TY - JOUR AU - Qiu, Abram AU - Meadows, Kristopher AU - Ye, Fei AU - Iyawe, Osasu AU - Kenneth-Nwosa, Kenneth PY - 2025/1/31 TI - Quantifying Patient Demand for Orthopedics Care by Region Through Google Trends Analysis: Descriptive Epidemiology Study JO - Online J Public Health Inform SP - e63560 VL - 17 KW - orthopedics KW - geographic factors KW - health care disparities KW - medical schools KW - internship and residency KW - epidemiology KW - public health informatics KW - physicians KW - assessment of health care needs KW - resource allocation N2 - Background: There is a growing gap between the supply of surgeons and the demand for orthopedic services in the United States. Objective: We analyzed publicly available online data to assess the correlation between the supply of orthopedic surgeons and patient demand across the United States. The geographic trends of this gap were assessed by using the relative demand index (RDI) to guide precision public health interventions such as resource allocation, residency program expansion, and workforce planning to specific regions. Methods: The data used were from the US Census Bureau, Association of American Medical Colleges (AAMC) through their 2024 Electronic Residency Application Service (ERAS) directory, AAMC State Physician Workforce Data Report, and Google Trends. We calculated the normalized relative search volume (RSV) and the RDI and compared them to the densities of orthopedic surgeons across the United States. We examined the disparities with the Spearman rank correlation coefficient. Results: The supply of orthopedic surgeons varied greatly across the United States, with a significantly higher demand for them in southern states (P=.02). The orthopedic surgeon concentration, normalized to the highest density, was the highest in Alaska (n=100), the District of Columbia (n=96), and Wyoming (n=72); and the lowest in Texas (n=0), Arkansas (n=6), and Oklahoma (n=64). The highest RDI values were observed in Utah (n=97), Florida (n=88), and Texas (n=83), while the lowest were observed in Alaska (n=0), the District of Columbia (n=5), and New Hampshire (n=7). The 7 states of Alaska, Maine, South Dakota, Wyoming, Montana, Delaware, and Idaho lacked orthopedic surgery residencies. In 2023, New York (n=19), Michigan (n=17), Ohio (n=17), Pennsylvania (n=16), and California (n=16) had the most residency programs. Demand and supply, represented by the RDI and orthopedic surgeon concentration, respectively, were strongly correlated negatively (?=?0.791, P<.001). States that were in the top quartile of residency programs (?4 residency programs) exhibited a high demand for orthopedic surgeons (?=.6035, P=.02). Conclusions: This study showed that regional disparities in access to orthopedic care can be addressed by increasing orthopedic residencies. The study highlights the novel application of the RDI to mapping the regional need for orthopedics, and this map allows for better targeted resource allocation to expand orthopedic surgery training. UR - https://ojphi.jmir.org/2025/1/e63560 UR - http://dx.doi.org/10.2196/63560 ID - info:doi/10.2196/63560 ER - TY - JOUR AU - Chen, Baozhan AU - Shi, Xiaobing AU - Feng, Tianyi AU - Jiang, Shuai AU - Zhai, Yunkai AU - Ren, Mingxing AU - Liu, Dongqing AU - Wang, Chengzeng AU - Gao, Jinghong PY - 2024/10/24 TI - Construction and Application of a Private 5G Standalone Medical Network in a Smart Health Environment: Exploratory Practice From China JO - J Med Internet Res SP - e52404 VL - 26 KW - 5G KW - medical private network KW - construction KW - application KW - performance test N2 - Background: To date, the differentiated requirements for network performance in various health care service scenarios?within, outside, and between hospitals?remain a key challenge that restricts the development and implementation of digital medical services. Objective: This study aims to construct and implement a private 5G (the 5th generation mobile communication technology) standalone (SA) medical network in a smart health environment to meet the diverse needs of various medical services. Methods: Based on an analysis of network differentiation requirements in medical applications, the system architecture and functional positioning of the proposed private 5G SA medical network are designed and implemented. The system architecture includes the development of exclusive and preferential channels for medical use, as well as an ordinary user channel. A 3-layer network function architecture is designed, encompassing resource, control, and intelligent operation layers to facilitate management arrangements and provide network open services. Core technologies, including edge cloud collaboration; service awareness; and slicing of access, bearer, and core networks, are employed in the construction and application of the 5G SA network. Results: The construction of the private 5G SA medical network primarily involves system architecture, standards, and security measures. The system, featuring exclusive, preferential, and common channels, supports a variety of medical applications. Relevant standards are adhered to in order to ensure the interaction and sharing of medical service information. Security is achieved through mechanisms such as authentication, abnormal behavior analysis, and dynamic access control. Three typical medical applications that rely on the 5G network in intrahospital, interhospital, and out-of-hospital scenarios?namely, mobile ward rounds, remote first aid, and remote ultrasound?were conducted. Testing of the 5G-enabled mobile ward rounds showed an average download rate of 790 Mbps and an average upload rate of 91 Mbps. Compared with 4G, the 5G network more effectively meets the diverse requirements of various business applications in prehospital emergency scenarios. For remote ultrasound, the average downlink rate of the 5G network is 4.82 Mbps, and the average uplink rate is 2 Mbps, with an average fluctuation of approximately 8 ms. The bandwidth, performance, and delay of the 5G SA network were also examined and confirmed to be effective. Conclusions: The proposed 5G SA medical network demonstrates strong performance in typical medical applications. Its construction and application could lead to the development of new medical service models and provide valuable references for the further advancement and implementation of 5G networks in other industries, both in China and globally. UR - https://www.jmir.org/2024/1/e52404 UR - http://dx.doi.org/10.2196/52404 UR - http://www.ncbi.nlm.nih.gov/pubmed/39446419 ID - info:doi/10.2196/52404 ER - TY - JOUR AU - Martonik, Rachel AU - Oleson, Caitlin AU - Marder, Ellyn PY - 2024/10/16 TI - Spatiotemporal Cluster Detection for COVID-19 Outbreak Surveillance: Descriptive Analysis Study JO - JMIR Public Health Surveill SP - e49871 VL - 10 KW - COVID-19 KW - cluster detection KW - disease outbreaks KW - surveillance KW - SaTScan KW - space-time surveillance KW - spatiotemporal KW - United States KW - outbreak KW - outbreaks KW - pandemic KW - real-time surveillance KW - detection KW - tool KW - tools KW - effectiveness KW - public health KW - intervention KW - interventions KW - community settings KW - outbreak detection N2 - Background: During the peak of the winter 2020-2021 surge, the number of weekly reported COVID-19 outbreaks in Washington State was 231; the majority occurred in high-priority settings such as workplaces, community settings, and schools. The Washington State Department of Health used automated address matching to identify clusters at health care facilities. No other systematic, statewide outbreak detection methods were in place. This was a gap given the high volume of cases, which delayed investigations and decreased data completeness, potentially leading to undetected outbreaks. We initiated statewide cluster detection using SaTScan, implementing a space-time permutation model to identify COVID-19 clusters for investigation. Objective: To improve outbreak detection, the Washington State Department of Health initiated a systematic cluster detection model to identify timely and actionable COVID-19 clusters for local health jurisdiction (LHJ) investigation and resource prioritization. This report details the model?s implementation and the assessment of the tool?s effectiveness. Methods: In total, 6 LHJs participated in a pilot to test model parameters including analysis type, geographic aggregation, cluster radius, and data lag. Parameters were determined through heuristic criteria to detect clusters early when they are smaller, making interventions more feasible. This study reviews all clusters detected after statewide implementation from July 17 to December 17, 2021. The clusters were analyzed by LHJ population and disease incidence. Clusters were compared with reported outbreaks. Results: A weekly, LHJ-specific retrospective space-time permutation model identified 2874 new clusters during this period. While the weekly analysis included case data from the prior 3 weeks, 58.25% (n=1674) of all clusters identified were timely?having occurred within 1 week of the analysis and early enough for intervention to prevent further transmission. There were 2874 reported outbreaks during this same period. Of those, 363 (12.63%) matched to at least one SaTScan cluster. The most frequent settings among reported and matched outbreaks were schools and youth programs (n=825, 28.71% and n=108, 29.8%), workplaces (n=617, 21.46% and n=56, 15%), and long-term care facilities (n=541, 18.82% and n=99, 27.3%). Settings with the highest percentage of clusters that matched outbreaks were community settings (16/72, 22%) and congregate housing (44/212, 20.8%). The model identified approximately one-third (119/363, 32.8%) of matched outbreaks before cases were associated with the outbreak event in our surveillance system. Conclusions: Our goal was to routinely and systematically identify timely and actionable COVID-19 clusters statewide. Regardless of population or incidence, the model identified reasonably sized, timely clusters statewide, meeting the objective. Among some high-priority settings subject to public health interventions throughout the pandemic, such as schools and community settings, the model identified clusters that were matched to reported outbreaks. In workplaces, another high-priority setting, results suggest the model might be able to identify outbreaks sooner than existing outbreak detection methods. UR - https://publichealth.jmir.org/2024/1/e49871 UR - http://dx.doi.org/10.2196/49871 UR - http://www.ncbi.nlm.nih.gov/pubmed/39412839 ID - info:doi/10.2196/49871 ER - TY - JOUR AU - Mollalo, Abolfazl AU - Hamidi, Bashir AU - Lenert, A. Leslie AU - Alekseyenko, V. Alexander PY - 2024/10/15 TI - Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review JO - JMIR Med Inform SP - e56343 VL - 12 KW - clinical phenotypes KW - electronic health records KW - geocoding KW - geographic information systems KW - patient phenotypes KW - spatial analysis N2 - Background: Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes. Objective: This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes. Methods: We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains. Results: A substantial proportion of studies (>85%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited. Conclusions: This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support. UR - https://medinform.jmir.org/2024/1/e56343 UR - http://dx.doi.org/10.2196/56343 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/56343 ER - TY - JOUR AU - Zhou, Weipeng AU - Youngbloom, Amy AU - Ren, Xinyang AU - Saelens, E. Brian AU - Mooney, D. Sean AU - Mooney, J. Stephen PY - 2024/10/4 TI - The Automatic Context Measurement Tool (ACMT) to Compile Participant-Specific Built and Social Environment Measures for Health Research: Development and Usability Study JO - JMIR Form Res SP - e56510 VL - 8 KW - built environment KW - social environment KW - geocoding KW - GIS KW - geographic information systems KW - ACMT KW - automatic context measurement tool KW - linkage KW - privacy N2 - Background: The environment shapes health behaviors and outcomes. Studies exploring this influence have been limited to research groups with the geographic information systems expertise required to develop built and social environment measures (eg, groups that include a researcher with geographic information system expertise). Objective: The goal of this study was to develop an open-source, user-friendly, and privacy-preserving tool for conveniently linking built, social, and natural environmental variables to study participant addresses. Methods: We built the automatic context measurement tool (ACMT). The ACMT comprises two components: (1) a geocoder, which identifies a latitude and longitude given an address (currently limited to the United States), and (2) a context measure assembler, which computes measures from publicly available data sources linked to a latitude and longitude. ACMT users access both of these components using an RStudio/RShiny-based web interface that is hosted within a Docker container, which runs on a local computer and keeps user data stored in local to protect sensitive data. We illustrate ACMT with 2 use cases: one comparing population density patterns within several major US cities, and one identifying correlates of cannabis licensure status in Washington State. Results: In the population density analysis, we created a line plot showing the population density (x-axis) in relation to distance from the center of the city (y-axis, using city hall location as a proxy) for Seattle, Los Angeles, Chicago, New York City, Nashville, Houston, and Boston with the distances being 1000, 2000, 3000, 4000, and 5000 m. We found the population density tended to decrease as distance from city hall increased except for Nashville and Houston, 2 cities that are notably more sprawling than the others. New York City had a significantly higher population density than the others. We also observed that Los Angeles and Seattle had similarly low population densities within up to 2500 m of City Hall. In the cannabis licensure status analysis, we gathered neighborhood measures such as age, sex, commute time, and education. We found the strongest predictive characteristic of cannabis license approval to be the count of female children aged 5 to 9 years and the proportion of females aged 62 to 64 years who were not in the labor force. However, after accounting for Bonferroni error correction, none of the measures were significantly associated with cannabis retail license approval status. Conclusions: The ACMT can be used to compile environmental measures to study the influence of environmental context on population health. The portable and flexible nature of ACMT makes it optimal for neighborhood study research seeking to attribute environmental data to specific locations within the United States. UR - https://formative.jmir.org/2024/1/e56510 UR - http://dx.doi.org/10.2196/56510 UR - http://www.ncbi.nlm.nih.gov/pubmed/39365663 ID - info:doi/10.2196/56510 ER - TY - JOUR AU - Chen, Yang AU - Zhou, Lidan AU - Zha, Yuanyi AU - Wang, Yujin AU - Wang, Kai AU - Lu, Lvliang AU - Guo, Pi AU - Zhang, Qingying PY - 2024/7/23 TI - Impact of Ambient Temperature on Mortality Burden and Spatial Heterogeneity in 16 Prefecture-Level Cities of a Low-Latitude Plateau Area in Yunnan Province: Time-Series Study JO - JMIR Public Health Surveill SP - e51883 VL - 10 KW - mortality burden KW - nonaccidental deaths KW - multivariate meta-analysis KW - distributed lagged nonlinear mode KW - attributable risk KW - climate change KW - human health KW - association KW - temperature KW - mortality KW - nonaccidental death KW - spatial heterogeneity KW - meteorological data KW - temperature esposure KW - heterogeneous KW - spatial planning KW - environmental temperature KW - prefecture-level KW - resource allocation N2 - Background: The relation between climate change and human health has become one of the major worldwide public health issues. However, the evidence for low-latitude plateau regions is limited, where the climate is unique and diverse with a complex geography and topography. Objectives: This study aimed to evaluate the effect of ambient temperature on the mortality burden of nonaccidental deaths in Yunnan Province and to further explore its spatial heterogeneity among different regions. Methods: We collected mortality and meteorological data from all 129 counties in Yunnan Province from 2014 to 2020, and 16 prefecture-level cities were analyzed as units. A distributed lagged nonlinear model was used to estimate the effect of temperature exposure on years of life lost (YLL) for nonaccidental deaths in each prefecture-level city. The attributable fraction of YLL due to ambient temperature was calculated. A multivariate meta-analysis was used to obtain an overall aggregated estimate of effects, and spatial heterogeneity among 16 prefecture-level cities was evaluated by adjusting the city-specific geographical characteristics, demographic characteristics, economic factors, and health resources factors. Results: The temperature-YLL association was nonlinear and followed slide-shaped curves in all regions. The cumulative cold and heat effect estimates along lag 0?21 days on YLL for nonaccidental deaths were 403.16 (95% empirical confidence interval [eCI] 148.14?615.18) and 247.83 (95% eCI 45.73?418.85), respectively. The attributable fraction for nonaccidental mortality due to daily mean temperature was 7.45% (95% eCI 3.73%?10.38%). Cold temperature was responsible for most of the mortality burden (4.61%, 95% eCI 1.70?7.04), whereas the burden due to heat was 2.84% (95% eCI 0.58?4.83). The vulnerable subpopulations include male individuals, people aged <75 years, people with education below junior college level, farmers, nonmarried individuals, and ethnic minorities. In the cause-specific subgroup analysis, the total attributable fraction (%) for mean temperature was 13.97% (95% eCI 6.70?14.02) for heart disease, 11.12% (95% eCI 2.52?16.82) for respiratory disease, 10.85% (95% eCI 6.70?14.02) for cardiovascular disease, and 10.13% (95% eCI 6.03?13.18) for stroke. The attributable risk of cold effect for cardiovascular disease was higher than that for respiratory disease cause of death (9.71% vs 4.54%). Furthermore, we found 48.2% heterogeneity in the effect of mean temperature on YLL after considering the inherent characteristics of the 16 prefecture-level cities, with urbanization rate accounting for the highest proportion of heterogeneity (15.7%) among urban characteristics. Conclusions: This study suggests that the cold effect dominated the total effect of temperature on mortality burden in Yunnan Province, and its effect was heterogeneous among different regions, which provides a basis for spatial planning and health policy formulation for disease prevention. UR - https://publichealth.jmir.org/2024/1/e51883 UR - http://dx.doi.org/10.2196/51883 ID - info:doi/10.2196/51883 ER - TY - JOUR AU - Guo, Huagui AU - Zhang, Shuyu AU - Xie, Xinwei AU - Liu, Jiang AU - Ho, Chak Hung PY - 2024/7/3 TI - Moderation Effects of Streetscape Perceptions on the Associations Between Accessibility, Land Use Mix, and Bike-Sharing Use: Cross-Sectional Study JO - JMIR Public Health Surveill SP - e58761 VL - 10 KW - built environment KW - streetscape perceptions KW - bike-sharing use KW - cycling KW - moderation effect KW - China N2 - Background: Cycling is known to be beneficial for human health. Studies have suggested significant associations of physical activity with macroscale built environments and streetscapes. However, whether good streetscapes can amplify the benefits of a favorable built environment on physical activity remains unknown. Objective: This study examines whether streetscape perceptions can modify the associations between accessibility, land use mix, and bike-sharing use. Methods: This cross-sectional study used data from 18,019,266 bike-sharing orders during weekends in Shanghai, China. A 500 × 500 m grid was selected as the analysis unit to allocate data. Bike-sharing use was defined as the number of bike-sharing origins. Street view images and a human-machine adversarial scoring framework were combined to evaluate lively, safety, and wealthy perceptions. Negative binomial regression was developed to examine the independent effects of the three perceptual factors in both the univariate model and fully adjusted model, controlling for population density, average building height, distance to nearest transit, number of bus stations, number of points of interest, distance to the nearest park, and distance to the central business district. The moderation effect was then investigated through the interaction term between streetscape perception and accessibility and land use mix, based on the fully adjusted model. We also tested whether the findings of streetscape moderation effects are robust when examinations are performed at different geographic scales, using a small-sample statistics approach and different operationalizations of land use mix and accessibility. Results: High levels of lively, safety, and wealthy perceptions were correlated with more bike-sharing activities. There were negative effects for the interactions between the land use Herfindahl-Hirschman index with the lively perception (?=?0.63; P=.01) and safety perception (?=?0.52; P=.001). The interaction between the lively perception and road intersection density was positively associated with the number of bike-sharing uses (?=0.43; P=.08). Among these, the lively perception showed the greatest independent effect (?=1.29; P<.001), followed by the safety perception (?=1.22; P=.001) and wealthy perception (?=0.72; P=.001). The findings were robust in the three sensitivity analyses. Conclusions: A safer and livelier streetscape can enhance the benefits of land use mix in promoting bike-sharing use, with a safer streetscape also intensifying the effect of accessibility. Interventions focused on streetscape perceptions can encourage cycling behavior and enhance the benefits of accessibility and land use mix. This study also contributes to the literature on potential moderators of built environment healthy behavior associations from the perspective of microscale environmental perceptions. UR - https://publichealth.jmir.org/2024/1/e58761 UR - http://dx.doi.org/10.2196/58761 ID - info:doi/10.2196/58761 ER - TY - JOUR AU - Lai, Peixuan AU - Cai, Weicong AU - Qu, Lin AU - Hong, Chuangyue AU - Lin, Kaihao AU - Tan, Weiguo AU - Zhao, Zhiguang PY - 2024/6/14 TI - Pulmonary Tuberculosis Notification Rate Within Shenzhen, China, 2010-2019: Spatial-Temporal Analysis JO - JMIR Public Health Surveill SP - e57209 VL - 10 KW - tuberculosis KW - spatial analysis KW - spatial-temporal cluster KW - Shenzhen KW - China N2 - Background: Pulmonary tuberculosis (PTB) is a chronic communicable disease of major public health and social concern. Although spatial-temporal analysis has been widely used to describe distribution characteristics and transmission patterns, few studies have revealed the changes in the small-scale clustering of PTB at the street level. Objective: The aim of this study was to analyze the temporal and spatial distribution characteristics and clusters of PTB at the street level in the Shenzhen municipality of China to provide a reference for PTB prevention and control. Methods: Data of reported PTB cases in Shenzhen from January 2010 to December 2019 were extracted from the China Information System for Disease Control and Prevention to describe the epidemiological characteristics. Time-series, spatial-autocorrelation, and spatial-temporal scanning analyses were performed to identify the spatial and temporal patterns and high-risk areas at the street level. Results: A total of 58,122 PTB cases from 2010 to 2019 were notified in Shenzhen. The annual notification rate of PTB decreased significantly from 64.97 per 100,000 population in 2010 to 43.43 per 100,000 population in 2019. PTB cases exhibited seasonal variations with peaks in late spring and summer each year. The PTB notification rate was nonrandomly distributed and spatially clustered with a Moran I value of 0.134 (P=.02). One most-likely cluster and 10 secondary clusters were detected, and the most-likely clustering area was centered at Nanshan Street of Nanshan District covering 6 streets, with the clustering time spanning from January 2010 to November 2012. Conclusions: This study identified seasonal patterns and spatial-temporal clusters of PTB cases at the street level in the Shenzhen municipality of China. Resources should be prioritized to the identified high-risk areas for PTB prevention and control. UR - https://publichealth.jmir.org/2024/1/e57209 UR - http://dx.doi.org/10.2196/57209 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875687 ID - info:doi/10.2196/57209 ER - TY - JOUR PY - 2021// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e11617 VL - 13 IS - 3 UR - UR - http://dx.doi.org/10.5210/ojphi.v13i3.11617 ID - info:doi/10.5210/ojphi.v13i3.11617 ER - TY - JOUR PY - 2021// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e11621 VL - 13 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v13i1.11621 UR - http://www.ncbi.nlm.nih.gov/pubmed/33936526 ID - info:doi/10.5210/ojphi.v13i1.11621 ER - TY - JOUR PY - 2019// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e10155 VL - 11 IS - 2 UR - UR - http://dx.doi.org/10.5210/ojphi.v11i2.10155 UR - http://www.ncbi.nlm.nih.gov/pubmed/31632602 ID - info:doi/10.5210/ojphi.v11i2.10155 ER - TY - JOUR PY - 2017// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e7758 VL - 9 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v9i1.7758 ID - info:doi/10.5210/ojphi.v9i1.7758 ER - TY - JOUR PY - 2015// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e5686 VL - 7 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v7i1.5686 ID - info:doi/10.5210/ojphi.v7i1.5686 ER - TY - JOUR PY - 2014// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e4982 VL - 5 IS - 3 UR - UR - http://dx.doi.org/10.5210/ojphi.v5i3.4982 UR - http://www.ncbi.nlm.nih.gov/pubmed/24678379 ID - info:doi/10.5210/ojphi.v5i3.4982 ER - TY - JOUR PY - 2013// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e4587 VL - 5 IS - 2 UR - UR - http://dx.doi.org/10.5210/ojphi.v5i2.4587 UR - http://www.ncbi.nlm.nih.gov/pubmed/23923097 ID - info:doi/10.5210/ojphi.v5i2.4587 ER - TY - JOUR PY - 2013// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e4397 VL - 5 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v5i1.4397 ID - info:doi/10.5210/ojphi.v5i1.4397 ER - TY - JOUR PY - 2013// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e4420 VL - 5 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v5i1.4420 ID - info:doi/10.5210/ojphi.v5i1.4420 ER - TY - JOUR PY - 2013// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e4443 VL - 5 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v5i1.4443 ID - info:doi/10.5210/ojphi.v5i1.4443 ER - TY - JOUR PY - 2013// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e4459 VL - 5 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v5i1.4459 ID - info:doi/10.5210/ojphi.v5i1.4459 ER - TY - JOUR PY - 2013// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e4379 VL - 5 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v5i1.4379 ID - info:doi/10.5210/ojphi.v5i1.4379 ER - TY - JOUR PY - 2013// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e4380 VL - 5 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v5i1.4380 ID - info:doi/10.5210/ojphi.v5i1.4380 ER - TY - JOUR PY - 2010// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e2893 VL - 2 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v2i1.2893 UR - http://www.ncbi.nlm.nih.gov/pubmed/23569578 ID - info:doi/10.5210/ojphi.v2i1.2893 ER - TY - JOUR PY - 2010// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e2910 VL - 2 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v2i1.2910 UR - http://www.ncbi.nlm.nih.gov/pubmed/23569576 ID - info:doi/10.5210/ojphi.v2i1.2910 ER - TY - JOUR PY - 2009// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e2771 VL - 1 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v1i1.2771 UR - http://www.ncbi.nlm.nih.gov/pubmed/23569571 ID - info:doi/10.5210/ojphi.v1i1.2771 ER -