mean weekly percentage of ILI encounters in which an influenza laboratory test is ordered is 5.62%, with a range from 0.68% in the off season to 19.2% during peak influenza activity. The mean weekly percentage of positive influenza laboratory results among all ILI encounters is 0.82%, with a range from 0.01% to 5.73% (Figure 2). The percent of ILI encounters in which a test is ordered increases as the influenza season progresses. Influenza laboratory tests conducted in the MHS include RIDTs, PCR, culture, and DFA. Among all influenza tests ordered in the MHS, 66.0% were RIDTs, 22.7% were PCR, and 11.3% were viral culture. Often, a confirmatory test is ordered following a RIDT; 20% of RIDTs have follow-up tests. The mean timeliness of influenza test result data in the MHS was 11.26 days for viral culture, 2.94 days for PCR, and 0.11 days for RIDTs. The RIDT results were moderately correlated with ILI encounters for the entire year (mean weekly Pearson correlation coefficient rho=0.60, 95% CI: 0.55, 0.66, Figure 3). During the influenza season, the mean weekly Pearson correlation coefficient increases to rho=0.75, 95% CI: 0.70, 0.79.ConclusionsThe DoD has the unique advantage of access to the electronic health record and laboratory tests and results of all MHS beneficiaries. This analysis provides evidence for increased utilization of positive RIDTs in ESSENCE. The moderate correlation between the ILI syndrome and positive RIDTs may be associated with ICD-10 codes included in the ILI syndrome definition that contribute to false positive influenza cases. Ongoing research is focused on improving this ILI syndrome definition using ICD-10 codes. Rapid influenza diagnostic tests provide more timely results than other influenza test types. In conjunction with ILI medical encounter data, positive RIDT data provides a more complete and timely picture of the true burden of influenza on the MHS population for early warning of influenza outbreaks. %R 10.5210/ojphi.v11i1.9757 %U %U https://doi.org/10.5210/ojphi.v11i1.9757 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9758 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo describe population-level response to influenza-like illness (ILI) as measured by wearable mobile health (mHealth) devices across multiple dimensions including steps, heart rate, and sleep duration and to assess the potential for using large networks of mHealth devices for influenza surveillance.IntroductionInfluenza surveillance has been a major focus of Data Science efforts to use novel data sources in population and public health [1]. This interest reflects the public health utility of timely identification of flu outbreaks and characterization of their severity and dynamics. Such information can inform mitigation efforts including the targeting of interventions and public health messaging. The key requirement for influenza surveillance systems based on novel data streams is establishing their relationship with underlying influenza patterns [2]. We assess the potential utility of wearable mHealth devices by establishing the aggregate responses to ILI along three dimensions: steps, sleep, and heart rate. Surveillance based on mHealth devices may have several desirable characteristics including 1) high resolution individual-level responses that can be prospectively analyzed in near real-time, 2) indications of physiological responses to flu that should be resistant to feedback loops, changes in health seeking behavior, and changes in technology use, 3) a growing user-base often organized into networks by providers or payers with increasing data quality and completeness, 4) the ability to query individual users underlying aggregate signals, and 5) demographic and geographic information enabling detailed characterization. These features suggest the potential of mHealth data to deliver “faster, more locally relevant” surveillance systems [3].MethodsDuring the 2017/2018 influenza season, surveys were conducted within the Achievement platform, a health app that integrates with a variety of wearable trackers and consumer health applications [4]. The Achievement population has given consent agreeing to participation in studies like the one presented here and permitting access to their data. Surveys queried users as to whether they had experienced flu-like (ILI) symptoms in the preceding 14 days. Respondents who had experienced symptoms were then asked to identify symptom days. Those who had not experienced symptoms were queried again two weeks later. Positive responses were re-indexed to align by date of symptom onset. Individual respondent’s measures were standardized on a per-individual level in the 6 week period centered on the index date. Population-level mean signals were directly computed across several dimensions including steps, sleep, and heart rate. Uncertainty was quantified using resampling.ResultsBeginning February 17th, 2018, surveys were distributed to Achievement users. Within the first week 31,934 users had responded to the survey. Over a 12-week period, 124,892 individuals completed the survey with 25,512 reporting flu-like symptoms in a two week period prior to the survey. Of these, 9,495 had wearable device data in the 90-day window surrounding their symptom dates and 3,362 respondents had “dense” data defined as no more than 4 consecutive missing days in the 6-week period surrounding the index date.Population-level signals to ILI were clearly evident for five measures across the three dimensions. Step count [fig. 1] and time spent active [fig. 2] decreased 1 day prior to reported symptom onset date (index date), with a minimum at day 2 of -.24 std. dev. for step count and -.25 std. dev for time spent active, and a return to baseline at day 8. Sleeplessness [fig.3] and time spent in bed [fig. 4] increased one day prior to index, peaking 4 days after index at a mean increase of .16 std. dev. for sleeplessness and .13 std. dev. for time spent in bed, and returning to baseline at 7 days. Heart rate was elevated from 1 day before index to day 6 with a peak increase of .18 std. dev. on days 2 and 3 after index.ConclusionsThe potential of mHealth devices to register illness has been recognized [5]. This study is the first to present population-level influenza signals in a large network of mHealth users. Mobile health device data linked to ILI-specific survey responses taken during the 2017/18 flu season demonstrate clear aggregate patterns across several dimensions including sleep, steps, and heart rate. These signals suggest the potential for systems to rapidly process individual-level responses to classify ILI and to use such classifiers for ILI surveillance. The data described here, high resolution individual-level behavioral and physiological data linked to timely survey responses, suggests the potential to further enhance outbreak detection and improve characterization of ILI patterns. The setting of our study, a very large network of mobile health device users who have consented to the prospective use of their data and to being queried about their health status, could provide a framework for automated prospective influenza surveillance using “real world evidence” [6]. Employed over a population-representative sample, this approach could provide adjunct to standard clinically-based sentinel systems.References[1] Althouse, Benjamin M., et al. \"Enhancing disease surveillance with novel data streams: challenges and opportunities.\" EPJ Data Science 4.1 (2015): 17.[2] Henning KJ. What is syndromic surveillance?. Morbidity and Mortality Weekly Report. 2004 Sep 24:7-11[3] Simonsen L, Gog JR, Olson D, Viboud C. Infectious disease surveillance in the big data era: towards faster and locally relevant systems. The Journal of infectious diseases. 2016 Nov 14;214(suppl_4):S380-5.[4] https://www.myachievement.com/[5] Li, Xiao, et al. \"Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information.\" PLoS biology 15.1 (2017): e2001402.[6]https://www.fda.gov/scienceresearch/specialtopics/realworldevidence/default.htm %R 10.5210/ojphi.v11i1.9758 %U %U https://doi.org/10.5210/ojphi.v11i1.9758 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9759 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveWe aim to 1) develop and implement a novel theoretical and technical framework able to dynamically model HIV transmission clusters in near-real time; 2) validate the model with real data; and 3) host focus groups with governmental stakeholders to identify optimal strategies for precision public health interventions.IntroductionReducing HIV incidence requires a ‘precision public health’ approach encompassing prevention campaigns, targeted interventions, and ‘next-generation’ surveillance through multimodal instruments, including sequencing. Molecular epidemiology methods (phylogenetics and phylodynamics) have recently gained traction for use in identifying and tracking epidemic transmission clusters, as well as reconstructing the demographic history of viral pathogen populations. However, such methods are not equipped to identify both transmission clusters and their corresponding dynamics in real time, and transmission clusters are assumed to be unrealistically static over the course of the epidemic. We will focus on the ongoing HIV epidemic in Florida, which has one of the highest HIV incidence rates in the United States. Although key HIV transmission risk groups have been identified in Florida through classical epidemiology surveillance methods, there remains a critical need for detection and tracking of expanding transmission clusters in near-real time.MethodsWe propose to develop and test a new phylodynamic method, HIV Dynamic Identification of Transmission Epicenters (HIV-DYNAMITE), that will support existing HIV surveillance efforts. In collaboration with the Florida Department of Health (FDOH), we will leverage an existing dataset, which contains over 44,300 sequences, and apply HIV-DYNAMITE to identify transmission clusters and infer growth trends of these clusters within epidemics. HIV-DYNAMITE will also be used to identify and predict infection trends and virus spread by conferring with demographic data. The system will be validated using newly obtained longitudinal data. Focus group discussions with the FDOH, the Centers for Disease Control and Prevention (CDC), and other stakeholders will be conducted to confer how to employ HIV-DYNAMITE into statewide informatics systems and to design future intervention strategies.ResultsThese methods are still under development.ConclusionsIn conclusion, this study aims to both complement and enhance existing efforts, such as the CDC’s HIV-TRACE, which is currently based on sequence data alone and lacks dynamic or geographic spread components. This approach has the potential to be incorporated into other settings within the US with comparable statewide surveillance and virus sequencing coverage through national reference centers. %R 10.5210/ojphi.v11i1.9759 %U %U https://doi.org/10.5210/ojphi.v11i1.9759 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9760 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo estimate effectiveness of PCR method for epidemiology surveillance for leptospirosis in Lviv Oblast and compare it with microscopic agglutination test (MAT).IntroductionLeptospirosis is one of the most important zoonotic diseases based on the severity of the clinical course, frequency of fatal outcome and long-term clinical consequences. In Ukraine, leptospirosis is one of the most widespread natural-focal infectious diseases. Based on data of the Public Health Center of the Ministry of Health of Ukraine in 2017, the incidence rate was 0.77 per 100,000 population (330 cases), mortality rate was 0,08 per 100 000 population (case fatality rate was 10,9 %). In Lviv Oblast, the disease was registered as sporadic cases that were not related to each other (in 2017, the incidence rate was 0.72 per 100,000 population [1]. Laboratory testing of samples collected from patients and environmental objects that may be the source of the pathogen is an integral part of the epidemiological surveillance of leptospirosis. Modern laboratory diagnostics of leptospirosis is based on microbiological, immunological and molecular-biological methods used in various combinations [2, 3]. Molecular genetic diagnostic methods that allow detection of the Leptospira spp. RNA/DNA are the most promising for diagnosis of leptospirosis in the early stages of the disease. Investigation of environmental objects allows timely detection of the pathogen in natural foci and conducting a set of anti-epidemic necessary measures.MethodsWe used the following PCR kits “Leptospira pathogenic-Real time (FR001) Genecam Biotechnology AG” and “LPS PCR kit variant FRT-50F “Amplisens” for leptospira DNA detection. “Ultra Clean Blood Spin DNA isolation kit MO BIO Laboratories, Inc.” and a set of reagents from the clinical materials “RIBO-prep” for the isolation of RNA / DNA loci of Leptospira spp. were used.In parallel, 37 human and 27 rodent serum samples were studied using MAT.PCR and MAT positive gray rats samples were additionally studied using the bacteriology method (adrenal cortex seeded on the liquid media).Epidemiological investigation (namely, patient interviewing, investigation of places where the infection was acquired, exploring the living conditions) and outbreak investigation report writing were conducted for all recorded cases (41).ResultsResults of the human samples investigation. During 2016-2017 and 7 months of 2018, 41 cases of leptospirosis were registered in Lviv Oblast. All these cases were confirmed with laboratory methods, including PCR; DNA of Leptospira spp. was detected in 15 patients (36,6 %), and MAT was positive in 26 cases (63,4%). In 8 patients (19,5%) both PCR and MAT testing gave positive results. Over the past three years, 5 fatal cases of leptospirosis (12.1%) have been registered, including two patients who died during the first week of the disease. For those two patients, the diagnosis was confirmed by PCR and MAT (leptospira lysis in MAT was noticed in the titre of 1:100-1:200); for other two patients, the diagnosis was confirmed using MAT only (1:800); and in the last patient from this group, leptospira lysis was noticed in low titres in MAT.Results of epidemiological investigation revealed that the most patients were infected through contact way of transmission (78.1%), including contact with objects and food contaminated with rodent excrement, and water-borne transmission (19.5%) during bathing, fishing, hunting, field work; in other 2.4% of cases the way of transmission was not identified.Epidemiological history showed that the main source of infection for humans in natural and urban foci were grey rats and rodents that could adapt to transforming ecosystems conditions.Results of animal samples investigation. Among 27 samples of gray rats, caught in places where patients probably got infected, in 11 samples (40.7%) a specific 16S rRNA of Leptospira spp. was detected and also MAT was positive; 1 samples (3,7 %) from this group was seropositive in MAT only.L. icterohaemorrhagiae live culture was isolated from 3 samples of grey rats that were positive in PCR and MAT.Results of environmental samples investigation showed the following: among 89 of water samples collected from recreation areas (lakes), 4 samples (4.5%) were positive (16S rRNA of Leptospira spp.). PCR of 8 samples of drinking water collected from leptospirosis foci gave negative results.ConclusionsIn Lviv Oblast, Ukraine, the potential of laboratory diagnostics of leptospirosis has increased due to introduction of PCR method in diagnostic algorithm. Results of clinical materials investigations revealed that with PCR it is became possible to confirm the diagnosis within the first several days from the onset of the disease (in 15 patients). Diagnosis was confirmed using MAT in 26 patients starting from the second week of the disease. At the same time, MAT is crucial, since it enables to identify the etiological structure of the disease and monitor the dynamics of the immune response.Investigation of animal and environmental samples with MAT and PCR methods allowed to establish causal relationships of patients with possible sources of infection.PCR method allowed to conduct epidemiological surveillance for leptospirosis at a new level, as the time for receiving results compare to the classical methods as well as biological risks during work with biomaterials have decreased.Currently, the combination of PCR and MAT methods for laboratory research in the surveillance of leptospirosis is optimal.Understanding environmental and epidemiological determinants allows for the identification of appropriate public health approaches to improve the situation with leptospirosis, such as reducing populations of pathogen reservoirs (rats) by conducting deratization measures, vaccinations of dogs and livestock, and regulatory compliance.References1. About the epidemiological situation with leptospirosis in Ukraine in 2017 and measures to prevent it / Information letter of PHC of the Ministry of Health of Ukraine , 20.07.2018 # 2651. – Kyiv. – 2018. – 26 p.2. Leptospirosis diagnosis: competancy of various laboratory tests / Suman Veerappa Budihal et all. / Journal of Clinical and Diagnostic Research. - 2014, Vol-8(1). – P. 199-202.3. World Health Organization. Human leptospirosis : guidance for diagnosis, surveillance and control. – 2003. – 122 p. ISBN 92 4 154589 5. %R 10.5210/ojphi.v11i1.9760 %U %U https://doi.org/10.5210/ojphi.v11i1.9760 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9761 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveUsing the two largest commercial laboratory data sources nationally, we estimated the annual rates of hepatitis C testing among individuals who were recommended to be tested (i.e., baby boomer cohort born between 1945 and 1965) by the CDC and United States Preventive Services Task Force. This panel will discuss strengths and weaknesses for monitoring hepatitis C testing using alternative data sources including self-reported data, insurance claims data, and laboratory testing data.IntroductionHepatitis C virus (HCV) infection is a leading cause of liver disease-related morbidity and mortality in the United States. Approximately 75% of people infected with chronic HCV were born between 1945 and 1965. Since 2012, the CDC has recommended one-time screening for chronic HCV infection for all persons in this birth cohort (baby boomers). The United States Preventive Services Task Force (USPSTF) subsequently made the same recommendation in June 2013. We estimated the rate of HCV testing between 2011 and 2017 among persons with commercial health insurance coverage and compared rates by birth cohort.MethodsHepatitis C virus testing data were obtained from Quest Diagnostics (Quest) and Laboratory Corporation of America (LabCorp), two large U.S. commercial laboratories serving clinicians and hospitals in all 50 U.S states and the District of Columbia. Analysis was based on de-identified person-level data from HCV antibody immunoassay tests ordered by clinicians in the U.S. between 2011 and in 2017 (with LabCorp data in 2017 limited to January through October). HCV antibody testing rates were calculated and defined as: the number of unique individuals who received their first HCV antibody test during a particular month per 100 unique individuals who had any laboratory test performed by the commercial laboratory during the same month, presented as an annual average (mean) testing rate. Persons born between 1945 and 1965 were classified as baby boomers and compared to persons born in all other years.ResultsIn 2011, prior to the CDC recommendation change, rates of HCV antibody testing relative to overall testing with each cohort were higher for the non-baby boomer cohort served by both Quest and LabCorp. In contrast, from 2012 thorugh2017, testing was more frequent among baby boomers than among non-baby boomers as a proportion of overall testing in each cohort. The rate of testing among baby boomers served by Quest rose from 1.7 per 100 test requests in 2011 to 3.8 per 100, an increase of 131%, while the rate of testing among non-baby boomers rose from 2.3 per 100 to 3.1 per 100, a 35% increase. Changes among patients served by LabCorp were nearly identical; a 132% increase among baby boomers (1.7 per 100 in 2011 to 4.0 per 100 in 2017) and a 31% increase among non-baby boomers (1.7 per 100 in 2011 to 3.2 per 100 in 2017).ConclusionsThis study demonstrates the utility of commercial laboratory data for assessing changes in HCV testing, as well as the potential impact of national recommendations supporting HCV testing of baby boomers. The study also highlights a prominent, the increase in HCV antibody testing in 2017 relative to 2011, prior to the recommendation change. %R 10.5210/ojphi.v11i1.9761 %U %U https://doi.org/10.5210/ojphi.v11i1.9761 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9762 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo identify best practices for combining public health data for multi-jurisdiction surveillance projects.IntroductionSentinel surveillance, where selected jurisdictions follow standardized protocols to collect and report enhanced public health data not available through other routine surveillance efforts, is a key part of national surveillance of sexually transmitted diseases (STDs). Although four STDs are nationally notifiable conditions (chlamydia, gonorrhea, syphilis and chancroid), the burden of these conditions (over 2.3 million cases were reported in 2017) limits the amount of detailed clinical and demographic data available for all cases. Sentinel surveillance in clinical settings serving at-risk populations, such as STD clinics, provides an opportunity to collect enhanced data elements on persons seeking STD-related services, such as sex of sex partners and anatomic site of infection. However, there are challenges in combining data across jurisdictions as estimated effect measures may vary by jurisdiction (e.g., some may have higher observed burden of disease among certain populations) and the amount of data contributed by jurisdiction may vary; combined this could lead to biased estimates if heterogeneity is not taken into account.MethodsUsing data from the STD Surveillance Network (SSuN), a sentinel surveillance project implemented in 10 jurisdictions, we investigated the effect of using different statistical methods to combine data across jurisdictions. We evaluated 5 methodologies:● “Fully stratified” where estimates were provided separately for each jurisdiction;● “Aggregated” where numerators and denominators were summed across jurisdictions without weighting;● “Mean estimate” where the mean of the jurisdiction-specific estimates was estimated;● “Random effects” where jurisdiction-specific estimates were combined using an inverse variance weighted random effects model to adjust for heterogeneity between jurisdictions; and● “Stratified random effects” where a possible effect modifier was identified and used to group jurisdictions prior to calculating the estimate from the random effects model.Through SSuN, jurisdictions collect visit-level data on patients attending selected STD clinics and report clinical and demographic data. As an illustrative example, we estimated rectal gonorrhea positivity among gay, bisexual, and other men who have sex with men (MSM) attending participating clinics. Jurisdiction-specific positivity was estimated as the # of unique MSM testing positive at least once for rectal gonorrhea divided by all MSM tested 1 or more times for rectal gonorrhea in all of the clinics in the jurisdiction. The stratifying variable for the stratified random effects method was the percent of MSM screened in the jurisdiction’s clinics, as low screening coverage may reflect targeted testing of MSM likely to be infected which may inflate observed positivity. For each of the five methods, we estimated rectal gonorrhea positivity and the corresponding 95% confidence interval (CI).ResultsIn 2017, 123,210 patients attended 30 STD clinics participating in the 10 SSuN jurisdictions, of which 31,052 (25.2%) were identified as MSM (jurisdiction-specific range: 8.8% to 70.0%). (Table 1) One jurisdiction (I) accounted for 39% of all MSM included in the analysis while one jurisdiction (J) accounted for only 1.6% of MSM included. The proportion of MSM tested for rectal gonorrhea at least once varied by jurisdiction, ranging from 44.3% to 76.9%. The fully stratified method identified differences in rectal gonorrhea positivity across jurisdictions, with jurisdiction-specific positivity ranging from 9.9% to 24.1%. Aggregating across jurisdictions masked this heterogeneity and provided a single summary estimate of 15.2% (95% CI: 14.7, 15.7). Taking the mean across the jurisdiction-specific estimates also provided a summary estimate; however, the uncertainty of the estimate increased (15.8%, 95% CI: 13.3, 18.7). Accounting for the heterogeneity by using a random effects model resulted in an estimate of 15.5% (95% CI: 13.9, 17.2). After stratifying by a likely confounder (% of MSM screened); the random effects estimate among 3 jurisdictions with lower screening coverage (<60%) was 19.7% (95% CI: 14.6, 24.8) and among 7 jurisdictions with higher screening coverage (≥60%) was 14.3% (95% CI: 12.9, 15.7).ConclusionsIn a sentinel surveillance project implemented in 10 jurisdictions, there was substantial heterogeneity in the observed proportion of MSM testing positive for rectal gonorrhea in selected STD clinics. Although a stratified analysis captured the heterogeneity across jurisdictions, it may not be feasible to present fully stratified estimates for all analyses (e.g., surveillance reports likely provide metrics for multiple diseases). Additionally, it limits the ability to succinctly communicate key findings. Aggregating numerators and denominators across jurisdictions to calculate a single summary estimate masks this heterogeneity and biases estimates toward high volume jurisdictions. Taking the mean across jurisdictions ensures that high-volume jurisdictions do not bias the overall estimate; however, the mean may be biased by very high or very low positivity estimates in a few jurisdictions. Using a random effects model accounted for both varying sample sizes and differences in observed heterogeneity; although the summary estimate was similar to the aggregate in this example, the wider 95% CI more accurately reflects the uncertainty in the estimate. Finally, stratifying by a likely effect measure modifier (% of MSM screened) prior to estimating the measure from the random effects model captured key differences in jurisdictions while still providing a limited number of summary estimates. Analysts using data from multi-jurisdiction surveillance projects should fully investigate possible biases when combining estimates across jurisdictions. If there is observed heterogeneity across jurisdictions and it is not feasible to provide fully stratified estimates, analysts could consider using methods to account for heterogeneity and minimize bias due to differing sample sizes, such as stratified random effects models. %R 10.5210/ojphi.v11i1.9762 %U %U https://doi.org/10.5210/ojphi.v11i1.9762 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9763 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveOne of the main objectives of these studies was to improve Anthrax laboratory diagnostics in order to properly monitor the prevalence and distribution of the disease in Georgia. For this geographic information system (GIS) was implemented and used as the additional tool to the laboratory tests for better visualization, summary results and risk assessment.IntroductionAnthrax is an acute infectious disease of historical importance caused by Bacillus anthracis (B. anthracis), a spore-forming, soil-borne bacterium with a remarkable ability to persist in the environment. Anthrax is endemic in many countries, including Georgia.Laboratory of the Ministry of Agriculture (LMA) has been actively working on the disease science 1907 and constantly improving diagnostics. In 2009-2017 the laboratory participated in cooperative biological studies. One of the main objectives of these studies was to improve Anthrax laboratory diagnostics in order to properly monitor the prevalence and distribution of the disease in Georgia.MethodsIn 2009 -2011, within GG18, LMA tested 130(animal and environmental) anthrax suspected samples collected from different regions of Georgia. Later, in 2014 – 2017, studies (TAP7; GG27) were focused on soil sample collection and 2825 specimens were collected from the entire country.Samples were tested according to Treat Agent Detection and Response (TADR) algorithm following standard operation procedures (SOPs). Cultures were isolated through Bacteriology tests - Gram strain, Lysis by gamma phage, Motility test, DFA and confirmed by Molecular Biology ( PCR).In 2009, within the studies , geographic information system (GIS) was implemented and used as the additional tool to the laboratory tests for better visualization, summary results and risk assessment.ResultsTotally, 2955 collected samples were tested. 86 cultures were isolated and confirmed. The results - anthrax cases were mapped by regions, rayons and villages, also positive cases were mapped by sample type and course, Majority of positive cases were in Kvemo Kartli (53%), 19% were from Kakheti, 19% - from Imereti and less distributed in other regions.Applying modern GIS the final map of anthrax foci in Georgia was created including both - old (historical data) and new (recent data) foci.ConclusionsThe studies aimed to improve Anthrax laboratory diagnostic in Georgia. Better Laboratory diagnostic with modern GIS analysis supports the monitoring of the disease prevalence in Georgia and significantly improves public health system in the country. %R 10.5210/ojphi.v11i1.9763 %U %U https://doi.org/10.5210/ojphi.v11i1.9763 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9764 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveWe present a new approach for pre-syndromic disease surveillance from free-text emergency department (ED) chief complaints, and evaluate the method using historical ED data from New York City’s Department of Health and Mental Hygiene (NYC DOHMH).IntroductionAn interdisciplinary team convened by ISDS to translate public health use-case needs into well-defined technical problems recently identified the need for new “pre-syndromic” surveillance methods that do not rely on existing syndromes or pre-defined illness categories1. Our group has recently developed Multidimensional Semantic Scan (MUSES), a pre-syndromic surveillance approach that (1) uses topic modeling to identify newly emerging syndromes that correspond to rare or novel diseases; and (2) uses multidimensional scan statistics to identify emerging outbreaks that correspond to these syndromes and are localized to a particular geography and/or subpopulation2,3. Through a blinded evaluation on retrospective free-text ED chief complaint data from NYC DOHMH, we demonstrate that MUSES has great potential to serve as a “safety net” for public health surveillance, facilitating a rapid, targeted, and effective response to emerging novel disease outbreaks and other events of relevance to public health that do not fit existing syndromes and might otherwise go undetected.MethodsMultidimensional semantic scan uses topic modeling to learn illness categories directly from the data, eliminating the need for pre-defined syndromes. Topic models are a set of algorithms that automatically summarize the content of large collections of documents by learning the main themes, or topics, contained in the documents4. Our method learns two sets of topics: a set of topics over the historical data designed to capture common illnesses, and a set of emerging topics over only the most recent data that are optimized to capture any new illnesses not captured by the historical topics. We then use multidimensional scan statistics to identify clusters of cases isolated to a certain topic, hospital, and/or demographic group of patients5.To evaluate the ability of MUSES to detect a diverse set of emerging patterns relevant to public health in large and complex data, we apply our algorithm to historical chief complaint data from NYC. This dataset has over 28 million ED cases from 53 NYC hospitals during 2010-2016. For each hospital we have data on the patients'' free-text chief complaint, date and time of arrival, age group, gender and discharge ICD-9 diagnosis code. Public health practitioners at NYC DOHMH performed a blinded evaluation of the top 500 highest-scoring clusters detected by our method and by a competing state of the art keyword-based approach6,7,8. For each of these clusters, the evaluators indicated if the cluster (1) represents a meaningful collection of cases and (2) is, in their judgement, of significant interest to public health.ResultsThe blinded evaluation by NYC DOHMH demonstrated that our method correctly identifies a larger number of events of interest to public health than the baseline keyword-based scan method. 320 (64%) of the top 500 results from MUSES corresponded to meaningful health events, while the keyword-based method only detected 246 such events (49.2%). MUSES also identified 6 more highly relevant events and 74 less meaningless clusters than the keyword-based method. Figure 1 shows that for any fixed number of clusters that public health officials choose to examine, MUSES identifies more meaningful events than keyword-based scan. Alternatively, for any desired number of true clusters detected, MUSES exhibits substantially higher precision: for example, in order to identify 100 true clusters, it had to report 159 total clusters (precision = 63%) as compared to 225 total clusters (precision = 44%) for the keyword-based scan. This corresponds to a 53% reduction in the number of false positive clusters.Additionally, to determine how our approach might provide situational awareness of emerging health concerns following a natural disaster, we examined the clusters identified by our approach in the week following October 29, 2012, when Hurricane Sandy struck New York City and caused a historic level of damage. These results show a progression of clusters from acute cases related to falls and shortness of breath, to mental health issues like depression and anxiety, to chronic health issues that require maintenance procedures, like dialysis and methadone distribution. It is of note that public health officials manually inspected emergency room data immediately following Hurricane Sandy and noticed an increase in the words “methadone”, “dialysis” and “oxygen”7. The ability of MUSES to automatically identify similar symptoms as human experts highlights its ability to learn meaningful but novel combinations of symptoms.ConclusionsOur MUSES system offers a novel method for pre-syndromic surveillance that achieves the goals set forth by public health practitioners during the ISDS Consultancy. When evaluated against a state of the art baseline, MUSES identifies a larger number of events of interest, has a lower false positive rate, and produces more coherent results. This ability to report newly emerging case clusters of high relevance to public health, without overwhelming the user with a large number of false positives, suggest high potential utility of the approach for day-to-day operational use as a “safety net” for public health surveillance, complementing existing syndromic surveillance approaches. We are currently building a pre-syndromic surveillance system based on the MUSES approach and plan to make this software widely available to public health partners in the near future.References1. Faigen Z, Deyneka L, Ising A, et al. Cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use case. Online J. Public Health Inform. 2015;7(3).2. Maurya A, Murray K, Liu Y, Dyer C, Cohen WW, Neill DB. Semantic scan: detecting subtle, spatially localized events in text streams. 2016. arXiv preprint arXiv:1602.04393.3. Nobles, M., Deyneka, L., Ising, A., & Neill, D. B. Identifying emerging novel outbreaks in textual emergency department data. Online J. Public Health Inform. 2015;7(1).4. Blei D, Ng A, Jordan M. Latent Dirichlet allocation. J Mach Learn Res. 2003; 3:993-1022.5. Neill DB. Fast subset scan for spatial pattern detection. J. Royal Stat. Soc. B. 2012; 74(2):337-60.6. Burkom H, Elbert Y, Piatko C, Fink C. A term-based approach to asyndromic determination of significant case clusters. Online J. Public Health Inform. 2015;7(1).7. Lall R, Levin-Rector A, Mathes R, Weiss D. Detecting unanticipated increases in emergency department chief complaint keywords. Online J. Public Health Inform. 2014;6(1).8. Walsh A, Hamby T, St John TL. Identifying clusters of rare and novel words in emergency department chief complaints. Online J. Public Health Inform. 2013;6(1). %R 10.5210/ojphi.v11i1.9764 %U %U https://doi.org/10.5210/ojphi.v11i1.9764 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9765 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo improve linkage between North Carolina’s Emergency Medical Services (EMS) and Emergency Department (ED) data using an iterative, deterministic approach.IntroductionThe opioid overdose crisis has rapidly expanded in North Carolina (NC), paralleling the epidemic across the United States. The number of opioid overdose deaths in NC has increased by nearly 40% each year since 2015.1 Critical to preventing overdose deaths is increasing access to the life-saving drug naloxone, which can reverse overdose symptoms and progression. Over 700 EMS agencies across NC respond to over 1,000,000 calls each year; naloxone administration was documented in over 15,000 calls in 2017.2Linking EMS encounters with naloxone administration to the corresponding ED visit assists in understanding the health outcomes of these patients. However, less than 66% of NC EMS records with naloxone administration in 2017 were successfully linked to an ED visit record. This study explored methods to improve EMS and ED data linkage, using a multistage process to maximize the number of correctly linked records while avoiding false linkages.MethodsEMS data were provided by the EMS Performance Improvement Center2 (EMSPIC); ED data were provided by NC DETECT.3 Optimization of current EMS/ED linkage methods began by extracting a non-random subset of EMS encounters with naloxone administration between January 1, 2017 and November 30, 2017 from 12 NC counties, representing eastern, central and western regions and the overall linkage performance of the larger dataset. Records were eligible for linkage if EMS recorded that the patient was “treated and transported” to the ED. All records in the subset were manually reviewed in NC DETECT to identify corresponding ED visit records. This produced a “gold standard” dataset of linked EMS/ED records.To evaluate linkage performance, we first identified all records eligible for linkage. Any EMS transport to either a hospital outside of NC or an NC ED not included in NC DETECT (e.g., military, VA and tribal hospitals) was excluded. Since existing linkage is performed daily and both EMS and ED records are updated over time to correct errors and missing data, existing linkage methods were re-run on updated data to evaluate the improvement provided solely by linking the most up-to-date data. Unlinked EMS records for which the encounter was an inter-facility transfer, transfer to helicopter transport, or the patient died during transfer were deemed ineligible for linkage, as these patients likely either bypassed or never made it to the ED.To initially improve linkage quality, we updated the mapping file of EMS/ED destinations. An exact destination match was required for linkage and the EMS destination variable is recorded as free-text; thus, all variations of a destination name and spelling were identified and mapped to a standardized name. The maximum time difference between EMS drop-off and ED intake was then allowed to exceed 60 minutes, in iterations of 90, 120, 240, and 360 minutes. With each iteration, we compared the linked IDs with the gold standard dataset to identify false links.Finally, a multistage linkage process was applied. First, deterministic linkage was run requiring exact matches for date of birth (DOB), sex/gender, and destination, and up to 360-minute difference between EMS/ED times. The unlinked records were then processed a second time, requiring exact matches for sex/gender and destination, DOB to be within +/- 10 days or +/- 1 year, and up to 60-minute difference between EMS/ED times.This multistage process was then run for all 2017 EMS encounters with naloxone administration to ensure that the new method was not over fit to the data subset. Potential bias in the linkage was assessed by comparing the distributions of age (mean and median) and gender (% male) among the linked and unlinked records in each dataset.Statistical analyses were completed using SAS 9.4 (Cary, NC). Linkage was executed using SQL Server.ResultsBetween 1/1/2017 and 11/30/2017, there were 14,793 EMS encounters with documented naloxone administration. Of these, 12,089 (81.7%) were recorded as “treated and transported”; 1,906 EMS encounters were included in the 12-county subset. The average age of patients was 45.1 years among all naloxone encounters and 45.2 years in the subset. 57.5% of all encounters were male; 58.1% were male in the subset.After removing EMS transports to non-NC or non-NC DETECT hospitals, the existing subset linkage was 61.8% (1,154/1,866). This included 38 (2.0%) false positives, apparently caused by ED records purged since this linkage was conducted. When the existing methods were run against the most current data, linkage improved to 72.2% (1,389/1,866), reflecting an absolute improvement of 10.4% by simply using updated data. Only 1 (0.05%) false positive was identified in this process.Following removal of unlinked inter-facility transfers, deaths during EMS transport, and transfers to helicopters, the records eligible for linkage dropped to 1,781. Linkage improved to 79.5% (1,417/1,781) when hospital names were standardized. Linkage using standardized hospital names and relaxing the EMS/ED time difference performed at the following levels: 82.3% at 90 minutes, 83.3% at 120 minutes, 87.9% at 240 minutes, and 89.4% at 360 minutes. Even when using the most relaxed time difference (+/- 360 minutes), only one false positive was identified, the same produced during initial linkage at +/- 60 minutes. The final multistage method produced linkage of 91.0% (1,620/1,781), with no additional false positives.Applying the initial methods to the statewide EMS dataset produced linkage of 64.8%. The multistage linkage process performed nearly identically on statewide data as observed for the subset, at 91.1%. For statewide data, the age of linked patients was younger (mean = 44.7 years [SD = 18.4], median = 41.0 years) than that of unlinked patients (mean = 48.0 years [SD = 19.3], median = 47.0 years). Additionally, linked patients were more likely to be male (58.1%) when compared to unlinked patients (54.2%).ConclusionsHigh quality linkage between EMS and ED records is essential for research and public health surveillance examining health outcomes. Using a multistage process, we improved the linkage of EMS encounters with documented naloxone administration to ED visits in North Carolina in 2017 from 64.8% to 91.1%, with less than 0.05% false positive rate. This improved linkage will facilitate future analyses of relationships between exposures during EMS encounters and outcomes experienced in hospitals. Future research should evaluate the generalizability of this linkage methodology to all EMS records, not just those with naloxone administration, as well as to pre-2017 data. Implementation of probabilistic linkage or machine learning as a final stage in a multistage process may further improve linkage outcomes, overcoming missing data or unpredictable errors in the data.References1. Kansagra SM, Cohen MK. The Opioid Epidemic in NC: Progress, Challenges, and Opportunities. N C Med J 2018; 79(3): 157-62.2. EMS Performance Improvement Center. About EMSPIC. https://www.emspic.org/about.3. NC DETECT. Background. http://ncdetect.org/background/ %R 10.5210/ojphi.v11i1.9765 %U %U https://doi.org/10.5210/ojphi.v11i1.9765 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9766 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo prove the role of partnerships in Disease Surveillance and Response to emerging public health threats in Kerala state, India.IntroductionKerala is a small state in India, having a population of only 34 million (2011 census) but with excellent health indices, human development index and a worthy model of decentralised governance. Integrated Disease Surveillance Program, a centrally supported surveillance program, in place since 2006 and have carved its own niche among the best performing states,in India. Laboratory confirmation of health related events/disease outbreaks is the key to successful and timely containement of such events, which need support from a wide range of Laboratories-from Primary care centers to advanced research laboratories, including private sector. In a resource constraint setting, an effective model of Partnership have helped this state in achieving great heights. Networking with laboratores of Medical Education Department, and Premier Private sector laboratories, Financing equipments and reagents through decentralised governance program, resource sharing with other National programs, Laboraotries of Food Safety, Fisheries and Water authorities have resulted in laboratory confirmation of public health events to the extend of 75-80% in the past 5 years in the state. Etiological confirmation accelerated response measures, often multidsciplinary, involving Human health sector, Animal Health, Agriculture, wild life and even environemntal sectors, all relevant in One Health context.MethodsDuring 2013-14,state launhced a laboratory networking initiative, with aid and guidance from central government,through a mutually beneficial MoU, linking all the 5 Govt Medical College Microbiology Laboratories with the State Health surveillance system. A State Laboratory Cordinator was designated, and these teaching Hospital were requested to assist the state in testing of outbreak samples from adjoining 3-4 districts.Additional funds were provided for these institutions after a team assessment and periodic monitoring.All the 14 disricts of state gained remarkably in laboratory confirmation of various outbreaks.During 2013, when one of the remote districts in the state detected an unusual fever cluster among the indigenous community, investigation by a multidisciplinary team, supported by a reputed private sector virology laboratory of an academic institution of the neighbouring state, confirmed Lyme disease, first time in the state. In 2014 and 2015, the same laboratory confirmed another hitherto unreported disease, Kyasanur Forest disease, in the same district. These two events lead to the establishing of a Private Public Partnership model in disease surveillance in the state. This model shared physical infrastructure in the govt hospital premises with technological support from the virology center. Since then, this laboratory has contributed to >90% of laboratory confirmation of health events in the district. Eventually, the same laboratory became the pioneer in confirmation of the first Nipah Virus outbreak in the state in 2018. This laboratory is also the Reference laboratory for H1N1 and Avian Influenza for whole of South India. This surveillance network, has since then, established additional units in other parts of the state through special government order.From the response perspective also, the state adopted similar partnership approach. The strategy for control of Kyasanur Forest Disease(KFD) is a classical example. Monkey deaths were autopsied by Wildlife experts, domestic animals were treated for tick infestation by the veterinary officers, research work done at Veterinary university, human cases treated and vulnerable population vaccinated by Human Health officers, Tribal and Revenue department addressed the welfare aspects of the affected indigenous communities, and the district collector cordinated all related activities. It was a pathbreaking experience, and since 2015, till date, no new case is reported from the district, unlike hotspots in other parts of India.In 2014, the state gained from Fisheries department laboratory, by confirmation of a fish toxin from an event of food borne infection outbreak. In the same year, Veterinary Univerisity laboratory isolated Vibrio Cholera from water samples from a Cholera outbreak.In 2018, the state surveillance unit, engaged with Veterinary University of the state to undertake MAT testing of Human Leptospirosis cases for facilitating the identification of serovars, another landmark effort, approved by Govt of India. The state surveillance system also receives tremendous support from laboratories of research centers like Rajeev Gandhi center for Biotechnology and Vector Control Research Center of ICMR (Indian council of Medical Research center). The state is now, preparing a draft action plan for constituing a One Health Governance Secretariate in Kerala, to bring together all the stakeholders in disease surveillance, for optimizing their contribution.ResultsState Health surveillance system detected 135,130,140,130,disease outbreaks during the years 2014,15,16,17,and 93, till date in 2018. The laboratory confirmation of 65%,75%,80%,82.5% and 65.5% in respective years facilitated prompt response by the state. This was made possible with an extensive laboratory collaboration with partners ranging from Institutional labs of state government as well as decentralised local self governments,(12.3%) Regional Public Health labs(13.8%), Referral Network Labs of Govt Medical College Hospitals (16.2%), Manipal Center for Viral Research Lab(11.5%) Kerala Water Authority Labs (6.2%), Food Security and Safety department (2.3%) and a small contribution by Private Laboratories (1.5%) during 2017. In 2018, 324 human samples were tested and 16 samples confirmed for Nipah virus disease,from MCVR Manipal. The same laboratory confirmed Lyme disease (2013) and Kyasanur Forest Disease (2014 and 2015) from human sampels. 3 environemntal samples were tested positive for Legionnaires bacteria from cooling system of 2 Tourist Hotels, following notification of Legionnaires Pneumonia among 2 foreign tourists.(2016 and 17). Fish toxin \"Ciguaterin\" was confrimed from an incident of food borne outbreak by a laboratory attached to Fisheries department ( 2015) - a unique example of One Health application in disease surveillance and outbreak response. Laboratories attached to Kerala Water Authority supports testing of water sampels during water borne infections and Food Safety department facilitates analysis of food items during food borne infections. 7 water samples tested positive for Vibrio Cholerae during a Cholera outbreak, done through Reserch wing of Veterinary Univerisity Micobiology Lab in 2016. An instance of Primary Amoebic Meningoencephalitis was confirmed through a premier private tertiary center laboraotry.Leptospira serovars are being identified through a collaborative project with a Veterinary University(2018).ConclusionsKerala state in India has shown many successful models in development sector. Partnership in Laboratory surveillance is the most recent one in the segment. Besides interdepartmental collaboration, a unique model of Private Public Partnership is also tried by this state, resulting in historic achievements like high eteological confirmation of outbreks including the mos recent and first ever Nipah virus disease,ample evidence for state''s commitment to IHR compliance as well.This model, I feel is replicable in similar situations in resource poor countries across the globe. %R 10.5210/ojphi.v11i1.9766 %U %U https://doi.org/10.5210/ojphi.v11i1.9766 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9767 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveThis study aims to implement and evaluate two automatic classification methods of free-text medical causes of death into Mortality Syndromic Groups (MSGs) in order to be used for reactive mortality surveillance.IntroductionMortality is an indicator of the severity of the impact of an event on the population. In France mortality surveillance is part of the syndromic surveillance system SurSaUD and is carried out by Santé publique France, the French public health agency. The set-up of an Electronic Death Registration System (EDRS) in 2007 enabled to receive in real-time medical causes of death in free-text format. This data source was considered as reactive and valuable to implement a reactive mortality surveillance system using medical causes of death (1).The reactive mortality surveillance system is based on the monitoring of Mortality Syndromic Groups (MSGs). An MSG is defined as a cluster of medical causes of death (pathologies, syndromes, symptoms) that meet the objectives of early detection and impact assessment of events (2).Since causes of death are entered in free-text format, their automatic classifications into MSGs require the use of natural language processing methods. We observe a constant increase in the use of these methods to classify medical information and for health surveillance over the last two decades (3).MethodsData consisted of the medical part of electronic death certificates received in routine by Santé publique France from 2012 to 2016. We split the dataset into training and test sets.Among each set, a subset of certificates was selected by a random sampling without replacement. Two annotators manually assigned MSGs to each death certificates in all subsets.Discordances were discussed and corrected if necessary. The agreement rate between the two annotators was 0.90 on the test set.Final annotated subsets represent the ground truth against which the methods tested were evaluated. The final evaluation was performed on the test set of 1,000 death certificates while the classifiers were trained on 3500 death certificates.Two classification methods were implemented: a rule-based method and a supervised machine learning method. The rule-based method was based on four processing steps: applying standardization rules, splitting of medical expression using delimiters, spelling correction and dictionary projection.The supervised machine learning method was set up using a linear Support Vector Machine (SVM) classifier. We trained a multi-label classifier using the one-versus-all strategy. We implemented two models: one based on surface features (SVM model) and the other, a hybrid model, combining surface features and features obtained by the rule-based method. Surface features were bags-of-word unigrams and bigrams and of character trigrams.The rule-based method and the two supervised machine learning models were evaluated using the three evaluation measures: precision (Positive Predictive Value), recall (Sensitivity) and F-measure (P/R/Fm).The study focused on the classification performance of MSGs defined for the reactive detection of outbreaks and are composed of unspecific or acute pathologies, or general symptoms (related to pain, fever, cognitive disorder…). Only the 40 MSGs mentioned at least 3 times in the test set were considered in this study, they belonged to 13 topics (Respiratory conditions, Cardio and cerebrovascular conditions, Infectious diseases, Digestive conditions…).ResultsWith the rule-based method, among the 40 MSGs, 24 obtained a P/R/Fm over 0.90. They belonged mainly to the topics Cardio and Cerebrovascular conditions (5 MSGs), Respiratory conditions (6), and General symptoms (5). Four MSGs obtained P/R/Fm below 0.85 belonging to the topics Infectious conditions (2), Blood condition (1) and Unspecified causes of death (1).The hybrid model obtained P/R/Fm over 0.90 for 25 MSGs. Among them, 21 were the same as the rule-base method. Performance of the rule-based method and the hybrid model were over 0.95 for the same 13 MSGs. The hybrid model obtained P/R/Fm below 0.85 for 4 MSGs also belonging to the same topics as those of the rule-based method.The SVM model had lower classification performance than the two other models.ConclusionsFor syndromic mortality surveillance both precision and recall are important for all MSGs. Indeed to meet the objective of a reactive detection of events, high precision is needed to limit false alarms. To measure the impact of an event, the surveillance system should have high recall, to avoid an underestimation of this impact. This is especially true for rarer diseases.The results showed that the rule-based method and the hybrid model are the most effective to classify causes of death into MSGs. For some MSGs with less than 5 mentions in the test set (7%), these results must be qualified. Also, to improve classification performance for MSGs with performance below 0.90, and to confirm these results further analysis must be conducted.The results suggest the relevance of these methods to set up a reactive mortality surveillance system for detection and alert based on free-text causes of death. Such a system will provide useful information to health authorities regarding the causes of death during an event, helping them to adapt counter and prevention measures.References1. Lassalle M, Caserio-Schönemann C, Gallay A, Rey G, Fouillet A. Pertinence of electronic death certificates for real-time surveillance and alert, France, 2012–2014. Public Health. 2017;143:85-93. (1)2. Baghdadi Y, Gallay A, Caserio-Schönemann C, Thiam M-M, Fouillet A. Towards real-time mortality surveillance by medical causes of death: A strategy of analysis for alert. Rev Epidemiol Sante Publique. 2018;66:S402. (2)3. Wang Y, Wang L, Rastegar-Mojarad M, Moon S, Shen F, Afzal N, et al. Clinical information extraction applications: A literature review. J Biomed Inf. 2018;77:34-49. (3) %R 10.5210/ojphi.v11i1.9767 %U %U https://doi.org/10.5210/ojphi.v11i1.9767 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9768 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveDue to the lack of information about the phylogenetic origins of Ukrainian Bacillus anthracis strains,the goal of this work was to make phylogenetic analysis of Ukrainian isolates obtained from various sources (soil, clinical material from infected humans and animal products) for better understanding of phylogenetic origins of this pathogen in Ukraine and Eastern Europe.IntroductionAnthrax is a widely spread zoonotic disease with natural transmissive cycle involving wildlife, livestock and humans [1]. It is caused by Bacillus anthracis, a highly pathogenic gram-positive, spore-producing bacterium, which poses a serious threat to public and animal health due to its mortality both for animals and for humans [2, 3, 4]. The ability of B. anthracis spores to remain viable in soils for decades enables their isolation from freely accessible environment [5]. This unique feature to form highly resistant spores in the environment plays a major role in the ecology and evolution of this pathogen [6]. During the spore phase, evolution is greatly reduced in rate, which limits the amount of genetic diversity found among isolates of this species [1]. All these factors demonstrate the need for reliable anthrax diagnosis and trace-back methods. This comprises bio forensic capabilities including state-of-the-art methods for accurate genotyping of B. anthracis strains.Methods23 thermolysates of B. anthracis broth cultures isolated from various sources (vesicles from eleven different people infected with cutaneous anthrax when disease’s sporadic outbreaks were detected in Ukraine in 1963-2002, as well as two samples from sheep wool, and eight soil samples) were obtained from the Central Epidemiological Station (Kyiv, Ukraine), as well as from I.I. Mechnikov Ukrainian Scientific and Research Anti-plaque Institute (Odessa, Ukraine). These anthrax cultures were confirmed with classical microbiological methods (microscopy, cultivation on solid and liquid media), “string of pearls” reaction, and using bioassay on living white mice (the mortality was observed two days after subcutaneous injection of 0,2-0,5 ml of cells’ suspension). All these tests were carried out at the institutions where samples were obtained. Besides, one B. anthracis isolate was cultivated from soil sample of an animal grave site nearby Koviagy village, Valky district, Kharkiv region. All samples were analyzed at the Bundeswehr Institute of Microbiology (Munich, Germany). To confirm the presence of the anthrax genome and plasmids, we isolated genomic DNA (gDNA) from thermolysates and studied the presence of the genomic marker dhp61 as well as the plasmid specific marker pagA (pXO1) and capC (pXO2) using qPCR. Quality of the isolated gDNA was tested using the Agilent bioanalyzer. To characterize regional and global phylogeographic patterns of these strains, canonical Single Nucleotide Polymorphisms analysis (canSNP) was conducted using high resolution melt (HRM). Three thermolysates of broth cultures isolated and soil sample isolated from animal grave site in Kharkiv region were analyzed using NewSeq Full genome sequencing.ResultsB. anthracis chromosomal DNA-marker dhp61 as well as pXO1 marker pagA and pXO2 plasmid marker capC could be detected in all thermolysates. However, the soil isolate from the Koviagy grave site was positive for dhp61 but contained only the pXO1 plasmid. The Bioanalyzer assay revealed that only 6 out of the 23 thermolysates had good enough DNA quality to be sequenced. So far only genomes of thermolysates of soil samples from Mykolaiv and Sumy regions, the thermolysate of sick patient''s vesicle from Kherson region as well as the soil sample from the animal grave site in Kharkiv region have been sequenced. For the residual 3 thermolysates the full genome analysis is still in progress. The sequencing results showed that the B. anthracis strain isolated from Mykolaiv soil sample belongs to the Vollum linage group and other thermolysates from Sumy and Kherson regions are closely clustering with isolates from Japan. Thus, human isolate from Kherson region is clustering with the Japanese isolate BA104 which was obtained from pig during sporadic anthrax incident in 1982 and soil isolate from Sumy region is clustering with the BA 103 isolate which was obtained from beef cattle in Japan in 1991. In contrast, we analyzed the genomic sequence of the pXO2-negative isolate from grave site in Kharkiv region using BioNumerics software and found that it has high similarity to STI strain.ConclusionsThe infrequent sporadic occurrence of anthrax in the country of Ukraine is likely caused by a heterogeneous population of B. anthracis. The found STI strain in the grave site of Kharkiv region is probably an environmental recovery of the Russian anthrax live vaccine which was commonly used for vaccination of animals in the former Soviet Union The sequencing result of the soil isolate from Mykolaiv region indicates the occurrence of another canSNP group, the Vollum group, which is quite untypical for Ukraine. The latter is mainly prevalent in the Asian regions (namely Pakistan) and therefore might have been introduced to Ukraine over the silk road. Other two thermolysates from Sumy and Kherson regions also showed unexpected results clustering with Japanese isolates. The further research of Ukrainian B. anthracis isolates will allow us to expand our knowledge about the population structure and evolution of anthrax in Ukraine.References1. Van Ert MN, Easterday WR, Huynh LY, Okinaka RT, Hugh-Jones ME, Ravel J, et al. (2007) Global Genetic Population Structure of Bacillus anthracis. PLoS ONE 2(5);2. Freidlander, A. M. 1997. Anthrax, p. 467–478. In F. R. Sidell, E. T. Takafuji, and D. R. Franz (ed.), Medical aspects of chemical and biological warfare. Office of the Surgeon General, Washington, D.C.3. Hoffmaster AR, Fitzgerald CC, Ribot E, Mayer LW, Popovic T (2002) Molecular subtyping of Bacillus anthracis and the 2001 bioterrorism-associated anthrax outbreak, United States. Emerg Infect Dis 8: 1111–1116.4. Keim P, Van Ert MN, Pearson T, Vogler AJ, Huynh LY, et al. (2004) Anthrax molecular epidemiology and forensics: using the appropriate marker for different evolutionary scales. Infect Genet Evol 4: 205–213.5. Eitzen, E. M. 1997. Use of biological weapons, p. 437–450. In F. R. Sidell, E. T. Takafuji, and D. R. Franz (ed.), Medical aspects of chemical and biological warfare. Office of the Surgeon General, Washington, D.C.6. Biloivan O, Duerr A, Schwarz J, Grass G, Arefiev V, Solodiankin O, Stegniy B, Gerilovych A (2018) Phylogenetic analysis of Ukrainian Bacillus anthracis strains. Third Annual BTRP Ukraine Regional One Health Research Symposium, abstract directory: 122. %R 10.5210/ojphi.v11i1.9768 %U %U https://doi.org/10.5210/ojphi.v11i1.9768 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9769 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo describe the use of syndromic surveillance data for real-time situational awareness of emergency department utilization during a localized mass overdose event related to the substance K2.IntroductionOn August 15, 2018, the Connecticut Department of Public Health (DPH) became aware of a cluster of suspected overdoses in an urban park related to the synthetic cannabinoid K2. Abuse of K2 has been associated with serious adverse effects and overdose clusters have been reported in multiple states. This investigation aimed to characterize the use of syndromic surveillance data to monitor a cluster of suspected overdoses in real time.MethodsThe EpiCenter syndromic surveillance system collects data on all emergency department (ED) visits at Connecticut hospitals. ED visits associated with the event were identified using ad hoc keyword analyses. The number of visits by facility location for the state, county, and city were communicated to state and local partners in real time. Gender, age, and repeated ED visits were assessed. After the event, surveillance findings were summarized for partnersResultsDuring the period of August 15–16, 2018 the number of ED visits with a mention of K2 in the chief complaint increased from three to 30 in the impacted county, compared to a peak of 5 visits during the period of March–July, 2018. An additional 25 ED visits were identified using other related keywords (e.g., weed). After the event, 72 ED visits were identified with K2 and location keywords in the chief complaint or triage notes. These 72 visits comprised 53 unique patients, with 12 patients returning to the ED 2–5 times over the two day period. Of 53 patients, 77% were male and the median age was 40 years (interquartile range 35–51 years). Surveillance findings were shared with partners in real time for situational awareness, and in a summary report on August 21.ConclusionsData from the EpiCenter system were consistent with reports from other data sources regarding this cluster of suspected drug overdoses. Next steps related to this event involve: monitoring data for reference to areas of concentrated substance use, enabling automated alerts to detect clusters of interest, and developing a plan to improve coordinate real-time communication with stakeholderswithin DPH and with external partners during events. %R 10.5210/ojphi.v11i1.9769 %U %U https://doi.org/10.5210/ojphi.v11i1.9769 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9770 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveBy systematically scoring algorithms and integrating outbreak data through statistical learning, evaluate and improve the performance of automated infectious-disease-outbreak detection. The improvements should be directly relevant to the epidemiological practice. A broader objective is to explore the usefulness of machine-learning approaches in epidemiology.IntroductionWithin the traditional surveillance of notifiable infectious diseases in Germany, not only are individual cases reported to the Robert Koch Institute, but also outbreaks themselves are recorded: A label is assigned by epidemiologists to each case, indicating whether it is part of an outbreak and of which. This expert knowledge represents, in the language of machine leaning, a \"ground truth\" for the algorithmic task of detecting outbreaks from a stream of surveillance data. The integration of this kind of information in the design and evaluation of algorithms is called supervised learning.MethodsReported cases were aggregated weekly and divided into two count time series, one for endemic (not part of an outbreak) and one for epidemic cases. Two new algorithms were developed for the analysis of such time series: farringtonOutbreak is an adaptation of the standard method farringtonFlexible as implemented in the surveillance R package: It trains on endemic case counts but detects anomalies on total case counts. The second algorithm is hmmOutbreak, which is based on a hidden Markov model (HMM): A binary hidden state indicates whether an outbreak was reported in a given week, the transition matrix for this state is learned from the outbreak data and this state is integrated as factor in a generalised linear model of the total case count. An explicit probability of being in a state of outbreak is then computed for each week (one-week ahead) and a signal is generated if it is higher than a user-defined threshold.To evaluate performance, we framed outbreak detection as a simple binary classification problem: Is there an outbreak in a given week, yes or no? Was a signal generated for this week, yes or no? One can thus count, for each time series, the true positives (outbreak data and signals agree), false positives, true negatives and false negatives. From those, classical performance scores can be computed, such as sensitivity, specificity, precision, F-score or area under the ROC curve (AUC).For the evaluation with real-word data we used time series of reported cases of salmonellosis and campylobacteriosis for each of the 412 German counties over 9 years. We also ran simple simulations with different parameter sets, generating count time series and outbreaks with the sim.pointSource function of the surveillance R package.ResultsWe have developed a supervised-learning framework for outbreak detection based on reported infections and outbreaks, proposing two algorithms and an evaluation method. hmmOutbreak performs overall much better than the standard farringtonFlexible, with e.g. a 60% improvement in sensitivity (0.5 compared to 0.3) at a fixed specificity of 0.9. The results were confirmed by simulations. Furthermore, the computation of explicit outbreak probabilities allows a better and clearer interpretation of detection results than the usual testing of the null hypothesis \"is endemic\".ConclusionsMethods of machine learning can be usefully applied in the context of infectious-disease surveillance. Already a simple HMM shows large improvements and better interpretability: More refined methods, in particular semi-supervised approaches, look thus very promising. The systematic integration of available expert knowledge, in this case the recording of outbreaks, allows an evaluation of algorithmic performance that is of direct relevance for the epidemiological practice, in contrast to the usual intrinsic statistical metrics. Beyond that, this knowledge can be readily used to improve that performance and, in the future, gain insights in outbreak dynamics. Moreover, other types of labels will be similarly integrated in automated surveillance analyses, e.g. user feedback on whether a signal was relevant (reinforcement learning) or messages on specialised internet platforms that were found to be useful warnings of international epidemic events. %R 10.5210/ojphi.v11i1.9770 %U %U https://doi.org/10.5210/ojphi.v11i1.9770 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9771 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo discuss the process for developing and revising suspected drug overdose queries in syndromic surveillance (SyS) systems.IntroductionState and local jurisdictions have been exploring the use of SyS data to monitor suspected drug overdose outbreaks in their communities. With the increasing awareness and use of SyS systems, staff from the Centers for Disease Control and Prevention (CDC) worked to develop several queries that jurisdictions could use to better capture suspected drug overdose visits. In 2017, CDC released their first two queries on heroin overdose and opioid overdose, followed in 2018 by stimulant and all drug overdose queries. Over time, and with the assistance from the SyS community and the CDC-funded Enhanced State Opioid Overdose Surveillance (ESOOS) state health departments, CDC has revised the queries to address suggestions from jurisdictions. However, it’s not clear how often and in what way the syndrome definitions are updated over time. This is particularly true as new drugs emerge and the names of those drugs are integrated into syndrome definitions (e.g., recent “Spice” and “K2” synthetic cannabinoid outbreaks).DescriptionThis roundtable will provide a forum for national, state, and local users of SyS and drug overdose syndrome queries to discuss the process of query development, with an eye towards determining when a definition is “good enough.” CDC staff will facilitate the discussion and present the current portfolio of drug-related overdose queries. Participants will be encouraged to provide feedback on the queries, share what has been/has not been working in their jurisdiction with regard to syndrome query development, and discuss the process for revising queries as the epidemic evolves. The focus of this roundtable will be on suspected drug overdose query development and revision with emergency department SyS data.How the Moderator Intends to Engage the Audience in Discussions on the TopicWith most jurisdictions grappling with the impact of the opioid epidemic, this roundtable is well suited for widespread audience participation. Though some jurisdictions have been using SyS to monitor suspected drug overdose outbreaks for some time, others are not using SyS in this way. Thus, opportunities for sharing of work, experiences, barriers, and facilitators will be useful for all SyS users %R 10.5210/ojphi.v11i1.9771 %U %U https://doi.org/10.5210/ojphi.v11i1.9771 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9772 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo increase the availability and accessibility of standardized environmental health data for public health surveillance and decision-making.IntroductionIn 2002, the United States (US) Centers for Disease Control and Prevention (CDC) launched the National Environmental Public Health Tracking Program (Tracking Program) to address the challenges in environmental health surveillance described by the Pew Environmental Commission (1). The report cited gaps in our understanding of how the environment affects our health and attributed these gaps to a dearth of surveillance data for environmental hazards, human exposures, and health effects. The Tracking Program’s mission is to provide information from a nationwide network of integrated health and environmental data that drives actions to improve the health of communities. Accomplishing this mission requires a range of expertise from environmental health scientists to programmers to communicators employing the best practices and latest technical advances of their disciplines. Critical to this mission, the Tracking Program must identify and prioritize what data are needed, address any gaps found, and integrate the data into the network for ongoing surveillance.MethodsThe Tracking Program identifies important environmental health topics with data challenges based on the recommendations in the Pew Commission report as well as input from federal, state, territorial, tribal, and local partners. For each topic, the first step is to formulate the key surveillance question, which includes identifying the decision-maker or end user. Next, available data are evaluated to determine if the data can answer the question and, if not, what enhancements or new data are needed. Standards are developed to establish data requirements and to ensure consistency and comparability. Standardized data are then integrated into the network at national, state, and local levels. Standardized measures are calculated to translate the data into the information needed. These measures are then publically disseminated via national, state, and local web-based portals. Data are updated annually or as they are available and new data are added regularly. All data undergo a multi-step validation process that is semi-automated, routinized, and reproducible.ResultsThe first set of nationally consistent data and measures (NCDM) was released in 2008 and covered 8 environmental health topics. Since then the NCDM have grown to cover 14 topics. Additional standardized data and measures are integrated into the national network resulting in 23 topics with standardized 450 measures (Figure). On the national network, measures can be queried via the Data Explorer, viewed in the info-by-location application, or connected to via the network’s Application Program Interface (API). On average, 15,000 and 3300 queries are run every month on the Data Explorer and the API respectfully. Additional locally relevant data are available on state and local tracking networks.Gaps in data have been addressed through standards for new data collections, models to extend available data, new methodologies for using existing data, and expansion of the utility of non-traditional public health data. For example, the program has collaborated with the Environmental Protection Agency to develop daily estimates of fine particulate matter and ozone for every county in the conterminous US and to develop the first national database of standardized radon testing data. The program also collaborated with the National Aeronautics and Space Administration and its academic partners to transform satellite data into data products for public health.The Tracking Program has analyzed the data to address important gaps in our understanding of the relationship between negative health outcomes and environmental hazards. Data have been used in epidemiologic studies to better quantify the association between fine particulate matter, ozone, wildfire smoke, and extreme heat on emergency department visits and hospitalizations. Results are translated into measures of health burden for public dissemination and can be used to inform regulatory standards and public health interventions.ConclusionsThe scope of the Tracking Program’s mission and the volume of data within the network requires the program to merge traditional public health expertise and practices with current technical and scientific advances. Data integrated into the network can be used to (1) describe temporal and spatial trends in health outcomes and potential environmental exposures, (2) identify populations most affected, (3) generate hypotheses about associations between health and environmental exposures, and (4) develop, guide, and assess the environmental public health policies and interventions aimed at reducing or eliminating health outcomes associated with environmental factors. The program continues to expand the data within the network and the applications deployed for others to access the data. Current data challenges include the need for more temporally and spatially resolved data to better understand the complex relationships between environmental hazards, health outcomes, and risk factors at a local level. National standards are in development for systematically generating, analyzing, and disseminating small area data and real-time data that will allow for comparisons between different datasets over geography and time.References1. Pew Environmental Health Tracking Project Team. America’s Environmental Health Gap: Why the Country Needs a Nationwide Health Tracking Network. Johns Hopkins School of Hygiene and Public Health, Department of Health Policy and Management; 2000. %R 10.5210/ojphi.v11i1.9772 %U %U https://doi.org/10.5210/ojphi.v11i1.9772 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9773 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveIn order to meet local mental health surveillance needs, we created multiple mental health-related indicators using emergency department data from the Colorado North Central Region (CO-NCR) Early Notification of Community Based Epidemics (ESSENCE), a Syndromic Surveillance (SyS) platform.IntroductionMental health is a common and costly concern; it is estimated that nearly 20 percent of adults in the United States live with a mental illness[1] and that more money is spent on mental illness than any other medical condition.[2] One spillover effect of unmet mental health needs may be increasing emergency department utilization. National analysis by Healthcare Cost and Utilization Project (H-CUP) found a 55% increase in emergency department visits for depression, anxiety, and stress reactions between 2006-2013.[3] Local public health agencies (LPHAs) can play an important role in reducing costs and burden associated with mental illness. There is opportunity to use emergency department data at a local level to monitor trends and evaluate the effectiveness of local strategies. ESSENCE, available in 31 states, provides near-real time observation-level emergency department data, which can be analyzed and disseminated according to local needs. Using ESSENCE data from 6 local counties in Colorado, we developed methods to estimate the overall burden of mental health and specific mental health disorders seen in the emergency department.MethodsBoulder County Public Health expanded on existing methods to develop multiple mental health queries in ESSENCE using data from the six Colorado counties that currently participate in the Colorado North Central Region (CO-NCR) SyS (i.e., Adams, Arapahoe, Boulder, Denver, Douglas, and Jefferson Counties). Our query was based solely off relevant International Classification of Disease version 10 Clinical Modification (ICD-10-CM) mental health codes: F20-F48, F99, R45.851, X71–X83, T14.91, and R45.851. We also included T36-T65 and T71 where intentional self-harm was specified. In addition to an overall mental health query we created 11 sub-queries for: anxiety disorder, conversion disorder, intentional self-harm/suicide attempt, mood disorder, obsessive compulsive disorder (OCD), dissociative disorder, schizophrenia, somatoform disorders, stress adjustment disorder, suicide ideation, and other mental health disorder). One observation could fall into multiple subcategories through inclusion of multiple discharge diagnosis (DD).One challenge of using the DD field in ESSENCE is that in Colorado, similar to other states, there can be excess of 40 unique ICD-10-CM codes listed in the DD field, and queries identify cases by searching all listed codes. For this project, that is problematic as codes may refer to historic and underlying health conditions, rather than acute cause of the ED visit. To handle this, we performed a secondary analysis to determine whether observations were “true mental health cases” based on order of codes listed in DD field, triage notes and chief complaint. We then calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value(NPV) of including observations where mental health was listed as the first (or primary) code, first or second, or first second or third code. Our analysis revealed that observations where mental health codes are listed later were less likely to be identifiable as true mental health cases, and led to our decision to only include observations with qualifying codes listed first or second.To assess the mental health burden, we developed code in SAS 9.4 that parsed ESSENCE output by discharge diagnosis, create aforementioned sub-queries, and calculated counts and age-adjusted rates (based on 2000 US Population) to summarize demographic and geographic trends.ResultsThere were 22,451 observations with mental health discharge diagnosis codes for the six Colorado counties between January and June 2018. Of these codes, 13,331 had a mental health code as the first and/or second listed DD and were counted as true mental health visits. The age-adjusted rates of any mental health visit ranged from approximately 425 per 100,000 in Douglas County to 1,026 per 100,000 in Denver County. The most common reasons for mental health visits across the region were anxiety, mood disorder, and suicide ideation (Figure 1). There was a significant spike in mental health ED visits among the 15-24 age group, followed by decreasing rates in older age groups (Figure 2). Younger age groups most commonly had ED visits for mood disorder (all age groups under 24), while in the age groups 25-34, 35-44, 65-74 and 75+ the most common reason for ED visit was anxiety. Also of note, ED visits for suicide ideation and self- harm were highest for the 15-24 age group. Males and females had similar rates of ED visits for most diagnoses, which is notable given males generally utilize healthcare services at lower rates than females.ConclusionsSyndromic surveillance is a valuable addition to available mental health surveillance. Our methods and results demonstrate the feasibility of tracking overall and specific mental health trends using the ESSENCE platform. Unlike other available mental health data, ESSENCE provides data that is local, observation level, and near-real time. Through continued collaboration with public health, medical and other stakeholders we hope this data can be pivotal in gauging disparities in mental health burden, monitoring trends, and prioritizing solutions.References[1] Mental Illness. National Institute of Mental Health. https://www.nimh.nih.gov/health/statistics/mental-illness.shtml[2] Roehrig C. Mental Disorders Top The List Of The Most Costly Conditions In The United States: $201 Billion. Health Aff (Millwood). 2016 Jun 1;35(6):1130-5. https://www-healthaffairs-org.ezp.welch.jhmi.edu/doi/pdf/10.1377/hlthaff.2015.1659[3]Weiss AJ, Barrett ML, Heslin KC. , Stocks C. Trends in Emergency Department Visits Involving Mental and Substance Use Disorders, 2006-2013. HCUP Statistical Brief #216. Agency for Healthcare Research and Quality. http://www.hcup-us.ahrq.gov/reports/statbriefs/sb216-Mental-Substance-Use-Disorder-ED-Visit-Trends.pdf. December 2016. %R 10.5210/ojphi.v11i1.9773 %U %U https://doi.org/10.5210/ojphi.v11i1.9773 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9774 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveLink syndromic surveillance data for potential opioid-involved overdoses with hospital discharge data to assess positive predictive value of CDC Opioid Classifiers for conducting surveillance on acute drug overdoses.IntroductionThe opioid drug overdose crisis presents serious challenges to state-based public health surveillance programs, not the least of which is uncertainty in the detection of cases in existing data systems. New Jersey historically had slightly higher unintentional drug overdose death rates than the national average, but by 2001 dramatic increases in drug overdose deaths in states like West Virginia began to drive up the national rate (Figure 1). Although the rise in New Jersey’s fatal overdose rates has mirrored the national rate since 1999, the rate has dramatically increased since 2011- from 9.7 per 100,000 (868 deaths) to 21.9 per 100,000 in 2016 (1,931 deaths), an increase of 125% in five years.1The New Jersey Department of Health has been funded by the Centers for Disease Control and Prevention (CDC) to conduct surveillance of opioid-involved overdoses through the Enhanced Surveillance of Opioid-Involved Overdose in States (ESOOS) program, and to conduct syndromic surveillance through the National Syndromic Surveillance Program (NSSP); this has presented a collaboration opportunity for the Department’s surveillance grantee programs to use existing resources to evaluate and refine New Jersey’s drug overdose case definitions and develop new indicators to measure the burden of overdose throughout the state and to facilitate effective responses.MethodsThis work examined using probabilistic matching strategies to assess how accurately syndromic surveillance data identifies potential opioid-involved overdose patients by linking to hospital discharge records after subsequent treatment in an emergency department or inpatient setting for either a confirmed opioid-involved overdose or another condition(s).New Jersey syndromic surveillance data from NSSP’s ESSENCE system from December 2016 with either CDC’s CCDD Classifiers “CDC Opioid Overdose V1” or “CDC Heroin Overdose V3” were selected for inclusion (“NJ ESSENCE data”). NJ ESSENCE data were restructured to produce one record per patient visit, with each record assigned one or more overdose classifiers; these records were then matched to the universe of acute care hospital discharge billing records from the New Jersey Hospital Discharge Data System (“UB data”) from the same time period. Confirmed drug overdoses were flagged in the UB data by using the CDC’s baseline ESOOS case definition, which searches all diagnosis fields for ICD-10-CM codes indicating an unintentional or undetermined intent drug overdose, an opioid overdose, or a heroin overdose. Optionally, there are suggested codes for mental and behavioral health conditions that indicate opioid abuse or dependence with intoxication (Table 1).Using SAS® software and PROC SQL, data were matched using a three-round “blocking” strategy based on facility identifier and admission date, and combinations of date of birth, sex, patient ZIP code, and age. Concordance of ESSENCE opioid overdose classifiers with indicator categories used by CDC’s ESOOS was evaluated. Suspected opioid overdoses from NJ ESSENCE that matched to UB records for mental health conditions that were not also acute overdoses were reviewed.ResultsThere were 253 records in NJ ESSENCE data with either “CDC Opioid Overdose V1” or “CDC Heroin Overdose V3” CCDD classifiers; restructuring the data resulted in 149 unique records of potential opioid overdoses. Of these, 106 (71%) records from NJ ESSENCE were successfully matched to emergency department or inpatient records. Eighty (80) records (54%), were matched in the first round using facility identifier and date of admission, date of birth, sex, and patient’s home ZIP code. Of the 43 unmatched NJ ESSENCE records, 33 (77%) were patients missing age and date of birth.Of the 106 matched records (Table 2):● 74 opioid-involved overdoses in NJ ESSENCE matched to any drug overdose records in the UB data, for an overall PPV of 70%.● 69 opioid-involved overdoses in NJ ESSENCE matched to opioid-involved overdose records, for an opioid-involved PPV of 65%.● 54 heroin-involved overdoses in NJ ESSENCE matched to heroin-involved overdose records, for a heroin-involved PPV of 92%.32 matched records were NJ ESSENCE positive for opioids and UB negative, and 24 (75%) were classified as potential heroin overdoses.●18 records had at least one mental and behavioral health condition code as part of the final discharge record.● 3 were flagged with the mental and behavioral health conditions with opioid intoxication indicator.Only one record appeared to be a possible false positive, with an NJ ESSENCE record indicating a “suspected heroin overdose or an overdose by unspecified drugs and of undetermined intent”, but a discharge record indicated a primary diagnosis code of I46.9 (sudden cardiac arrest) and other systemic diagnoses but no poisoning or mental or behavioral health codes reported.ConclusionsNJ ESSENCE data with CDC Opioid or Heroin Overdose Classifiers was able to correctly identify opioid-involved overdoses in matched records for patients experiencing an acute overdose better than 2 out of 3 times. For patients experiencing an acute heroin overdose the PPV was over 90%. Cases with discordance in classification matched to records that may have been possible undetected drug intoxications or other mental and behavioral health conditions.This work does not confirm that the CDC Opioid or Heroin Overdose Classifiers accurately capture all or even most drug overdoses treated in New Jersey hospitals reported to NSSP ESSENCE as of December 2016. A total of 1,461 discharges for acute drug overdoses were identified in UB data using the ESOOS case definition; 1,069 were treated and released from the emergency department, and 392 were admitted for further inpatient care. The 106 matched records only represent 7% of total overdose records identified in the UB data.Further suggested work includes follow-up on possible data quality issues, pursuing a comprehensive project using all UB-identified overdoses matched to a broader selection of NJ ESSENCE data to examine what may be missed by the CDC’s NSSP overdose classifiers, and using more recent data to test improvements made to the system since the original data pull.References1. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. Web-based Injury Statistics Query and Reporting System (WISQARS) [online]. (2005) [2018 Oct 1]. Available from URL: http://www.cdc.gov/injury/wisqars %R 10.5210/ojphi.v11i1.9774 %U %U https://doi.org/10.5210/ojphi.v11i1.9774 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9776 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectivesTo evaluate the use of a real-time surveillance tool to track a variety of occupationally-related emergency room visits through the state based syndromic surveillance system, EpiCenter.IntroductionThis study uses data from the New Jersey syndromic surveillance system (EpiCenter) as a data source to enhance surveillance of current non-fatal occupational injuries, illnesses, and poisonings. EpiCenter was originally developed for early detection and monitoring of the health of communities using chief complaints from people seeking acute care in hospital emergency rooms to identify health trends. Currently, syndromic surveillance has not been widely applied to identify occupational injuries and illnesses. Incorporating syndromic surveillance data from EpiCenter, along with hospital discharge data, will enhance the classification and capture of work-related non-fatal injuries with possible improved efforts at prevention.MethodsEpiCenter Emergency Department data from January to December 2014 was evaluated, using work-related keywords and ICD-9 codes, to determine its ability to capture non-fatal work-related injuries. A collection of keywords and phrases specific to work-related injuries was developed by manually assessing the free text chief complaint data field’s. Sensitivity, specificity, and positive predictive value (PPV), along with descriptive statistics was used to evaluate and summarize the occupational injuries identified in EpiCenter.ResultsOverall, 11,919 (0.3%) possible work-related injuries were identified via EpiCenter. Of these visits 956 (8%) indicated Workman’s Compensation as payer. Events that resulted in the greatest number of ED visits were falls, slips, trips (1,679, 14%). Nature of injury included cuts, lacerations (1,041, 9%), burns (255, 2%), and sprains, strains, tears (185, 2). The part of the body most affected were the back (1,414, 12%). This work-related classifier achieved a sensitivity of 5.4%, a specificity of 99.8%, and a PPV of 2.8%.ConclusionsEvaluating the ability and performance of a new and existing surveillance data source to capture work-related injuries can lead to enhancements in current data collection methods. This evaluation successfully demonstrated that the chief complaint reporting system can yield real-time knowledge of incidents and local conditions for use in identifying opportunities for prevention of work-related injuries. %R 10.5210/ojphi.v11i1.9776 %U %U https://doi.org/10.5210/ojphi.v11i1.9776 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9779 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveAssess the validity of Florida (FL) Enhanced State Opioid Overdose Surveillance (ESOOS) non-fatal syndromic case definitions.IntroductionIn 2017, FL Department of Health (DOH) became one of thirty-two states plus Washington, D.C funded by the Center for Disease Control and Prevention (CDC) under the ESOOS program. One of the objectives of this funding was to increase the timeliness of reporting on non-fatal opioid overdoses through syndromic surveillance utilizing either the emergency department (ED) or Emergency Medical Services (EMS) data systems. Syndromic case validation is an essential requirement under ESOOS for non-fatal opioid-involved overdose (OIOD). FL’s ESOOS program conducted OIOD validation and quality monitoring of EMS case definitions, using data from FL’s Emergency Medical Services Tracking and Reporting System (EMSTARS). We examined measurement validity with OIOD cases identified from FL’s statewide hospital billing database, FL Agency for Health Care Administration (AHCA).MethodsFrom FL-EMSTARS, we extracted EMS data where the type of service requested was a 911 response, the patient was treated then transported by EMS to a hospital facility in Florida and was 11 years of age or older. Additionally, all incident-patient encounters excluded those who were dead at the scene. We included all responses with dispatch dates between January 1, 2016, and December 31, 2016. From FL-AHCA, we extracted ED and inpatient discharge information with admission dates and patient age covering the same ranges as our EMS encounters. We classified FL-EMSTARS cases based on combinations, like that of Rhode Island,1 using providers primary impression (PPI), providers secondary impression (PSI) and response to the administration of naloxone. FL-AHCA cases were defined by the following T and F codes from the International Classification of Diseases 10: T40.0-T40.4, T40.60, T40.69, F11.12, F11.120, F11.121, F11.122, F11.129, F11.22, F11.220, F11.221, F11.222, F11.229, F11.92, F11.920, F11.921, F11.922, F11.929. For all “T” codes, the 6th character was either a “1” or “4,” because ESOOS is focused on unintentional and undetermined drug overdoses, ergo we excluded ED visits that are related to intentional self-harm (i.e., “2”) or assault (i.e., “3”). Lastly, for all “T” codes, the 7th character we included was the initial ED encounter (i.e., “A”) because the purpose of the system is to capture increases or decreases in acute overdoses. To improve our match rate, account for typographical errors, and account for the discriminatory power some values may contain, we employed probabilistic linkage using Link Plus software developed by the CDC Cancer Division. Blocking occurred among social security number (SSN), event date, patient age in years, and date of birth (DOB). Next, we matched both datasets on ten variables: event date, age, sex, DOB, ethnicity, facility code, hospital zip code, race, SSN, and patient’s residence zip code. Further pruning was performed to ensure all matches were within a 24-hour time interval. Data management and statistical analyses were performed using SAS® statistical software, version 9.4 (SAS Institute Inc., Cary, NC, USA). We assessed EMS measurement validity by sensitivity, specificity, and positive predictive value (PPV). Next, risk factors were identified by stepwise multivariable logistic regression to improve the accuracy of the FL-ESOOS definition. Significant risk factors from the parsimonious multivariable model were used to simulate unique combinations to estimate the maximum sensitivity and PPV for OIOD.ResultsPrior to merging, FL-EMSTARS contained 1,308,825 unique incident-patient records, where FL-AHCA contained 8,862,566 unique incident-patient records. Of these, we conservatively linked 892,593 (68.2%) of the FL-EMSTARS dataset with FL-AHCA. Our probabilistic linkage represents an 18.2% linkage improvement over previous FL-DOH deterministic strategies (J Jiang, unpublished CSTE presentation, 2018). Among the matched pairs we estimated 8,526 OIOD, 0.96% prevalence, using the FL-AHCA case definition. Whereas the FL-ESOOS syndromic case definition estimated 6,188 OIOD, 0.69% prevalence. The FL-ESOOS OIOD syndromic case definition demonstrated 31.64% sensitivity, 99.61% specificity, and 43.60% PPV. Among false negatives, the response to administrated naloxone among OIOD was 39.37% “not known,” 37.95% “unchanged,” and 0.28% “worse.” We altered the FL-ESOOS EMSTARS case definition for OIOD to include those who were administered naloxone regardless of their response to the medication. We observed 12.37% sensitivity increase to 44.01%, 0.56% specificity decrease to 99.05%, and 12.78% PPV decrease to 30.82%.Are final multivariable model is as follows: lnOdds(Opioid Overdose)= 12.66 – 0.5459(Med Albuterol) – 0.9568(Med Aspirin) – 0.5765(Med Midazolam Hydrochloride) – 0.8690(Med Morphine Sulfate) + 1.4103 (Med Naloxone) – 0.7694(Med Nitroglycerine) + 0.3622(Med Oxygen) – 0.3702(Med Phenergan) – 0.8820(Med Epinephrine 1:10000) – 0.7397(Med Fentanyl) – 0.6376(Med Sodium Bicarbonate) – 0.2725(Med Normal Saline) + 0.3935(Med Other-Not Listed) + 0.6300(PPI General Malaise) + 0.8476(PPI Other, Non-Traumatic Pain) + 0.8725(PPI Airway Obstruction) + 0.4808(PPI Allergic reaction) + 1.4948(PPI Altered level of consciousness) + 1.5481(PPI Behavioral/psychiatric disorder) + 1.3843(PPI Cardiac arrest) + 2.3913(PPI Poisoning/drug ingestion) + 2.2418(PPI Intentional Drug Use; Related Problems) + 0.2783(PPI Respiratory distress) + 2.0305(PPI Respiratory arrest) + 0.4292(PPI Stroke/CVA) + 0.5402(PPI Syncope/fainting) + 0.5219(PSI Other, Non-Traumatic Pain) + 0.9355(PSI Allergic reaction) + 0.3521(PSI Altered level of consciousness) + 0.9036(PSI Poisoning/drug ingestion) + 0.9661(PSI Intentional Drug Use; Related Problems) + 0.3766(PSI Respiratory Distress) + 1.1802(PSI Respiratory Arrest).We plotted the multivariable sensitivity and PPV by probaiblity cutoff value to determine which would produce the best discrimination (see Figure 1). By incorporating a probability cutoff value ≥ 0.22, we can inprove both sensitivity and PPV. Specifically, we can achieve 45.48% sensitivity, 99.32% specificity, and 45.48% PPV.ConclusionsThe sensitivity of the FL-ESOOS surveillance system is not generally high but could still be useful if subsequent validation shows sensitivity stability. Regarding maximizing FL-ESOOS sensitivity and PPV, we deomonstrated that our mulitvariable model with an appropriate probability cutoff value performes better than the current case definition. This study contributes to the limited literature on Florida non-fatal opioid overdoses with a specific emphasis on validating EMS records. New unique indicator combinations are possible to increase sensitivity and PPV but should be thoroughly investigated to balance the tradeoffs to optimize the system’s ability to detect non-fatal overdoses and to discriminate true cases.References1. Rhode Island Department of Health. Rhode Island Enhanced State Opioid Overdose Surveillance (ESOOS) Case Definition For Emergency Medical Services (EMS).; 2017.2. Jiang J, Mai A, Card K, Sturms J, McCoy S. EMS Naloxone Administration for Implication of Opioid Overdose. Presentation presented at the: 2018; CSTE Annual Conference. %R 10.5210/ojphi.v11i1.9779 %U %U https://doi.org/10.5210/ojphi.v11i1.9779 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9780 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveFind practical ways to sort through statistical noise in syndromic data and make use of alerts most likely to have public health importance.IntroductionThe National Syndromic Surveillance Program’s (NSSP) instance of ESSENCE* in the BioSense Platform generates about 35,000 statistical alerts each week. Local ESSENCE instances can generate as many as 5,000 statistical alerts each week. While some states have well-coordinated processes for delegating data and statistical alerts to local public health jurisdictions for review, many do not have adequate resources. By design, statistical alerts should indicate potential clusters that warrant a syndromic surveillance practitioner‘s time and focus. However, practitioners frequently ignore statistical alerts altogether because of the overwhelming volume of data and alerts. In 2008, staff in the Virginia Department of Health experimented with rules that could be used to rank the statistical output generated in ESSENCE alert lists. Results were shared with Johns Hopkins University Applied Physics Lab (JHU/APL), the developer of ESSENCE, and were early inputs into what is now known as “myAlerts,” an ESSENCE function that syndromic surveillance practitioners can use to customize alerting and sort through statistical noise. NSSP–ESSENCE produces a shared alert list by syndrome, county, and age-group strata, which generates an unwieldy but rich data set that can be studied to learn more about the importance of these statistical alerts. Ultimately, guidance can be developed to help syndromic surveillance practitioners set up meaningful ESSENCE myAlerts effective in identifying clusters with public health importance.MethodsThe region/syndrome alert list generated from NSSP’s instance of ESSENCE on the BioSense Platform was downloaded and ranked based on five criteria:1. Observed count causing the alert2. Expected count generated by ESSENCE3. Total number of alerts for that syndrome in that county and number of prior alerts during that week for the same syndrome, county, and age group4. Density of alerts during the prior week5. Recency of the latest alertAlerts were then ranked based on:1. Higher absolute counts (regardless of expected value)2. Higher partial chi-square, (Obs-Exp)2 / Exp3. Higher total alerts for a given county/syndrome4. Higher number of earlier alerts for same county/syndrome/age group5. Multiple alerts same day > alerts on consecutive days > alerts separated by days without alerts6. Alerts present on more recent daysThe top 20 alerts with the highest scores were then reviewed and if anything unusual was noticed (i.e. problems unrelated to recent data quality problems or onboardings, seasonal trends, etc.) then there was follow-up with the site. The alert list rankings were then evaluated for differences among factors available in the ESSENCE myAlert function. We compared the top 5% of ranked alerts to the remaining 95% to determine if there were significant differences in the following factors:1. Total number of alerts across six age groups (including all ages) within 8 days of each syndrome and county stratum;2. Average alert frequency across six age groups (including all ages) within 8 days for each stratum;3. Average count across the strata;4. Average expected value across the strata;5. Average of the difference between the count and expected values for each stratum; and6. Average Level across the strata.ResultsPreliminary interactions with sites revealed important clusters – some already known and some not. For example, a cluster of healthcare workers exposed to Neisseria meningitides, and kids exposed to a bat at summer camp and presenting for prophylaxis were among the clusters identified. Additionally there were differences seen in the adjustable myAlert parameters when comparing the top 5% to the lower 95% of ranked alerts.ConclusionsThe differences seen and preliminary feedback suggests that this ranking method may be effective in identifying alerts representing true clusters of public health importance. Testing designed to evaluate myAlert parameters based on the differences seen in the top 5% of ranked alerts is underway in sites where more detailed data access is available. More study is needed; however, there are indications that cutoff values for these parameters may be a valuable way for syndromic surveillance practitioners to reduce the review burden and focus on the most important statistical clusters identified by ESSENCE statistical algorithms.References*ESSENCE stands for the Electronic Surveillance System for the Early Notification of Community-based Epidemics and is designed by Johns Hopkins University Applied Physics Laboratory. %R 10.5210/ojphi.v11i1.9780 %U %U https://doi.org/10.5210/ojphi.v11i1.9780 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9789 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveEmergency department (ED) visits related to mental health (MH) disorders have increased since 2006 (1), indicating a potential burden on the healthcare delivery system. Surveillance systems has been developed to identify and understand these changing trends in how EDs are used and to characterize populations seeking care. Many state and local health departments are using syndromic surveillance to monitor MH-related ED visits in near real-time. This presentation describes how queries can be created and customized to identify select MH sub-indicators (for adults) by using chief complaint text terms and diagnoses codes. The MH sub-indicators examined are mood and depressive disorders, schizophrenic disorders, and anxiety disorders. Wider adoption of syndromic surveillance for characterizing MH disorders can support long-term planning for healthcare resources and service delivery.IntroductionSyndromic surveillance systems, although initially developed in response to bioterrorist threats, are increasingly being used at the local, state, and national level to support early identification of infectious disease and other emerging threats to public health. To facilitate detection, one of the goals of CDC’s National Syndromic Surveillance Program (NSSP) is to develop and share new sets of syndrome codes with the syndromic surveillance Community of Practice. Before analysts, epidemiologists, and other practitioners begin customizing queries to meet local needs, especially monitoring ED visits in near-real time during public health emergencies, they need to understand how syndromes are developed.More than 4,000 hospital routinely send data to NSSP’s BioSense Platform, representing about 55 percent of ED visits in the United States (2). The platform’s surveillance component, ESSENCE,* is a web-based application for analyzing and visualizing prediagnostic hospital ED data. ESSENCE’s Chief Complaint Query Validation (CCQV) data source, which is a national-level data source with access to chief complaint (CC) and discharge diagnoses (DD) from reporting sites, was designed for testing new queries.MethodsWe used ESSENCE CCQV to query weekly data for the nine week period from the first quarter of 2018 and looked at three common MH sub-indicators: mood and depressive disorders, schizophrenic disorders, and anxiety disorders. We developed four query types for each MH sub-indicator. Query-1 focused on DD codes; query-2 focused on CC text terms; query-3 focused on a combination of CC, DD, and no exclusion for mental health co-morbidity; and query-4 focused on a combination of CC and DD and excluded mental health co-morbidity. We also examined the summary distribution of CC texts to identify keywords related to MH sub-indicators.For mood and depressive disorders, we queried ICD-9 codes 296, 311; ICD-10 codes F30–F39; CC text terms for words “depressive disorder,” bipolar disorder,” “mood disorder,” “depression,” “manic episodes,” and “psychotic.” For schizophrenic disorders, we queried ICD-9 codes 295; ICD-10 codes F20–F29; CC text terms for words “psychosis,” “psychotic,” “schizo,” “delusional,” “paranoid,” “auditory,” “hallucinations,” and “hearing voices.” For anxiety disorders, we queried ICD-9 codes 300, 306, 307, 308, 309; ICD-10 codes F40–F48; CC text terms for words “anxiety,” “anexiy,” “aniety,” “aniexty,” “ansiety,” “anxety,” “anxity,” “anxiety,” “phobia,” and “panic attack.”ResultsWe identified 2.3 million average weekly ED visits for the 9-week period queried. Table 1 shows average weekly ED visits of select MH sub-indicators from the four query types. Because query 4 focused on specific MH outcomes and excluded MH co-morbidities, the average weekly ED visit for all three sub-indicators was almost half that of query 3, which focused on broader concepts by including MH co-morbidities. Among mood and depressive disorders, query 4 identified on average 23,352 ED visits per week versus 45,504 visits per week for query 3. Similarly, for schizophrenic disorders and anxiety disorders, query 4 identified on average 4,988 and 32,790 visits per week compared with 9,816 and 53,868 visits, respectively, for query 3. Further, more MH-related visits were identified using the DD-coded query (query 1) than CC-based text terms (query 2).ConclusionsAnalysts can benefit from having queries on select sub-indicators readily available and can use these to facilitate routine MH-related monitoring of ED visits, or customize the queries by including local text terms. Consistent with our previous work (3), this analysis demonstrated that MH-related ED visits are more likely to be found in DD codes than in CC alone.* Electronic Surveillance for the Early Notification of Community-based EpidemicsReferences[1] Weiss AJ, Barrett ML, Heslin KC , Stocks C. Trends in Emergency Department Visits Involving Mental and Substance Use Disorders, 2006–2013. HCUP Statistical Brief #216 [Internet]. Rockville (MD): Agency for Healthcare Research and Quality; 2016 Dec [cited 2018 Aug 14]. Available from: http://www.hcup-us.ahrq.gov/reports/statbriefs/sb216-Mental-Substance-Use-Disorder-ED-Visit-Trends.pdf.[2] Gould DW, Walker D, Yoon PW. The Evolution of BioSense: Lessons Learned and Future Directions. Public Health Reports. 2017 Jul/Aug;132(Suppl 1):S7–S11.[3] Dey AN, Gould D, Adekoya N, Hicks P, Ejigu GS, English R, Couse J, Zhou H. Use of Diagnosis Code in Mental Health Syndrome Definition. Online Journal of Public Health Informatics [Internet]. 2018 [cited 2018 Aug 14];10(1). Available from: https://doi.org/10.5210/ojphi.v10i1.8983 %R 10.5210/ojphi.v11i1.9789 %U %U https://doi.org/10.5210/ojphi.v11i1.9789 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9817 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveWe report the findings of Andhra Pradesh state’s mobile medical service programme and how It is currently used to strengthen the disease surveillance mechanisms at the village level.IntroductionIndia has an Integrated Disease Surveillance project that reports key communicable and infectious diseases at the district and sub-district level. However, recent reviews suggest structural and functional deficiencies resulting in poor data quality (1). Hence evidence-based actions are often delayed. Piramal Swasthya in collaboration with Government of Andhra Pradesh launched a mobile medical unit (MMU) programme in 2016. This Mobile medical service delivers primary care services to rural population besides reporting and alerting unusual health events to district and state health authorities for timely and appropriate action.The MMU service in the Indian state of Andhra Pradesh is one of the oldest and largest public-private initiatives in India. Two hundred and ninety-two MMUs provide fixed-day services to nearly 20,000 patients a day across 14,000 villages in rural Andhra Pradesh. Every day an MMU equipped with medical ( a doctor) and non-medical (1 nurse, 1 registration officer, 1 driver, 1 pharmacist, 1 lab technician, 1 driver) staff visit 2 service points (villages) as per prefixed route map. Each MMU also has its own mobile tablet operated by registration officer for capturing patient details. The core services delivered through MMUs are the diagnosis, treatment, counseling, and free drug distribution to the beneficiaries suffering from common ailments ranging from seasonal diseases to acute communicable and common chronic non-communicable diseases. The routinely collected patient data is daily synchronized on a centrally managed data servers.MethodsFor this analysis, we used aggregated and pooled data that were routinely collected from August 2016-March 2018. Patient details such as socio-demographic variables (age, sex etc.) medical history and key vitals (random blood sugar, blood pressure, pulse rate etc.) and disease diagnosis variables were analyzed. Besides, communication and action taken reports shared with Government of Andhra Pradesh were also analyzed. We report the findings of the programme with reference to strengthing the village level communicable disease surveillance. Unusual health events were defined as more than 3 patients reporting the epidemiologically linked and similar conditions clustered in the same village.ResultsWe observed 4,352,859 unique beneficiaries registrations and 9,122,349 patient visits. Of all unique beneficiaries, 79.3% had complete diagnosis details (53% non-communicable disease, 39% communicable and 8% others conditions). A total of 7 unusual health events related to specific and suspected conditions (3 vector-borne diseases related, 4 diarrhea-related) were reported to district health authorities, of which 3 were confirmed outbreaks (1 dengue, 1 malaria, and 1 typhoid) as investigated by local health authorities.ConclusionsMobile medical services are useful to detect unusual health events in areas with limited resources. It increases accountability and response from the Government authorities if the timely information is shared with competent health authorities. Careful evaluation of the mobile health interventions is needed before scaling-up such services in other remote rural areas.References1. Kumar A, Goel MK, Jain RB, Khanna P. Tracking the Implementation to identify gaps in Integrated Disease Surveillance Program in a Block of District Jhajjar (Haryana). Journal of Family Medicine and Primary Care. 2014;3(3):213-215.2. Raut D, Bhola A. Integrated disease surveillance in India: Way forward. Global Journal of Medicine and Public Health.2014;3(4):1-10 %R 10.5210/ojphi.v11i1.9817 %U %U https://doi.org/10.5210/ojphi.v11i1.9817 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9942 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo propose a computationally simple, fast, and reliable temporal method for early event detection in multiple data streamsIntroductionCurrent biosurveillance systems run multiple univariate statistical process control (SPC) charts to detect increases in multiple data streams1. The method of using multiple univariate SPC charts is easy to implement and easy to interpret. By examining alarms from each control chart, it is easy to identify which data stream is causing the alarm. However, testing multiple data streams simultaneously can lead to multiple testing problems that inflate the combined false alarm probability. Although methods such as the Bonferroni correction can be applied to address the multiple testing problem by lowering the false alarm probability in each control chart, these approaches can be extremely conservative.Biosurveillance systems often make use of variations of popular univariate SPC charts such as the Shewart Chart, the cumulative sum chart (CUSUM), and the exponentially weighted moving average chart (EWMA). In these control charts an alarm is signaled when the charting statistic exceeds a pre-defined control limit. With the standard SPC charts, the false alarm rate is specified using the in-control average run length (ARL0). If multiple charts are used, the resulting multiple testing problem is often addressed using family-wise error rate (FWER) based methods – that are known to be conservative - for error control.A new temporal method is proposed for early event detection in multiple data streams. The proposed method uses p-values instead of the control limits that are commonly used with standard SPC charts. In addition, the proposed method uses false discovery rate (FDR) for error control over the standard ARL0 used with conventional SPC charts. With the use of FDR for error control, the proposed method makes use of more powerful and up-to-date procedures for handling the multiple testing problem than FWER-based methods.MethodsThe proposed method can be applied to multiple univariate CUSUM or EWMA control charts. It can also be applied to a variation of the Hotelling T2 chart which is a common multivariate process monitoring method. The Hotelling T2 chart is analogous to the Shewart chart. Montgomery et. al2 proposed a variation of the Hotelling T2 chart where the T2 statistic is decomposed into components that reflect the contribution of each data stream.First, a tolerable FDR level specified. Then, at each new time step disease counts from each of the m geographic regions Y1t, Y2t, … , Ymt are collected. These disease counts are used to calculate the charting statistics S1t, S2t, … , Smt for each region. Meanwhile by inspecting historical data from each region, a non-outbreak period is identified. Using data from the non-outbreak period, bootstrap samples are drawn with replacement from each region and charting statistics are calculated. Using the charting statistics, empirical non-outbreak distributions are generated for each region. With the empirical non-outbreak distributions and the current charting statistic for each region S1t, S2t, … , Smt , corresponding p-values p1t, p2t, … , pmt are calculated. The multiple testing problem that occurs in comparing multiple p-values simultaneously is handled using the Storey -Tibshirani multiple comparison procedure3 to signal alarms.ResultsAs an illustration, all three methods – EWMA, CUSUM, and Hotelling T2 (components) - were applied to a data set consisting of weekly disease count data from 16 German federal sates gathered over a 11 year period from 2004-2014. The first two years of data from 2004-2005 were used to calibrate the model. Figure 1 shows the results for the state of Rhineland Palatinate. The three plots in Figure 1 show (a) the weekly disease counts for Rhineland Palatinate (b) the EWMA statistic (shown in red), the CUSUM statistic (shown in dark green) and (c) the component of the Hotelling T2 statistic corresponding to the illustrated state (shown in blue). The actual outbreak occurred on week 306 (shown by the orange line). Notice the two false alarms – alarms that occur before week 306 - with the Hotelling T2 statistic (dark green) on weeks 34 and 292; similarly, the CUSUM statistic signals a false alarm on week 57. However, the EWMA statistic does not signal any false alarms before the outbreak (red). Figure 2 zooms on the alarm statistics for the time period from weeks 280 – 330. The Hotelling T2 statistic misses the onset of actual outbreak on week 306. The CUSUM statistic detects the outbreak on week 307 – one week later. However, the EWMA statistic detects the outbreak right at the onset on week 306.ConclusionsExtensive simulation studies were conducted to compare the performance of the three control charts. Performance was compared in terms of (i) speed of detection and (ii) false alarm rates. Simulation results provide convincing evidence that the EWMA and the CUSUM are considerably speedier in detecting outbreaks compared to Hotelling T2 statistic: compared to the CUSUM, the EWMA is relatively faster. Similarly, the false alarm rates are larger for Hotelling T2 statistic compared to the EWMA and the CUSUM: false alarms are rare with both the EWMA and the CUSUM statistics with EWMA statistic having a slight edge. Overall, EWMA has the best performance out of the three methods with the new algorithm. Thus, the new algorithm applied to the EWMA statistic provides a simple, fast, and a reliable method for early event detection in multiple data streams.References1. Fricker RD. Introduction to Statistical Methods for Biosurveillance. New York, NY: Cambridge University Press; 2013. 399p.2. Runger GC, Alt FB, Montgomery DC. Contributors to Multivariate Statistical Process Control Signal. Communications in Statistics – Theory and Methods. 1996; 25(10): 2203-2213.3. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences USA 2003; 100:9440–9445. %R 10.5210/ojphi.v11i1.9942 %U %U https://doi.org/10.5210/ojphi.v11i1.9942 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 11 %N 1 %P e9943 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2019 %7 ..2019 %9 %J Online J Public Health Inform %G English %X ObjectiveTo assess the relationship between seasonal increases in emergency department (ED) and urgent care center (UCC) visits for hand, foot, and mouth disease (HFMD) among children 0-4 years old and average dew point temperatures in Virginia. To determine if this relationship can be used to develop an early warning tool for high intensity seasons of HFMD, allowing for earlier targeted public health action and communication to the community and local childcare centers during these high intensity seasons.IntroductionHand, foot, and mouth disease is a highly infectious disease common among early childhood populations caused by human enteroviruses (Enterovirus genus).1 The enteroviruses responsible for HFMD generally cause mild illness among children in the United States with symptoms of fever and rash/blisters, but have also been linked to small outbreaks of severe neurological disease such as meningitis, encephalitis, and acute flaccid myelitis.2Enteroviruses circulate year-round but increase in the summer-fall months across much of the United States.3 The drivers of this seasonality are not fully understood, but research indicates climatic factors, rather than demographic ones, are most likely to drive the amplitude and timing of the seasonal peaks.3 A recent CDC study on nonpolio enteroviruses identified dew point temperature as a strong predictor of local enterovirus seasonality, explaining around 30% of the variation in intensity of transmission across the United States.3MethodsSyndromic surveillance data on ED and UCC visits among 0-4 year olds in Virginia were analyzed from January 1, 2012 to August 31, 2018. Visits for HFMD were identified using the following chief complaint and discharge diagnosis terms: hand, foot, and mouth; HFM; fever with rash, lesions, or blisters; ICD-10 code: B08.4; or SNOMED CT code: 266108008. Visits for HFMD among 0-4 year olds were aggregated by week and calculated as a proportion of all ED and UCC visits among this age group during the study period.Hourly dew point readings from the Richmond International Airport from January 1, 2012 to August 31, 2018 were obtained from the National Oceanic and Atmospheric Administration (NOAA), National Climatic Data Center (NCDC). NOAA readings were averaged by week to establish a mean dew point for each week during the study period. Correlation analyses were performed on weekly dew point temperatures and weekly percent of HFMD visits. Weekly dew point averages were used to determine low-activity weeks at which to measure baseline percentages of HFMD visits. A low-activity week was defined as periods of two or more consecutive weeks in which each week had an average dew point temperature of less than 55.4 degrees Fahrenheit.3 To assess if HFMD seasons varied in intensity from year to year, a Kruskal-Wallis test was used to assess significant differences by year among the mean weekly percent of HFMD visits during high-activity weeks.An early warning threshold for a high intensity season was developed by calculating the mean percent of HFMD visits during low-activity weeks for the previous three years and adding two standard deviations. Threshold rates were calculated for years 2015 through 2018 and compared to the percentage of 0-4 year old HFMD visits during high-activity weeks. The week where percent of HFMD visits crossed the early warning threshold in 2018 was assessed to determine when public health notifications could have been made to alert the community about a high intensity (above threshold) HFMD season if this early warning tool had been utilized.ResultsBetween January 1, 2012 and August 31, 2018, there were 27,181 visits for HFMD among children aged 0-4 years. Mean and median weekly percent of HFMD visits were 1.33% and 1.01% of total 0-4 year old visits, respectively, with a range from 0.18% to 5.32%. These visits were most prominent during the summer or fall each year, with annual peaks occurring between weeks 22-46.Weekly percent of HFMD visits and average weekly dew point temperatures were significantly correlated (r=0.562, p<.0001). The mean weekly dew point temperature for high-activity weeks was 67.2 degrees Fahrenheit, with a range between 49.3 and 73.5 degrees. A Kruskal-Wallis test showed a significant difference in the mean weekly percent of visits by year for high-activity weeks (p<.0001).Over the 4 years of data to which the threshold was applied, percent of HFMD visits crossed the threshold in 2016 and 2018, indicating both years experienced high intensity HFMD seasons (Fig. 1). Percent of HFMD visits never crossed the early warning threshold in 2015 nor 2017. In 2018, the threshold was met on Week 21 (week ending June 2, 2018) which was more than 3 weeks prior to when public health notifications were made using routine surveillance methods through ESSENCE.ConclusionsVisits for HFMD among the young childhood population (0-4 year olds) in Virginia exhibit annual summer-fall seasonality with significant differences between the percent of visits from year to year. Seasons exhibiting a significantly higher percent of HFMD visits during high-activity weeks warrant a greater level of public health communication and outreach to educate parents, physicians and childcare centers about the disease and prevention measures. It can be difficult to differentiate high intensity seasons from low intensity seasons in the early weeks of increasing disease activity. Traditional syndromic surveillance methods using ESSENCE identify significant increases in HFMD visits from the previous 90 days, but do not readily alert on differences in seasonality from year to year. These results support the use of dew point temperature data to develop an early warning tool for high intensity seasons of HFMD. This early warning tool will allow for more efficient use of resources and targeted outreach during years with particularly high HFMD activity within the young childhood population. This early warning tool will be implemented by the Virginia Department of Health in 2019 to evaluate its effectiveness at identifying high HFMD activity in real-time.References1. Khetsuriani N, Lamonte-Fowlkes A, Oberst S, Pallansch MA. Enterovirus surveillance—United States, 1970–2005. MMWR Surveill Summ 2006;55(No. SS-8). https://www.ncbi.nlm.nih.gov/pubmed/169718902. Centers for Disease Control and Prevention (2018). Hand, Foot, and Mouth Disease (HFMD). Retrieved Sept 25, 2018, from https://www.cdc.gov/hand-foot-mouth/about/complications.html.3. Pons-Salort M, Oberste MS, Pallansch MA, et al. The seasonality of nonpolio enteroviruses in the United States: patterns and drivers. Proc Natl Acad Sci U S A 2018;115:3078–83 http://doi.org/10.1073/pnas.1721159115 %R 10.5210/ojphi.v11i1.9943 %U %U https://doi.org/10.5210/ojphi.v11i1.9943 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8299 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveWe will demonstrate tools that allow mechanistic contraints on disease progression and epidemic spread to play off against interventions, mitigation, and control measures. The fundamental mechanisms of disease progression and epidemic spread provide important constraints on interpreting changing epidemic cases counts with time and geography in the context of on-going interventions, mitigations, and controls. Models such as these that account for the effects of human actions can also allow evaluation of the importance of categories of epidemic and disease controls.IntroductionWe present the EpiEarly, EpiGrid, and EpiCast tools for mechanistically-based biological decision support. The range of tools covers coarse-, medium-, and fine-grained models. The coarse-grained, aggregated time-series only data tool (EpiEarly) provides a statistic quantifying epidemic growth potential and associated uncertainties. The medium grained, geographically-resolved model (EpiGrid) is based on differential equation type simulations of disease and epidemic progression in the presence of various human interventions geared toward understanding the role of infection control, early vs. late diagnosis, vaccination, etc. in outbreak control. A fine-grained hybrid-agent epidemic model (EpiCast) with diurnal agent travel and contagion allows the analysis of the importance of contact-networks, travel, and detailed intervention strategies for the control of outbreaks and epidemics.MethodsWe use three types of methods for simulation and analysis. They are: (1) Bayesian and regression methods allowing estimation of the basic reproductive number from case-count data; (2) ordinary-differential equation integration with modifications to account for discreteness of disease spread when case counts are small (we include space- and time-dependent effects); and (3) methods that hybridize agent-based travel, mixing, and disease progression with nested-mass action contagion (i.e. not fully agent-based). From the perspective of decision support, the crucial feature of mechanistic infectious epidemiological models is a way to capture the human interventions that determine epidemic outcome. Categorizing types of mitigation into those that change the force of infection, and those that branch disease progression allows a common framework that can be extended from medium-grained models through fine-grained. Our canonical example is our EpiGrid tool which allows for the modulation of the force of infection (i.e. contagion) with time (and potentially space), the vaccination of a susceptible population in a geographically-targeted manner, movement controls, and branching our disease progression model to account for early- vs. late-intervention during host disease progression.ResultsWe will present analysis of diseases that exemplify the various aspects of analysis in support of outbreak and epidemic control. Human and animal diseases relevant to this demonstration include rinderpest, avian influenza, and measles. We will begin with EpiEarly’s estimate of epidemic potential using aggregated time-dependent case-count data. The key observation for EpiEarly is that under a wide range of situations a disease''s reproductive number should be generalized to a distribution of possibilities to account for inherent randomness and other factors (including the variability of a disease contact network). We will then continue with a demonstration of EpiGrid’s capabilities for understanding and modelling the role of interventions including contagion control (the force of infection), treatment (changing disease progression and infectiousness depending on treatment), vaccination, culling, and movement controls. We will briefly touch on the capabilities of EpiCast for more detailed analysis of specific intervention strategies.ConclusionsWe will demonstrate examples where modeling either contributed or plausibly would contribute to informing epidemic and outbreak control constrained by the possibilities of the underlying epidemic and disease dynamics. %R 10.5210/ojphi.v10i1.8299 %U %U https://doi.org/10.5210/ojphi.v10i1.8299 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8302 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo investigate whether alternative statistical approaches can improve daily aberration detection using syndromic surveillance in England.IntroductionSyndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. Public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. The algorithm currently in use at Public Health England (PHE) for syndromic surveillance is the ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method (Morbey et al, 2015), which fits a mixed model to counts of syndromes on a daily basis. This research checks whether the RAMMIE method works across a range of public health scenarios and how it compares to alternative methods.MethodsFor this purpose, we compare RAMMIE to the improved quasi-Poisson regression-based approach (Noufaily et al, 2013), currently implemented at PHE for weekly infectious disease laboratory surveillance, and to the Early Aberration Reporting System (EARS) method (Rossi et al, 1999), which is used for syndromic surveillance aberration detection in many other countries. We model syndromic datasets, capturing real data aspects such as long-term trends, seasonality, public holidays, and day-of-the-week effects, with or without added outbreaks. Then, we compute the sensitivity and specificity to compare how well each of the algorithms detects synthetic outbreaks to provide recommendations for the most suitable statistical methods to use during different public health scenarios.ResultsPreliminary results suggest all methods provide high sensitivity and specificity, with the (Noufaily et al, 2013) approach having the highest sensitivity and specificity. We showed that for syndromes with long-term increasing trends, RAMMIE required modificaiton to prevent excess false alarms. Also, our study suggests further work is needed to fully account for public holidays and day-of-the-week effects.ConclusionsOur study will provide recommendations for which algorithm is most effective for PHE''s syndromic surveillance for a range of different syndromes. Furthermore our work to generate standardised synthetic syndromic datasets and a range of outbreaks can be used for future evaluations in England and elsewhere.ReferencesNoufaily, A., Enki, D. G., Farrington, C. P., Garthwaite, P., Andrews, N. and Charlett, A. (2013). An Improved Algorithm for Outbreak Detection in Multiple Surveillance Systems. Statistics in Medicine, 32(7), 1206-1222.Morbey, R. A., Elliot, A. J., Charlett, A., Verlander, A. Q, Andrews, N. and Smith, G. (2013). The application of a novel ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method for syndromic surveillance in England, Bioinformatics, 31(22), 3660-3665.Rossi, G, Lampugnani, L, Marchi, M. (1999), An approximate CUSUM procedure for surveillance of health events. Statistics in Medicine, 18, 2111–2122 %R 10.5210/ojphi.v10i1.8302 %U %U https://doi.org/10.5210/ojphi.v10i1.8302 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8319 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo develop a computational model to assess the risk of epidemics in global mass gatherings and evaluate the impact of various measures of prevention and control of infectious diseases.IntroductionGlobal Mass gatherings (MGs) such as Olympic Games, FIFA World Cup, and Hajj (Muslim pilgrimage to Makkah), attract millions of people from different countries. The gathering of a large population in a proximity facilitates transmission of infectious diseases [1]. Attendees arrive from different geographical areas with diverse disease history and immune responses. The associated travel patterns with global events can contribute to a further disease spread affecting a large number of people within a short period and lead to a potential pandemic. Global MGs pose serious health threats and challenges to the hosting countries and home countries of the participants [2]. Advanced planning and disease surveillance systems are required to control health risks in these events. The success of computational models in different areas of public health and epidemiology motivates using these models in MGs to study transmission of infectious diseases and assess the risk of epidemics. Computational models enable simulation and analysis of different disease transmission scenarios in global MGs. Epidemic models can be used to evaluate the impact of various measures of prevention and control of infectious diseases.MethodsThe annual event of the Hajj is selected to illustrate the main aspects of the proposed model and to address the associated challenges. Every year, more than two million pilgrims from over 186 countries arrive in Makkah to perform Hajj with the majority arriving by air. Foreign pilgrims can stay at one of the holy cities of Makkah and Madinah up to 30-35 days prior the starting date of the Hajj. The long duration of the arrival phase of the Hajj allows a potential epidemic to proceed in the population of international pilgrims. Stochastic SEIR (Susceptible−Exposed−Infected−Recovered) agent-based model is developed to simulate the disease transmission among pilgrims. The agent-based model is used to simulate pilgrims and their interactions during the various phases of the Hajj. Each agent represents a pilgrim and maintains a record of demographic data (gender, country of origin, age), health data (infectivity, susceptibility, number of days being exposed or infected), event related data (location, arrival date and time), and precautionary or health-related behaviors.Each pilgrim can be either healthy but susceptible to a disease, exposed who are infected but cannot transmit the infection, or infectious (asymptomatic or symptomatic) who are infected and can transmit the disease to other susceptibles. Exposed individuals transfer to the infectious compartment after 1/α days, and infectious individuals will recover and gain immunity to that disease after 1/γ days. Where α is the latent period and γ is the infectious period. Moving susceptible individuals to exposed compartment depends on a successful disease transmission given a contact with an infectious individual. The disease transmission rate is determined by the contact rate and thetransmission probability per contact. Contact rate and mixing patterns are defined by probabilistic weights based on the features of infectious pilgrims and the duration and setting of the stage where contacts are taking place. The initial infections are seeded in the population using two scenarios (Figure 1) to measure the effects of changing, the timing for introducing a disease into the population and the likelihood that a particular flight will arrive with one or more infected individuals.ResultsThe results showed that the number of initial infections is influenced by increasing the value of λ and selecting starting date within peak arrival days. When starting from the first day, the average size of the initial infectious ranges from 0.05% to 1% of the total arriving pilgrims. Using the SEIR agent-based model, a simulation of the H1N1 Influenza epidemic was completed for the 35-days arrival stage of the Hajj. The epidemic is initiated with one infectious pilgrim per flight resulting in infected 0.5% of the total arriving pilgrims. As pilgrims spend few hours at the airport, the results obtained from running the epidemic model showed only new cases of susceptible individuals entering the exposed state in a range of 0.20% to 0.35% of total susceptibles. The number of new cases is reduced by almost the same rate of the number of infectious individuals following precautionary behaviors.ConclusionsA data-driven stochastic SEIR agent-based model is developed to simulate disease spread at global mass gatherings. The proposed model can provide initial indicators of infectious disease epidemic at these events and evaluate the possible effects of intervention measures and health-related behaviors. The proposed model can be generalized to model the spread of various diseases in different mass gatherings, as it allows different factors to vary and entered as parameters.References1. Memish ZA, Stephens GM, Steffen R, Ahmed QA. Emergence of medicine for mass gatherings: lessons from the Hajj. The Lancet infectious diseases. 2012 Jan 31;12(1):56-65.2. Chowell G, Nishiura H, Viboud C. Modeling rapidly disseminating infectious disease during mass gatherings. BMC medicine. 2012 Dec 7;10(1):159. %R 10.5210/ojphi.v10i1.8319 %U %U https://doi.org/10.5210/ojphi.v10i1.8319 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8321 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveA team of data scientists from Booz Allen competed in an opioid hackathon and developed a prototype opioid surveillance system using data science methods. This presentation intends to 1) describe the positives and negatives of our data science approach, 2) demo the prototype applications built, and 3) discuss next steps for local implementation of a similar capability.IntroductionAt the Governor’s Opioid Addiction Crisis Datathon in September 2017, a team of Booz Allen data scientists participated in a two-day hackathon to develop a prototype surveillance system for business users to locate areas of high risk across multiple indicators in the State of Virginia. We addressed 1) how different geographic regions experience the opioid overdose epidemic differently by clustering similar counties by socieconomic indicators, and 2) facilitating better data sharing between health care providers and law enforcement. We believe this inexpensive, open source, surveillance approach could be applied for states across the nation, particularly those with high rates of death due to drug overdoses and those with significant increases in death.MethodsThe Datathon provided a combination of publicly available data and State of Virginia datasets consisting of crime data, treatment center data, funding data, mortality and morbidity data for opioid, prescription drugs (i.e. oxycodone, fentanyl), and heroin cases, where dates started as early as 2010. The team focused on three data sources: U.S. Census Bureau (American Community Survey), State of Virginia Opioid Mortality and Overdose Data, and State of Virginia Department of Corrections Data. All data was cleaned and mapped to county-levels using FIPS codes. The prototype system allowed users to cluster similar counties together based on socioeconomic indicators so that underlying demographic patterns like food stamp usage and poverty levels might be revealed as indicative of mortality and overdose rates. This was important because neighboring counties like Goochland and Henrico Counties, while sharing a border, do not necessarily share similar behavioral and population characteristics. As a result, counties in close proximity may require different approaches for community messaging, law enforcement, and treatment infrastructure. The prototype also ingests crime and mortality data at the county-level for dynamic data exploration across multiple time and geographic parameters, a potential vehicle for data exchange in real-time.ResultsThe team wrote an agglomerative algorithm similar to k-means clustering in Python, with a Flask API back-end, and visualized using FIPS county codes in R Shiny. Users were allowed to select 2 to 5 clusters for visualization. The second part of the prototype featured two dashboards built in ElasticSearch and Kibana, open source software built on a noSQL database designed for information retrieval. Annual data on number of criminal commits and major offenses and mortality and overdose data on opioid usage were ingested and displayed using multiple descriptive charts and basic NLP. The clustering algorithm indicated that when using five clusters, counties in the east of Virginia are more dissimilar to each other, than counties in the west. The farther west, the more socioeconomically homogenous counties become, which may explain why counties in the west have greater rates of opioid overdose than in the east which involve more recreational use of non-prescription drugs. The dashboards indicated that between 2011 and 2017, the majority of crimes associated with heavy-use of drugs included Larceny/Fraud, Drug Sales, Assault, Burglary, Drug Possession, and Sexual Assault. Filtering by year, county, and offense, allowed for very focused analysis at the county level.ConclusionsData science methods using geospatial analytics, unsupervised machine learning, and leverage of noSQL databases for unstructured data, offer powerful and inexpensive ways for local officials to develop their own opioid surveillance system. Our approach of using clustering algorithms could be advanced by including several dozen socioeconomic features, tied to a potential risk score that the group was considering calculating. Further, as the team became more familiar with the data, they considered building a supervised machine learning to not only predict overdoses in each county, but more so, to extract from the model which features would be most predictive county-to-county. Next, because of the fast-paced nature of an overnight hackathon, a variety of open source applications were used to build solutions quickly. The team recommends generating a single architecture that would seamlessly tie together Python, R Shiny, and ElasticSearch/Kibana into one system. Ultimately, the goal of the entire prototype is to ingest and update the models with real-time data dispatched by police, public health, emergency departments, and medical examiners.Referenceshttps://data.virginia.gov/datathon-2017/https://vimeo.com/236131006?ref=tw-sharehttps://vimeo.com/236131182?ref=tw-share %R 10.5210/ojphi.v10i1.8321 %U %U https://doi.org/10.5210/ojphi.v10i1.8321 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8322 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo utilize ED chief complaint data obtained from syndromic surveillance to quantify the effect of the Illinois smoking ban on acute myocardial infarction (AMI), acute coronary syndrome (ACS), stroke, and chronic obstructive pulmonary disease (COPD) related ED visits in adults in Cook County, IL.IntroductionTobacco use is the leading global cause of preventable death, killing more than five million people per year [1]. In addition, exposure to secondhand smoke is estimated to kill an additional 600,000 people globally each year [1]. In 1986, the US Surgeon General’s Report declared secondhand smoke to be a cause of lung cancer in healthy nonsmokers [2].The first law restricting smoking in public places was enacted in 1973 in Arizona that followed the 1972 Surgeon General’s Report providing awareness of the negative health effects associated with the exposure to air pollution from tobacco smoke [3]. Smoke-free laws were slowly enacted after this time point with most occurring after the year 2000 [4].In July 2007, the Smoke Free Illinois Act (SB0500, Public Act 095-0017) was passed in IL [5]. The ban went into effect on Jan 1, 2008 and Illinois joined 22 other states in prohibiting smoking in virtually all public places and workplaces including offices, theaters, museums, libraries, schools, commercial establishments, retail stores, bars, private clubs, and gaming facilities [5-6].While many studies have examined the effect of smoking bans on hospitalizations, this study would be the first to examine the effect of the comprehensive smoking ban in IL on ED visits by utilizing chronic disease categories created with ED chief complaint data captured by syndromic surveillance [7]. The author hypothesizes that the comprehensive smoking ban in IL significantly reduced the ED visits associated with AMI, ACS, stroke, and COPD in adults in Cook County, IL.MethodsED visits with chief complaints consistent with categories for AMI, ACS, stroke and COPD captured by the Cook Co. Dept. of Public Health local instance of ESSENCE from Jan 1, 2006 – Dec 31, 2013 were included in the analysis. Proc Genmod with a log link and negative binomial distribution was utilized for the analysis. All data was aggregated at the monthly level. The total number of ED visits of the health effect of interest was the dependent variable. The total ED visits during the same period of time, was used as the offset variable, sub-grouped by age and gender where appropriate. A binary variable was utilized to capture the effect of the time period after the implementation of the statewide smoking ban; 0 for before the ban and 1 for after the ban. When examining the effect of the statewide ban, Cook Co. as an entirety was examined as well as ED visits stratified by zip codes that already had a smoking ban in place at that time point and those that did not, and stratifying by urban (Chicago) vs. suburban Cook Co. Seasonality was addressed by including month, month squared and month cubed in the model. Influenza was addressed by including a binary variable to indicate when influenza was occurring in the area based on percent influenza-like-illness ED visits that were occurring above the threshold for the area during that time period. Age and gender were also evaluated as confounders and effect modifiers. SAS 9.4 was utilized to perform the analyses.ResultsResults are presented in Table 1. Reductions of ED visits after the smoking ban implementation were seen in AMI and ACS disease categories for the overall adjusted model, at 3% and 3.5% respectively. Stroke associated ED visits were not affected by the smoking ban. COPD associated ED visits were not reduced immediately by the smoking ban, but did have a significant reduction 6 months after implementation of the ban at 3.6%. Stronger effects were seen in individuals 70 years and older, females, the urban population, and zip codes without a prior ban for AMI, ACS, and COPD.ConclusionsAn immediate, significant reduction in ED visits associated with AMI and ACS was associated with the IL statewide smoking ban in Cook Co., IL. COPD associated ED visits were significantly reduced 6 months after the ban implementation. The effect was greater in individuals 70 years and older, females, the urban population, and zip codes without a prior ban.References1. WHO, WHO report on the global tobacco epidemic. Implementing smoke-free environments. 2009, WHO: Geneva, Switzerland.2. DC, The health consequences of involuntary exposure to tobacco smoke : a report of the Surgeon General. 2006, U.S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, Coordinating Center for Health Promotion, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health: Atlanta, GA.3. Eriksen, M. and F. Chaloupka, The economic impact of clean indoor air laws. CA Cancer J Clin, 2007. 57(6): p. 367-78.4. Foundation, A.N.R. Overview List - How many Smokefree Laws? 2015 10/2/2015 [cited 2015 10/5/2015]; Available from: http://www.no-smoke.org/pdf/mediaordlist.pdf.5. Smoke Free Illinois Act, in Public Act 095-0017. 2007.6. Goodman, P., et al., Effects of the Irish smoking ban on respiratory health of bar workers and air quality in Dublin pubs. Am J Respir Crit Care Med, 2007. 175(8): p. 840-5.7. Callinan, J.E., et al., Legislative smoking bans for reducing secondhand smoke exposure, smoking prevalence and tobacco consumption. Cochrane Database Syst Rev, 2010(4): p. CD005992. %R 10.5210/ojphi.v10i1.8322 %U %U https://doi.org/10.5210/ojphi.v10i1.8322 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8323 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveLANL has built software that automatically collects global notifiable disease data, synthesizes the data, and makes it available to humans and computers within the Biosurveillance Ecosystem (BSVE) as a novel data stream. These data have many applications including improving the prediction and early warning of disease events.IntroductionMost countries do not report national notifiable disease data in a machine-readable format. Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health.While most nations likely store incident data in a machine-readable format, governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues1.A survey conducted by LANL of notifiable disease data reporting in over fifty countries identified only a few websites that report data in a machine-readable format. The majority (>70%) produce reports as PDF files on a regular basis. The bulk of the PDF reports present data in a structured tabular format, while some report in natural language.The structure and format of PDF reports change often; this adds to the complexity of identifying and parsing the desired data. Not all websites publish in English, and it is common to find typos and clerical errors.LANL has developed a tool, Epi Archive, to collect global notifiable disease data automatically and continuously and make it uniform and readily accessible.MethodsWe conducted a survey of the national notifiable disease reporting systems notating how the data are reported and in what formats. We determined the minimal metadata that is required to contextualize incident counts properly, as well as optional metadata that is commonly found.The development of software to regularly ingest notifiable disease data and make it available involves three or four main steps: scraping, detecting, parsing and persisting.Scraping: we examine website design and determine reporting mechanisms for each country/website, as well as what varies across the reporting mechanisms. We then designed and wrote code to automate the downloading of the data for each country. We store all artifacts presented as files (PDF, XLSX, etc.) in their original form, along with appropriate metadata for parsing and data provenance.Detecting: This step is required when parsing structured non-machine-readable data such as tabular data in PDF files. We combined the Nurminen methodology of PDF table detection with in-house heuristics to find the desired data within PDF reports2.Parsing: We determined what to extract from each dataset and parsed these data into uniform data structures, correctly accommodating the variations in metadata (e.g., time interval definitions) and the various human languages.Persisting: We store the data in the Epi Archive database and make it available on the internet and through the BSVE. The data is persisted into a structured and normalized SQL database.ResultsThe Epi Archive tool currently contains national and/or subnational notifiable disease data from twenty nations. When a user accesses the Epi Archive site, they are prompted with four fields: country, subregion, disease of interest, and date duration. Upon form submission, a time series is generated from the users’ specifications. The generated graph can then be downloaded into a CSV file if a user is interested in performing personal analysis. Additionally, the data from Epi Archive can be reached through a REST API (Representational State Transfer Application Programming Interface).ConclusionsLANL, as part of a currently funded DTRA effort, is automatically and continually collecting global notifiable disease data. While 20 nations are in production, more are being brought online in the near future. These data are already being utilized and will have many applications including improving the prediction and early warning of disease events.References[1] van Panhuis WG, Paul P, Emerson C, et al. A systematic review of barriers to data sharing in public health. BMC Public Health. 2014. 14:1144. doi:10.1186/1471-2458-14-1144[2] Nurminen, Anssi. \"Algorithmic extraction of data in tables in PDF documents.\" (2013). %R 10.5210/ojphi.v10i1.8323 %U %U https://doi.org/10.5210/ojphi.v10i1.8323 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8324 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveEpi Evident is a web based application built to empower public health analysts by providing a platform that improves monitoring, comparing, and forecasting case counts and period prevalence of notifiable diseases for any scale jurisdiction at regional, country, or global-level. This proof of concept application development addresses improving visualization, access, situational awareness, and prediction of disease behavior.IntroductionThe Epi Evident application was designed for clear and comprehensive visualization for monitoring, comparing, and forecasting notifiable diseases simultaneously across chosen countries. Epi Evident addresses the taxing analytical evaluation of how diseases behave differently across countries. This application provides a user-friendly platform with easily interpretable analytics which allows analysts to conduct biosurveillance with minimal user tasks. Developed at the Pacific Northwest National Laboratory (PNNL), Epi Evident utilizes time-series disease case count data from the Biosurveillance Ecosystem (BSVE) application Epi Archive (1). This diverse data source is filtered through the flexible Epi Evident workflow for forecast model building designed to integrate any entering combination of country and disease. The application aims to quickly inform analysts of anomalies in disease & location specific behavior and aid in evidence based decision making to help control or prevent disease outbreaks.MethodsA workflow was constructed to define the best disease forecast model for each location based on an adjustable method approach. The differences in disease behavior across countries was achieved through a React/Python application with a user-friendly output for monitoring and comparing different combinations.The forecast model building workflow consisted of three major steps to determine the best fit model for a given disease-country pair: data type, model type, and model comparison & selection. Testing various disease-country combinations allowed for direct evaluation of the workflow efficiency, flexibility, and criteria for determining the best fit model. Data type was characterized as either seasonal, cyclic, or sporadic. Depending on data type, a specific time series forecasting model was applied. In general, seasonal or cyclic data required either an Auto-Regression Integrated Moving Average (ARIMA) model or a Seasonal Auto-Regression Integrated Moving Average (SARIMA) model while sporadic datasets employed a Poisson model. Several model candidates for a single country and disease combination were then compared to determine which was the best fit model. ARIMA and SARIMA model selection criteria included their respective order significance, residual diagnostics, and lowest possible combination of Akaike Information Criterion and Root Mean Square Error (RMSE) values. Poisson model selection criteria involved Poisson or negative binomial distribution and event probability, lag dependency of immediate past events or seasonality, and lowest possible RMSE. To enhance the user’s monitoring and comparisons across multiple countries and diseases, each forecasted case counts supplied a corresponding period prevalence. This period prevalence was calculated by dividing the case counts by the population in the selected country and timeframe. Population records were obtained through the public World Health Organization database (2).ResultsA variety of visualization tools on Epi Evident allows convenient interpretation on behaviors of diseases spanning multiple countries simultaneously (Figure 1). Countries, diseases, and timeframe are selected and displayed within a matrix alongside with their corresponding forecasts for case counts and period prevalence. By providing this full representation, users can easily interpret and anticipate disease behavior while monitoring, comparing, and forecasting case counts and period prevalence across multiple countries. For future work, the Epi Evident workflow can be scaled to accommodate any disease-country combination with automated model selection to allow easier and more efficient biosurveillance.ConclusionsEpi Evident empowers analysts to visualize, monitor, compare, and forecast disease case counts and period prevalence across countries. Epi Evident exemplifies how filtering diverse data through a flexible workflow can be scalable to output distinctive models for any given country and disease combination. Thus, providing accurate forecasting and enhanced situational awareness throughout the globe. Implementing this application’s methodology helps enhance and expand biosurveillance efficacy for multiple diseases across multiple countries simultaneously.References1. Generous Nicholas, Fairchild Geoffrey, Khalsa Hari, Tasseff Byron, Arnold James. Epi Archive: An automated data collection of notifiable disease data. Online Journal of Public Health Informatics. 2017. 9(1):e372. http://apps.who.int/gho/data/view.main.POP2040?lang=en Accessed: 6/20/2017 %R 10.5210/ojphi.v10i1.8324 %U %U https://doi.org/10.5210/ojphi.v10i1.8324 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8325 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo validate and improve the syndromic algorithm used to describe pneumonia emergency department (ED) visit trends in New York City (NYC).IntroductionThe NYC Department of Health and Mental Hygiene (DOHMH) uses ED syndromic surveillance to monitor near real-time trends in pneumonia visits. The original pneumonia algorithm was developed based on ED chief complaints, and more recently was modified following a legionella outbreak in NYC. In 2016, syndromic data was matched to New York State all payer database (SPARCS) for 2010 through 2015. We leveraged this matched dataset to validate ED visits identified by our pneumonia algorithm and suggest improvements. An effective algorithm for tracking trends in pneumonia could provide critical information to inform and facilitate public health decision-making.MethodsThe DOHMH syndromic surveillance system includes daily ED data from 53 NYC hospitals. Most syndrome algorithms rely solely on chief complaint, which has historically been reported more consistently than discharge diagnosis. For this analysis, the validation dataset was restricted to matched visits with consistent age (plus or minus two years) and sex between the syndromic and SPARCS datasets.The original pneumonia algorithm used a basic text search function to identify any mention of ICD-9-CM and ICD-10-CM diagnosis codes indicating pneumonia or key words “PNEUMON” or “MONIA” within the chief complaint only. The updated algorithm additionally searches the chief complaint for any mention of key words specific to legionella (“LEGIONA”, “LEGIONN”, “LEGIONE”) and also searches for pneumonia ICD codes in the discharge diagnosis field. Syndrome sensitivity and positive predictive value (PPV) were evaluated by comparing visits identified by each algorithm to visits identified by billing diagnosis codes. A true SPARCS pneumonia ED visit was defined to contain an admitting or principal diagnosis billing code indicating pneumonia.Alternate algorithms were created using regular expressions, which allowed for more flexible and accurate pattern matching. The algorithms were further revised based on additional inclusion and exclusion key words identified using the validation dataset.ResultsBetween 2010 and 2015, there were a total of 204,101 true pneumonia visits based on the SPARCS billing records. Evaluation of the original algorithm found a sensitivity of 15.6% (31,771/204,101) and a PPV of 55.6% (31,771/57,180). Over the same time period, syndromic surveillance identified a total of 127,560 pneumonia visits using the updated algorithm; 86,590 of the 127,560 syndromic cases identified were determined to be a true visit based on the billing diagnosis codes, resulting in an algorithm sensitivity of 42.4% and PPV of 67.9%. Of the 127,560 cases identified by the updated algorithm, 19 cases were classified as a pneumonia visit solely due to the presence of legionella key words in the chief complaint. Regular expression usage as opposed to the basic text search on the updated algorithm found similar sensitivity (42.4%, 86,561/204,101) and PPV (68.0%, 86,561/127,238).Among all true pneumonia visits with a non-blank discharge diagnosis field, 65.3% (68,001/104,223) had mention of a pneumonia diagnosis code. Use of the discharge diagnosis code field in addition to the chief complaint found the algorithm to be almost three times more sensitive and 1.2 times greater in PPV than an algorithm without use of discharge diagnosis. Seasonal trends captured with and without use of discharge diagnosis were both similar to the true pneumonia trend indicated by SPARCS.Among the 117,540 pneumonia cases missed by the updated algorithm, 58.6% had fewer than three words in the chief complaint. With the most popular key words among the missed cases being highly non-specific (i.e., “fever”, “cough”, “pain”), inclusion of these key words in addition to regular expression and discharge diagnosis field usage elevated algorithm sensitivity at a severe cost to the PPV. Including “fever” in the list of pneumonia key words resulted in a sensitivity of 56.5% (115,280/204,101) and a PPV of 9.0% (115,280/1,282,342), while addition of the key word combination “fever” and “cough” led to a sensitivity of 46.7% (95,264/204,101) and a PPV of 29.8% (95,264/319,876).As we were unable to identify novel key word indicators that were good markers for pneumonia events, regular expression search functionality was the best improvement to the pneumonia syndrome algorithm. This revised, new algorithm maintained sensitivity (42.4%, 86,561/204,101) and provided slight improvements to PPV (68.0%, 86,561/127,219).However, performance of the updated algorithm varied across age groups. The algorithm was most effective in identifying younger cases (43.9% sensitivity, 80.4% PPV for those 17 years and younger), while it performed the worst among those 65 years and older (39.6% sensitivity, 58.7% PPV).ConclusionsBased on our evaluation of the pneumonia syndromic surveillance algorithm, we found that search of the discharge diagnosis field greatly improved algorithm sensitivity and PPV and usage of regular expressions increased PPV slightly. Including additional words possibly indicating pneumonia did not substantially improve sensitivity or PPV. However, integration of the ED chief complaint triage notes which are not currently utilized could further enhance the effectiveness of the pneumonia syndrome algorithm and better characterize daily pneumonia trends in NYC. %R 10.5210/ojphi.v10i1.8325 %U %U https://doi.org/10.5210/ojphi.v10i1.8325 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8326 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo better define and automate biosurveillance syndrome categorization using modern unsupervised vector embedding techniques.IntroductionComprehensive medical syndrome definitions are critical for outbreak investigation, disease trend monitoring, and public health surveillance. However, because current definitions are based on keyword string-matching, they may miss important distributional information in free text and medical codes that could be used to build a more general classifier. Here, we explore the idea that individual ICD codes can be categorized by examining their contextual relationships across all other ICD codes. We extend previous work in representation learning with medical data [1] by generating dense vector embeddings of these ICD codes found in emergency department (ED) visit records. The resulting representations capture information about disease co-occurrence that would typically require SME involvement and support the development of more robust syndrome definitions.MethodsWe evaluate our method on anonymized ED visit records obtained from the New York City Department of Health and Mental Hygiene. The data set consists of approximately 3 million records spanning January 2016 to December 2016, each containing from one to ten ICD-9 or ICD-10 codes.We use these data to embed each ICD code into a high-dimensional vector space following techniques described in Mikolov, et al. [2], colloquially known as word2vec. We define an individual code’s context window as the entirety of its current health record. Final vector embeddings are generated using the gensim machine learning library in Python. We generate 300-dimensional embeddings using a skip-gram network for qualitative evaluation.We use the TensorFlow Embedding Projector to visualize the resulting embedding space. We generate a three-dimensional t-SNE visualization with a perplexity of 32 and a learning rate of 10, run for 1,000 iterations (Figure 1). Finally, we use cosine distance to measure the nearest neighbors of common ICD-10 codes to evaluate the consistency of the generated vector embeddings (Table 1).ResultsT-SNE visualization of the generated vector embeddings confirms our hypothesis that ICD codes can be contextually grouped into distinct syndrome clusters (Figure 1). Manual examination of the resulting embeddings confirms consistency across codes from the same top-level category but also reveals cross-category relationships that would be missed from a strictly hierarchical analysis (Table 1). For example, not only does the method appropriately discover the close relationship between influenza codes J10.1 and A49.2, it also reveals a link between asthma code J45.20 and obesity code E66.09. We believe these learned relationships will be useful both for refining existing syndrome categories and developing new ones.ConclusionsThe embedding structure supports the hypothesis of distinct syndrome clusters, and nearest-neighbor results expose relationships between categorically unrelated codes (appropriate upon examination). The method works automatically without the need for SME analysis and it provides an objective, data-driven baseline for the development of syndrome definitions and their refinement.References[1] Choi Y, Chiu CY-I, Sontag D. Learning Low-Dimensional Representations of Medical Concepts. AMIA Summits on Translational Science Proceedings. 2016;2016:41-50.[2] Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. InAdvances in neural information processing systems 2013 (pp. 3111-3119). %R 10.5210/ojphi.v10i1.8326 %U %U https://doi.org/10.5210/ojphi.v10i1.8326 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8327 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo develop a forecasting model for weekly emergency department admissions due to pneumonia using information from hospital-based, community-based and laboratory-based surveillance systems.IntroductionPneumonia, an infection of the lung due to bacterial, viral or fungal pathogens, is a significant cause of morbidity and mortality worldwide. In the past few decades, the threat of emerging pathogens presenting as pneumonia, such as Severe Acute Respiratory Syndrome, avian influenza A(H5N1) and A(H7N9), and Middle East Respiratory Syndrome coronavirus has emphasised the importance of the surveillance of pneumonia and other severe respiratory infections. An unexpected increase in the number of hospital admissions for pneumonia or severe respiratory infections could be a signal of a change in the virulence of the influenza viruses or other respiratory pathogens circulating in the community, or an alert of an emerging pathogen which warrants further public health investigation.The purpose of this study was to develop a forecasting model to prospectively forecast the number of emergency department (ED) admissions due to pneumonia in Singapore, a tropical country. We hypothesise that there is complementary information between hospital-based and community-based surveillance systems. The clinical spectrum of many respiratory pathogens causing pneumonia ranges from asymptomatic or subclinical infection to severe or fatal pneumonia, and it is usually difficult to distinguish between the different pathogens in the absence of a laboratory test. Infected persons could present with varying degrees of severity of the infection, and seek treatment at different healthcare facilities. Hospital-based surveillance captures the more severe manifestation of the infection while community-based surveillance captures the less severe manifestation of the infection and enables earlier detection of the infection. Thus, the integration of information from the two surveillance systems should improve the prospective forecasting of ED admissions due to pneumonia. We also investigate if the inclusion of influenza data from the laboratory surveillance system would improve the forecasting model, since influenza circulates all-year round in Singapore and is a common aetiology for pneumonia.MethodsThis was a retrospective study using aggregated national surveillance data and meteorological data during the period 3 January 2011 to 1 January 2017.We compared the performance of autoregressive integrated moving average model (ARIMA) with multiple linear regression models with ARIMA errors, with and without the inclusion of influenza predictors at forecast horizons of 2, 4, 6 and 8 weeks in advance. Weekly data between the study period of 3 January 2011 and 1 January 2017 were split into training and validation sets, with the first three years of data used as the base training set. Time series cross validation was used to estimate the models’ accuracy and out-of-sample forecast accuracy was based on the calculation of the mean absolute error (MAE) and mean absolute percent forecast error (MAPE).ResultsThe multiple linear regression model with ARIMA errors that included influenza predictors was the best performing model while the basic ARIMA model was the worst performing model for all forecast horizons. The two multiple linear regression models with ARIMA errors had a MAPE of less than 10% for all forecast horizons.ConclusionsData from different multiple surveillance systems and the inclusion of influenza trends can be used to improve the forecast of ED admissions due to pneumonia in a tropical setting, despite the absence of large differences between seasons. Accurate forecasting at the national level can prepare healthcare facilities for an impending surge. %R 10.5210/ojphi.v10i1.8327 %U %U https://doi.org/10.5210/ojphi.v10i1.8327 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8328 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveWe sought to use free text mining tools to improve emergency department (ED) chief complaint and discharge diagnosis data syndrome definition matching across facilities with differing robustness of data in the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) application in Idaho’s syndromic surveillance system.IntroductionStandard syndrome definitions for ED visits in ESSENCE rely on chief complaints. Visits with more words in the chief complaint field are more likely to match syndrome definitions. While using ESSENCE, we observed geographic differences in chief complaint length, apparently related to differences in electronic health record (EHR) systems, which resulted in disparate syndrome matching across Idaho regions. We hypothesized that chief complaint and diagnosis code co-occurrence among ED visits to facilities with long chief complaints could help identify terms that would improve syndrome match among facilities with short chief complaints.MethodsThe ESSENCE-defined influenza-like illness (ILI) chief complaint syndrome was used as the base syndrome for this analysis. Syndrome-matched visits were defined as visits that match the syndrome definition.We assessed chief complaints and diagnosis code co-occurrence of syndrome-matched visits using the RCRAN TidyText package and developed a bigram network from normalized, concatenated chief complaint and diagnosis code (CCDD) fields and normalized diagnosis code (DD) fields per previously described methodologies.1 Common connections were defined by a natural break in frequency of pair occurrence for CCDD pairs (30 occurrences) and DD pairs (5 occurrences).The ESSENCE syndrome was revised by adding relevant bigram network clusters and logic operators. We compared time series of the percent of ED visits matched to the ESSENCE syndrome with those matched to the revised syndrome. We stratified the time series by facilities grouped by short (average < 4 words, “Group A”) and long (average ≥ 4 words, “Group B”) chief complaint fields (Figure 1). Influenza season start was defined as two consecutive weeks above baseline, or the 95% upper confidence limit of percent syndrome-matched visits outside of the CDC ILI surveillance season. Season trends and influenza-related deaths in Idaho residents were compared.ResultsDuring August 1, 2016 through July 31, 2017, 1,587 (1.17%) of 135,789 ED visits matched the ESSENCE syndrome. Bigram networks of CCDD fields produced clusters already included by the ESSENCE syndrome. The bigram network of DD fields (Figure 2) produced six clusters. The revised syndrome definition included the ESSENCE syndrome, 3 single DD terms, and 3 two DD terms combined. The start of influenza season was identified as the same week for both ILI syndrome definitions (ESSENCE baseline 0.70%; revised baseline 2.21%). The ESSENCE syndrome indicated the season peaked during Morbidity and Mortality Weekly Report (MMWR) week 2017-05 with the season ending MMWR week 2017-14. The revised syndrome indicated 2017-20 as the season end. Multiple peaks seen with the revised syndrome during MMWR weeks 2017-02, 2017-05, and 2017-10 mirrored peaks in influenza-related deaths during MMWR weeks 2017-03, 2017-06, and 2017-11.ILI season onset was five weeks earlier with the revised syndrome compared with the ESSENCE syndrome in Group A facilities, but remained the same in Group B. The annual percentage of ED visits related to ILI was more uniform between facility groups under the revised syndrome than the ESSENCE syndrome. Unlike the trend seen with the ESSENCE syndrome, the revised syndrome shows low-level ILI activity in both groups year-round.ConclusionsIn Idaho, dramatic differences in ED visit chief complaint word counts were seen between facilities; bigram networks were found to be an important tool to identify diagnosis codes and logical operators that built more inclusive syndrome definitions when added to an existing chief complaint syndrome. Bigram networks may aid understanding the relationship between chief complaints and diagnosis codes in syndrome-matched visits.Use of trade names and commercial sources is for identification only and does not imply endorsement by the Centers for Disease Control and Prevention, the Public Health Service, or the U.S. Department of Health and Human Services.References1. Silge, J., Robinson, D. (2017). “Text Mining with R”. O’Reilly. %R 10.5210/ojphi.v10i1.8328 %U %U https://doi.org/10.5210/ojphi.v10i1.8328 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8329 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveThis study aimed to assess the effects of urban physical environment on individual obesity using geographically aggregated health behavior surveillance data applying a geo-imputation method.Introduction''Where we live'' affects ''How we live''. Information about ''how one lives'' collected from the public health surveillance data such as the Behavioral Risk Factor Surveillance System (BRFSS). Neighborhood environment surrounding individuals affects their health behavior or health status are influenced as well as their own traits. Meanwhile, geographical information of subjects recruited in the health behavior surveillance data is usually aggregated at administrative levels such as a county. Even if we do not know accurate addresses of individuals, we can allocate them to the random locations where is analogous to their real home within a locality using a geo-imputation method. In this study, we assess the association between obesity and built environment by applying random property allocation (1).MethodsData from the Korean Community Health Survey (KCHS), which is the nationwide community-based cross-sectional survey conducted by 253 community health centers in South Korea, were used (2). More than 90000 subjects recruited in the capital city Seoul from 2011 to 2014. They were selected by two-step stratified random sampling (424 administrative communities with an average area of 1.16km2 and two house types) in each 25 counties. We re-allocated them randomly on the nested locality based on their community (administrative boundaries) and hose type (land-use) using GIS program (Figure 1). Surrounding built environment elements such as fast-food markets, driving roads, public transit and road-crosse were measured within 500m buffer from randomly allocated locations as density or distance. Variables associating obesity are measured by : 1) self-reported obesity (self-reported body mass index(BMI) ≥ 25) (Figure 2), 2) perceived obesity, 3) intention to weight control. We implemented logistic regression models to estimate the effect of physical environmental factors on obesity.ResultsThe person who lives in a detached house, nearer fast food markets or with higher driving road density was more likely to be obese. Who lives in a detached house was less perceived their obesity. Who lives in a detached house, nearer fast food markets or with higher driving road density was less likely to intend to control their body weights. Association between intention to weight control and accessibility to subway station showed marginal effect.ConclusionsUrban environments influenced individual''s obesity, perception, and intention to weight loss. Since we used cross-sectional survey data, we do not account cumulative environmental influence. Moreover, individuals'' self-selection of more healthier places were not accounted. Even though we did not measure the environment at individuals'' real address, we can measure the effects of neighborhood environment more efficiently by using random property allocation.References1. Walter SR, Rose N. Random property allocation: A novel geographic imputation procedure based on a complete geocoded address file. Spatial and spatio-temporal epidemiology. 2013;6:7-16. Epub 2013/08/27. doi: 10.1016/j.sste.2013.04.005. PubMed PMID: 23973177.2. Kim YT, Choi BY, Lee KO, Kim H, Chun JH, Kim SY, et al. Overview of Korean Community Health Survey. J Korean Med Assoc. 2012;55(1):74-83. (in Korean) %R 10.5210/ojphi.v10i1.8329 %U %U https://doi.org/10.5210/ojphi.v10i1.8329 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8331 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo access the potential health impact on the population during mass gathering over time using labelling procedure in emergency department (ED).IntroductionThe massive flow of people to mass gathering events, such as festivals or sports events like EURO 2016, may increase public health risks. In the particular context of several terrorist attacks that took place in France in 2015, the French national Public Health agency has decided to strengthen the population health surveillance systems using the mandatory notification disease system and the French national syndromic surveillance SurSaUD®.The objectives in terms of health surveillance of mass gathering are: 1/ the timely detection of a health event (infectious cluster, environmental exposure, collective foodborne disease…) 2/ the health impact assessment of an unexpected event such as a terrorist attack.In collaboration with the Regional Emergency Observatory (ORU), a procedure for the labeling of emergencies has been tested to identify the ED records that could be considered as linked to the event.MethodsDuring summer 2016, the procedure was tested on seven major festive events throughout the region. In addition to the main medical diagnosis, a specific ICD-10 code “Y3388” was chosen to be used in associated diagnosis for records that were supposed to linked to the event.Information on the labeling procedure was insured by the ORU to the emergency departments.All records with medical diagnoses or medical pattern beginning by Y33 have been analyzed.ResultsNo significant increase in the global indicator was observed in the ED impacted by mass gathering. The ED labelling procedure identified 260 records: two thirds corresponded to young men and 17% came from abroad. Among the 250 records labeled in associated diagnosis, 39% were associated to traumatisms and 31% corresponded to alcohol intake.ConclusionsThis study shows that a labelling procedure allows the identification, quantification and characterization of the population ED records associated with mass gathering. Additionally, a labelling procedure to assess a potential impact of an event as mass gathering can be implemented fairly rapidly. %R 10.5210/ojphi.v10i1.8331 %U %U https://doi.org/10.5210/ojphi.v10i1.8331 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8332 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveMadagascar is one of the low-income countries with limited resources. In order to minimize the cost of the fight against malaria, the main objective of this study is to identify the priority zone for Indoor Residual Spraying (IRS).IntroductionMalaria remains a major public health problem in Madagascar. Indoor Residual Spraying (IRS) is the adopted strategy for malaria control in the CHs and Fringe regions of Madagascar. Remotely sensed data analysis combined with Multi-Criteria Evaluation become crucial to target priority areas for intervention.MethodsSatellite images were used to update land cover information using object based image analysis method, NOAA and MODIS for temperature and rainfall data. Multi-Criteria Evaluation was performed by weighted linear combination to obtain the gradient of malaria transmission risk. Factor weights were determined by pair-wise comparison based on literature review and expert knowledge. Fuzzy set theory was used to perform the factors weighting. To estimate a best fit risk magnitude probability per commune, we used per pixel values for inhabited locations, and chose an adjusted mean. The Jenks Natural Breaks algorithm was used to classify the obtained malaria risk gradient. All the process was compiled in a semi-automatic plugin working in an open source software. Comparison of risk magnitude between two consecutive years was performed to assess the environmental change.ResultsThree models of malaria risk are available for 2014, 2015 and 2016. The updated land cover map showed suitable breeding sites for mosquito responsible of malaria transmission in CHs with an accuracy of 84%. A change of 64.4% and 35.6% unchanged were obtained concerning change detection of malaria risk between 2014 and 2015. Between the years 2015 and 2016, 11.2% of the area of interest remains unchanged while 88.8% changed. Respectively 26.9% decreased and 61.9% increased.ConclusionsIt is crucial to focus the indoor residual spraying efforts according to the risk gradient. This allows to increase the effectiveness of the intervention targeting areas with the most need, as well as to optimize financial and logistical resource management.References1. Rakotomanana, F, Randremanana R, Rabarijaona L, et al. Determining areas that require indoor insecticide spraying using Multi Criteria Evaluation, a decision-support tool for malaria vector control programmes in the Central Highlands of Madagascar. International Journal of Health Geographics, 2007, 6:2. 10.1186/1476-072X-6-22. Saaty TL: A scaling method for priorities in hierarchical structures . Journal of Mathematical Psychology. 1977, 15: 234-281. 10.1016/0022-2496(77)90033-5. %R 10.5210/ojphi.v10i1.8332 %U %U https://doi.org/10.5210/ojphi.v10i1.8332 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8333 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo identify predictors of the risk of developing exertional heat illness (EHI) among basic training populations in the Department of Defense.IntroductionAlthough effective preventive measures for heat-related illness have been recommended and mandated for military personnel, there continues to be incident cases. In 2016, there were 401 incident cases of heat stroke and 2,135 incident cases of “other heat illness” among all active component service members. Current military guidelines utilize the wet bulb globe temperature (WBGT) index to measure heat risk, guiding work/rest and hydration practices. The WBGT requires calibrated instrumentation and is based on fixed cutoff values. We propose using readily available meteorological data inputs and EHI cases to identify and validate an EHI risk prediction model. Prior studies have found that combinations of WBGT and the previous day’s WBGT and relative humidity and temperature have predictive value for EHI.1 We build upon prior work by using generalized additive models (GAMs).MethodsA case-control study was conducted among active component service members from all basic training installations from January 1, 2010 to May 31, 2017. Incident cases of EHI were identified utilizing diagnosis codes extracted from inpatient and outpatient medical encounters and confirmed reportable medical events. An equal number of random controls, matched by installation, were selected. Mean weather data during daylight hours from the Air Force Weather Squadron were provided for the closest weather station to the installation during the same time period. A GAM was used due to the non-linear association between EHI and weather predictors, to develop models for the risk of incident EHI. Training (75% of data) and test (25% of data) datasets were generated for model training and model validation. Three hundred sets of training and test datasets were randomly generated. For each set, sensitivity and specificity for EHI prediction was calculated. Four models with different combinations of predictors were compared: model 1 contains month, day of week, and installation; model 2 contains WBGT, month, day of week, and installation; model 3 contains WBGT, previous day’s WBGT, month, day of week, and installation; and model 4 contains relative humidity, temperature, month, day of week, and installation. Each predictor was significantly associated with EHI. The mean differences in sensitivity and specificity between all models and model 1 were compared and 95% confidence intervals were generated by bootstrapping. GAMs were generated using the mgcv package and odds ratios were generated using the oddsratio package in R.ResultsThere were 5,258 incident cases of EHI from 2010-2017 among active component service members stationed at basic training installations. There was not a significant difference in model performance when comparing the four models. The mean differences in sensitivity and specificity of each model compared to model 1 are displayed in Table 1. The association between log odds of EHI and WBGT, controlling for month, day of week, and installation (model 2) is displayed in Figure 1. There is not a single representative odds ratio generated for GAMs due to the non-linear relationship between predictors and the log odds of EHI. As an example, the odds ratio between two arbitrary WBGT points is displayed. The odds of EHI among those exposed to a mean WBGT of 85°F is 2.55 (95% CI: 2.45, 2.64) times the odds of EHI among those exposed to a mean WBGT of 80°F. The association between the log odds of EHI and relative humidity, controlling for month, day of week, installation, and temperature (model 4) is displayed in Figure 2. The odds of EHI among those exposed to 80% relative humidity is 1.36 (95% CI: 1.33, 1.39) times the odds of EHI among those exposed to 60% relative humidity.ConclusionsOur results provide evidence that there is no significant difference in model prediction of EHI utilizing various combinations of weather predictors. However, there is a significant non-linear association between weather predictors and EHI and examples of these relationships are given using different models. Model performance can be improved by including more granular exposure data (i.e. physical activity during EHI episode, biometric and physiological measures).References1. Wallace RF, Kriebel D, Punnett L, Wegman DH, Wenger CB, Gardner JW, Gonzalez RR. The effects of continuous hot weather training on risk of exertional heat illness. Med Sci Sports Exerc. 2005 Jan; 37(1):84-90. %R 10.5210/ojphi.v10i1.8333 %U %U https://doi.org/10.5210/ojphi.v10i1.8333 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8336 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveIn this paper we used Boosted Regression Tree analysis coupled with environmental factors gathered from satellite data, such as temperature, elevation, and precipitation, to model the niche of Dengue Fever (DF) in Colombia.IntroductionDengue Fever (DF) is a vector-borne disease of the flavivirus family carried by the Aedes aegypti mosquito, and one of the leading causes of illness and death in tropical regions of the world. Nearly 400 million people become infected each year, while roughly one-third of the world’s population live in areas of risk. Dengue fever has been endemic to Colombia since the late 1970s and is a serious health problem for the country with over 36 million people at risk. We used the Magdalena watershed of central Colombia as the site for this study due to its natural separation from other geographical regions in the country, its wide range of climatic conditions, the fact that it includes the main urban centers in Colombia, and houses 80% of the country’s population. Advances in the quality and types of remote sensing (RS) satellite imagery has made it possible to enhance or replace the field collection of environmental data such as precipitation, temperature, and land use, especially in remote areas of the world such as the mountainous areas of Colombia. We modeled the cases of DF by municipality with the environmental factors derived from the satellite data using boosted regression tree analysis. Boosted regression tree analysis (BRT), has proven useful in a wide range of studies, from predicting forest productivity to other vector-borne diseases such as Leishmaniosis, and Crimean-Congo hemorrhagic fever. Using this framework, we set out to determine what are the differences between using presence/absence and case counts of DF in this type of analysis?MethodsWe combined data on Dengue fever cases downloaded from the Instituto Nacional de Salud (INS) Programa SIVIGILA INS site with population data downloaded from the 2005 General Census administered by the National Administrative Department of Statistics (Departamento Administrativo Nacional de Estadística, DANE) and projected to 2012–2014 levels. We acquired remote sensing data from the National Aeronautics and Space Administration (NASA) data servers for each day of the study period. Imagery for each environmental variable was composited to reduce the effects of cloud cover and to match the ISO Week Date format reporting of the case data. We aggregated these weekly composite images for each variable using GIS to create annual minimum, maximum, and mean for a raster cell. These data were further aggregated to the municipality level using the GIS, again for minimum, maximum, and mean. Land use and elevation were only downloaded for one period given they change very little over time. The BRT analysis was conducted twice: once using the Bernoulli family of presence/absence and again using the Poisson family of actual case counts. In the first analysis (Bernoulli), any municipality reporting one or more cases of DF in the year was coded as having disease “presence”, while all others were coded as not having disease “absence”. The BRT model was run, using a twenty-five percent hold out of the data as a testing set, for each year. In the second analysis (Poisson), the only change to the models consisted of replacing the presence/absence data with the actual cases of reported DF within the municipality. The Poisson family was chosen in the model since the count data were highly skewed.ResultsWe calculated RMSE and Pearson r values for each of the three years. The Poisson model out-performed the Bernoulli model across all years. The RMSE values were considerably lower for the Poisson model compared to the Bernoulli model, reflecting a better model fit. The Pearson r values were higher for the Poisson model compared to the Bernoulli model, again across all three years. We created maps to compare Cases with the Poisson and the Bernoulli results. The maps shown in the figure reflect the results for 2012. The left panel represents the cases per 10,000 population per square kilometer for each municipality. The dark green color represents very low ratios of DF, while the red color reflects a higher incidence of DF. All maps used the same classification as the reported cases map for comparison, with an additional symbol (black) used for values outside the reported cases range.ConclusionsUsing actual reported case data and the Poisson function within the BRT functions created by Elith et al. and the gbm package in R, we show that the differences between using presence/absence and case counts of DF in a BRT analysis gives a clearer picture of the spatial distribution of DF. By using readily available and freely accessible data, we have shown that practitioners both within and outside of Colombia can quickly create accurate maps of annual DF incidence. The methods described here could also be extended to other regions and diseases, making it useful to a wide range of end users. %R 10.5210/ojphi.v10i1.8336 %U %U https://doi.org/10.5210/ojphi.v10i1.8336 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8337 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveCharacterize the behavior of nonparametric regression models for message arrival probability as outage detection tools.IntroductionTimely and accurate syndromic surveillance depends on continuous data feeds from healthcare facilities. Typical outlier detection methodologies in syndromic surveillance compare predictions of counts for an interval to observed event counts, either to detect increases in volume associated with public health incidents or decreases in volume associated with compromised data transmission.Accurate predictions of total facility volume need to account for significant variance associated with the time of day and week; at the extreme are facilities which are only open during limited hours and on select days. Models need to account for the cross-product of all hours and days, creating a significant data burden. Timely detection of outages may require sub-hour aggregation, increasing this burden by increasing the number of intervals for which parameters need to be estimated.Nonparametric models for the probability of message arrival offer an alternative approach to generating predictions. The data requirements are reduced by assuming some time-dependent structure in the data rather than allowing each interval to be independent of all others, allowing for predictions at sub-hour intervals.MethodsHealthcare facility data was collected as HL7 messages via the EpiCenter syndromic surveillance system from June 1, 2017 through August 31, 2017. 713 facilities sent at least 1,000 messages during this period and were included in the analysis.Standard Poisson regression models were fit to counts of messages per quarter hour. Predictors were indicators for day of week, hour of day, and quarter of hour, along with interaction terms between them.Nonparametric logistic regression models were fit to data on the presence or absence of any message for each minute of the first two months of the study period, using the minute within the week as a predictor. The last month of data was scanned for outages at 15-minute intervals and calculating the probability of no messages since the last received message per facility as:P(Gap from mlast to mnow) = ∏t 1 - Pmessage(t)Four consecutive intervals with probability below 1-10 were considered outages.ResultsA total of 12,710,275 ADT A04 messages were received from 713 facilities from June 1, 2017 through August 31, 2017.Estimation of Poisson regression models averaged 1 minute, while nonparametric models averaged 1.5 minutes to estimate. Poisson models required 672 parameters to specify, whereas nonparametric models required 29. Calculating predictions from fitted models averaged 0.2 seconds for Poisson models and 2 seconds for nonparametric models. Although predictions from the two models are not on identical scales and thus not directly comparable, they did correlate well with each other with an average correlation of 0.8.The nonparametric regression method detected 175 resolved outages and 9 open outages in August, 2017. The resolved outages lasted an average of 1.5 days (1.75 hours to 15 days). The likelihood of these outages averaged 6e-13 (3e-160 to 4e-11).Figure 1 illustrates how the nonparametric models can be used in a dashboard for all 713 connections. Likelihood of an outage is available for each facility based on how long it has been since the last message was received; this can be updated every minute as needed. Figure 2 illustrates the predictions from a nonparametric model for a single facility and a detected outage.ConclusionsNonparametric regression models of message arrival demonstrated suitable performance for use in detecting connection outages. Compared to standard Poisson regression models, computation time for nonparametric models was longer but within acceptable ranges for operational needs and storage was significantly reduced. Further, storage and computation time for standard models will increase if greater time granularity is desired, whereas the nonparametric models require no additional storage or computation. Model predictions were sufficiently similar between both models for the two to give comparable performance in detecting outages. Given the greater time flexibility of the nonparametric models and the smaller data requirements for initial model estimation (due to fewer estimated parameters), the nonparametric approach represents a promising new option for monitoring syndromic surveillance data quality. %R 10.5210/ojphi.v10i1.8337 %U %U https://doi.org/10.5210/ojphi.v10i1.8337 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8338 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveCompare rate changes over time for Emergency Department (ED) visits due to opioid overdose in urban versus rural areas of the state of Missouri.IntroductionLike many other states in the U.S., Missouri has experienced large increases in opioid abuse resulting in hundreds dying each year and thousands of ED visits due to overdose. Missouri has two major urban areas, St. Louis and Kansas City and a few smaller cities, while the remainder of the state is more rural in nature. The opioid epidemic has impacted all areas in the state but the magnitude of that impact varies as well as the type of opioid used. Missouri Department of Health and Senior Services (MODHSS) maintains the Patient Abstract System (PAS) which contains data from hospitals and ambulatory surgical centers throughout the state. PAS includes data from ED visits including information on diagnoses, patient demographics, and other information about the visit. MODHSS also participates in the Enhanced State Surveillance of Opioid-involved Morbidity and Mortality project (ESOOS). One major aspect of this surveillance project is the collection of data on non-fatal opioid overdoses from ED visits. Through this collection of data, MODHSS analyzed opioid overdose visits throughout the state, how rates compare across urban and rural areas, and how those rates have changed over time.MethodsThe 115 counties in Missouri were organized into the six-level urban-rural classification scheme developed by the National Center for Health Statistics (NCHS). The attached table shows the breakout of counties into the six different categories. The data years analyzed were 2012 through 2016. ED visits due to opioid overdose were identified using case definitions supplied by ESOOS. Overdoses were analyzed in three different categories—all opioids, heroin, and non-heroin opioids. The all opioid category combines heroin and non-heroin opioids. Non-heroin opioids includes prescription drugs such as oxycodone, hydrocodone, fentanyl, and fentanyl analogues. Annual rates per 10,000 were calculated for each county classification using population estimates. Confidence intervals (at 95%) were then calculated using either inverse gamma when the number of ED visits was under 500, or Poisson when the number was 500 or more. Changes over time were calculated using both a year over year method and a 5 year change method.ResultsOverall opioid rates have increased in all geographic areas during the 5 year period analyzed. Large Central Metro and Large Fringe Metro counties had the highest rates of ED visits due to opioid overdose. These two classifications also saw the largest increases in rates. The Large Central Metro counties collectively increased over 125%, while the Large Fringe Metro area increased 130%. Both areas experienced statistically significant increases year-to-year between 2014 and 2016 in addition to the overall 5 year period of 2012-2016.Analysis was also conducted for heroin and non-heroin subsets of opioid abuse. There were important differences in these two groups. For heroin ED visits, the highest rates were found in the Large Central Metro and Large Fringe Metro regions. However, the largest increase in percentage terms were found in the Medium Metropolitan, Micropolitan and Noncore regions which all saw increases of over 300%. Notably, every region experienced increases of over 150%. The Medium Metro had two consecutive years (2013/2014 and 2014/2015) where the heroin ED rate more than doubled.In contrast, non-heroin ED visits did not experience such a large increase over time. Most areas saw small fluctuations year-to-year with moderate overall increases over the 5-year time period. The exception to this trend is the Large Fringe Metro area, which saw increases every year most notably between 2014 and 2015 and had by far the largest 5 year increase at 82%.ConclusionsThe urban areas in Missouri continue to have the highest rates of opioid overdose, however all areas within the state have experienced very large increases in heroin ED visits within the past five years. The increase in heroin ED visits in the rural areas suggests the abuse of heroin has now spread throughout the state, as rates were much lower in 2012. The steady increase in non-heroin opioids unique to the Large Fringe Metro may be due to the availability of fentanyl in urban areas especially the St. Louis area. This possible finding would correspond with the increased deaths due to fentanyl experienced in and around the St. Louis urban area that has been identified through analysis of death certificate data. %R 10.5210/ojphi.v10i1.8338 %U %U https://doi.org/10.5210/ojphi.v10i1.8338 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8339 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveWe present a mathematical framework for non-parametric estimation of the force of infection, together with statistical upper and lower confidence bands. The resulting estimates allow to assess how well simpler models, such as SEIR, fit the observed time series of incidence data.IntroductionUncertainty Quantification (UQ), the ability to quantify the impact of sample-to-sample variations and model misspecification on predictions and forecasts, is a critical aspect of disease surveillance. While quantifying the impact of stochastic uncertainty in the data is well understood, quantifying the impact of model misspecification is significantly harder. For the latter, one needs a \"universal model\" to which more restrictive parametric models are compared too.MethodsThis talk presents a useful modeling framework for time series of incidence data from contagious diseases that enables one to identify and quantify the impact of model form uncertainty. Specifically, we propose to focus on estimating the timedependent force of infection. The latter is a universal parameters for all contagious disease model. Using a machine learning technique for estimating monotone functions, i.e., isotonic regression and its variants, one can estimate the force ofinfection without addtional assumptions. We note that most contagious disease model do satisfy this monotonicity assumption, due to a combination of factors: depletion of susceptibles, implementation of mitigation strategies, behavior change, etc. Comparing the resulting \"non-parametric\" estimate with parametric estimates, obtained by fitting an SEIR for example, can reveal model deficiencies and help quantify model form uncertainties.Finally, we discuss how ideas from \"strict bound theory\" can be used to develop upper and lower uncertainty bands for force of infection that acknowledge the intrinsic stochasticity in the data.ResultsWe demonstrate the application of the methodology to weekly Influenza Like Illness (ILI) incidence data from France andcompare the results to fitted SIR and SEIR models. This comparison can be seen as a nonparametric goodness of fit test, providing one with tools to do simple model selection.ConclusionsWe present a novel and flexible model to statistically describe the force of infection as a function of time. Comparing the fit to incidence data of that model with the fit of simpler parametric models enables the quantification of model form uncertainty and associated model selection. %R 10.5210/ojphi.v10i1.8339 %U %U https://doi.org/10.5210/ojphi.v10i1.8339 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8340 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveStandardize selection of indicator data streams and corresponding alerting algorithms for syndromic, reportable disease, and confirmed diagnostic categories derived from veterinary laboratory test order data for bovines.IntroductionThe Johns Hopkins University Applied Physics Laboratory is collaborating with epidemiologists of the US Dept. of Agriculture''s Animal and Plant Health Inspection Service (APHIS) Center for Epidemiology and Animal Health (CEAH) to increase animal health surveillance capacity. CEAH monitors selected syndromic animal health indicators for stakeholder reporting. This project’s goal was to extend this capacity to bovine veterinary laboratory test accession data.MethodsIndicators for weekly monitoring were derived from bovine test records from the Colorado State University Veterinary Diagnostic Laboratory System from 27 Jun 2010 - 29 May 2016. Selected indicator types were syndromic test orders, disease-specific orders, and disease-specific positive results. Indicators were adopted if APHIS epidemiologists considered them worth monitoring and if they were represented by at least 100 lab accessions.Ten syndromes were chosen for routine monitoring based on body systems, bovine-specific concerns (e.g. mastitis), and concepts to capture novel threats. Reportable diseases were chosen from the list published by the Colorado Dept. of Agriculture [1]. Based on APHIS concerns and test order frequencies, 4 diseases were chosen for weekly monitoring: Bluetongue, Brucellosis, Epizootic Hemorrhagic Disease, and Paratuberculosis. To monitor positives, we considered both the number and the ratio of herds with at least one positive result for each disease. For included tests (excluding results quantified with antibody levels), we counted an accession as “positive” if the result field contained strings “positive”, “suspect”, or “detect” without negation terms. For weekly counts, we added the number of herds with any positives after deduplication. Diseases adopted for monitoring of positive results were Bovine Viral Diarrhea, Trichomoniasis, and Paratuberculosis.From experience and literature, we compared variants of 4 algorithm types, including: the C2 method of the CDC Early Aberration Reporting System, a CuSUM control chart with a sliding baseline, the temporal scan statistic Gscan applied to hospital infection counts, and the CDC Historical Limits method.We adapted a semisynthetic simulation approach for algorithm comparison in which authentic disease count data are used as baseline, and simulated signals are added to the background as detection targets. In discussions about specific diseases and veterinary testing practice, CEAH required sensitivity to one-week data spikes as well as effects of health threats with multi-week incubation periods and more gradual test ordering. For such gradual signals, we chose the lognormal signal model of Sartwell applied to incidence data for many diseases. Incubation periods vary widely by disease, and for this project, we chose lognormal parameters such that 90% of reported cases would occur within 6 weeks. We conducted separate algorithm detection trials for spike and gradual signals.Calculations of sensitivity, alert rate, and timeliness were derived with sets of 1000 repeated trials for each combination of algorithm and syndrome or disease. We applied minimum performance requirements of 95% sensitivity, ≤1 alert per 8 weeks, and mean detection delays of <2 weeks. The rule adopted for recommending an alerting method was to seek the method with the lowest alert rate that satisfied the sensitivity, alert rate, and delay criteria.ResultsThe Table shows the syndromes with chosen algorithms and thresholds for detection of the gradual signals. The scan statistic Gscan and the historical limits method HistLim achieved consistently higher sensitivities with acceptable alert rates than the other methods applied . The presentation will extend the results to reportable disease and clinical positive indicators and to the spike signals for all indicators.ConclusionsAmong results for both signal types, the results yielded a few preferred methods covering all chosen indicator streams. Monitored indicators with median weekly counts = 0 remain a challenge requiring more background data and veterinarian judgment. From analysis of orders from the few available laboratories, manual review will be required to achieve accurate syndromic categorization for each lab. Monitoring of test positives will require combined analysis of positive herd counts and percentages (of all tested herds) due to routine variation in laboratory submissions.References[1] Colorado Department of Agriculture, Livestock Health: Reportable Diseases in Colorado, https://www.colorado.gov/pacific/aganimals/livestock-health, last accessed Aug. 23, 2017. %R 10.5210/ojphi.v10i1.8340 %U %U https://doi.org/10.5210/ojphi.v10i1.8340 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8341 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveThe application of spatial analysis to improve the awareness and use of surveillance data.IntroductionThe re-emergence of an infectious disease is dependent on social, political, behavioral, and disease-specific factors. Global disease surveillance is a requisite of early detection that facilitates coordinated interventions to these events. Novel informatics tools developed from publicly available data are constantly evolving with the incorporation of new data streams. Re-emerging Infectious Disease (RED) Alert is an open-source tool designed to help analysts develop a contextual framework when planning for future events, given what has occurred in the past. Geospatial methods assist researchers in making informed decisions by incorporating the power of place to better explain the relationships between variables.MethodsDisease incidence and indicator data derived for the RED Alert project were analyzed for spatial associations. Using aggregate country-level data, spatial and spatiotemporal clusters were identified in ArcMap 10.5.1. The identified clusters were then used as the outcome for a series of binary logistic regression models to determine significant covariates that help explain global hotspots. These methods will continue to evolve and be incorporated into the RED Alert decision support ecosystem to provide analysts with a global perspective on potential re-emergence.ResultsHotspots of high disease incidence in relation to neighboring countries were identified for measles, cholera, dengue, and yellow fever between 2000 and 2014. Disease-specific predictors were identified using aggregate estimates from World Bank indicator dataset. Data was imputed where possible to enhance the validity of the Gi * statistic for clustering. In the future, as data streams become more readily available, hotspot modeling at a finer resolution will help to improve the precision of spatial epidemiology.ConclusionsSpatial methods enhance the capability of understanding complex population and disease relationships, which in turn improves surveillance and the ability to predict re-emergence. With tools like RED Alert, public health analysts can better prepare to respond rapidly to future re-emerging disease threats. %R 10.5210/ojphi.v10i1.8341 %U %U https://doi.org/10.5210/ojphi.v10i1.8341 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8342 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo analyze differences in utilization of Emergency Departments for primary care sensitive conditions by facility and by patient ZIP code.IntroductionSyndromic surveillance has been widely implemented for the collection of Emergency Department (ED) data. EDs may be the only option for seeking care in underserved areas, but they do not represent population-based measures. This analysis provides insight on health-seeking behaviors within the context of the type of care sought.MethodsThe NSSP BioSense database in Adminer was queried for Illinois ED visits that occurred in August 2016, November 2016, February 2017, or May 2017. These months were chosen to account for seasonality and holidays. For each visit, as defined by the BioSense ID, the first listed diagnosis code, defined to be the primary diagnosis, and the latest valid patient ZIP code were determined.Next, an algorithm1 developed by New York University (NYU) which uses diagnosis codes to classify ED visits was applied to each visit''s primary diagnosis. With this algorithm, a percentage (possibly zero) of each visit was classified as primary care sensitive (PCS), where the percentage is based on the diagnosis code.The visits were tabulated to find the percentage of visits to each facility or from each ZIP code which were classified as PCS. (Visits whose diagnosis was not matched by the algorithm were excluded.) The relationships between the percentages of PCS visits in each facility or ZIP code and characteristics of the facilities or ZIP codes were then analyzed.Facilities were grouped by Critical Access Hospital (CAH) status2 and by location (within, or not within, a primary care Health Professional Shortage Area (HPSA), as determined using a tool from the U.S. Department of Health and Human Services3). Percentages of PCS visits at different types of facilities were compared using t-tests.Variables reported in the Social Vulnerability Index (SVI)4 at the census tract level were converted to ZIP code-level data using a crosswalk from the U.S. Department of Housing and Urban Development5. An ordinary least squares regression model in which these variables were used to predict the percentage of PCS visits in each ZIP code was fitted. The R package geoR6 was used to fit an additional model which accounted for spatial correlation across ZIP codes. In this model, ZCTA latitude and longitude coordinates from the U.S. Census7 were used as the ZIP codes'' locations. Only ZIP codes for which the NYU algorithm matched diagnoses from at least 70% of visits were included in these models.ResultsThe overall proportion of PCS visits across all CAHs is significantly greater than the proportion at other facilities (p < 0.0001). Likewise, the proportion of PCS visits at facilities in primary care HPSAs is significantly greater than the proportion at other facilities (p < 0.0001). Among facilities for which the NYU algorithm matched diagnoses from at least 70% of visits, the mean percentage of PCS visits at facilities in primary care HPSAs is significantly higher than the mean at other facilities (p = 0.0009).The regression model for ZIP code-level data with spatial correlation was found to be better than the regression without spatial weighting. The spatial model found 3 of 16 SVI variables to be significant predictors of the percentage of ED visits which are PCS: after adjusting for all other variables, a one percentage point increase in minority makeup is associated with a 0.09 percentage point increase in PCS visits (p < 0.0001), a one percentage point increase in persons in group quarters is associated with a 0.13 percentage point decrease in PCS visits (p = 0.0009), and a $1000 increase in per capita income is associated with a 0.12 percentage point decrease in PCS visits (p = 0.0011).ConclusionsED-based syndromic surveillance can only provide part of the picture for monitoring health conditions across Illinois. Understanding rates of PCS ED visits can enhance the interpretation of health trends. Lower rates can inform recruiting plans for capturing data from additional sources, such as urgent or immediate care facilities, while higher rates of PCS visits at EDs may indicate areas in need of more healthcare resources.References1. New York University Center for Health and Public Service Research. Algorithm for classifying ED utilization. https://wagner.nyu.edu/faculty/billings/nyued-background . Datasets available from: http://wagner.nyu.edu/files/faculty/NYU_ED_Algorithm_-_ICD-9_Codes_-_6.23.15.xlsx and http://wagner.nyu.edu/files/faculty/NYU_ED_Algorithm_-_ICD-10_Codes_-_6.23.15.xlsx2. Illinois Health Facilities & Services Review Board. 2016 Hospital Data Spreadsheet [dataset on the Internet]. Available from: https://www.illinois.gov/sites/hfsrb/InventoriesData/FacilityProfiles/Documents/AHQ%20Data%20File%202016.xls3. U.S. Department of Health & Human Services. Health Resources & Services Administration Data Warehouse shortage area tool [Internet]. Available from: https://datawarehouse.hrsa.gov/tools/analyzers/geo/ShortageArea.aspx4. CDC Agency for Toxic Substances and Disease Registry / Geospatial Research, Analysis, and Services Program. Social Vulnerability Index 2014 Database – Illinois tract text file [dataset on the Internet]. Available from: https://svi.cdc.gov/SVIDataToolsDownload.html5. U.S. Department of Housing and Urban Development. HUD USPS ZIP Code Crosswalk File: ZIP-TRACT, 2nd Quarter 2017 [dataset on the Internet]. Available from: https://www.huduser.gov/portal/datasets/usps_crosswalk.html6. Ribeiro Jr PJ, Diggle PJ. geoR: Analysis of Geostatistical Data [computer software]. R package version 1.7-5.2. 2016. https://CRAN.R-project.org/package=geoR7. U.S. Census. 2010 ZIP Code Tabulation Areas Gazetteer File [dataset on the Internet]. Available from: https://www.census.gov/geo/maps-data/data/gazetteer2010.html %R 10.5210/ojphi.v10i1.8342 %U %U https://doi.org/10.5210/ojphi.v10i1.8342 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8343 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo adjust modelled baselines used for syndromic surveillance to account for public health interventions. Specifically to account for a change in the seasonality of diarrhoea and vomiting indicators following the introduction of a rotavirus vaccine in England.IntroductionPublic Health England''s syndromic surveillance service monitor presentations for gastrointestinal illness to detect increases in health care seeking behaviour driven by infectious gastrointestinal disease. We use regression models to create baselines for expected activity and then identify any periods of signficant increases. The introduction of a rotavirus vaccine in England during July 2013 (Bawa, Z. et al. 2015) led to a reduction in incidence of the disease, requiring a readjustment of baselines.MethodsWe identified syndromes where rates had dropped significantly following the vaccine’s introduction. For these indicators, we introduced new variables into the regression models used to create baselines. Specifically we tested for a ‘step-change’ drop in rates and a change in the seasonality of baselines. Finally we checked the new models accuracy against actual syndromic data before and after the vaccine introduction.ResultsWe were able to improve model fit post-intervention, with the best-fitting models based on a change in seasonality. All post-intervention regression models had reduced average residual square error. Reductions in residual errors ranged from <1% to 60% when a ‘step-change’ variable was included and 4% to 75% when accounting for seasonality. Furthermore, every syndrome showed a better model fit when a change in seasonality was included.ConclusionsPrior to the vaccine’s introduction, rotavirus caused a spring-time peak in vomiting and diarrhoea recorded by syndromic surveillance systems. Failure to account for the reduction in this peak post-vaccine would have made surveillance systems less effective. In particular, any increased activity during spring may have been undetected. Moreover, models that did not account for changes in seasonality would increase the chances of false alarms during other seasons. By adjusting our baselines for the changes in seasonality due to the vaccine we were able to maintain effective surveillance systems.ReferencesBawa, Z., et al. Assessing the Likely Impact of a Rotavirus Vaccination Program in England: The Contribution of Syndromic Surveillance. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2015;61(1):77-85. %R 10.5210/ojphi.v10i1.8343 %U %U https://doi.org/10.5210/ojphi.v10i1.8343 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8344 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo utilize syndromic surveillance data timely detecting herion overdose outbreaks in the community.IntroductionEarly detection of heroin overdose clusters is important in the current battle against the opioid crisis to effectively implement prevention and control measures. The New York State syndromic surveillance system collects hospital emergency department (ED) visit data, including visit time, chief complaint, and patient zip code. This data can be used to timely identify potential heroin overdose outbreaks by detecting spatial-temporal case clusters with scan statistic.MethodsHeroin overdose cases (Heroin_OD) were identified from ED visits by searching Heroin_OD key terms in the chief complaints. Then the space-time permutation model (using the SaTScan package) was applied to detect clusters of Heroin_OD. ED visit date served as the time variable and the case residential zip code was the spatial coordinate variable for the SaTScan analysis. A SAS program was developed to carry out the prospective scan statistics analysis weekly and produces reports of detected clusters in table and map format. Cluster detection parameters were set to detect heroin overdose aggregation in maximum geographic radium of 20 kilometer (km) and maximum time span up of 21 days at the P-value <= 0.05. Chief complaints within the clusters are reviewed to ensure accuracy of detection. Messages have been developed and are shared with community members including law enforcement and public health identifying the cluster and offering suggestions of activities that can occur at the local level to identify and address the cause of the cluster, as well as to reduce potential harm. This includes the 23 syringe exchange programs (SEPs) regulated by the New York State Department of Health.ResultsUsing ED visit data from 138 NY upstate hospitals, a total of 12 Heroin_OD clusters were detected by the SaTScan analysis during the period of 9/1/2016 through 9/17/2017. There were 845 cases identified. The average age was 35 years and ranged from 7 to 95 years. Sixty nine percent (69%) of the cases was in 20 to 39 age group and 66% in males. A cluster was identified earlier 2017 in Suffolk County, and the local SEP was alerted. This encouraged communication between partners within the alerted county which ultimately resulted in identifying the substance endangering people who used drugs in the area. It also helped public health to partner with public safety, ensuring that the availability of the substance was interrupted.ConclusionsAs the space-time permutation scan statistic only requires disease counts, event date and disease location, the method can be easily implemented for detecting disease outbreaks using data routinely collected from disease surveillance systems. The current study showed that scan statistic is a useful tool for identifying clusters of non-fatal overdoses from specific drugs. This method also returns important information to assist outbreak investigations, such as geographic location and time-span of the potential outbreaks. %R 10.5210/ojphi.v10i1.8344 %U %U https://doi.org/10.5210/ojphi.v10i1.8344 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8347 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo carry out an observational study to explore what added value Google search data can provide to existing routine syndromic surveillance systems in England for a range of conditions of public health importance and summarise lessons learned for other countries.IntroductionGlobally, there have been various studies assessing trends in Google search terms in the context of public health surveillance1. However, there has been a predominant focus on individual health outcomes such as influenza, with limited evidence on the added value and practical impact on public health action for a range of diseases and conditions routinely monitored by existing surveillance programmes. A proposed advantage is improved timeliness relative to established surveillance systems. However, these studies did not compare performance against other syndromic data sources, which are often monitored daily and already offer early warning over traditional surveillance methods. Google search data could also potentially contribute to assessing the wider population health impact of public health events by supporting estimation of the proportion of the population who are symptomatic but may not present to healthcare services.MethodsWe sought to determine the added public health utility of Google search data alongside established syndromic surveillance systems in England2 for a range of conditions of public health importance, including allergic rhinitis, scarlet fever, bronchitis, pertussis, measles, rotavirus and the health impact of heatwaves. Google search term selection was based on diagnostic and clinical codes underlying the syndromic indicators, with Google Trends3 used to identify additional related internet search terms. Daily data was extracted from syndromic surveillance systems2 and from the Google Health Trends Application Programming Interface (API) from 2012 to 2017 and a retrospective daily analysis undertaken during pre-identified public health events to identify a) whether signals were detected during these events and b) assess the correlation with analogous syndromic surveillance indicators through calculation of Spearman correlation coefficients and lag assessment to determine timeliness.ResultsWe detected increases in Google search term frequency during public health events of interest. Good correlation was seen with comparable syndromic surveillance indicators on a daily timescale for several health outcomes, including the search terms hayfever, scarlet fever, bronchiolitis and heatstroke. Weaker correlation was seen for conditions which occur in small numbers and are vaccine preventable such as measles and pertussis. Lag analysis showed similar timeliness between daily syndromic and Google data, suggesting that, overall, Google data did not provide an earlier or delayed signal compared to syndromic surveillance indicators in England.ConclusionsTo the best of our knowledge this is the first time trends in Google search data have been compared against syndromic data for a range of public health conditions in England. These findings demonstrate the potential utility of internet search query data in conjunction with existing systems in England, with syndromic surveillance data found to be as timely as Google data. These findings also have important implications for countries where there are no such healthcare-based syndromic surveillance systems in place. Factors to consider with analyses of Google search trend data in the context of disease surveillance have been highlighted, including the choice of search terms and interpretation of the reasons behind searching the internet.References1Nuti SV, Wayda B, Ranasinghe I, Wang S, Dreyer RP, Chen SI, Murugiah K. The use of google trends in health care research: a systematic review. PLoS One. 2014 Oct 22;9(10):e109583.2Public Health England. Syndromic surveillance: systems and analyses. 2017. Available online: https://www.gov.uk/government/collections/syndromic-surveillance-systems-and-analyses3Google. 2017. Google Trends. Available online:https://trends.google.com/trends/ %R 10.5210/ojphi.v10i1.8347 %U %U https://doi.org/10.5210/ojphi.v10i1.8347 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8903 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveThe objective of this abstract is to illustrate how the Utah Department of Health processes a high volume of electronic data in an automated way. We do this by a series of rules engines that does not require human intervention.IntroductionNational initiatives, such as Meaningful Use, are automating the detection and reporting of reportable disease events to public health, which has led to more complete, timely, and accurate public health surveillance data. However, electronic reporting has also lead to significant increases in the number of cases reported to public health. In order for this data to be useful to public health, it must be processed and made available to epidemiologists and investigators in a timely fashion for intervention and monitoring. To meet this challenge, the Utah Department of Health (UDOH)’s Disease Control and Prevention Informatics Program (DCPIP) has developed the Electronic Message Staging Area (EMSA). EMSA is a system capable of automatically filtering, processing, and evaluating incoming electronic laboratory reporting (ELR) messages for relevance to public health, and entering those laboratory results into Utah’s integrated disease surveillance system (UT-NEDSS) without impacting the overall efficiency of UT-NEDSS or increasing the workload of epidemiologists.MethodsAfter parsing and translating messages, EMSA runs the messages through a series of rules to determine if a test result should update an existing UT-NEDSS event, create a new UT-NEDSS event, be archived for possible use in future cases (e.g. to help identify seroconversion) or if the test result should be discarded. All of these rules can be configured specifically for each reportable condition. First, EMSA runs age-based rules. If the incoming message is too old for the indicated condition, EMSA does not continue processing and the message is discarded. EMSA then attempts to person match to determine if the person reported in the ELR message matches a known person in UT-NEDSS. If the person matches, EMSA will then evaluate whether the laboratory result should append to any events associated with the person, create a new event under that person, or create a new person and event. This process occurs through two different rule sets: whitelist rules, and test specific rules. Whitelist Rules are condition-specific and, when available, based on CDC''s case definition guidelines to determine when a new lab test result should be considered part of an existing case or a catalyst to trigger a new event. Whitelist Rules run against all existing events found for the person matched, and once a single event is matched, then the more-specific test result-based rules come into play. Within an event matched by the whitelist rules, we have another set of rules based on the test result, collection date, accession number, and test status, to determine whether to add the laboratory report to the event, update an existing laboratory report, or if the laboratory report is a duplicate to be discarded. The message also runs through rules based on test and test result, and sometimes off organism, that determine whether that result can even be used to update the case or not. Whitelist rules also determine if too much time has passed since the matching event occurred for the incoming laboratory result to be appended to the matching event. Whitelist rules exist for both morbidity and contact events, and are based on timeframes such as onset date and treatment dates. If a particular incoming laboratory test result matches a known person in UT-NEDSS, and the whitelist rules determine that the laboratory result matches that person’s disease condition and can “update an existing event”, the laboratory result is run through another set of rules, called “test specific rules”. Test specific rules match incoming laboratory tests results to a UT-NEDSS disease condition, and determine whether each unique test type and test result combination can “create a new event” and/or “update an existing event”. All tests that do not meet the criteria for inclusion into UT-NEDSS, either by updating an event or creating a new event, are held in EMSA, in what is termed the “graylist” for a period of 18 months. When EMSA creates a new event, it queries the graylist to determine if a previous reported lab should be pulled and added to the new event. Graylist rules determine how far back EMSA is allowed to search for previous test results.ResultsFrom 10/10/2016 to 9/30/2017, the Utah Department of Health has received a total of 995,486 electronic messages that required processing. Of those 995,486 messages, 23,787 (2.4%) were deleted, 17,839 (1.8%) were identified as duplicates and subsequently deleted, 853,853 (85.8%) were sent to graylist, and 99,657 (10%) were added to UT-NEDSS. Of the 99,657 messages, 85,705 (86%) were processed from raw electronic messages to assignment into UT-NEDSS without any human intervention.ConclusionsELR improves the timeliness, completeness, and accuracy of laboratory reporting to public health, but often results in a significant increase in laboratory reporting to public health agencies. This increase in volume can overwhelm epidemiologists and investigators if manual processes for reviewing all incoming ELR messages are needed for processing laboratory results and entering data into surveillance systems. In order to fully leverage the benefits of ELR for public health surveillance, we knew we needed a highly automated process for receiving, parsing, translating, and entering data into UT-NEDSS that would mitigate the challenges associated with the increased volume. We developed EMSA and its series of rule sets to meet this challenge. %R 10.5210/ojphi.v10i1.8903 %U %U https://doi.org/10.5210/ojphi.v10i1.8903 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 10 %N 1 %P e8905 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2018 %7 ..2018 %9 %J Online J Public Health Inform %G English %X ObjectiveTo share practical, user-friendly data validation methods in R that result in shorter validation time and simpler code.IntroductionThere are currently 123 healthcare facilities sending data to the Washington (WA) State syndromic surveillance program. Of these facilities, 30 are sending to the National Syndromic Surveillance Program’s (NSSP) production environment. The remainder are undergoing validation or in queue for validation. Given the large number of WA healthcare facilities awaiting validation, staff within the state syndromic surveillance program developed methods in R to reduce the amount of time required to validate data from an individual facility.MethodsThe dplyr package and R Markdown file format were used to more rapidly conduct syndromic data validation. Dplyr, written by Hadley Wickham, was created for easy data manipulation.<span style=\"font-size:10.8333px\">1</span> The syntax of this package is user-friendly, providing a function for almost every common data manipulation task and utilizing the piping operator from the magrittr package. Data fields of interest for syndromic surveillance are classified as required (R), required but may be empty (RE), or optional (O). For R or RE data fields, dplyr can be used to check for patterns of missingness as well as verify that the correct value sets are being used for code fields. For character fields, dplyr can be used to pull samples of free-text, calculate word or character counts, or search for string patterns of interest.ResultsThe amount of time spent validating any single facility has decreased significantly. This has allowed the number of facilities undergoing data validation at one time to increase from 12 to 22. However, the length of time between beginning and completing data validation per facility has not decreased. While reporting data issues to facilities takes less time, the lag in the validation process still occurs while waiting for facilities to correct these issues at the feed origination.ConclusionsIn order to increase the number of healthcare facilities that are sending production quality data more quickly, more resources need to be directed at providing facilities with support on how to correct data issues rather than solely reporting the problems.References1. Anderson, S. dplyr and pipes: the basics [Internet]. 2014 [cited 2017 Oct 10]. Available from: http://seananderson.ca/2014/09/13/dplyr-intro.html2. Broman, K. Knitr with R Markdown [Internet]. [cited 2017 Oct 10]. Available from: http://kbroman.org/knitr_knutshell/pages/Rmarkdown.html. %R 10.5210/ojphi.v10i1.8905 %U %U https://doi.org/10.5210/ojphi.v10i1.8905 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7600 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveTo improve the ability of syndromic surveillance systems to detectunusual events.IntroductionSyndromic surveillance systems are used by Public Health England(PHE) to detect changes in health care activity that are indicative ofpotential threats to public health. By providing early warning andsituational awareness, these systems play a key role in supportinginfectious disease surveillance programmes, decision making andsupporting public health interventions.In order to improve the identification ofunusualactivity, wecreated new baselines to modelseasonally expectedactivity inthe absence of outbreaks or other incidents. Although historicaldata could be used to model seasonality, changes due to publichealth interventions or working practices affected comparability.Specific examples of these changes included a major change in theway telehealth services were provided in England and the rotavirusvaccination programme introduced in July 2013 that changed theseasonality of gastrointestinal consultations. Therefore, we needed toincorporate these temporal changes in our baselines.MethodsWe used negative binominal regression to model daily syndromicsurveillance, allowing for day of week and public holiday effects.To account for step changes in data caused by changes in healthcaresystem working practices or public health interventions we introducedspecific independent variables into the models. Finally, we smoothedthe regression models to provide short term forecasts of expectedtrends.The new baselines were applied to PHE’s four syndromicsurveillance systems for daily surveillance and public-facing weeklybulletins.ResultsWe replaced traditional surveillance baselines (based on simpleaverages of historical data) with the regression models for dailysurveillance of 53 syndromes across four syndromic surveillancesystems. The improved models captured current seasonal trends andmore closely reflected actual data outside of outbreaks.ConclusionsSyndromic surveillance baselines provide context forepidemiologists to make decisions about seasonal disease activity andemerging public health threats. The improved baselines developedhere showed whether current activity was consistent with expectedactivity, given all available information, and improved interpretationwhen trends diverged from expectations. %R 10.5210/ojphi.v9i1.7600 %U %U https://doi.org/10.5210/ojphi.v9i1.7600 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7661 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveTo describe the challenges and lessons learned for public healthand providers to successfully implement public health MeaningfulUse readiness guidelines and navigate from intent to submission ofproduction data while simultaneously upgrading surveillance systems.IntroductionThe Syndromic Surveillance Consortium of Southeast Texas(SSCSeT) consists of 13 stakeholders who represent 19 counties orjurisdictions in the Texas Gulf Coast region and receives health datafrom over 100 providers. The Houston Health Department (HHD)maintains and operates the syndromic surveillance system for the GulfCoast region since 2007. In preparation for Meaningful Use (MU) theHHD has adapted and implemented guidance and recommendationsfrom Centers for Disease Control and Prevention, Office of NationalCoordinator for Health Information Technology and others. HHDsgoal is to make it possible for providers meet MU specification byfacilitating the transmission of health related data for syndromicsurveillance. The timing of the transition into MU overlaps with thechange in syndromic surveillance systems. %R 10.5210/ojphi.v9i1.7661 %U %U https://doi.org/10.5210/ojphi.v9i1.7661 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7662 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveOur primary goal is to move towards establishing a causal linkbetween binge drinking, mental health, employment and income.IntroductionOne of the key questions in health economics is what is thedirection of causality: does poverty cause poor health outcomes; doeslow education cause poor health outcomes; does poor health resultin lack of productivity; does poor health cause poor educational andincome outcomes; and how is this all related to mental health if at all.We are used to breaking down data into fragments as researchers:an investigator who is predominantly focused on health outcomeswill approach the problem with disease as the dependent variable andincome as the conditioning variable. However, if we are interested inincome inequality we will reverse the direction and income will bethe dependent variable with health status as the conditioning variable.The representation above allows us to visualize data as a functionof multiple fragments. For example if we want to understand howdepression is related to income, one can look at the figure to observethat with lower income there is a higher likelihood of being depressed.With this simple illustration we can see that establishing causal linkscan be very tricky, if not incredibly challenging.MethodsTwo methods are: applied descriptive analysis and estimation.We approach this without causality in mind, but with an intentionto explore how behavior responds to income, education, labor andhealth. Our descriptive approach looks at trends in binge drinking andmental health as it affects key economic outcomes such as education,employment, and income. For each outcome we then run a simpleprobit model controlling for a variety of characteristics. The keyco-variates in these models are income, employment and health.It is very useful to look at these simple probits because often itis hard to separate the effects of income on health, employment onincome, health on employment, education on employment, health andincome, and finally income, employment, health and education onmental health and substance abuse.ResultsOur estimated results are rather interesting. Examining themarginal probits, e.g. figures 1.3, and 1.5, we show that there isn’ta significant income effect, nor do we find significant education oremployment effects associated with binge drinking. In fact we findthat in Wisconsin binge drinking is a health burden for those whoare eligible to drink irrespective of education and that the effect issignificant; we also find that higher levels of education increase theprobability of being unemployed but not significantly. The secondset of probit estimates, e.g. figure 1.7, show that poor health is indeedassociated with outcomes lower employment as compared to othergroups, and higher probability of depression. The last set of probits,e.g. figure 1.1, show that retired, self employed and employed areless likely to be depressed but not significantly so, and those who areunable to work have a higher estimated probablilty to be depressed.Income doesn’t appear to have a significant estimated effect ondepression.ConclusionsOur analysis provide insights into the question of socio-economicstatus (SES), binge drinking, and depression in three important ways.First, we explore the relationship between SES and binge drinkingand we find that binge drinking is SES invariant. Second we findthat depression is not associated with income it does have a strongrelationship with employment status. We are in the process ofunpacking the effects of SES, binge drinking and depression to movebeyond associational inferences to causal inferences. %R 10.5210/ojphi.v9i1.7662 %U %U https://doi.org/10.5210/ojphi.v9i1.7662 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 9 %N 1 %P e7664 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2017 %7 ..2017 %9 %J Online J Public Health Inform %G English %X ObjectiveTo analyze tobacco use in Georgia to influence policy, systemsand environmental changes as tools to reduce its burden on healthoutcomesIntroductionTobacco use is the leading cause of preventable illness and deathsin Georgia. About 10.1% of deaths among adults in Georgia arelinked to smoking related illnesses. Most first use of cigarettes occursby age 18 (87%), with nearly all-first use by 26 years of age (98%).Although cigarette smoking has declined significantly since 1964,very large disparities in tobacco use remain across different sub-groups of the population. Multiple environmental, psychological, andsocial factors have been associated with tobacco use, including raceand ethnicity, age, SES, educational accomplishment, gender, andsexual orientation. These factors within the social environment havea huge influence on motivation to begin and to continue using tobaccoproducts for not just the individual but also certain community groupwithin the population. Established in 2000, Georgia Tobacco UsePrevention Program (GTUPP) is a program designed to meet theoverall goal of reducing the health and economic burden associatedwith tobacco use for all members of the community. By working withvarious partners, GTUPP plans, implements and evaluates policy,systems, and environmental changes designed to reduce tobacco-related illnesses and deaths. Best practice strategies focus on thefollowing goals: preventing the initiation of tobacco use amongyoung people; promoting quitting among young people and adults(e.g. Georgia Tobacco Quit Line (GTQL); eliminating exposureto secondhand tobacco smoke; and identifying and eliminating thedisparities related to tobacco use among various population groups.MethodsThe following data collection tools were used to educatecommunity members, local coalition groups and policy decisionmakers on the burden of tobacco use in Georgia: Youth TobaccoSurvey (YTS), Youth Risk Behavioral Survey (YRBS) and BehavioralRisk Factor Surveillance System (BRFSS). These tools allows publichealth professionals to create messaging needed to reach differentstakeholders. The following are examples of key data points thatwere used to influence policy, systems, and environmental change:27,000 of middle school students and 79,000 of high school currentlyuse tobacco (cigarettes, smokeless tobacco or cigars). Approximately32,400 of middle school students and 72,900 of high school studentssay they have tried smoking electronic cigarettes (e-cigarettes).Smoking prevalence among adult males 740,000 is significantlyhigher than among females 510,000, and the overall smokingprevalence is highest among adults’ ages 25-34 years 292,000.ResultsCurrently, the following policies have been adopted as a resultof using surveillance to educate policy decision makers and multi-sector groups in the community at large: 116 school district are100% tobacco free, 28 parks and recreation are 100% tobacco/smokefree, 46 colleges/universities are tobacco free, 6 cities in Georgiahave a comprehensive smoke free air law, 65 multi-unit housing(private/public) are smoke free, and 132 hospitals are tobacco free.Between June 2015 and July 2016, over 15,000 Georgia tobaccousers used the GTQL services to make a quit attempt, and healthcareproviders through a systems change referral approach referred 13%of the users to the GTQL.ConclusionsWorking with schools (K-12), parks, colleges/universities,hospitals, worksites, and municipalities to adopt tobacco freepolicies and promote cessation services provides an opportunityfor all members of the community to be tobacco free. As tobaccouse is associated with chronic diseases it is imperative to engageall members of the community in tobacco free living. Removingavoidable structural and social barriers and equally implementingtobacco use prevention programs and policies is essential. %R 10.5210/ojphi.v9i1.7664 %U %U https://doi.org/10.5210/ojphi.v9i1.7664 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 8 %N 1 %P e6415 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2016 %7 ..2016 %9 %J Online J Public Health Inform %G English %X The decision as to whether an alarm (excess activity in syndromic surveillance indicators) leads to an alert (a public health response) is often based on expert knowledge. Expert-based approaches may produce faster results than automated approaches but could be difficult to replicate. Moreover, the effectiveness of a syndromic surveillance system could be compromised in the absence of such experts. Bayesian network structural learning provides a mechanism to identify and represent relations between syndromic indicators, and between these indicators and alerts. Their outputs have the potential to assist decision-makers determine more effectively which alarms are most likely to lead to alerts. %R 10.5210/ojphi.v8i1.6415 %U %U https://doi.org/10.5210/ojphi.v8i1.6415 %0 Journal Article %I %V %N %P %T %D %7 .. %9 %J %G English %X %U %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4574 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X The purpose of our work is to develop a system for automatic contact tracing with the goal of identifying individuals who are most likely infected, even if we do not have direct diagnostic information on their health status. We developed a dynamic Bayesian network to process the sensors information from users'' cellphones to track the spreading of the pandemic in the population. Our Bayesian data analysis algorithms track the real-time proximity contacts in the population and provide the public health agencies, the probabilistic likelihood for each individual of being infected by the novel virus. %R 10.5210/ojphi.v5i1.4574 %U %U https://doi.org/10.5210/ojphi.v5i1.4574 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4385 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X This project aimed to document and assess the variation in state legislation relating to foodborne disease surveillance and outbreak response for all 50 states and the District of Columbia by creating a database and appendix of laws and regulations that will be made available to researchers and policymakers. Through compilation of the state laws and regulations and analysis of previous multistate outbreaks, we are able to present trends, variations, and gaps in the legislation that directly impacts the ability of public health officials to conduct foodborne outbreak investigations. %R 10.5210/ojphi.v5i1.4385 %U %U https://doi.org/10.5210/ojphi.v5i1.4385 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4387 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X For the purpose of developing a national system of outbreak surveillance, we compared local outbreak signals in three sources of syndromic data: telephone triage of acute gastroenteritis, web queries about symptoms of gastrointestinal illness, and OTC pharmacy sales of anti-diarrhea medication. The sensitivity and specificity were highest for telephone triage data. It provided the most promising source of syndromic data for surveillance of point-source outbreaks. Currently, a project has been initialized to develop and implement a national system in Sweden for daily syndromic surveillance based on 1177 Health Care Direct, supporting regional and local outbreak detection and investigation. %R 10.5210/ojphi.v5i1.4387 %U %U https://doi.org/10.5210/ojphi.v5i1.4387 %0 Journal Article %I %V %N %P %T %D %7 .. %9 %J %G English %X %U %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4391 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X Multidimensional Subset Scan (MD-Scan) is a new method for early outbreak detection and characterization using multivariate case data from individuals in a population. MD-Scan extends previous work on multivariate event detection by identifying the characteristics of the affected subpopulation (e.g. affected gender(s), age groups, and behavioral risk factors), and enables more timely and more accurate detection while maintaining computational tractability. %R 10.5210/ojphi.v5i1.4391 %U %U https://doi.org/10.5210/ojphi.v5i1.4391 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4396 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. We analysed twenty years‰Û ª data from a large laboratory surveillance database used for outbreak detection in England and Wales. Our aim is to inform the development of more effective outbreak detection algorithms. We describe the diversity of seasonal patterns, trends, artefacts and extra-Poisson variability that an effective multiple laboratory-based outbreak detection system must cope with. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms. %R 10.5210/ojphi.v5i1.4396 %U %U https://doi.org/10.5210/ojphi.v5i1.4396 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4397 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X This study was to elucidate the spatio-temporal correlations between the mild and severe enterovirus cases through integrating enterovirus-related three surveillance systems, including the sentinel physician, national notifiable diseases and laboratory surveillance systems in Taiwan. With these fully understanding epidemiological characteristics, hopefuly, we can develop better measures and indicators from mild cases to provide early warning signals and thus minimizing subsequent numbers of severe cases. Taiwan‰Û ªs surveillance data indicate that public health professionals can monitor the trends in the numbers of mild EV cases in community to provide early warning signals for local residents to prevent the severity of future waves. %R 10.5210/ojphi.v5i1.4397 %U %U https://doi.org/10.5210/ojphi.v5i1.4397 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4401 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X The choice of outbreak detection algorithm and its configuration result in variations in the performance of public health surveillance systems. The ability of predicting the performance of detection algorithms under different circumstances will guide the method selection and algorithm configuration. Our work characterizes the dependence of the detection performance on the type and severity of outbreak. We examined the influence of determinants on the performance of C-algorithms and W-algorithms. We used Bayesian Networks to model relationships between determinants and the performance. The results on a sophisticated simulated data set show that algorithm performance can be predicted well using this methodology. %R 10.5210/ojphi.v5i1.4401 %U %U https://doi.org/10.5210/ojphi.v5i1.4401 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4404 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X To aid in developing a global biosurveillance program, it is critical to develop a framework to capture and understand the myriad of data streams and evaluate them in context of surveillance goals. Toward this goal, Los Alamos National Laboratory has developed a new method of evaluating the effectiveness of data stream types through the use of a novel concept called the surveillance window, a technique that integrates operational systems analysis, surveillance system analysis and epidemiological analysis. In this presentation application of this methodology to Foot and Mouth Disease, Ebola and Influenza and E.coli related gastrointestinal disease surveillance will be demonstrated. %R 10.5210/ojphi.v5i1.4404 %U %U https://doi.org/10.5210/ojphi.v5i1.4404 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4408 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X COPD is a prevalent chronic disease among older adults, responsible for substantial healthcare utilization. We used the NC DETECT surveillance system to investigate patterns of hospitalizations and short-term return visits resulting from COPD-related ED visits. We found a high prevalence of hospital admissions and return ED visits, including many repeat hospitalizations. We also provide new information about the impact of age, sex, and insurance status on these short-term outcomes. %R 10.5210/ojphi.v5i1.4408 %U %U https://doi.org/10.5210/ojphi.v5i1.4408 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4411 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X We compared detection performance of univariate alerting methods on real and simulated events in different types of biosurveillance data. Both kinds of detection performance analysis showed the method based on Holt-Winters exponential smoothing superior on non-sparse time series with day-of-week effects. The adaptive CUSUM and Shewhart methods proved optimal on sparse data and data without weekly patterns. %R 10.5210/ojphi.v5i1.4411 %U %U https://doi.org/10.5210/ojphi.v5i1.4411 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4415 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X Estimates of influenza based on influenza like illness (ILI) may not capture the full spectrum of illness or result in early warning. We tested a syndromic surveillance method using hospital staff influenza like absence (ILA) to potentially enhance ILI. Rates of ILA were compared to regional surveillance data on ILI and confirmed positive influenza A test results (PITR) in hospitalised patients. ILA demonstrated accurate seasonal trends in influenza as defined by ILI, but provided more realistic estimates of the relative burden of pH1N1, and potentially earlier warning than ILI and PITR, which is likely to improve accuracy of influenza monitoring. %R 10.5210/ojphi.v5i1.4415 %U %U https://doi.org/10.5210/ojphi.v5i1.4415 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4417 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X Outbreaks of gastrointestinal disease occur with some frequency in North America, resulting in considerably morbidity, mortality, and cost. Outbreak detection can be improved by using simulated outbreak data to build, validate, and evaluate models that aim to improve accuracy and timeliness of outbreak detection. We have constructed a microsimulation model that depicts reasonable outbreak scenarios in space and time, and explore the use of a hidden Markov model along with supervised learning algorithms to find unique space-time outbreak signatures useful for outbreak classification. %R 10.5210/ojphi.v5i1.4417 %U %U https://doi.org/10.5210/ojphi.v5i1.4417 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4422 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X The goal of this project is to enhance surveillance for Arboviruses by establish a baseline for the emerging threat of Dengue fever the border region of Arizona. This will be accomplished by: enhancement and exchange of dengue laboratory testing techniques, a seroprevalence sentinel-hospital site study of symptomatic patients, and mapping techniques to better understand the presence of mosquito vectors. %R 10.5210/ojphi.v5i1.4422 %U %U https://doi.org/10.5210/ojphi.v5i1.4422 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4428 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X This presentation reviews the experiences of a meteorology-inspired infectious disease forecast station operating within a rural community. The forecast station promoted routine communication of a broader array of infectious disease activity than that monitored by public health; facilitated proactive, cost effective healthcare; and enabled recognition of unusual, disruptive infectious activity with enough time to enable mitigation of clinical, infrastructure, and financial impact to the community. Routine communication of comprehensive infectious disease forecast and situational awareness information promoted community adaptive fitness to a wide variety of infectious hazards. %R 10.5210/ojphi.v5i1.4428 %U %U https://doi.org/10.5210/ojphi.v5i1.4428 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4473 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X Establishing automated syndromic surveillance in rural China was improper due to lack of required hardware facilities. Thus, more convenient syndromic surveillance method is needed. Before establishing system, ten targeted symptoms (i.e, fever, cough, sore throat, diarrhea, nausea/vomiting, headache, rash, mucocutaneous hemorrhage, convulsion and disturbance of consciousness) were determined under surveillance after epidemiological analysis on historical data of infectious diseases, literature review, expert consultation meeting, workshop and field investigation. This abstract describes the process of selecting the targeted symptoms, which may provide methods and evidences for other resource poor settings to construct similar surveillance system. %R 10.5210/ojphi.v5i1.4473 %U %U https://doi.org/10.5210/ojphi.v5i1.4473 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4491 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X The International Health Regulations (IHR) 2005, provides a framework that supports efforts to improve global health security and as such, there is an expectation that member states will take necessary steps to develop and strengthen systems and capacity for disease surveillance and detection and response to public health threats. To this end, a collaborative project was set up in 2010 to contribute to training a cadre of future trainers in a manner that sustainably supports ongoing efforts to improve the capability and capacity to undertake disease surveillance and Emergency Preparedness and Response (EPR) activities in India. %R 10.5210/ojphi.v5i1.4491 %U %U https://doi.org/10.5210/ojphi.v5i1.4491 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4493 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X Deaths tend to lag extreme heat and mortality data is generally not available for timely surveillance during heat waves. We analyzed daily weather, emergency medical system (EMS) calls flagged as heat-related, emergency department (ED) visits classified as heat-related, and natural cause deaths. We observed a 10% (95% CI: 4-16) mortality increase associated with one-day lagged heat-related EMS calls and a 5% mortality increase with one-day lagged ED visits (95% CI: 2-8). We conclude heat-related illness can be tracked during heat waves using EMS and ED data which are indicators of heat associated excess natural cause mortality. %R 10.5210/ojphi.v5i1.4493 %U %U https://doi.org/10.5210/ojphi.v5i1.4493 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4375 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X Since March 2007 the ISDS Research Committee (RC) has been identifying high-quality literature related to disease surveillance. These articles are made available to committee members and selected articles are discussed on bimonthly literature review calls. These calls have been a rich opportunity for participants to stay up-to-date on literature and collaborate with colleagues from diverse fields. However, the process for capturing relevant articles was not always the most efficient or inclusive. In response, ISDS RC has updated its Literature Review process, which has resulted in more articles being captured from a wider range of disciplines. %R 10.5210/ojphi.v5i1.4375 %U %U https://doi.org/10.5210/ojphi.v5i1.4375 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 3 %N 3 %P e3794 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2011 %7 ..2011 %9 %J Online J Public Health Inform %G English %X Electronic medical record (EMR) systems are a rich potential source for detailed, timely, and efficient surveillance of large populations. We created the Electronic medical record Support for Public Health (ESP) system to facilitate and demonstrate the potential advantages of harnessing EMRs for public health surveillance. ESP organizes and analyzes EMR data for events of public health interest and transmits electronic case reports or aggregate population summaries to public health agencies as appropriate. It is designed to be compatible with any EMR system and can be customized to different states’ messaging requirements. All ESP code is open source and freely available. ESP currently has modules for notifiable disease, influenza-like illness syndrome, and diabetes surveillance.An intelligent presentation system for ESP called the RiskScape is under development. The RiskScape displays surveillance data in an accessible and intelligible format by automatically mapping results by zip code, stratifying outcomes by demographic and clinical parameters, and enabling users to specify custom queries and stratifications. The goal of RiskScape is to provide public health practitioners with rich, up-to-date views of health measures that facilitate timely identification of health disparities and opportunities for targeted interventions. ESP installations are currently operational in Massachusetts and Ohio, providing live, automated surveillance on over 1 million patients. Additional installations are underway at two more large practices in Massachusetts. %M 23569616 %R 10.5210/ojphi.v3i3.3794 %U %U https://doi.org/10.5210/ojphi.v3i3.3794 %U http://www.ncbi.nlm.nih.gov/pubmed/23569616 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 3 %N 3 %P e3903 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2011 %7 ..2011 %9 %J Online J Public Health Inform %G English %X To control disease, laboratories and providers are required to report conditions to public health authorities. Reporting logic is defined in a variety of resources, but there is no single resource available for reporters to access the list of reportable events and computable reporting logic for any jurisdiction. In order to develop evidence-based requirements for authoring such knowledge, we evaluated reporting logic in the Council of State and Territorial Epidemiologist (CSTE) position statements to assess its readiness for automated systems and identify features that should be considered when designing an authoring interface; we evaluated codes in the Reportable Condition Mapping Tables (RCMT) relative to the nationally-defined reporting logic, and described the high level business processes and knowledge required to support laboratory-based public health reporting. We focused on logic for viral hepatitis. We found that CSTE tabular logic was unnecessarily complex (sufficient conditions superseded necessary and optional con¬ditions) and was sometimes true for more than one reportable event: we uncovered major overlap in the logic between acute and chronic hepatitis B (52%), acute and Past and Present hepatitis C (90%). We found that the RCMT includes codes for all hepatitis criteria, but includes addition codes for tests not included in the criteria. The proportion of hepatitis variant-related codes included in RCMT that correspond to a criterion in the hepatitis-related position statements varied between hepatitis A (36%), acute hepatitis B (16%), chronic hepatitis B (64%), acute hepatitis C (96%), and past and present hepatitis C (96%). Public health epidemiologists have the need to communicate parameters other than just the name of a disease or organism that should be reported, such as the status and specimen sources. Existing knowledge resources should be integrated, harmonized and made computable. Our findings identified functionality that should be provided by future knowledge management systems to support epidemiologists as they communicate reporting rules for their jurisdiction. %M 23569619 %R 10.5210/ojphi.v3i3.3903 %U %U https://doi.org/10.5210/ojphi.v3i3.3903 %U http://www.ncbi.nlm.nih.gov/pubmed/23569619 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 2 %N 3 %P e3041 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2010 %7 ..2010 %9 %J Online J Public Health Inform %G English %X Laboratory information systems may fulfill many of the requirements for individual result management within a public health laboratory but typically system access by data users, timely data extraction, integration and analysis is difficult. This is further complicated by often having multiple laboratory results for specific analytes or related analytes per specimen tested as part of complex laboratory algorithms requiring specialized expertise for result interpretation. We describe DIAL, (Data Integration for Alberta Laboratories), a platform allowing laboratory data to be extracted, interpreted, collated and analyzed in near real-time using secure web based technology, which is adapted from CNPHI`s Canadian Early Warning System (CEWS) technology. The development of DIAL represents a major technical advancement in the public health information management domain, building capacity for laboratory based surveillance. %M 23569594 %R 10.5210/ojphi.v2i3.3041 %U %U https://doi.org/10.5210/ojphi.v2i3.3041 %U http://www.ncbi.nlm.nih.gov/pubmed/23569594 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 2 %N 3 %P e3028 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2010 %7 ..2010 %9 %J Online J Public Health Inform %G English %X The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) enables health care practitioners to detect and monitor health indicators of public health importance. ESSENCE is used by public health departments in the National Capital Region (NCR); a cross-jurisdictional data sharing agreement has allowed cooperative health information sharing in the region since 2004. Emergency department visits for influenza-like illness (ILI) in the NCR from 2008 are compared to those of 2009. Important differences in the rates, timing, and demographic composition of ILI visits were found. By monitoring a regional surveillance system, public health practitioners had an increased ability to understand the magnitude and character of different ILI outbreaks. This increased ability provided crucial community-level information on which to base response and control measures for the novel 2009 H1N1 influenza outbreak. This report underscores the utility of automated surveillance systems in monitoring community-based outbreaks. %M 23569593 %R 10.5210/ojphi.v2i3.3028 %U %U https://doi.org/10.5210/ojphi.v2i3.3028 %U http://www.ncbi.nlm.nih.gov/pubmed/23569593 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 2 %N 3 %P e3031 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2010 %7 ..2010 %9 %J Online J Public Health Inform %G English %X The 2009 Inauguration and H1N1 outbreak called for real-time electronic information-sharing and surveillance across multiple jurisdictions to better understand the health of migrating populations. The InfoShare web application proved to be an efficient tool for users to share disease surveillance information. During both high profile events, public health users shared information within a secure access-controlled website across regions in the U.S. and among agencies. Due to its flexible design, InfoShare was quickly modified from its 2009 Inauguration interface to an interface that supports H1N1 surveillance. Through discussions and post-use surveys, a majority of InfoShare users revealed that the tool had provided a valuable and needed function. InfoShare allowed individual jurisdictions to receive timely and useful information, which, when merged with neighboring jurisdictions, significantly enhanced situational awareness for better decision-making and improved public health outcomes. %M 23569596 %R 10.5210/ojphi.v2i3.3031 %U %U https://doi.org/10.5210/ojphi.v2i3.3031 %U http://www.ncbi.nlm.nih.gov/pubmed/23569596 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 2 %N 1 %P e2837 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2010 %7 ..2010 %9 %J Online J Public Health Inform %G English %X Health surveillance can be viewed as an ongoing systematic collection, analysis, and interpretation of data for use in planning, implementation, and evaluation of a given health system, in potentially multiple spheres (ex: animal, human, environment). As we move into a sophisticated technologically advanced era, there is a need for cost-effective and efficient health surveillance methods and systems that will rapidly identify potential bioterrorism attacks and infectious disease outbreaks. The main objective of such methods and systems would be to reduce the impact of an outbreak by enabling appropriate officials to detect it quickly and implement timely and appropriate interventions. Identifying an outbreak and/or potential bioterrorism attack days to weeks earlier than traditional surveillance methods would potentially result in a reduction in morbidity, mortality, and outbreak associated economic consequences. Proposed here is a novel framework that would enable a user and/or a system to interpret the anomaly detection results generated via multiple aberration detection algorithms with some indication of confidence. A framework that takes into account the relationships between algorithms and produces an unbiased confidence measure for identification of start of an outbreak. %M 23569580 %R 10.5210/ojphi.v2i1.2837 %U %U https://doi.org/10.5210/ojphi.v2i1.2837 %U http://www.ncbi.nlm.nih.gov/pubmed/23569580