@Article{info:doi/10.2196/63560,
author="Qiu, Abram
and Meadows, Kristopher
and Ye, Fei
and Iyawe, Osasu
and Kenneth-Nwosa, Kenneth",
title="Quantifying Patient Demand for Orthopedics Care by Region Through Google Trends Analysis: Descriptive Epidemiology Study",
journal="Online J Public Health Inform",
year="2025",
month="Jan",
day="31",
volume="17",
pages="e63560",
keywords="orthopedics",
keywords="geographic factors",
keywords="health care disparities",
keywords="medical schools",
keywords="internship and residency",
keywords="epidemiology",
keywords="public health informatics",
keywords="physicians",
keywords="assessment of health care needs",
keywords="resource allocation",
abstract="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 ($\rho$=?0.791, P<.001). States that were in the top quartile of residency programs (?4 residency programs) exhibited a high demand for orthopedic surgeons ($\rho$=.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. ",
doi="10.2196/63560",
url="https://ojphi.jmir.org/2025/1/e63560"
}


@Article{info:doi/10.2196/52404,
author="Chen, Baozhan
and Shi, Xiaobing
and Feng, Tianyi
and Jiang, Shuai
and Zhai, Yunkai
and Ren, Mingxing
and Liu, Dongqing
and Wang, Chengzeng
and Gao, Jinghong",
title="Construction and Application of a Private 5G Standalone Medical Network in a Smart Health Environment: Exploratory Practice From China",
journal="J Med Internet Res",
year="2024",
month="Oct",
day="24",
volume="26",
pages="e52404",
keywords="5G",
keywords="medical private network",
keywords="construction",
keywords="application",
keywords="performance test",
abstract="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. ",
doi="10.2196/52404",
url="https://www.jmir.org/2024/1/e52404",
url="http://www.ncbi.nlm.nih.gov/pubmed/39446419"
}


@Article{info:doi/10.2196/49871,
author="Martonik, Rachel
and Oleson, Caitlin
and Marder, Ellyn",
title="Spatiotemporal Cluster Detection for COVID-19 Outbreak Surveillance: Descriptive Analysis Study",
journal="JMIR Public Health Surveill",
year="2024",
month="Oct",
day="16",
volume="10",
pages="e49871",
keywords="COVID-19",
keywords="cluster detection",
keywords="disease outbreaks",
keywords="surveillance",
keywords="SaTScan",
keywords="space-time surveillance",
keywords="spatiotemporal",
keywords="United States",
keywords="outbreak",
keywords="outbreaks",
keywords="pandemic",
keywords="real-time surveillance",
keywords="detection",
keywords="tool",
keywords="tools",
keywords="effectiveness",
keywords="public health",
keywords="intervention",
keywords="interventions",
keywords="community settings",
keywords="outbreak detection",
abstract="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. ",
doi="10.2196/49871",
url="https://publichealth.jmir.org/2024/1/e49871",
url="http://www.ncbi.nlm.nih.gov/pubmed/39412839"
}


@Article{info:doi/10.2196/56343,
author="Mollalo, Abolfazl
and Hamidi, Bashir
and Lenert, A. Leslie
and Alekseyenko, V. Alexander",
title="Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review",
journal="JMIR Med Inform",
year="2024",
month="Oct",
day="15",
volume="12",
pages="e56343",
keywords="clinical phenotypes",
keywords="electronic health records",
keywords="geocoding",
keywords="geographic information systems",
keywords="patient phenotypes",
keywords="spatial analysis",
abstract="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. ",
doi="10.2196/56343",
url="https://medinform.jmir.org/2024/1/e56343"
}


@Article{info:doi/10.2196/56510,
author="Zhou, Weipeng
and Youngbloom, Amy
and Ren, Xinyang
and Saelens, E. Brian
and Mooney, D. Sean
and Mooney, J. Stephen",
title="The Automatic Context Measurement Tool (ACMT) to Compile Participant-Specific Built and Social Environment Measures for Health Research: Development and Usability Study",
journal="JMIR Form Res",
year="2024",
month="Oct",
day="4",
volume="8",
pages="e56510",
keywords="built environment",
keywords="social environment",
keywords="geocoding",
keywords="GIS",
keywords="geographic information systems",
keywords="ACMT",
keywords="automatic context measurement tool",
keywords="linkage",
keywords="privacy",
abstract="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. ",
doi="10.2196/56510",
url="https://formative.jmir.org/2024/1/e56510",
url="http://www.ncbi.nlm.nih.gov/pubmed/39365663"
}


@Article{info:doi/10.2196/51883,
author="Chen, Yang
and Zhou, Lidan
and Zha, Yuanyi
and Wang, Yujin
and Wang, Kai
and Lu, Lvliang
and Guo, Pi
and Zhang, Qingying",
title="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",
journal="JMIR Public Health Surveill",
year="2024",
month="Jul",
day="23",
volume="10",
pages="e51883",
keywords="mortality burden",
keywords="nonaccidental deaths",
keywords="multivariate meta-analysis",
keywords="distributed lagged nonlinear mode",
keywords="attributable risk",
keywords="climate change",
keywords="human health",
keywords="association",
keywords="temperature",
keywords="mortality",
keywords="nonaccidental death",
keywords="spatial heterogeneity",
keywords="meteorological data",
keywords="temperature esposure",
keywords="heterogeneous",
keywords="spatial planning",
keywords="environmental temperature",
keywords="prefecture-level",
keywords="resource allocation",
abstract="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. ",
doi="10.2196/51883",
url="https://publichealth.jmir.org/2024/1/e51883"
}


@Article{info:doi/10.2196/58761,
author="Guo, Huagui
and Zhang, Shuyu
and Xie, Xinwei
and Liu, Jiang
and Ho, Chak Hung",
title="Moderation Effects of Streetscape Perceptions on the Associations Between Accessibility, Land Use Mix, and Bike-Sharing Use: Cross-Sectional Study",
journal="JMIR Public Health Surveill",
year="2024",
month="Jul",
day="3",
volume="10",
pages="e58761",
keywords="built environment",
keywords="streetscape perceptions",
keywords="bike-sharing use",
keywords="cycling",
keywords="moderation effect",
keywords="China",
abstract="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 {\texttimes} 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 ($\beta$=--0.63; P=.01) and safety perception ($\beta$=--0.52; P=.001). The interaction between the lively perception and road intersection density was positively associated with the number of bike-sharing uses ($\beta$=0.43; P=.08). Among these, the lively perception showed the greatest independent effect ($\beta$=1.29; P<.001), followed by the safety perception ($\beta$=1.22; P=.001) and wealthy perception ($\beta$=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. ",
doi="10.2196/58761",
url="https://publichealth.jmir.org/2024/1/e58761"
}


@Article{info:doi/10.2196/57209,
author="Lai, Peixuan
and Cai, Weicong
and Qu, Lin
and Hong, Chuangyue
and Lin, Kaihao
and Tan, Weiguo
and Zhao, Zhiguang",
title="Pulmonary Tuberculosis Notification Rate Within Shenzhen, China, 2010-2019: Spatial-Temporal Analysis",
journal="JMIR Public Health Surveill",
year="2024",
month="Jun",
day="14",
volume="10",
pages="e57209",
keywords="tuberculosis",
keywords="spatial analysis",
keywords="spatial-temporal cluster",
keywords="Shenzhen",
keywords="China",
abstract="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. ",
doi="10.2196/57209",
url="https://publichealth.jmir.org/2024/1/e57209",
url="http://www.ncbi.nlm.nih.gov/pubmed/38875687"
}


@Article{info:doi/10.5210/ojphi.v13i3.11617,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2021",
volume="13",
number="3",
pages="e11617",
doi="10.5210/ojphi.v13i3.11617",
url=""
}


@Article{info:doi/10.5210/ojphi.v13i1.11621,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2021",
volume="13",
number="1",
pages="e11621",
doi="10.5210/ojphi.v13i1.11621",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/33936526"
}


@Article{info:doi/10.5210/ojphi.v11i2.10155,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2019",
volume="11",
number="2",
pages="e10155",
doi="10.5210/ojphi.v11i2.10155",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/31632602"
}


@Article{info:doi/10.5210/ojphi.v9i1.7758,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2017",
volume="9",
number="1",
pages="e7758",
doi="10.5210/ojphi.v9i1.7758",
url=""
}


@Article{info:doi/10.5210/ojphi.v7i1.5686,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2015",
volume="7",
number="1",
pages="e5686",
doi="10.5210/ojphi.v7i1.5686",
url=""
}


@Article{info:doi/10.5210/ojphi.v5i3.4982,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2014",
volume="5",
number="3",
pages="e4982",
doi="10.5210/ojphi.v5i3.4982",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/24678379"
}


@Article{info:doi/10.5210/ojphi.v5i2.4587,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2013",
volume="5",
number="2",
pages="e4587",
doi="10.5210/ojphi.v5i2.4587",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/23923097"
}


@Article{info:doi/10.5210/ojphi.v5i1.4397,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2013",
volume="5",
number="1",
pages="e4397",
doi="10.5210/ojphi.v5i1.4397",
url=""
}


@Article{info:doi/10.5210/ojphi.v5i1.4420,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2013",
volume="5",
number="1",
pages="e4420",
doi="10.5210/ojphi.v5i1.4420",
url=""
}


@Article{info:doi/10.5210/ojphi.v5i1.4443,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2013",
volume="5",
number="1",
pages="e4443",
doi="10.5210/ojphi.v5i1.4443",
url=""
}


@Article{info:doi/10.5210/ojphi.v5i1.4459,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2013",
volume="5",
number="1",
pages="e4459",
doi="10.5210/ojphi.v5i1.4459",
url=""
}


@Article{info:doi/10.5210/ojphi.v5i1.4379,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2013",
volume="5",
number="1",
pages="e4379",
doi="10.5210/ojphi.v5i1.4379",
url=""
}


@Article{info:doi/10.5210/ojphi.v5i1.4380,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2013",
volume="5",
number="1",
pages="e4380",
doi="10.5210/ojphi.v5i1.4380",
url=""
}


@Article{info:doi/10.5210/ojphi.v2i1.2893,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2010",
volume="2",
number="1",
pages="e2893",
doi="10.5210/ojphi.v2i1.2893",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/23569578"
}


@Article{info:doi/10.5210/ojphi.v2i1.2910,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2010",
volume="2",
number="1",
pages="e2910",
doi="10.5210/ojphi.v2i1.2910",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/23569576"
}


@Article{info:doi/10.5210/ojphi.v1i1.2771,
title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review",
journal="Online J Public Health Inform",
year="2009",
volume="1",
number="1",
pages="e2771",
doi="10.5210/ojphi.v1i1.2771",
url="",
url="http://www.ncbi.nlm.nih.gov/pubmed/23569571"
}