@Article{info:doi/10.2196/69569,
author="Wei, Lan
and Wu, Yongsheng
and Chen, Lin
and Cheng, Jinquan
and Zhao, Jin",
title="Spatiotemporal and Behavioral Patterns of Men Who Have Sex With Men Using Geosocial Networking Apps in Shenzhen From Mobile Big Data Perspective: Longitudinal Observational Study",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="20",
volume="27",
pages="e69569",
keywords="men who have sex with men",
keywords="geosocial networking apps",
keywords="distribution",
keywords="heterogeneity",
keywords="mobility",
keywords="HIV testing",
keywords="risk behavior",
keywords="big data",
abstract="Background: The use of geosocial networking apps is linked to increased risky sexual behaviors among men who have sex with men, but their relationship with HIV and other sexually transmitted infections remains inconclusive. Since 2015, the prevalence of app use among men who have sex with men in Shenzhen has surged, highlighting the need for research on their spatiotemporal and behavioral patterns to inform targeted prevention and intervention strategies. Objective: This study aims to investigate the population size, spatiotemporal and behavioral patterns, and mobility of app-using men who have sex with men in Shenzhen using mobile big data. The goal is to inform enhanced and innovative intervention strategies and guide health resource allocation. Methods: By leveraging mobile big data application technology, we collected demographic and geographic location data from 3 target apps---Blued (Blued Inc), Jack'd (Online Buddies Inc), and Zank (Zank Group)---over continuous time periods. Spatial autocorrelation (Global Moran I) and hot spot analysis (Getis-Ord Gi) were used to identify the geographic clusters. The Geodetector tool (Chinese Academy of Sciences) was adopted to measure spatially stratified heterogeneity features. Results: From September 2017 to August 2018, a total of 158,387 males aged 15-69 years in Shenzhen used one of the 3 apps, with the majority (71,318, 45.03\%) aged 25-34 years. The app user-to-male ratio was approximately 2.6\% among all males aged 15-69 years. The estimated population of app-using men who have sex with men in Shenzhen during this period was 268,817. The geographic distribution of app-using men who have sex with men in Shenzhen was clustered, with hot spots primarily located in central and western Shenzhen, while the distribution of HIV testing and counseling was more concentrated in central-eastern Shenzhen. Approximately 60,202 (38\%) app-using men who have sex with men left Shenzhen during the Spring Festival, and 37,756 (62.7\%) of them returned after the holiday. The destination distribution showed a relatively centralized flow throughout the country, with the largest mobility within Guangdong province (67.7\%), followed by lower mobility to Hunan province (7.9\%) and other neighboring provinces (3\%-5\%), such as Jiangxi, Guangxi, and Hubei Provinces. Conclusions: Shenzhen has a large population of men who have sex with men. The variation and inconsistent spatiotemporal distribution of app use and HIV testing and counseling emphasize the need to adapt traditional venue-based prevention and intervention to identified hot spots and to launch outreach initiatives that extend beyond traditional healthcare settings. Given the relatively high internal and interprovincial mobility of app-using men who have sex with men, further smartphone-based behavioral monitoring could provide valuable insights for developing enhanced and innovative HIV prevention and intervention strategies. Moreover, our study demonstrates the potential of mobile big data to address critical research gaps often overlooked by traditional methods. ",
doi="10.2196/69569",
url="https://www.jmir.org/2025/1/e69569"
}


@Article{info:doi/10.2196/64914,
author="Pullano, Giulia
and Alvarez-Zuzek, Gisele Lucila
and Colizza, Vittoria
and Bansal, Shweta",
title="Characterizing US Spatial Connectivity and Implications for Geographical Disease Dynamics and Metapopulation Modeling: Longitudinal Observational Study",
journal="JMIR Public Health Surveill",
year="2025",
month="Feb",
day="18",
volume="11",
pages="e64914",
keywords="geographical disease dynamics",
keywords="spatial connectivity",
keywords="mobility data",
keywords="metapopulation modeling",
keywords="COVID-19",
keywords="human mobility",
keywords="infectious diseases",
keywords="social distancing",
keywords="epidemic",
keywords="mobile apps",
keywords="SafeGraph",
keywords="SARS-CoV-2",
keywords="coronavirus",
keywords="pandemic",
keywords="spatio-temporal",
keywords="US",
keywords="public health",
keywords="mobile health",
keywords="mHealth",
keywords="digital health",
keywords="health informatics",
abstract="Background: Human mobility is expected to be a critical factor in the geographic diffusion of infectious diseases, and this assumption led to the implementation of social distancing policies during the early fight against the COVID-19 emergency in the United States. Yet, because of substantial data gaps in the past, what still eludes our understanding are the following questions: (1) How does mobility contribute to the spread of infection within the United States at local, regional, and national scales? (2) How do seasonality and shifts in behavior affect mobility over time? (3) At what geographic level is mobility homogeneous across the United States? Objective: This study aimed to address the questions that are critical for developing accurate transmission models, predicting the spatial propagation of disease across scales, and understanding the optimal geographical and temporal scale for the implementation of control policies. Methods: We analyzed high-resolution mobility data from mobile app usage from SafeGraph Inc, mapping daily connectivity between the US counties to grasp spatial clustering and temporal stability. Integrating this into a spatially explicit transmission model, we replicated SARS-CoV-2's first wave invasion, assessing mobility's spatiotemporal impact on disease predictions. Results: Analysis from 2019 to 2021 showed that mobility patterns remained stable, except for a decline in April 2020 due to lockdowns, which reduced daily movements from 45 million to approximately 25 million nationwide. Despite this reduction, intercounty connectivity remained seasonally stable, largely unaffected during the early COVID-19 phase, with a median Spearman coefficient of 0.62 (SD 0.01) between daily connectivity and gravity networks. We identified 104 geographic clusters of US counties with strong internal mobility connectivity and weaker links to counties outside these clusters. These clusters were stable over time, largely overlapping state boundaries (normalized mutual information=0.82) and demonstrating high temporal stability (normalized mutual information=0.95). Our findings suggest that intercounty connectivity is relatively static and homogeneous at the substate level. Furthermore, while county-level, daily mobility data best captures disease invasion, static mobility data aggregated to the cluster level also effectively models spatial diffusion. Conclusions: Our work demonstrates that intercounty mobility was negligibly affected outside the lockdown period in April 2020, explaining the broad spatial distribution of COVID-19 outbreaks in the United States during the early phase of the pandemic. Such geographically dispersed outbreaks place a significant strain on national public health resources and necessitate complex metapopulation modeling approaches for predicting disease dynamics and control design. We thus inform the design of such metapopulation models to balance high disease predictability with low data requirements. ",
doi="10.2196/64914",
url="https://publichealth.jmir.org/2025/1/e64914"
}



@Article{info:doi/10.2196/58539,
author="Arifi, Dorian
and Resch, Bernd
and Santillana, Mauricio
and Guan, Wendy Weihe
and Knoblauch, Steffen
and Lautenbach, Sven
and Jaenisch, Thomas
and Morales, Ivonne
and Havas, Clemens",
title="Geosocial Media's Early Warning Capabilities Across US County-Level Political Clusters: Observational Study",
journal="JMIR Infodemiology",
year="2025",
month="Jan",
day="30",
volume="5",
pages="e58539",
keywords="spatiotemporal epidemiology",
keywords="geo-social media data",
keywords="digital disease surveillance",
keywords="political polarization",
keywords="epidemiological early warning",
keywords="digital early warning",
abstract="Background: The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and health care experts to implement nonpharmaceutical public health interventions, such as stay-at-home orders and mask mandates, to slow the spread of the virus. While these interventions proved essential in controlling transmission, they also caused substantial economic and societal costs and should therefore be used strategically, particularly when disease activity is on the rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown to provide a promising tool for anticipating moments of potential health care crises. However, previous studies on the early warning capabilities of geosocial media data have largely been constrained by coarse spatial resolutions or short temporal scopes, with limited understanding of how local political beliefs may influence these capabilities. Objective: This study aimed to assess how the epidemiological early warning capabilities of geosocial media posts for COVID-19 vary over time and across US counties with differing political beliefs. Methods: We classified US counties into 3 political clusters, democrat, republican, and swing counties, based on voting data from the last 6 federal election cycles. In these clusters, we analyzed the early warning capabilities of geosocial media posts across 6 consecutive COVID-19 waves (February 2020-April 2022). We specifically examined the temporal lag between geosocial media signals and surges in COVID-19 cases, measuring both the number of days by which the geosocial media signals preceded the surges in COVID-19 cases (temporal lag) and the correlation between their respective time series. Results: The early warning capabilities of geosocial media data differed across political clusters and COVID-19 waves. On average, geosocial media posts preceded COVID-19 cases by 21 days in republican counties compared with 14.6 days in democrat counties and 24.2 days in swing counties. In general, geosocial media posts were preceding COVID-19 cases in 5 out of 6 waves across all political clusters. However, we observed a decrease over time in the number of days that posts preceded COVID-19 cases, particularly in democrat and republican counties. Furthermore, a decline in signal strength and the impact of trending topics presented challenges for the reliability of the early warning signals. Conclusions: This study provides valuable insights into the strengths and limitations of geosocial media data as an epidemiological early warning tool, particularly highlighting how they can change across county-level political clusters. Thus, these findings indicate that future geosocial media based epidemiological early warning systems might benefit from accounting for political beliefs. In addition, the impact of declining geosocial media signal strength over time and the role of trending topics for signal reliability in early warning systems need to be assessed in future research. ",
doi="10.2196/58539",
url="https://infodemiology.jmir.org/2025/1/e58539"
}



@Article{info:doi/10.2196/66655,
author="Cui, Zixuan
and Suo, Chen
and Zhao, Yidan
and Wang, Shuo
and Zhao, Ming
and Chen, Ruilin
and Lu, Linyao
and Zhang, Tiejun
and Chen, Xingdong",
title="Spatiotemporal Correlation Analysis for the Incidence of Esophageal and Gastric Cancer From 2010 to 2019: Ecological Study",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="29",
volume="11",
pages="e66655",
keywords="spatiotemporal analysis",
keywords="spatiotemporal correlation",
keywords="esophageal cancer",
keywords="gastric cancer",
keywords="cancer",
keywords="global burden of disease",
keywords="GBD",
keywords="average annual percentage change",
keywords="incidence",
keywords="epidemiology",
abstract="Background: Esophageal and gastric cancer were among the top 10 most common cancers worldwide. In addition, sex-specific differences were observed in the incidence. Due to their anatomic proximity, the 2 cancers have both different but also shared risk factors and epidemiological features. Exploring the potential correlated incidence pattern of them, holds significant importance in providing clues in the etiology and preventive strategies. Objective: This study aims to explore the spatiotemporal correlation between the incidence patterns of esophageal and gastric cancer in 204 countries and territories from 2010 to 2019 so that prevention and control strategies can be more effective. Methods: The data of esophageal and gastric cancer were sourced from the Global Burden of Disease (GBD). Spatial autocorrelation analysis using Moran I in ArcGIS 10.8 (Esri) was performed to determine spatial clustering of each cancer incidence. We classified different risk areas based on the risk ratio (RR) of the 2 cancers in various countries to the global, and the correlation between their RR was evaluated using Pearson correlation coefficient. Temporal trends were quantified by calculating the average annual percent change (AAPC), and the correlation between the temporal trends of both cancers was evaluated using Pearson correlation coefficients. Results: In 2019, among 204 countries and territories, the age-standardized incidence rates (ASIR) of esophageal cancer ranged from 0.91 (95\% CI 0.65-1.58) to 24.53 (95\% CI 18.74-32.51), and the ASIR of gastric cancer ranged from 3.28 (95\% CI 2.67-3.91) to 43.70 (95\% CI 34.29-55.10). Malawi was identified as the highest risk for esophageal cancer (male RR=3.27; female RR=5.19) and low risk for gastric cancer (male RR=0.21; female RR=0.23) in both sexes. Spatial autocorrelation analysis revealed significant spatial clustering of the incidence for both cancers (Moran I>0.20 and P<.001). A positive correlation between the risk of esophageal and gastric cancer was observed in males (r=0.25, P<.001). The ASIR of both cancers showed a decreasing trend globally. The ASIR for esophageal and gastric cancer showed an AAPC of ?1.43 (95\% CI ?1.58 to ?1.27) and ?1.76 (95\% CI ?2.08 to ?1.43) in males, and ?1.93 (95\% CI ?2.11 to ?1.75) and ?1.79 (95\% CI ?2.13 to ?1.46) in females. In addition, a positive correlation between the temporal trends in ASIR for both cancers was observed at the global level across sexes (male r=0.98; female r=0.98). Conclusions: Our study shows that there was a significant spatial clustering of the incidence for esophageal and gastric cancer and a positive correlation between the risk of both cancers across countries was observed in males. In addition, a codescending incidence trend between both cancers was observed at the global level. ",
doi="10.2196/66655",
url="https://cancer.jmir.org/2025/1/e66655"
}










@Article{info:doi/10.2196/53361,
author="Li, Juntong
and Zhang, Runxi
and Lan, Guanghua
and Lin, Mei
and Tan, Shengkui
and Zhu, Qiuying
and Chen, Huanhuan
and Huang, Jinghua
and Ding, Dongni
and Li, Chunying
and Ruan, Yuhua
and Wang, Na",
title="The Distribution and Associated Factors of HIV/AIDS Among Youths in Guangxi, China, From 2014 to 2021: Bayesian Spatiotemporal Analysis",
journal="JMIR Public Health Surveill",
year="2024",
month="Sep",
day="27",
volume="10",
pages="e53361",
keywords="HIV/AIDS",
keywords="acquired immunodeficiency syndrome",
keywords="youth",
keywords="reported incidence",
keywords="Bayesian model",
keywords="spatiotemporal distribution",
abstract="Background: In recent years, the number of HIV/AIDS cases among youth has increased year by year around the world. A spatial and temporal analysis of these AIDS cases is necessary for the development of youth AIDS prevention and control policies. Objective: This study aimed to analyze the spatial and temporal distribution and associated factors of HIV/AIDS among youth in Guangxi as an example. Methods: The reported HIV/AIDS cases of youths aged 15?24 years in Guangxi from January 2014 to December 2021 were extracted from the Chinese Comprehensive Response Information Management System of HIV/AIDS. Data on population, economy, and health resources were obtained from the Guangxi Statistical Yearbook. The ArcGIS (version 10.8; ESRI Inc) software was used to describe the spatial distribution of AIDS incidence among youths in Guangxi. A Bayesian spatiotemporal model was used to analyze the distribution and associated factors of HIV/AIDS, such as gross domestic product per capita, population density, number of health technicians, and road mileage per unit area. Results: From 2014 to 2021, a total of 4638 cases of HIV/AIDS infection among youths were reported in Guangxi. The reported incidence of HIV/AIDS cases among youths in Guangxi increased from 9.13/100,000 in 2014 to 11.15/100,000 in 2019 and then plummeted to a low of 8.37/100,000 in 2020, followed by a small increase to 9.66/100,000 in 2021. The districts (counties) with relatively high HIV/AIDS prevalence among youths were Xixiangtang, Xingning, Qingxiu, Chengzhong, and Diecai. The reported incidence of HIV/AIDS among youths was negatively significantly associated with road mileage per unit area (km) at a posterior mean of ?0.510 (95\% CI ?0.818 to 0.209). It was positively associated with population density (100 persons) at a posterior mean of 0.025 (95\% CI 0.012?0.038), with the number of health technicians (100 persons) having a posterior mean of 0.007 (95\% CI 0.004?0.009). Conclusions: In Guangxi, current HIV and AIDS prevention and control among young people should focus on areas with a high risk of disease. It is suggested to strengthen the allocation of AIDS health resources and balance urban development and AIDS prevention. In addition, AIDS awareness, detection, and intervention among Guangxi youths need to be strengthened. ",
doi="10.2196/53361",
url="https://publichealth.jmir.org/2024/1/e53361"
}


@Article{info:doi/10.2196/48794,
author="Iacoella, Francesco
and Tirivayi, Nyasha",
title="Mobile Phones and HIV Testing: Multicountry Instrumental Variable Analysis From Sub-Saharan Africa",
journal="J Med Internet Res",
year="2024",
month="Sep",
day="27",
volume="26",
pages="e48794",
keywords="information and knowledge",
keywords="communication",
keywords="health and economic development",
keywords="public health",
keywords="technological change",
keywords="choices and consequences",
keywords="mobile phone",
keywords="connectivity",
keywords="access",
keywords="HIV",
keywords="testing",
keywords="Sub-Saharan Africa",
keywords="women's health",
abstract="Background: Sub-Saharan Africa has been a technological hothouse when it comes to mobile phone technology adoption. However, evidence on the role played by mobile technology on infectious disease prevention has been mostly limited to experimental studies. Objective: This observational study investigates the role of mobile phone connectivity on HIV testing in sub-Saharan Africa. Methods: We make use of the novel and comprehensive OpenCelliD cell tower database and Demographic and Health Survey geocoded information for over 400,000 women in 29 sub-Saharan African countries. We examine, through ordinary least square and instrumental variable regressions, whether women's community distance from the closest cell tower influences knowledge about HIV testing facilities and the likelihood of ever being tested for HIV. Results: After finding a negative and significant impact of distance to the nearest cell tower on knowledge of HIV testing facility (--0.7 percentage points per unit increase in distance) and HIV testing (--0.5 percentage points per unit increase), we investigate the mechanisms through which such effects might occur. Our analysis shows that distance to a cell tower reduces HIV-related knowledge (--0.4 percentage points per unit increase) as well as reproductive health knowledge (--0.4 percentage points per unit increase). Similar results are observed when the analysis is performed at community level. Conclusions: Results suggest that the effect of mobile phone connectivity is channeled through increased knowledge of HIV, sexually transmittable infections, and modern contraceptive methods. Further analysis shows that cell phone ownership has an even larger impact on HIV testing and knowledge. This paper adds to the recent literature on the impact of mobile-based HIV prevention schemes by showing through large-scale analysis that better mobile network access is a powerful tool to spread reproductive health knowledge and increase HIV awareness. ",
doi="10.2196/48794",
url="https://www.jmir.org/2024/1/e48794"
}



@Article{info:doi/10.2196/56571,
author="Jones, S. Brie
and DeWitt, E. Michael
and Wenner, J. Jennifer
and Sanders, W. John",
title="Lyme Disease Under-Ascertainment During the COVID-19 Pandemic in the United States: Retrospective Study",
journal="JMIR Public Health Surveill",
year="2024",
month="Sep",
day="12",
volume="10",
pages="e56571",
keywords="surveillance",
keywords="ascertainment",
keywords="Lyme diseases",
keywords="vector-borne diseases",
keywords="vector-borne disease",
keywords="vector-borne pathogens",
keywords="public health",
keywords="Lyme disease",
keywords="United States",
keywords="North Carolina",
keywords="COVID-19",
keywords="pandemic",
keywords="hospital",
keywords="hospitals",
keywords="clinic-based",
keywords="surveillance program",
keywords="geospatial model",
keywords="spatiotemporal",
abstract="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. ",
doi="10.2196/56571",
url="https://publichealth.jmir.org/2024/1/e56571"
}


@Article{info:doi/10.2196/47416,
author="Kost, Joseph Gerald
and Eng, Muyngim
and Zadran, Amanullah",
title="Geospatial Point-of-Care Testing Strategies for COVID-19 Resilience in Resource-Poor Settings: Rural Cambodia Field Study",
journal="JMIR Public Health Surveill",
year="2024",
month="Aug",
day="27",
volume="10",
pages="e47416",
keywords="Cambodia",
keywords="COVID-19",
keywords="diagnostic portals",
keywords="mobile-testing van and clinic",
keywords="molecular diagnostics",
keywords="point-of-care testing",
keywords="POCT",
keywords="public health resilience",
keywords="rapid antigen test",
keywords="RAgT",
keywords="SARS-CoV-2",
keywords="Solano and Yolo counties",
keywords="California",
abstract="Background: Point-of-care testing (POCT) generates intrinsically fast, inherently spatial, and immediately actionable results. Lessons learned in rural Cambodia and California create a framework for planning and mobilizing POCT with telehealth interventions. Timely diagnosis can help communities assess the spread of highly infectious diseases, mitigate outbreaks, and manage risks. Objective: The aims of this study were to identify the need for POCT in Cambodian border provinces during peak COVID-19 outbreaks and to quantify geospatial gaps in access to diagnostics during community lockdowns. Methods: Data sources comprised focus groups, interactive learners, webinar participants, online contacts, academic experts, public health experts, and officials who determined diagnostic needs and priorities in rural Cambodia during peak COVID-19 outbreaks. We analyzed geographic distances and transit times to testing in border provinces and assessed a high-risk province, Banteay Meanchey, where people crossed borders daily leading to disease spread. We strategized access to rapid antigen testing and molecular diagnostics in the aforementioned province and applied mobile-testing experience among the impacted population. Results: COVID-19 outbreaks were difficult to manage in rural and isolated areas where diagnostics were insufficient to meet needs. The median transit time from border provinces (n=17) to testing sites was 73 (range 1-494) minutes, and in the high-risk Banteay Meanchey Province (n=9 districts), this transit time was 90 (range 10-150) minutes. Within border provinces, maximum versus minimum distances and access times for testing differed significantly (P<.001). Pareto plots revealed geospatial gaps in access to testing for people who are not centrally located. At the time of epidemic peaks in Southeast Asia, mathematical analyses showed that only one available rapid antigen test met the World Health Organization requirement of sensitivity >80\%. We observed that in rural Solano and Yolo counties, California, vending machines and public libraries dispensing free COVID-19 test kits 24-7 improved public access to diagnostics. Mobile-testing vans equipped with COVID-19 antigen, reverse transcription polymerase chain reaction, and multiplex influenza A/B testing proved useful for differential diagnosis, public awareness, travel certifications, and telehealth treatment. Conclusions: Rural diagnostic portals implemented in California demonstrated a feasible public health strategy for Cambodia. Automated dispensers and mobile POCT can respond to COVID-19 case surges and enhance preparedness. Point-of-need planning can enhance resilience and assure spatial justice. Public health assets should include higher-quality, lower-cost, readily accessible, and user-friendly POCT, such as self-testing for diagnosis, home molecular tests, distributed border detection for surveillance, and mobile diagnostics vans for quick telehealth treatment. High-risk settings will benefit from the synthesis of geospatially optimized POCT, automated 24-7 test access, and timely diagnosis of asymptomatic and symptomatic patients at points of need now, during new outbreaks, and in future pandemics. ",
doi="10.2196/47416",
url="https://publichealth.jmir.org/2024/1/e47416",
url="http://www.ncbi.nlm.nih.gov/pubmed/39190459"
}



@Article{info:doi/10.2196/48825,
author="Riggins, P. Daniel
and Zhang, Huiyuan
and Trick, E. William",
title="Using Social Vulnerability Indices to Predict Priority Areas for Prevention of Sudden Unexpected Infant Death in Cook County, IL: Cross-Sectional Study",
journal="JMIR Public Health Surveill",
year="2024",
month="Aug",
day="20",
volume="10",
pages="e48825",
keywords="infant",
keywords="socioeconomic disparities in health",
keywords="sudden unexpected infant death",
keywords="SUID",
keywords="sudden infant death",
keywords="SID",
keywords="geographic information systems",
keywords="structural racism",
keywords="predict",
keywords="social vulnerability",
keywords="racial disparity",
keywords="socioeconomic",
keywords="disparity",
keywords="child",
keywords="infancy",
keywords="pediatric",
keywords="sudden infant death syndrome",
keywords="SIDS",
abstract="Background: The incidence of sudden unexpected infant death (SUID) in the United States has persisted at roughly the same level since the mid-2000s, despite intensive prevention efforts around safe sleep. Disparities in outcomes across racial and socioeconomic lines also persist. These disparities are reflected in the spatial distribution of cases across neighborhoods. Strategies for prevention should be targeted precisely in space and time to further reduce SUID and correct disparities. Objective: We sought to aid neighborhood-level prevention efforts by characterizing communities where SUID occurred in Cook County, IL, from 2015 to 2019 and predicting where it would occur in 2021?2025 using a semiautomated, reproducible workflow based on open-source software and data. Methods: This cross-sectional retrospective study queried geocoded medical examiner data from 2015?2019 to identify SUID cases in Cook County, IL, and aggregated them to ``communities'' as the unit of analysis. We compared demographic factors in communities affected by SUID versus those unaffected using Wilcoxon rank sum statistical testing. We used social vulnerability indicators from 2014 to train a negative binomial prediction model for SUID case counts in each given community for 2015?2019. We applied indicators from 2020 to the trained model to make predictions for 2021?2025. Results: Validation of our query of medical examiner data produced 325 finalized cases with a sensitivity of 95\% (95\% CI 93\%?97\%) and a specificity of 98\% (95\% CI 94\%?100\%). Case counts at the community level ranged from a minimum of 0 to a maximum of 17. A map of SUID case counts showed clusters of communities in the south and west regions of the county. All communities with the highest case counts were located within Chicago city limits. Communities affected by SUID exhibited lower median proportions of non-Hispanic White residents at 17\% versus 60\% (P<.001) and higher median proportions of non-Hispanic Black residents at 32\% versus 3\% (P<.001). Our predictive model showed moderate accuracy when assessed on the training data (Nagelkerke R2=70.2\% and RMSE=17.49). It predicted Austin (17 cases), Englewood (14 cases), Auburn Gresham (12 cases), Chicago Lawn (12 cases), and South Shore (11 cases) would have the largest case counts between 2021 and 2025. Conclusions: Sharp racial and socioeconomic disparities in SUID incidence persisted within Cook County from 2015 to 2019. Our predictive model and maps identify precise regions within the county for local health departments to target for intervention. Other jurisdictions can adapt our coding workflows and data sources to predict which of their own communities will be most affected by SUID. ",
doi="10.2196/48825",
url="https://publichealth.jmir.org/2024/1/e48825"
}






@Article{info:doi/10.2196/49648,
author="Sawires, Rana
and Clothier, J. Hazel
and Burgner, David
and Fahey, Collingwood Michael
and Buttery, Jim",
title="Kawasaki Disease and Respiratory Viruses: Ecological Spatiotemporal Analysis",
journal="JMIR Public Health Surveill",
year="2024",
month="Jul",
day="25",
volume="10",
pages="e49648",
keywords="Kawasaki disease",
keywords="pediatric",
keywords="infection",
keywords="RSV",
keywords="human metapneumovirus",
keywords="respiratory virus",
keywords="virology",
keywords="community",
keywords="viral infection",
keywords="respiratory disease",
keywords="respiratory diseases",
keywords="children",
keywords="epidemiology",
keywords="respiratory syncytial virus",
abstract="Background: Kawasaki disease is an uncommon vasculitis affecting young children. Its etiology is not completely understood, although infections have been frequently postulated as the triggers. Respiratory viruses, specifically, have often been implicated as causative agents for Kawasaki disease presentations. Objective: We aimed to conduct an ecological spatiotemporal analysis to determine whether Kawasaki disease incidence was related to community respiratory virus circulation in a shared region and population, and to describe viral associations before and during the COVID-19 pandemic. Methods: We obtained independent statewide data sets of hospital admissions of Kawasaki disease and respiratory multiplex polymerase chain reaction tests performed at two large hospital networks in Victoria, Australia, from July 2011 to November 2021. We studied spatiotemporal relationships by negative binomial regression analysis of the monthly incidence of Kawasaki disease and the rate of positive respiratory polymerase chain reaction tests in different regions of Victoria. Peak viral seasons (95th percentile incidence) were compared to median viral circulation (50th percentile incidence) to calculate peak season increased rate ratios. Results: While no seasonal trend in Kawasaki disease incidence was identified throughout the study period, we found a 1.52 (99\% CI 1.27?1.82) and a 1.43 (99\% CI 1.17?1.73) increased rate ratio of Kawasaki disease presentations in association with human metapneumovirus and respiratory syncytial virus circulation, respectively, before the COVID-19 pandemic. No respiratory viral associations with Kawasaki disease were observed during the COVID-19 pandemic. Conclusions: Our large ecological analysis demonstrates novel spatiotemporal relationships between human metapneumovirus and respiratory syncytial virus circulation with Kawasaki disease. The disappearance of these associations in the COVID-19 pandemic may reflect the reduced circulation of non--SARS-CoV-2 viruses during this period, supporting the prepandemic associations identified in this study. The roles of human metapneumovirus and respiratory syncytial virus in Kawasaki disease etiology warrant further investigation. ",
doi="10.2196/49648",
url="https://publichealth.jmir.org/2024/1/e49648"
}


@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/10.2196/54611,
author="Mustafa, Hazim Ali
and Khaleel, Abdulghafoor Hanan
and Lami, Faris",
title="Human Brucellosis in Iraq: Spatiotemporal Data Analysis From 2007-2018",
journal="JMIRx Med",
year="2024",
month="Jul",
day="3",
volume="5",
pages="e54611",
keywords="human brucellosis",
keywords="livestock",
keywords="clustering",
keywords="spatial",
keywords="temporal",
keywords="Iraq",
abstract="Background: Brucellosis is both endemic and enzootic in Iraq, resulting in long-term morbidity for humans as well as economic loss. No previous study of the spatial and temporal patterns of brucellosis in Iraq was done to identify potential clustering of cases. Objective: This study aims to detect the spatial and temporal distribution of human brucellosis in Iraq and identify any changes that occurred from 2007 to 2018. Methods: A descriptive, cross-sectional study was conducted using secondary data from the Surveillance Section at the Communicable Diseases Control Center, Public Health Directorate, Ministry of Health in Iraq. The trends of cases by sex and age group from 2007 to 2018 were displayed. The seasonal distribution of the cases from 2007 to 2012 was graphed. We calculated the incidence of human brucellosis per district per year and used local Getis-Ord Gi* statistics to detect the spatial distribution of the data. The data were analyzed using Microsoft Excel and GeoDa software. Results: A total of 51,508 human brucellosis cases were reported during the 12-year study period, with some missing data for age groups. Human brucellosis persisted annually in Iraq across the study period with no specific temporal clustering of cases. In contrast, spatial clustering was predominant in northern Iraq. Conclusions: There were significant differences in the geographic distribution of brucellosis. The number of cases is the highest in the north and northeast regions of the country, which has borders with nearby countries. In addition, people in these areas depend more on locally made dairy products, which can be inadequately pasteurized. Despite the lack of significant temporal clustering of cases, the highest number of cases were reported during summer and spring. Considering these patterns when allocating resources to combat this disease, determining public health priorities, and planning prevention and control strategies is important. ",
doi="10.2196/54611",
url="https://xmed.jmir.org/2024/1/e54611"
}






TY  - JOUR
AU  - Zhou, Qingqing
AU  - Zeng, Huatang
AU  - Wu, Liqun
AU  - Diao, Kaichuan
AU  - He, Rongxin
AU  - Zhu, Bin
PY  - 2024/6/12
TI  - Geographic Disparities in Access to Assisted Reproductive Technology Centers in China: Spatial-Statistical Study
JO  - JMIR Public Health Surveill
SP  - e55418
VL  - 10
KW  - assisted reproductive technology
KW  - spatial accessibility
KW  - travel time
KW  - travel cost
KW  - China
UR  - https://publichealth.jmir.org/2024/1/e55418
UR  - http://dx.doi.org/10.2196/55418
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38865169
ID  - info:doi/10.2196/55418
ER  - 
nlm.nih.gov/pubmed/38865169"
}




@Article{info:doi/10.2196/56229,
author="Xie, Ziyi
and Chen, Bowen
and Duan, Zhizhuang",
title="Spatiotemporal Analysis of HIV/AIDS Incidence in China From 2009 to 2019 and Its Association With Socioeconomic Factors: Geospatial Study",
journal="JMIR Public Health Surveill",
year="2024",
month="Jun",
day="7",
volume="10",
pages="e56229",
keywords="HIV/AIDS",
keywords="spatiotemporal distribution",
keywords="cluster analysis",
keywords="socioeconomic factors",
keywords="China",
abstract="Background: The Joint United Nations Program on HIV/AIDS (UNAIDS) has set the ``95-95-95'' targets to ensure that 95\% of all people living with HIV will know their HIV status, 95\% of all people living with HIV will receive sustained antiretroviral therapy (ART), and 95\% of all people receiving ART will achieve viral suppression (<1000 copies/mL). However, few countries have currently achieved these targets, posing challenges to the realization of the UNAIDS goal to eliminate the global HIV/AIDS epidemic by 2030. The Chinese government has implemented corresponding policies for HIV/AIDS prevention and control; however, it still faces the challenge of a large number of HIV/AIDS cases. Existing research predominantly focuses on the study of a particular region or population in China, and there is relatively limited research on the macro-level analysis of the spatiotemporal distribution of HIV/AIDS across China and its association with socioeconomic factors. Objective: This study seeks to identify the impact of these factors on the spatiotemporal distribution of HIV/AIDS incidence in China, aiming to provide scientific recommendations for future policy development. Methods: This study employed ArcGIS 10.2 (Esri) for spatial analysis, encompassing measures such as the imbalance index, geographical concentration index, spatial autocorrelation analysis (Moran I), and hot spot analysis (Getis-Ord Gi*). These methods were used to unveil the spatiotemporal distribution characteristics of HIV/AIDS incidence in 31 provinces of China from 2009 to 2019. Geographical Detector was used for ecological detection, risk area detection, factor detection, and interaction detection. The analysis focused on 9 selected socioeconomic indicators to further investigate the influence of socioeconomic factors on HIV/AIDS incidence in China. Results: The spatiotemporal distribution analysis of HIV/AIDS incidence in China from 2009 to 2019 revealed distinct patterns. The spatial distribution type of HIV/AIDS incidence in China was random in 2009-2010. However, from 2011 to 2019, the distribution pattern evolved toward a clustered arrangement, with the degree of clustering increasing each year. Notably, from 2012 onwards, there was a significant and rapid growth in the aggregation of cold and hot spot clusters of HIV/AIDS incidence in China, stabilizing only by the year 2016. An analysis of the impact of socioeconomic factors on HIV/AIDS incidence in China highlighted the ``urbanization rate'' and ``urban basic medical insurance fund expenditure'' as the primary factors influencing the spatial distribution of HIV/AIDS incidence. Additionally, among social factors, indicators related to medical resources exerted a crucial influence on HIV/AIDS incidence. Conclusions: From 2009 to 2019, HIV/AIDS incidence in China was influenced by various socioeconomic factors. In the future, it is imperative to optimize the combination of different socioeconomic indicators based on regional incidence patterns. This optimization will facilitate the formulation of corresponding policies to address the challenges posed by the HIV/AIDS epidemic. ",
doi="10.2196/56229",
url="https://publichealth.jmir.org/2024/1/e56229",
url="http://www.ncbi.nlm.nih.gov/pubmed/38848123"
}










@Article{info:doi/10.2196/45671,
author="Koenig, R. Leah
and Becker, Andr{\'e}a
and Ko, Jennifer
and Upadhyay, D. Ushma",
title="The Role of Telehealth in Promoting Equitable Abortion Access in the United States: Spatial Analysis",
journal="JMIR Public Health Surveill",
year="2023",
month="Nov",
day="7",
volume="9",
pages="e45671",
keywords="telehealth",
keywords="abortion",
keywords="spatial analysis",
keywords="health equity",
keywords="barriers",
keywords="abortion access",
keywords="legal",
keywords="young people",
keywords="remote",
keywords="rural",
abstract="Background: Even preceding the Supreme Court's 2022 Dobbs v. Jackson Women's Health Organization decision, patients in the United States faced exceptional barriers to reach abortion providers. Abortion restrictions disproportionately limited abortion access among people of color, young people, and those living on low incomes. Presently, clinics in states where abortion remains legal are experiencing an influx of out-of-state patients and wait times for in-person appointments are increasing. Direct-to-patient telehealth for abortion care has expanded since its introduction in the United States in 2020. However, the role of this telehealth model in addressing geographic barriers to and inequities in abortion access remains unclear. Objective: We sought to examine the amount of travel that patients averted by using telehealth for abortion care, and the role of telehealth in mitigating inequities in abortion access by race or ethnicity, age, pregnancy duration, socioeconomic status, rural residence, and distance to a facility. Methods: We used geospatial analyses and data from patients in the California Home Abortion by Telehealth Study, residing in 31 states and Washington DC, who obtained telehealth abortion care at 1 of 3 virtual abortion clinics. We used patients' residential ZIP code data and data from US abortion facility locations to document the round-trip driving distance in miles, driving time, and public transit time to the nearest abortion facility that patients averted by using telehealth abortion services from April 2021 to January 2022, before the Dobbs decision. We used binomial regression to assess whether patients reported that telehealth was more likely to make it possible to access a timely abortion among patients of color, those experiencing food insecurity, younger patients, those with longer pregnancy durations, rural patients, and those residing further from their closest abortion facility. Results: The 6027 patients averted a median of 10 (IQR 5-26) miles and 25 (IQR 14-46) minutes of round-trip driving, and 1 hour 25 minutes (IQR 46 minutes to 2 hours 30 minutes) of round-trip public transit time. Among a subsample of 1586 patients surveyed, 43\% (n=683) reported that telehealth made it possible to obtain timely abortion care. Telehealth was most likely to make it possible to have a timely abortion for younger patients (prevalence ratio [PR] 1.4, 95\% CI 1.2-1.6) for patients younger than 25 years of age compared to those 35 years of age or older), rural patients (PR 1.4, 95\% CI 1.2-1.6), those experiencing food insecurity (PR 1.3, 95\% CI 1.1-1.4), and those who averted over 100 miles of driving to their closest abortion facility (PR 1.6, 95\% CI 1.3-1.9). Conclusions: These findings support the role of telehealth in reducing abortion-related travel barriers in states where abortion remains legal, especially among patient populations who already face structural barriers to abortion care. Restrictions on telehealth abortion threaten health equity. ",
doi="10.2196/45671",
url="https://publichealth.jmir.org/2023/1/e45671",
url="http://www.ncbi.nlm.nih.gov/pubmed/37934583"
}


@Article{info:doi/10.2196/47151,
author="Skeen, J. Simone
and Tokarz, Stephanie
and Gasik, E. Rayna
and Solano, McGettigan Chelsea
and Smith, A. Ethan
and Sagoe, Binaifer Momi
and Hudson, V. Lauryn
and Steele, Kara
and Theall, P. Katherine
and Clum, A. Gretchen",
title="A Trauma-Informed, Geospatially Aware, Just-in-Time Adaptive mHealth Intervention to Support Effective Coping Skills Among People Living With HIV in New Orleans: Development and Protocol for a Pilot Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2023",
month="Oct",
day="24",
volume="12",
pages="e47151",
keywords="mobile health",
keywords="mHealth",
keywords="HIV",
keywords="traumatic stress",
keywords="posttraumatic growth",
keywords="coping",
keywords="geospatial",
keywords="just-in-time adaptive intervention",
keywords="JITAI",
keywords="just-in-time",
keywords="posttraumatic",
keywords="medication adherence",
keywords="mental well-being",
keywords="viral suppression",
keywords="development",
keywords="design",
keywords="acceptability",
keywords="feasibility",
keywords="mobile phone",
abstract="Background: In 2020, Greater New Orleans, Louisiana, was home to 7048 people living with HIV---1083 per 100,000 residents, 2.85 times the US national rate. With Louisiana routinely ranked last in indexes of health equity, violent crime rates in Orleans Parish quintupling national averages, and in-care New Orleans people living with HIV surviving twice the US average of adverse childhood experiences, accessible, trauma-focused, evidence-based interventions (EBIs) for violence-affected people living with HIV are urgently needed. Objective: To meet this need, we adapted Living in the Face of Trauma, a well-established EBI tailored for people living with HIV, into NOLA GEM, a just-in-time adaptive mobile health (mHealth) intervention. This study aimed to culturally tailor and refine the NOLA GEM app and assess its acceptability; feasibility; and preliminary efficacy on care engagement, medication adherence, viral suppression, and mental well-being among in-care people living with HIV in Greater New Orleans. Methods: The development of NOLA GEM entailed identifying real-time tailoring variables via a geographic ecological momentary assessment (GEMA) study (n=49; aim 1) and place-based and user-centered tailoring, responsive to the unique cultural contexts of HIV survivorship in New Orleans, via formative interviews (n=12; aim 2). The iOS- and Android-enabled NOLA GEM app leverages twice-daily GEMA prompts to offer just-in-time, in-app recommendations for effective coping skills practice and app-delivered Living in the Face of Trauma session content. For aim 3, the pilot trial will enroll an analytic sample of 60 New Orleans people living with HIV individually randomized to parallel NOLA GEM (intervention) or GEMA-alone (control) arms at a 1:1 allocation for a 21-day period. Acceptability and feasibility will be assessed via enrollment, attrition, active daily use through paradata metrics, and prevalidated usability measures. At the postassessment time point, primary end points will be assessed via a range of well-validated, domain-specific scales. Care engagement and viral suppression will be assessed via past missed appointments and self-reported viral load at 30 and 90 days, respectively, and through well-demonstrated adherence self-efficacy measures. Results: Aims 1 and 2 have been achieved, NOLA GEM is in Beta, and all aim-3 methods have been reviewed and approved by the institutional review board of Tulane University. Recruitment was launched in July 2023, with a target date for follow-up assessment completion in December 2023. Conclusions: By leveraging user-centered development and embracing principles that elevate the lived expertise of New Orleans people living with HIV, mHealth-adapted EBIs can reflect community wisdom on posttraumatic resilience. Sustainable adoption of the NOLA GEM app and a promising early efficacy profile will support the feasibility of a future fully powered clinical trial and potential translation to new underserved settings in service of holistic survivorship and well-being of people living with HIV. Trial Registration: ClinicalTrials.gov NCT05784714; https://clinicaltrials.gov/ct2/show/NCT05784714 International Registered Report Identifier (IRRID): PRR1-10.2196/47151 ",
doi="10.2196/47151",
url="https://www.researchprotocols.org/2023/1/e47151",
url="http://www.ncbi.nlm.nih.gov/pubmed/37874637"
}


@Article{info:doi/10.2196/44031,
author="Du, Min
and Yan, Wenxin
and Zhu, Lin
and Liang, Wannian
and Liu, Min
and Liu, Jue",
title="Trends in the Baidu Index in Search Activity Related to Mpox at Geographical and Economic Levels and Associated Factors in China: National Longitudinal Analysis",
journal="JMIR Form Res",
year="2023",
month="Aug",
day="23",
volume="7",
pages="e44031",
keywords="mpox",
keywords="internet attention",
keywords="emergency",
keywords="disparities",
keywords="China",
abstract="Background: Research assessing trends in online search activity related to mpox in China is scarce. Objective: We aimed to provide evidence for an overview of online information searching during an infectious disease outbreak by analyzing trends in online search activity related to mpox at geographical and economic levels in China and explore influencing factors. Methods: We used the Baidu index to present online search activity related to mpox from May 19 to September 19, 2022. Segmented interrupted time-series analysis was used to estimate trends in online search activity. Factors influencing these trends were analyzed using a general linear regression (GLM) model. We calculated the concentration index to measure economic-related inequality in online search activity and related trends. Results: Online search activity was highest on the day the first imported case of mpox appeared in Chongqing compared to 3 other cutoff time points. After the day of the first imported mpox case in Taiwan, the declaration of a public health emergency of international concern, the first imported mpox case in Hong Kong, and the first imported mpox case in Chongqing, national online search activity increased by 0.642\%, 1.035\%, 1.199\%, and 2.023\%, respectively. The eastern regions had higher increases than the central and western regions. Across 31 provinces, municipalities, and autonomous regions, the top 3 areas with higher increases were Beijing, Shanghai, and Tianjin at 3 time points, with the exception of the day of the first imported mpox case in Chongqing (Chongqing replaced Tianjin on that day). When AIDS incidence increased by 1 per 100,000 people, there was an increase after the day of the first imported mpox case in Chongqing of 36.22\% (95\% CI 3.29\%-69.15\%; P=.04) after controlling for other covariates. Online search activity (concentration index=0.18; P<.001) was more concentrated among populations with a higher economic status. Unlike the central area, the eastern (concentration index=0.234; P<.001) and western areas (concentration index=0.047; P=.04) had significant economic-related disparities (P for difference <.001) in online search activity. The overall concentration index of changes in online search activity became lower over time. Conclusions: Regions with a higher economic level showed more interest in mpox, especially Beijing and Shanghai. After the day of the first imported mpox case in Chongqing, changes in online search activity were affected by AIDS incidence rate. Economic-related disparities in changes in online search activity became lower over time. It would be desirable to construct a reliable information source in regions with a higher economic level and higher AIDS incidence rate and promote public knowledge in regions with a lower economic level in China, especially after important public events. ",
doi="10.2196/44031",
url="https://formative.jmir.org/2023/1/e44031",
url="http://www.ncbi.nlm.nih.gov/pubmed/37610816"
}






@Article{info:doi/10.2196/43243,
author="Dantas, Costa Janmilli da
and Lopes, Horacio Rayssa
and Marinho, Ramos Cristiane da Silva
and Pinheiro, Tavares Yago
and Silva, da Richardson Augusto Rosendo",
title="The Use of Spatial Analysis in Syphilis-Related Research: Protocol for a Scoping Review",
journal="JMIR Res Protoc",
year="2023",
month="Apr",
day="25",
volume="12",
pages="e43243",
keywords="treponema pallidum",
keywords="syphilis",
keywords="infectious diseases",
keywords="spatial distribution",
keywords="systems of geographic information",
keywords="spatiotemporal analysis",
keywords="health care",
keywords="surveillance",
keywords="spatial analysis",
keywords="geographic information",
keywords="policy maker",
abstract="Background: Latin America, Africa, and Asia have high incidences of syphilis. New approaches are needed to understand and reduce disease transmissibility. In health care, spatial analysis is important to map diseases and understand their epidemiologic aspects. Objective: The proposed scoping review will identify and map the use of spatial analysis as a tool for syphilis-related research in health care. Methods: This protocol was based on the Joanna Briggs Institute manual, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). We will conduct searches in Embase; Lilacs, via the Virtual Health Library (Biblioteca Virtual en Salud; BVS), in Portuguese and English; Medline/PubMed; Web of Science; Cumulative Index to Nursing and Allied Health Literature (CINAHL); and Scopus. Gray literature will be searched for in Google Scholar, the Digital Library of Theses and Dissertations, the Catalog of Theses and Dissertations of the Coordination of Improvement of Higher Education Personnel (Coordena{\c{c}}{\~a}o de Aperfei{\c{c}}oamento de Pessoal de N{\'i}vel Superior; CAPES), Open Access Theses and Dissertations, ProQuest Dissertations and Theses Global, and the Networked Digital Library of Theses and Dissertations. The main research question is ``How has spatial analysis been used in syphilis-related research in health care?'' Studies are included if they have the full text available, address syphilis, and use geographic information systems software and spatial analysis techniques, regardless of sample characteristics or size. Studies published as research articles, theses, dissertations, and government documents will also be considered, with no location, time, or language restrictions. Data will be extracted using a spreadsheet adapted from the Joanna Briggs Institute. Quantitative and qualitative data will be analyzed using descriptive statistics and a thematic analysis, respectively. Results: The results will be presented according to the PRISMA-ScR guidelines and will summarize the use of spatial analysis in syphilis-related research in health care in countries with different contexts, factors associated with spatial cluster formation, population health impacts, contributions to health systems, challenges, limitations, and possible research gaps. The results will guide future research and may be useful for health and safety professionals, managers, public policy makers, the general population, the academic community, and health professionals who work directly with people with syphilis. Data collection is projected to start in June 2023 and end in July 2023. Data analysis is scheduled to take place in August and September 2023. We expect to publish results in the final months of 2023. Conclusions: The review may reveal where syphilis incidence has the highest incidence, which countries most use spatial analysis to study syphilis, and whether spatial analysis is applicable to syphilis in each continent, thereby contributing to discussion and knowledge dissemination on the use of spatial analysis as a tool for syphilis-related research in health care. Trial Registration: Open Science Framework CNVXE; https://osf.io/cnvxe International Registered Report Identifier (IRRID): PRR1-10.2196/43243 ",
doi="10.2196/43243",
url="https://www.researchprotocols.org/2023/1/e43243",
url="http://www.ncbi.nlm.nih.gov/pubmed/37097740"
}


@Article{info:doi/10.2196/43623,
author="Beauchamp, M. Alaina
and Lehmann, U. Christoph
and Medford, J. Richard
and Hughes, E. Amy",
title="The Association of a Geographically Wide Social Media Network on Depression: County-Level Ecological Analysis",
journal="J Med Internet Res",
year="2023",
month="Mar",
day="27",
volume="25",
pages="e43623",
keywords="Facebook",
keywords="social connectedness",
keywords="depression",
keywords="county-level analysis",
keywords="social media",
keywords="mental health",
keywords="research",
keywords="ecological",
keywords="geography",
keywords="GIS",
abstract="Background: Social connectedness decreases human mortality, improves cancer survival, cardiovascular health, and body mass, results in better-controlled glucose levels, and strengthens mental health. However, few public health studies have leveraged large social media data sets to classify user network structure and geographic reach rather than the sole use of social media platforms. Objective: The objective of this study was to determine the association between population-level digital social connectedness and reach and depression in the population across geographies of the United States. Methods: Our study used an ecological assessment of aggregated, cross-sectional population measures of social connectedness, and self-reported depression across all counties in the United States. This study included all 3142 counties in the contiguous United States. We used measures obtained between 2018 and 2020 for adult residents in the study area. The study's main exposure of interest is the Social Connectedness Index (SCI), a pair-wise composite index describing the ``strength of connectedness between 2 geographic areas as represented by Facebook friendship ties.'' This measure describes the density and geographical reach of average county residents' social network using Facebook friendships and can differentiate between local and long-distance Facebook connections. The study's outcome of interest is self-reported depressive disorder as published by the Centers for Disease Control and Prevention. Results: On average, 21\% (21/100) of all adult residents in the United States reported a depressive disorder. Depression frequency was the lowest for counties in the Northeast (18.6\%) and was highest for southern counties (22.4\%). Social networks in northeastern counties involved moderately local connections (SCI 5-10 the 20th percentile for n=70, 36\% of counties), whereas social networks in Midwest, southern, and western counties contained mostly local connections (SCI 1-2 the 20th percentile for n=598, 56.7\%, n=401, 28.2\%, and n=159, 38.4\%, respectively). As the quantity and distance that social connections span (ie, SCI) increased, the prevalence of depressive disorders decreased by 0.3\% (SE 0.1\%) per rank. Conclusions: Social connectedness and depression showed, after adjusting for confounding factors such as income, education, cohabitation, natural resources, employment categories, accessibility, and urbanicity, that a greater social connectedness score is associated with a decreased prevalence of depression. ",
doi="10.2196/43623",
url="https://www.jmir.org/2023/1/e43623",
url="http://www.ncbi.nlm.nih.gov/pubmed/36972109"
}


@Article{info:doi/10.2196/42425,
author="Zhang, Qi
and Ding, Huan
and Gao, Song
and Zhang, Shipeng
and Shen, Shiya
and Chen, Xiaoyan
and Xu, Zhuping",
title="Spatiotemporal Changes in Pulmonary Tuberculosis Incidence in a Low-Epidemic Area of China in 2005-2020: Retrospective Spatiotemporal Analysis",
journal="JMIR Public Health Surveill",
year="2023",
month="Mar",
day="8",
volume="9",
pages="e42425",
keywords="pulmonary tuberculosis",
keywords="spatial analysis",
keywords="temporal analysis",
keywords="epidemiology",
keywords="China",
abstract="Background: In China, tuberculosis (TB) is still a major public health problem, and the incidence of TB has significant spatial heterogeneity. Objective: This study aimed to investigate the temporal trends and spatial patterns of pulmonary tuberculosis (PTB) in a low-epidemic area of eastern China, Wuxi city, from 2005 to 2020. Methods: The data of PTB cases from 2005 to 2020 were obtained from the Tuberculosis Information Management System. The joinpoint regression model was used to identify the changes in the secular temporal trend. Kernel density analysis and hot spot analysis were used to explore the spatial distribution characteristics and clusters of the PTB incidence rate. Results: A total of 37,592 cases were registered during 2005-2020, with an average annual incidence rate of 34.6 per 100,000 population. The population older than 60 years had the highest incidence rate of 59.0 per 100,000 population. In the study period, the incidence rate decreased from 50.4 to 23.9 per 100,000 population, with an average annual percent change of --4.9\% (95\% CI --6.8\% to --2.9\%). The incidence rate of pathogen-positive patients increased during 2017-2020, with an annual percent change of 13.4\% (95\% CI 4.3\%-23.2\%). The TB cases were mainly concentrated in the city center, and the incidence of hot spots areas gradually changed from rural areas to urban areas during the study period. Conclusions: The PTB incidence rate in Wuxi city has been declining rapidly with the effective implementation of strategies and projects. The populated urban centers will become key areas of TB prevention and control, especially in the older population. ",
doi="10.2196/42425",
url="https://publichealth.jmir.org/2023/1/e42425",
url="http://www.ncbi.nlm.nih.gov/pubmed/36884278"
}


@Article{info:doi/10.2196/42258,
author="Spang, P. Robert
and Haeger, Christine
and M{\"u}mken, A. Sandra
and Brauer, Max
and Voigt-Antons, Jan-Niklas
and Gellert, Paul",
title="Smartphone Global Positioning System--Based System to Assess Mobility in Health Research: Development, Accuracy, and Usability Study",
journal="JMIR Rehabil Assist Technol",
year="2023",
month="Mar",
day="2",
volume="10",
pages="e42258",
keywords="geographic information system",
keywords="rehabilitation",
keywords="prevention medicine",
keywords="geoinformatics",
keywords="out-of-home mobility",
abstract="Background: As global positioning system (GPS) measurement is getting more precise and affordable, health researchers can now objectively measure mobility using GPS sensors. Available systems, however, often lack data security and means of adaptation and often rely on a permanent internet connection. Objective: To overcome these issues, we aimed to develop and test an easy-to-use, easy-to-adapt, and offline working app using smartphone sensors (GPS and accelerometry) for the quantification of mobility parameters. Methods: An Android app, a server backend, and a specialized analysis pipeline have been developed (development substudy). Parameters of mobility by the study team members were extracted from the recorded GPS data using existing and newly developed algorithms. Test measurements were performed with participants to complete accuracy and reliability tests (accuracy substudy). Usability was examined by interviewing community-dwelling older adults after 1 week of device use, followed by an iterative app design process (usability substudy). Results: The study protocol and the software toolchain worked reliably and accurately, even under suboptimal conditions, such as narrow streets and rural areas. The developed algorithms had high accuracy (97.4\% correctness, F1-score=0.975) in distinguishing dwelling periods from moving intervals. The accuracy of the stop/trip classification is fundamental to second-order analyses such as the time out of home, as they rely on a precise discrimination between the 2 classes. The usability of the app and the study protocol was piloted with older adults, which showed low barriers and easy implementation into daily routines. Conclusions: Based on accuracy analyses and users' experience with the proposed system for GPS assessments, the developed algorithm showed great potential for app-based estimation of mobility in diverse health research contexts, including mobility patterns of community-dwelling older adults living in rural areas. International Registered Report Identifier (IRRID): RR2-10.1186/s12877-021-02739-0 ",
doi="10.2196/42258",
url="https://rehab.jmir.org/2023/1/e42258",
url="http://www.ncbi.nlm.nih.gov/pubmed/36862498"
}


@Article{info:doi/10.2196/37039,
author="Brakefield, S. Whitney
and Olusanya, A. Olufunto
and Shaban-Nejad, Arash",
title="Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach",
journal="JMIR Public Health Surveill",
year="2022",
month="Aug",
day="9",
volume="8",
number="8",
pages="e37039",
keywords="obesity",
keywords="obesity surveillance",
keywords="disease surveillance",
keywords="machine learning",
keywords="geographic information systems",
keywords="social determinants of health",
keywords="SDOH",
keywords="disparities",
abstract="Background: Obesity is a global epidemic causing at least 2.8 million deaths per year. This complex disease is associated with significant socioeconomic burden, reduced work productivity, unemployment, and other social determinants of health (SDOH) disparities. Objective: The objective of this study was to investigate the effects of SDOH on obesity prevalence among adults in Shelby County, Tennessee, the United States, using a geospatial machine learning approach. Methods: Obesity prevalence was obtained from the publicly available 500 Cities database of Centers for Disease Control and Prevention, and SDOH indicators were extracted from the US census and the US Department of Agriculture. We examined the geographic distributions of obesity prevalence patterns, using Getis-Ord Gi* statistics and calibrated multiple models to study the association between SDOH and adult obesity. Unsupervised machine learning was used to conduct grouping analysis to investigate the distribution of obesity prevalence and associated SDOH indicators. Results: Results depicted a high percentage of neighborhoods experiencing high adult obesity prevalence within Shelby County. In the census tract, the median household income, as well as the percentage of individuals who were Black, home renters, living below the poverty level, 55 years or older, unmarried, and uninsured, had a significant association with adult obesity prevalence. The grouping analysis revealed disparities in obesity prevalence among disadvantaged neighborhoods. Conclusions: More research is needed to examine links between geographical location, SDOH, and chronic diseases. The findings of this study, which depict a significantly higher prevalence of obesity within disadvantaged neighborhoods, and other geospatial information can be leveraged to offer valuable insights, informing health decision-making and interventions that mitigate risk factors of increasing obesity prevalence. ",
doi="10.2196/37039",
url="https://publichealth.jmir.org/2022/8/e37039",
url="http://www.ncbi.nlm.nih.gov/pubmed/35943795"
}


@Article{info:doi/10.2196/37756,
author="Krzyzanowski, Brittany
and Manson, M. Steven",
title="Twenty Years of the Health Insurance Portability and Accountability Act Safe Harbor Provision: Unsolved Challenges and Ways Forward",
journal="JMIR Med Inform",
year="2022",
month="Aug",
day="3",
volume="10",
number="8",
pages="e37756",
keywords="Health Insurance Portability and Accountability Act",
keywords="HIPAA",
keywords="data privacy",
keywords="health",
keywords="maps",
keywords="safe harbor",
keywords="visualization",
keywords="patient privacy",
doi="10.2196/37756",
url="https://medinform.jmir.org/2022/8/e37756",
url="http://www.ncbi.nlm.nih.gov/pubmed/35921140"
}


@Article{info:doi/10.2196/34782,
author="Abdullah, Md Abu Yousuf
and Law, Jane
and Perlman, M. Christopher
and Butt, A. Zahid",
title="Age- and Sex-Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study",
journal="JMIR Public Health Surveill",
year="2022",
month="Jul",
day="28",
volume="8",
number="7",
pages="e34782",
keywords="mental health disorders",
keywords="vegetation cover",
keywords="age- and sex- specific association",
keywords="Enhanced Vegetation Index",
keywords="Bayesian",
keywords="spatial",
keywords="hierarchical modeling",
keywords="marginalization",
keywords="latent covariates",
abstract="Background: Despite growing evidence that reduced vegetation cover could be a putative risk factor for mental health disorders, the age- and the sex-specific association between vegetation and mental health disorder cases in urban areas is poorly understood. However, with rapid urbanization across the globe, there is an urgent need to study this association and understand the potential impact of vegetation loss on the mental well-being of urban residents. Objective: This study aims to analyze the spatial association between vegetation cover and the age- and sex-stratified mental health disorder cases in the neighborhoods of Toronto, Canada. Methods: We used remote sensing to detect urban vegetation and Bayesian spatial hierarchical modeling to analyze the relationship between vegetation cover and mental health disorder cases. Specifically, an Enhanced Vegetation Index was used to detect urban vegetation, and Bayesian Poisson lognormal models were implemented to study the association between vegetation and mental health disorder cases of males and females in the 0-19, 20-44, 45-64, and ?65 years age groups, after controlling for marginalization and unmeasured (latent) spatial and nonspatial covariates at the neighborhood level. Results: The results suggest that even after adjusting for marginalization, there were significant age- and sex-specific effects of vegetation on the prevalence of mental health disorders in Toronto. Mental health disorders were negatively associated with the vegetation cover for males aged 0-19 years (?7.009; 95\% CI ?13.130 to ?0.980) and for both males (?4.544; 95\% CI ?8.224 to ?0.895) and females (?3.513; 95\% CI ?6.289 to ?0.681) aged 20-44 years. However, for older adults in the 45-64 and ?65 years age groups, only the marginalization covariates were significantly associated with mental health disorder cases. In addition, a substantial influence of the unmeasured (latent) and spatially structured covariates was detected in each model (relative contributions>0.7), suggesting that the variations in area-specific relative risk were mainly spatial in nature. Conclusions: As significant and negative associations between vegetation and mental health disorder cases were found for young males and females, investments in urban greenery can help reduce the future burden of mental health disorders in Canada. The findings highlight the urgent need to understand the age-sex dynamics of the interaction between surrounding vegetation and urban dwellers and its subsequent impact on mental well-being. ",
doi="10.2196/34782",
url="https://publichealth.jmir.org/2022/7/e34782",
url="http://www.ncbi.nlm.nih.gov/pubmed/35900816"
}


@Article{info:doi/10.2196/34285,
author="Sigalo, Nekabari
and St Jean, Beth
and Frias-Martinez, Vanessa",
title="Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets",
journal="JMIR Public Health Surveill",
year="2022",
month="Jul",
day="5",
volume="8",
number="7",
pages="e34285",
keywords="social media",
keywords="Twitter",
keywords="food deserts",
keywords="food insecurity",
abstract="Background: The issue of food insecurity is becoming increasingly important to public health practitioners because of the adverse health outcomes and underlying racial disparities associated with insufficient access to healthy foods. Prior research has used data sources such as surveys, geographic information systems, and food store assessments to identify regions classified as food deserts but perhaps the individuals in these regions unknowingly provide their own accounts of food consumption and food insecurity through social media. Social media data have proved useful in answering questions related to public health; therefore, these data are a rich source for identifying food deserts in the United States. Objective: The aim of this study was to develop, from geotagged Twitter data, a predictive model for the identification of food deserts in the United States using the linguistic constructs found in food-related tweets. Methods: Twitter's streaming application programming interface was used to collect a random 1\% sample of public geolocated tweets across 25 major cities from March 2020 to December 2020. A total of 60,174 geolocated food-related tweets were collected across the 25 cities. Each geolocated tweet was mapped to its respective census tract using point-to-polygon mapping, which allowed us to develop census tract--level features derived from the linguistic constructs found in food-related tweets, such as tweet sentiment and average nutritional value of foods mentioned in the tweets. These features were then used to examine the associations between food desert status and the food ingestion language and sentiment of tweets in a census tract and to determine whether food-related tweets can be used to infer census tract--level food desert status. Results: We found associations between a census tract being classified as a food desert and an increase in the number of tweets in a census tract that mentioned unhealthy foods (P=.03), including foods high in cholesterol (P=.02) or low in key nutrients such as potassium (P=.01). We also found an association between a census tract being classified as a food desert and an increase in the proportion of tweets that mentioned healthy foods (P=.03) and fast-food restaurants (P=.01) with positive sentiment. In addition, we found that including food ingestion language derived from tweets in classification models that predict food desert status improves model performance compared with baseline models that only include socioeconomic characteristics. Conclusions: Social media data have been increasingly used to answer questions related to health and well-being. Using Twitter data, we found that food-related tweets can be used to develop models for predicting census tract food desert status with high accuracy and improve over baseline models. Food ingestion language found in tweets, such as census tract--level measures of food sentiment and healthiness, are associated with census tract--level food desert status. ",
doi="10.2196/34285",
url="https://publichealth.jmir.org/2022/7/e34285",
url="http://www.ncbi.nlm.nih.gov/pubmed/35788108"
}


@Article{info:doi/10.2196/37858,
author="Shi, Qiming
and Herbert, Carly
and Ward, V. Doyle
and Simin, Karl
and McCormick, A. Beth
and Ellison III, T. Richard
and Zai, H. Adrian",
title="COVID-19 Variant Surveillance and Social Determinants in Central Massachusetts: Development Study",
journal="JMIR Form Res",
year="2022",
month="Jun",
day="13",
volume="6",
number="6",
pages="e37858",
keywords="geographic information science",
keywords="GIS",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="variants",
keywords="surveillance",
keywords="dashboard",
keywords="web mapping",
keywords="public health",
keywords="web-based information",
keywords="digital health",
keywords="epidemiology",
abstract="Background: Public health scientists have used spatial tools such as web-based Geographical Information System (GIS) applications to monitor and forecast the progression of the COVID-19 pandemic and track the impact of their interventions. The ability to track SARS-CoV-2 variants and incorporate the social determinants of health with street-level granularity can facilitate the identification of local outbreaks, highlight variant-specific geospatial epidemiology, and inform effective interventions. We developed a novel dashboard, the University of Massachusetts' Graphical user interface for Geographic Information (MAGGI) variant tracking system that combines GIS, health-associated sociodemographic data, and viral genomic data to visualize the spatiotemporal incidence of SARS-CoV-2 variants with street-level resolution while safeguarding protected health information. The specificity and richness of the dashboard enhance the local understanding of variant introductions and transmissions so that appropriate public health strategies can be devised and evaluated. Objective: We developed a web-based dashboard that simultaneously visualizes the geographic distribution of SARS-CoV-2 variants in Central Massachusetts, the social determinants of health, and vaccination data to support public health efforts to locally mitigate the impact of the COVID-19 pandemic. Methods: MAGGI uses a server-client model--based system, enabling users to access data and visualizations via an encrypted web browser, thus securing patient health information. We integrated data from electronic medical records, SARS-CoV-2 genomic analysis, and public health resources. We developed the following functionalities into MAGGI: spatial and temporal selection capability by zip codes of interest, the detection of variant clusters, and a tool to display variant distribution by the social determinants of health. MAGGI was built on the Environmental Systems Research Institute ecosystem and is readily adaptable to monitor other infectious diseases and their variants in real-time. Results: We created a geo-referenced database and added sociodemographic and viral genomic data to the ArcGIS dashboard that interactively displays Central Massachusetts' spatiotemporal variants distribution. Genomic epidemiologists and public health officials use MAGGI to show the occurrence of SARS-CoV-2 genomic variants at high geographic resolution and refine the display by selecting a combination of data features such as variant subtype, subject zip codes, or date of COVID-19--positive sample collection. Furthermore, they use it to scale time and space to visualize association patterns between socioeconomics, social vulnerability based on the Centers for Disease Control and Prevention's social vulnerability index, and vaccination rates. We launched the system at the University of Massachusetts Chan Medical School to support internal research projects starting in March 2021. Conclusions: We developed a COVID-19 variant surveillance dashboard to advance our geospatial technologies to study SARS-CoV-2 variants transmission dynamics. This real-time, GIS-based tool exemplifies how spatial informatics can support public health officials, genomics epidemiologists, infectious disease specialists, and other researchers to track and study the spread patterns of SARS-CoV-2 variants in our communities. ",
doi="10.2196/37858",
url="https://formative.jmir.org/2022/6/e37858",
url="http://www.ncbi.nlm.nih.gov/pubmed/35658093"
}


@Article{info:doi/10.2196/32133,
author="Abell-Hart, Kayley
and Rashidian, Sina
and Teng, Dejun
and Rosenthal, N. Richard
and Wang, Fusheng",
title="Where Opioid Overdose Patients Live Far From Treatment: Geospatial Analysis of Underserved Populations in New York State",
journal="JMIR Public Health Surveill",
year="2022",
month="Apr",
day="12",
volume="8",
number="4",
pages="e32133",
keywords="opioid use disorder",
keywords="opioid overdose",
keywords="buprenorphine",
keywords="naloxone",
keywords="geospatial analysis",
keywords="epidemiology",
keywords="opioid pandemic",
keywords="public health",
abstract="Background: Opioid addiction and overdose have a large burden of disease and mortality in New York State (NYS). The medication naloxone can reverse an overdose, and buprenorphine can treat opioid use disorder. Efforts to increase the accessibility of both medications include a naloxone standing order and a waiver program for prescribing buprenorphine outside a licensed drug treatment program. However, only a slim majority of NYS pharmacies are listed as participating in the naloxone standing order, and less than 7\% of prescribers in NYS have a buprenorphine waiver. Therefore, there is a significant opportunity to increase access. Objective: Identifying the geographic regions of NYS that are farthest from resources can help target interventions to improve access to naloxone and buprenorphine. To maximize the efficiency of such efforts, we also sought to determine where these underserved regions overlap with the largest numbers of actual patients who have experienced opioid overdose. Methods: We used address data to assess the spatial distribution of naloxone pharmacies and buprenorphine prescribers. Using the home addresses of patients who had an opioid overdose, we identified geographic locations of resource deficits. We report findings at the high spatial granularity of census tracts, with some neighboring census tracts merged to preserve privacy. Results: We identified several hot spots, where many patients live far from the nearest resource of each type. The highest density of patients in areas far from naloxone pharmacies was found in eastern Broome county. For areas far from buprenorphine prescribers, we identified subregions of Oswego county and Wayne county as having a high number of potentially underserved patients. Conclusions: Although NYS is home to thousands of naloxone pharmacies and potential buprenorphine prescribers, access is not uniform. Spatial analysis revealed census tract areas that are far from resources, yet contain the residences of many patients who have experienced opioid overdose. Our findings have implications for public health decision support in NYS. Our methods for privacy can also be applied to other spatial supply-demand problems involving sensitive data. ",
doi="10.2196/32133",
url="https://publichealth.jmir.org/2022/4/e32133",
url="http://www.ncbi.nlm.nih.gov/pubmed/35412467"
}


@Article{info:doi/10.2196/35073,
author="Haithcoat, Timothy
and Liu, Danlu
and Young, Tiffany
and Shyu, Chi-Ren",
title="Investigating Health Context Using a Spatial Data Analytical Tool: Development of a Geospatial Big Data Ecosystem",
journal="JMIR Med Inform",
year="2022",
month="Apr",
day="6",
volume="10",
number="4",
pages="e35073",
keywords="context",
keywords="Geographic Information System",
keywords="big data",
keywords="equity",
keywords="population health",
keywords="public health",
keywords="digital health",
keywords="eHealth",
keywords="location",
keywords="geospatial",
keywords="data analytics",
keywords="analytical framework",
keywords="medical informatics",
keywords="research knowledgebase",
abstract="Background: Enabling the use of spatial context is vital to understanding today's digital health problems. Any given location is associated with many different contexts. The strategic transformation of population health, epidemiology, and eHealth studies requires vast amounts of integrated digital data. Needed is a novel analytical framework designed to leverage location to create new contextual knowledge. The Geospatial Analytical Research Knowledgebase (GeoARK), a web-based research resource has robust, locationally integrated, social, environmental, and infrastructural information to address today's complex questions, investigate context, and spatially enable health investigations. GeoARK is different from other Geographic Information System (GIS) resources in that it has taken the layered world of the GIS and flattened it into a big data table that ties all the data and information together using location and developing its context. Objective: It is paramount to build a robust spatial data analytics framework that integrates social, environmental, and infrastructural knowledge to empower health researchers' use of geospatial context to timely answer population health issues. The goal is twofold in that it embodies an innovative technological approach and serves to ease the educational burden for health researchers to think spatially about their problems. Methods: A unique analytical tool using location as the key was developed. It allows integration across source, geography, and time to create a geospatial big table with over 162 million individual locations (X-Y points that serve as rows) and 5549 attributes (represented as columns). The concept of context (adjacency, proximity, distance, etc) is quantified through geoanalytics and captured as new distance, density, or neighbor attributes within the system. Development of geospatial analytics permits contextual extraction and investigator-initiated eHealth and mobile health (mHealth) analysis across multiple attributes. Results: We built a unique geospatial big data ecosystem called GeoARK. Analytics on this big table occur across resolution groups, sources, and geographies for extraction and analysis of information to gain new insights. Case studies, including telehealth assessment in North Carolina, national income inequality and health outcome disparity, and a Missouri COVID-19 risk assessment, demonstrate the capability to support robust and efficient geospatial understanding of a wide spectrum of population health questions. Conclusions: This research identified, compiled, transformed, standardized, and integrated multifaceted data required to better understand the context of health events within a large location-enabled database. The GeoARK system empowers health professionals to engage more complex research where the synergisms of health and geospatial information will be robustly studied beyond what could be accomplished today. No longer is the need to know how to perform geospatial processing an impediment to the health researcher, but rather the development of how to think spatially becomes the greater challenge. ",
doi="10.2196/35073",
url="https://medinform.jmir.org/2022/4/e35073",
url="http://www.ncbi.nlm.nih.gov/pubmed/35311683"
}


@Article{info:doi/10.2196/35253,
author="Wang, Alex
and McCarron, Robert
and Azzam, Daniel
and Stehli, Annamarie
and Xiong, Glen
and DeMartini, Jeremy",
title="Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study",
journal="JMIR Ment Health",
year="2022",
month="Mar",
day="31",
volume="9",
number="3",
pages="e35253",
keywords="depression",
keywords="epidemiology",
keywords="internet",
keywords="google trends",
keywords="big data",
keywords="mental health",
abstract="Background: The epidemiology of mental health disorders has important theoretical and practical implications for health care service and planning. The recent increase in big data storage and subsequent development of analytical tools suggest that mining search databases may yield important trends on mental health, which can be used to support existing population health studies. Objective: This study aimed to map depression search intent in the United States based on internet-based mental health queries. Methods: Weekly data on mental health searches were extracted from Google Trends for an 11-year period (2010-2021) and separated by US state for the following terms: ``feeling sad,'' ``depressed,'' ``depression,'' ``empty,'' ``insomnia,'' ``fatigue,'' ``guilty,'' ``feeling guilty,'' and ``suicide.'' Multivariable regression models were created based on geographic and environmental factors and normalized to the following control terms: ``sports,'' ``news,'' ``google,'' ``youtube,'' ``facebook,'' and ``netflix.'' Heat maps of population depression were generated based on search intent. Results: Depression search intent grew 67\% from January 2010 to March 2021. Depression search intent showed significant seasonal patterns with peak intensity during winter (adjusted P<.001) and early spring months (adjusted P<.001), relative to summer months. Geographic location correlated with depression search intent with states in the Northeast (adjusted P=.01) having higher search intent than states in the South. Conclusions: The trends extrapolated from Google Trends successfully correlate with known risk factors for depression, such as seasonality and increasing latitude. These findings suggest that Google Trends may be a valid novel epidemiological tool to map depression prevalence in the United States. ",
doi="10.2196/35253",
url="https://mental.jmir.org/2022/3/e35253",
url="http://www.ncbi.nlm.nih.gov/pubmed/35357320"
}


@Article{info:doi/10.2196/31557,
author="Lyu, Zeyu
and Takikawa, Hiroki",
title="The Disparity and Dynamics of Social Distancing Behaviors in Japan: Investigation of Mobile Phone Mobility Data",
journal="JMIR Med Inform",
year="2022",
month="Mar",
day="22",
volume="10",
number="3",
pages="e31557",
keywords="COVID-19",
keywords="social distancing",
keywords="mobility",
keywords="time series",
keywords="tracking",
keywords="policy",
abstract="Background: The availability of large-scale and fine-grained aggregated mobility data has allowed researchers to observe the dynamics of social distancing behaviors at high spatial and temporal resolutions. Despite the increasing attention paid to this research agenda, limited studies have focused on the demographic factors related to mobility, and the dynamics of social distancing behaviors have not been fully investigated. Objective: This study aims to assist in designing and implementing public health policies by exploring how social distancing behaviors varied among various demographic groups over time. Methods: We combined several data sources, including mobile tracking mobility data and geographical statistics, to estimate the visiting population of entertainment venues across demographic groups, which can be considered the proxy of social distancing behaviors. Next, we used time series analysis methods to investigate how voluntary and policy-induced social distancing behaviors shifted over time across demographic groups. Results: Our findings demonstrate distinct patterns of social distancing behaviors and their dynamics across age groups. On the one hand, although entertainment venues' population comprises mainly individuals aged 20-40 years, a more significant proportion of the youth has adopted social distancing behaviors and complied with policy implementations compared to older age groups. From this perspective, the increasing contribution to infections by the youth should be more likely to be attributed to their number rather than their violation of social distancing behaviors. On the other hand, although risk perception and self-restriction recommendations can induce social distancing behaviors, their impact and effectiveness appear to be largely weakened during Japan's second state of emergency. Conclusions: This study provides a timely reference for policymakers about the current situation on how different demographic groups adopt social distancing behaviors over time. On the one hand, the age-dependent disparity requires more nuanced and targeted mitigation strategies to increase the intention of elderly individuals to adopt mobility restriction behaviors. On the other hand, considering that the effectiveness of policy implementations requesting social distancing behaviors appears to decline over time, in extreme cases, the government should consider imposing stricter social distancing interventions, as they are necessary to promote social distancing behaviors and mitigate the transmission of COVID-19. ",
doi="10.2196/31557",
url="https://medinform.jmir.org/2022/3/e31557",
url="http://www.ncbi.nlm.nih.gov/pubmed/35297764"
}


@Article{info:doi/10.2196/28857,
author="Beukenhorst, L. Anna
and Sergeant, C. Jamie
and Schultz, M. David
and McBeth, John
and Yimer, B. Belay
and Dixon, G. Will",
title="Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Nov",
day="16",
volume="9",
number="11",
pages="e28857",
keywords="geolocation",
keywords="global positioning system",
keywords="smartphones",
keywords="mobile phone",
keywords="mobile health",
keywords="environmental exposures",
keywords="data analysis",
keywords="digital epidemiology",
keywords="missing data",
keywords="location data",
keywords="mobile application",
abstract="Background: Smartphone location data can be used for observational health studies (to determine participant exposure or behavior) or to deliver a location-based health intervention. However, missing location data are more common when using smartphones compared to when using research-grade location trackers. Missing location data can affect study validity and intervention safety. Objective: The objective of this study was to investigate the distribution of missing location data and its predictors to inform design, analysis, and interpretation of future smartphone (observational and interventional) studies. Methods: We analyzed hourly smartphone location data collected from 9665 research participants on 488,400 participant days in a national smartphone study investigating the association between weather conditions and chronic pain in the United Kingdom. We used a generalized mixed-effects linear model with logistic regression to identify whether a successfully recorded geolocation was associated with the time of day, participants' time in study, operating system, time since previous survey completion, participant age, sex, and weather sensitivity. Results: For most participants, the app collected a median of 2 out of a maximum of 24 locations (1760/9665, 18.2\% of participants), no location data (1664/9665, 17.2\%), or complete location data (1575/9665, 16.3\%). The median locations per day differed by the operating system: participants with an Android phone most often had complete data (a median of 24/24 locations) whereas iPhone users most often had a median of 2 out of 24 locations. The odds of a successfully recorded location for Android phones were 22.91 times higher than those for iPhones (95\% CI 19.53-26.87). The odds of a successfully recorded location were lower during weekends (odds ratio [OR] 0.94, 95\% CI 0.94-0.95) and nights (OR 0.37, 95\% CI 0.37-0.38), if time in study was longer (OR 0.99 per additional day in study, 95\% CI 0.99-1.00), and if a participant had not used the app recently (OR 0.96 per additional day since last survey entry, 95\% CI 0.96-0.96). Participant age and sex did not predict missing location data. Conclusions: The predictors of missing location data reported in our study could inform app settings and user instructions for future smartphone (observational and interventional) studies. These predictors have implications for analysis methods to deal with missing location data, such as imputation of missing values or case-only analysis. Health studies using smartphones for data collection should assess context-specific consequences of high missing data, especially among iPhone users, during the night and for disengaged participants. ",
doi="10.2196/28857",
url="https://mhealth.jmir.org/2021/11/e28857",
url="http://www.ncbi.nlm.nih.gov/pubmed/34783661"
}


@Article{info:doi/10.2196/31161,
author="Bos, C. V{\'e}ronique L. L.
and Jansen, Tessa
and Klazinga, S. Niek
and Kringos, S. Dionne",
title="Development and Actionability of the Dutch COVID-19 Dashboard: Descriptive Assessment and Expert Appraisal Study",
journal="JMIR Public Health Surveill",
year="2021",
month="Oct",
day="12",
volume="7",
number="10",
pages="e31161",
keywords="COVID-19",
keywords="dashboard",
keywords="performance intelligence",
keywords="Netherlands",
keywords="actionability",
keywords="communication",
keywords="government",
keywords="pandemic",
keywords="public health",
abstract="Background: Web-based public reporting by means of dashboards has become an essential tool for governments worldwide to monitor COVID-19 information and communicate it to the public. The actionability of such dashboards is determined by their fitness for purpose---meeting a specific information need---and fitness for use---placing the right information into the right hands at the right time and in a manner that can be understood. Objective: The aim of this study was to identify specific areas where the actionability of the Dutch government's COVID-19 dashboard could be improved, with the ultimate goal of enhancing public understanding of the pandemic. Methods: The study was conducted from February 2020 to April 2021. A mixed methods approach was carried out, using (1) a descriptive checklist over time to monitor changes made to the dashboard, (2) an actionability scoring of the dashboard to pinpoint areas for improvement, and (3) a reflection meeting with the dashboard development team to contextualize findings and discuss areas for improvement. Results: The dashboard predominantly showed epidemiological information on COVID-19. It had been developed and adapted by adding more in-depth indicators, more geographic disaggregation options, and new indicator themes. It also changed in target audience from policy makers to the general public; thus, a homepage was added with the most important information, using news-like items to explain the provided indicators and conducting research to enhance public understanding of the dashboard. However, disaggregation options such as sex, socioeconomic status, and ethnicity and indicators on dual-track health system management and social and economic impact that have proven to give important insights in other countries are missing from the Dutch COVID-19 dashboard, limiting its actionability. Conclusions: The Dutch COVID-19 dashboard developed over time its fitness for purpose and use in terms of providing epidemiological information to the general public as a target audience. However, to strengthen the Dutch health system's ability to cope with upcoming phases of the COVID-19 pandemic or future public health emergencies, we advise (1) establishing timely indicators relating to health system capacity, (2) including relevant data disaggregation options (eg, sex, socioeconomic status), and (3) enabling interoperability between social, health, and economic data sources. ",
doi="10.2196/31161",
url="https://publichealth.jmir.org/2021/10/e31161",
url="http://www.ncbi.nlm.nih.gov/pubmed/34543229"
}


@Article{info:doi/10.2196/23648,
author="Jo, Youngji
and Barthel, Nathan
and Stierman, Elizabeth
and Clifton, Kathryn
and Pak, Semee Esther
and Ezeiru, Sonachi
and Ekweremadu, Diwe
and Onugu, Nnaemeka
and Ali, Zainab
and Egwu, Elijah
and Akoh, Ochayi
and Uzunyayla, Orkan
and Van Hulle, Suzanne",
title="The Potential of Digital Data Collection Tools for Long-lasting Insecticide-Treated Net Mass Campaigns in Nigeria: Formative Study",
journal="JMIR Form Res",
year="2021",
month="Oct",
day="8",
volume="5",
number="10",
pages="e23648",
keywords="long-lasting insecticide-treated nets",
keywords="malaria",
keywords="Nigeria",
keywords="information communication technology",
keywords="geographic information system",
keywords="supply chain management",
abstract="Background: Nigeria has the world's largest malaria burden, accounting for 27\% of the world's malaria cases and 23\% of malaria mortality globally. This formative study describes the operational process of the mass distribution of long-lasting insecticide-treated nets (LLINs) during a campaign program in Nigeria. Objective: This study aims to assess whether and how digital data collection and management tools can change current practices and help resolve major implementation issues. Methods: Qualitative data on the technical features and operational processes of paper-based and information and communication technology (ICT)--based systems in the Edo and Kwara states from June 2 to 30, 2017, were collected on the basis of documented operation manuals, field observations, and informant interviews. During the LLIN campaign in Edo State, we recruited 6 local government area focal persons and monitors and documented daily review meetings during household mobilization (9 days) and net distribution (5 days) to understand the major program implementation issues associated with the following three aspects: logistic issues, technical issues, and demand creation. Each issue was categorized according to the expected degree (low, mid, and high) of change by the ICT system. Results: The net campaign started with microplanning and training, followed by a month-long implementation process, which included household mobilization, net movement, net distribution, and end process monitoring. The ICT system can improve management and oversight issues related to data reporting and processes through user-centered interface design, built-in data quality control logic flow or algorithms, and workflow automation. These often require more than 50\% of staff time and effort in the current paper-based practice. Compared with the current paper-based system, the real-time system is expected to reduce the time to payment compensation for health workers by about 20 days and produce summary campaign statistics for at least 20 to 30 days. Conclusions: The ICT system can facilitate the measurement of population coverage beyond program coverage during an LLIN campaign with greater data reliability and timeliness, which are often compromised due to the limited workforce capacity in a paper-based system. ",
doi="10.2196/23648",
url="https://formative.jmir.org/2021/10/e23648",
url="http://www.ncbi.nlm.nih.gov/pubmed/34623310"
}


@Article{info:doi/10.2196/30444,
author="De Ridder, David
and Loizeau, Jutta Andrea
and Sandoval, Luis Jos{\'e}
and Ehrler, Fr{\'e}d{\'e}ric
and Perrier, Myriam
and Ritch, Albert
and Violot, Guillemette
and Santolini, Marc
and Greshake Tzovaras, Bastian
and Stringhini, Silvia
and Kaiser, Laurent
and Pradeau, Jean-Fran{\c{c}}ois
and Joost, St{\'e}phane
and Guessous, Idris",
title="Detection of Spatiotemporal Clusters of COVID-19--Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study",
journal="JMIR Res Protoc",
year="2021",
month="Oct",
day="6",
volume="10",
number="10",
pages="e30444",
keywords="participatory surveillance",
keywords="infectious disease",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="space-time clustering",
keywords="digital health",
keywords="mobile app",
keywords="mHealth",
keywords="epidemiology",
keywords="surveillance",
keywords="digital surveillance",
keywords="public health",
abstract="Background: The early detection of clusters of infectious diseases such as the SARS-CoV-2--related COVID-19 disease can promote timely testing recommendation compliance and help to prevent disease outbreaks. Prior research revealed the potential of COVID-19 participatory syndromic surveillance systems to complement traditional surveillance systems. However, most existing systems did not integrate geographic information at a local scale, which could improve the management of the SARS-CoV-2 pandemic. Objective: The aim of this study is to detect active and emerging spatiotemporal clusters of COVID-19--associated symptoms, and to examine (a posteriori) the association between the clusters' characteristics and sociodemographic and environmental determinants. Methods: This report presents the methodology and development of the @choum (English: ``achoo'') study, evaluating an epidemiological digital surveillance tool to detect and prevent clusters of individuals (target sample size, N=5000), aged 18 years or above, with COVID-19--associated symptoms living and/or working in the canton of Geneva, Switzerland. The tool is a 5-minute survey integrated into a free and secure mobile app (CoronApp-HUG). Participants are enrolled through a comprehensive communication campaign conducted throughout the 12-month data collection phase. Participants register to the tool by providing electronic informed consent and nonsensitive information (gender, age, geographically masked addresses). Symptomatic participants can then report COVID-19--associated symptoms at their onset (eg, symptoms type, test date) by tapping on the @choum button. Those who have not yet been tested are offered the possibility to be informed on their cluster status (information returned by daily automated clustering analysis). At each participation step, participants are redirected to the official COVID-19 recommendations websites. Geospatial clustering analyses are performed using the modified space-time density-based spatial clustering of applications with noise (MST-DBSCAN) algorithm. Results: The study began on September 1, 2020, and will be completed on February 28, 2022. Multiple tests performed at various time points throughout the 5-month preparation phase have helped to improve the tool's user experience and the accuracy of the clustering analyses. A 1-month pilot study performed among 38 pharmacists working in 7 Geneva-based pharmacies confirmed the proper functioning of the tool. Since the tool's launch to the entire population of Geneva on February 11, 2021, data are being collected and clusters are being carefully monitored. The primary study outcomes are expected to be published in mid-2022. Conclusions: The @choum study evaluates an innovative participatory epidemiological digital surveillance tool to detect and prevent clusters of COVID-19--associated symptoms. @choum collects precise geographic information while protecting the user's privacy by using geomasking methods. By providing an evidence base to inform citizens and local authorities on areas potentially facing a high COVID-19 burden, the tool supports the targeted allocation of public health resources and promotes testing. International Registered Report Identifier (IRRID): DERR1-10.2196/30444 ",
doi="10.2196/30444",
url="https://www.researchprotocols.org/2021/10/e30444",
url="http://www.ncbi.nlm.nih.gov/pubmed/34449403"
}


@Article{info:doi/10.2196/30854,
author="Hu, Tao
and Wang, Siqin
and Luo, Wei
and Zhang, Mengxi
and Huang, Xiao
and Yan, Yingwei
and Liu, Regina
and Ly, Kelly
and Kacker, Viraj
and She, Bing
and Li, Zhenlong",
title="Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective",
journal="J Med Internet Res",
year="2021",
month="Sep",
day="10",
volume="23",
number="9",
pages="e30854",
keywords="Twitter",
keywords="public opinion",
keywords="COVID-19 vaccines",
keywords="sentiment analysis",
keywords="emotion analysis",
keywords="topic modeling",
keywords="COVID-19",
abstract="Background: The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. Objective: The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter. Methods: We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes. Results: An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis. Conclusions: The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines. ",
doi="10.2196/30854",
url="https://www.jmir.org/2021/9/e30854",
url="http://www.ncbi.nlm.nih.gov/pubmed/34346888"
}


@Article{info:doi/10.2196/26231,
author="Liu, Lei
and Ni, Yizhao
and Beck, F. Andrew
and Brokamp, Cole
and Ramphul, C. Ryan
and Highfield, D. Linda
and Kanjia, Karkera Megha
and Pratap, ``Nick'' J.",
title="Understanding Pediatric Surgery Cancellation: Geospatial Analysis",
journal="J Med Internet Res",
year="2021",
month="Sep",
day="10",
volume="23",
number="9",
pages="e26231",
keywords="surgery cancellation",
keywords="socioeconomic factors",
keywords="spatial regression models",
keywords="machine learning",
abstract="Background: Day-of-surgery cancellation (DoSC) represents a substantial wastage of hospital resources and can cause significant inconvenience to patients and families. Cancellation is reported to impact between 2\% and 20\% of the 50 million procedures performed annually in American hospitals. Up to 85\% of cancellations may be amenable to the modification of patients' and families' behaviors. However, the factors underlying DoSC and the barriers experienced by families are not well understood. Objective: This study aims to conduct a geospatial analysis of patient-specific variables from electronic health records (EHRs) of Cincinnati Children's Hospital Medical Center (CCHMC) and of Texas Children's Hospital (TCH), as well as linked socioeconomic factors measured at the census tract level, to understand potential underlying contributors to disparities in DoSC rates across neighborhoods. Methods: The study population included pediatric patients who underwent scheduled surgeries at CCHMC and TCH. A 5-year data set was extracted from the CCHMC EHR, and addresses were geocoded. An equivalent set of data >5.7 years was extracted from the TCH EHR. Case-based data related to patients' health care use were aggregated at the census tract level. Community-level variables were extracted from the American Community Survey as surrogates for patients' socioeconomic and minority status as well as markers of the surrounding context. Leveraging the selected variables, we built spatial models to understand the variation in DoSC rates across census tracts. The findings were compared to those of the nonspatial regression and deep learning models. Model performance was evaluated from the root mean squared error (RMSE) using nested 10-fold cross-validation. Feature importance was evaluated by computing the increment of the RMSE when a single variable was shuffled within the data set. Results: Data collection yielded sets of 463 census tracts at CCHMC (DoSC rates 1.2\%-12.5\%) and 1024 census tracts at TCH (DoSC rates 3\%-12.2\%). For CCHMC, an L2-normalized generalized linear regression model achieved the best performance in predicting all-cause DoSC rate (RMSE 1.299\%, 95\% CI 1.21\%-1.387\%); however, its improvement over others was marginal. For TCH, an L2-normalized generalized linear regression model also performed best (RMSE 1.305\%, 95\% CI 1.257\%-1.352\%). All-cause DoSC rate at CCHMC was predicted most strongly by previous no show. As for community-level data, the proportion of African American inhabitants per census tract was consistently an important predictor. In the Texas area, the proportion of overcrowded households was salient to DoSC rate. Conclusions: Our findings suggest that geospatial analysis offers potential for use in targeting interventions for census tracts at a higher risk of cancellation. Our study also demonstrates the importance of home location, socioeconomic disadvantage, and racial minority status on the DoSC of children's surgery. The success of future efforts to reduce cancellation may benefit from taking social, economic, and cultural issues into account. ",
doi="10.2196/26231",
url="https://www.jmir.org/2021/9/e26231",
url="http://www.ncbi.nlm.nih.gov/pubmed/34505837"
}


@Article{info:doi/10.2196/15864,
author="Trotter II, Robert
and Baldwin, Julie
and Buck, Loren Charles
and Remiker, Mark
and Aguirre, Amanda
and Milner, Trudie
and Torres, Emma
and von Hippel, Arthur Frank",
title="Health Impacts of Perchlorate and Pesticide Exposure: Protocol for Community-Engaged Research to Evaluate Environmental Toxicants in a US Border Community",
journal="JMIR Res Protoc",
year="2021",
month="Aug",
day="11",
volume="10",
number="8",
pages="e15864",
keywords="community-engaged research",
keywords="endocrine disruption",
keywords="environmental contaminants",
keywords="health disparities",
keywords="toxic metal contamination",
keywords="perchlorates",
keywords="pesticides",
keywords="population health",
keywords="thyroid disease",
abstract="Background: The Northern Arizona University (NAU) Center for Health Equity Research (CHER) is conducting community-engaged health research involving ``environmental scans'' in Yuma County in collaboration with community health stakeholders, including the Yuma Regional Medical Center (YRMC), Regional Center for Border Health, Inc. (RCBH), Campesinos Sin Fronteras (CSF), Yuma County Public Health District, and government agencies and nongovernmental organizations (NGOs) working on border health issues. The purpose of these efforts is to address community-generated environmental health hazards identified through ongoing coalitions among NAU, and local health care and research institutions. Objective: We are undertaking joint community/university efforts to examine human exposures to perchlorate and agricultural pesticides. This project also includes the parallel development of a new animal model for investigating the mechanisms of toxicity following a ``one health'' approach. The ultimate goal of this community-engaged effort is to develop interventions to reduce exposures and health impacts of contaminants in Yuma populations. Methods: All participants completed the informed consent process, which included information on the purpose of the study, a request for access to health histories and medical records, and interviews. The interview included questions related to (1) demographics, (2) social determinants of health, (3) health screening, (4) occupational and environmental exposures to perchlorate and pesticides, and (5) access to health services. Each participant provided a hair sample for quantifying the metals used in pesticides, urine sample for perchlorate quantification, and blood sample for endocrine assays. Modeling will examine the relationships between the concentrations of contaminants and hormones, demographics and social determinants of health, and health status of the study population, including health markers known to be impacted by perchlorate and pesticides. Results: We recruited 323 adults residing in Yuma County during a 1-year pilot/feasibility study. Among these, 147 residents were patients from either YRMC or RCBH with a primary diagnosis of thyroid disease, including hyperthyroidism, hypothyroidism, thyroid cancer, or goiter. The remaining 176 participants were from the general population but with no history of thyroid disorder. The pilot study confirmed the feasibility of using the identified community-engaged protocol to recruit, consent, and collect data from a difficult-to-access, vulnerable population. The demographics of the pilot study population and positive feedback on the success of the community-engaged approach indicate that the project can be scaled up to a broader study with replicable population health findings. Conclusions: Using a community-engaged approach, the research protocol provided substantial evidence regarding the effectiveness of designing and implementing culturally relevant recruitment and dissemination processes that combine laboratory findings and public health information. Future findings will elucidate the mechanisms of toxicity and the population health effects of the contaminants of concern, as well as provide a new animal model to develop precision medicine capabilities for the population. International Registered Report Identifier (IRRID): DERR1-10.2196/15864 ",
doi="10.2196/15864",
url="https://www.researchprotocols.org/2021/8/e15864",
url="http://www.ncbi.nlm.nih.gov/pubmed/34383679"
}


@Article{info:doi/10.2196/29759,
author="Chaney, Cunard Sarah
and Mechael, Patricia
and Thu, Myo Nay
and Diallo, S. Mamadou
and Gachen, Carine",
title="Every Child on the Map: A Theory of Change Framework for Improving Childhood Immunization Coverage and Equity Using Geospatial Data and Technologies",
journal="J Med Internet Res",
year="2021",
month="Aug",
day="3",
volume="23",
number="8",
pages="e29759",
keywords="geospatial data",
keywords="immunization",
keywords="health information systems",
keywords="service delivery",
keywords="equity mapping",
keywords="theory",
keywords="framework",
keywords="children",
keywords="vaccine",
keywords="equity",
keywords="geospatial",
keywords="data",
keywords="outcome",
keywords="coverage",
keywords="low- and middle-income",
keywords="LMIC",
doi="10.2196/29759",
url="https://www.jmir.org/2021/8/e29759",
url="http://www.ncbi.nlm.nih.gov/pubmed/34342584"
}


@Article{info:doi/10.2196/23528,
author="Zheng, Shuai
and Edwards, R. Jonathan
and Dudeck, A. Margaret
and Patel, R. Prachi
and Wattenmaker, Lauren
and Mirza, Muzna
and Tejedor, Chernetsky Sheri
and Lemoine, Kent
and Benin, L. Andrea
and Pollock, A. Daniel",
title="Building an Interactive Geospatial Visualization Application for National Health Care--Associated Infection Surveillance: Development Study",
journal="JMIR Public Health Surveill",
year="2021",
month="Jul",
day="30",
volume="7",
number="7",
pages="e23528",
keywords="data visualization",
keywords="geospatial information system",
keywords="health care--associated infection",
abstract="Background: The Centers for Disease Control and Prevention's (CDC's) National Healthcare Safety Network (NHSN) is the most widely used health care--associated infection (HAI) and antimicrobial use and resistance surveillance program in the United States. Over 37,000 health care facilities participate in the program and submit a large volume of surveillance data. These data are used by the facilities themselves, the CDC, and other agencies and organizations for a variety of purposes, including infection prevention, antimicrobial stewardship, and clinical quality measurement. Among the summary metrics made available by the NHSN are standardized infection ratios, which are used to identify HAI prevention needs and measure progress at the national, regional, state, and local levels. Objective: To extend the use of geospatial methods and tools to NHSN data, and in turn to promote and inspire new uses of the rendered data for analysis and prevention purposes, we developed a web-enabled system that enables integrated visualization of HAI metrics and supporting data. Methods: We leveraged geocoding and visualization technologies that are readily available and in current use to develop a web-enabled system designed to support visualization and interpretation of data submitted to the NHSN from geographically dispersed sites. The server--client model--based system enables users to access the application via a web browser. Results: We integrated multiple data sets into a single-page dashboard designed to enable users to navigate across different HAI event types, choose specific health care facility or geographic locations for data displays, and scale across time units within identified periods. We launched the system for internal CDC use in January 2019. Conclusions: CDC NHSN statisticians, data analysts, and subject matter experts identified opportunities to extend the use of geospatial methods and tools to NHSN data and provided the impetus to develop NHSNViz. The development effort proceeded iteratively, with the developer adding or enhancing functionality and including additional data sets in a series of prototype versions, each of which incorporated user feedback. The initial production version of NHSNViz provides a new geospatial analytic resource built in accordance with CDC user requirements and extensible to additional users and uses in subsequent versions. ",
doi="10.2196/23528",
url="https://publichealth.jmir.org/2021/7/e23528",
url="http://www.ncbi.nlm.nih.gov/pubmed/34328436"
}


@Article{info:doi/10.2196/29191,
author="Tamura, Kosuke
and Curlin, Kaveri
and Neally, J. Sam
and Vijayakumar, P. Nithya
and Mitchell, M. Valerie
and Collins, S. Billy
and Gutierrez-Huerta, Cristhian
and Troendle, F. James
and Baumer, Yvonne
and Osei Baah, Foster
and Turner, S. Briana
and Gray, Veronica
and Tirado, A. Brian
and Ortiz-Chaparro, Erika
and Berrigan, David
and Mehta, N. Nehal
and Vaccarino, Viola
and Zenk, N. Shannon
and Powell-Wiley, M. Tiffany",
title="Geospatial Analysis of Neighborhood Environmental Stress in Relation to Biological Markers of Cardiovascular Health and Health Behaviors in Women: Protocol for a Pilot Study",
journal="JMIR Res Protoc",
year="2021",
month="Jul",
day="22",
volume="10",
number="7",
pages="e29191",
keywords="wearables",
keywords="global positioning system",
keywords="ecological momentary assessment",
keywords="accelerometer",
keywords="biomarkers of stress",
keywords="mobile phone",
abstract="Background: Innovative analyses of cardiovascular (CV) risk markers and health behaviors linked to neighborhood stressors are essential to further elucidate the mechanisms by which adverse neighborhood social conditions lead to poor CV outcomes. We propose to objectively measure physical activity (PA), sedentary behavior, and neighborhood stress using accelerometers, GPS, and real-time perceived ecological momentary assessment via smartphone apps and to link these to biological measures in a sample of White and African American women in Washington, DC, neighborhoods. Objective: The primary aim of this study is to test the hypothesis that living in adverse neighborhood social conditions is associated with higher stress-related neural activity among 60 healthy women living in high or low socioeconomic status neighborhoods in Washington, DC. Sub-aim 1 of this study is to test the hypothesis that the association is moderated by objectively measured PA using an accelerometer. A secondary objective is to test the hypothesis that residing in adverse neighborhood social environment conditions is related to differences in vascular function. Sub-aim 2 of this study is to test the hypothesis that the association is moderated by objectively measured PA. The third aim of this study is to test the hypothesis that adverse neighborhood social environment conditions are related to differences in immune system activation. Methods: The proposed study will be cross-sectional, with a sample of at least 60 women (30 healthy White women and 30 healthy Black women) from Wards 3 and 5 in Washington, DC. A sample of the women (n=30) will be recruited from high-income areas in Ward 3 from census tracts within a 15\% of Ward 3's range for median household income. The other participants (n=30) will be recruited from low-income areas in Wards 5 from census tracts within a 15\% of Ward 5's range for median household income. Finally, participants from Wards 3 and 5 will be matched based on age, race, and BMI. Participants will wear a GPS unit and accelerometer and report their stress and mood in real time using a smartphone. We will then examine the associations between GPS-derived neighborhood variables, stress-related neural activity measures, and adverse biological markers. Results: The National Institutes of Health Institutional Review Board has approved this study. Recruitment will begin in the summer of 2021. Conclusions: Findings from this research could inform the development of multilevel behavioral interventions and policies to better manage environmental factors that promote immune system activation or psychosocial stress while concurrently working to increase PA, thereby influencing CV health. International Registered Report Identifier (IRRID): PRR1-10.2196/29191 ",
doi="10.2196/29191",
url="https://www.researchprotocols.org/2021/7/e29191",
url="http://www.ncbi.nlm.nih.gov/pubmed/34292168"
}


@Article{info:doi/10.2196/19587,
author="Onovo, Amobi
and Kalaiwo, Abiye
and Katbi, Moses
and Ogorry, Otse
and Jaquet, Antoine
and Keiser, Olivia",
title="Geographical Disparities in HIV Seroprevalence Among Men Who Have Sex with Men and People Who Inject Drugs in Nigeria: Exploratory Spatial Data Analysis",
journal="JMIR Public Health Surveill",
year="2021",
month="May",
day="24",
volume="7",
number="5",
pages="e19587",
keywords="key population",
keywords="MSM",
keywords="PWID",
keywords="HIV seroprevalence",
keywords="HIV testing modality",
keywords="hotspots",
keywords="geospatial",
keywords="Getis-Ord-Gi*",
keywords="IBBSS",
keywords="Nigeria",
abstract="Background: The assessment of geographical heterogeneity of HIV among men who have sex with men (MSM) and people who inject drugs (PWID) can usefully inform targeted HIV prevention and care strategies. Objective: We aimed to measure HIV seroprevalence and identify hotspots of HIV infection among MSM and PWID in Nigeria. Methods: We included all MSM and PWID accessing HIV testing services across 7 prioritized states (Lagos, Nasarawa, Akwa Ibom, Cross Rivers, Rivers, Benue, and the Federal Capital Territory) in 3 geographic regions (North Central, South South, and South West) between October 1, 2016, and September 30, 2017. We extracted data from national testing registers, georeferenced all HIV test results aggregated at the local government area level, and calculated HIV seroprevalence. We calculated and compared HIV seroprevalence from our study to the 2014 integrated biological and behavioural surveillance survey and used global spatial autocorrelation and hotspot analysis to highlight patterns of HIV infection and identify areas of significant clustering of HIV cases. Results: MSM and PWID had HIV seroprevalence rates of 12.14\% (3209/26,423) and 11.88\% (1126/9474), respectively. Global spatial autocorrelation Moran I statistics revealed a clustered distribution of HIV infection among MSM and PWID with a <5\% and <1\% likelihood that this clustered pattern could be due to chance, respectively. Significant clusters of HIV infection (Getis-Ord-Gi* statistics) confined to the North Central and South South regions were identified among MSM and PWID. Compared to the 2014 integrated biological and behavioural surveillance survey, our results suggest an increased HIV seroprevalence among PWID and a substantial decrease among MSM. Conclusions: This study identified geographical areas to prioritize for control of HIV infection among MSM and PWID, thus demonstrating that geographical information system technology is a useful tool to inform public health planning for interventions targeting epidemic control of HIV infection. ",
doi="10.2196/19587",
url="https://publichealth.jmir.org/2021/5/e19587",
url="http://www.ncbi.nlm.nih.gov/pubmed/34028360"
}


@Article{info:doi/10.2196/19502,
author="Lima, Yuri
and Pinheiro, Wallace
and Barbosa, Eduardo Carlos
and Magalh{\~a}es, Matheus
and Chaves, Miriam
and de Souza, Moreira Jano
and Rodrigues, S{\'e}rgio
and Xex{\'e}o, Geraldo",
title="Development of an Index for the Inspection of Aedes aegypti Breeding Sites in Brazil: Multi-criteria Analysis",
journal="JMIR Public Health Surveill",
year="2021",
month="May",
day="10",
volume="7",
number="5",
pages="e19502",
keywords="multi-criteria analysis",
keywords="public health",
keywords="human sensors",
keywords="vector surveillance",
keywords="tropical diseases",
abstract="Background: Aedes aegypti is a vector for the transmission of diseases such as dengue fever, chikungunya, Zika fever, and yellow fever. In 2016, over 1 million cases of these diseases were reported in Brazil, which is an alarming public health issue. One of the ways of controlling this disease is by inspecting and neutralizing the places where A. aegypti lays its eggs. The Ministry of Planning, Development, and Administration of Brazil maintains the inspection statistics. Objective: We propose a multi-criteria analysis to create an index for A. aegypti inspections reported through the Ministry of Planning, Development, and Administration system of Brazil. Methods: Based on the repository from urban cleaning services combined with data on inspections conducted by government agencies in several Brazilian cities and municipalities, we selected and combined metrics, which we further ranked using the analytic hierarchy process methodology. We also developed risk maps based on the analytic hierarchy process ranking of the A. aegypti breeding sites. Results: Based on our analysis and the available data, the priority for inspections should consider the number of sick people (weight 0.350), medical evaluations (weight 0.239), inspections (weight 0.201), mosquito breeding sites (weight 0.126), and days of absence from work (weight 0.096). Conclusions: The proposed index could aid public health practitioners in preventing the appearance of new A. aegypti breeding sites. This information technology application can help solve such public health challenges. ",
doi="10.2196/19502",
url="https://publichealth.jmir.org/2021/5/e19502",
url="http://www.ncbi.nlm.nih.gov/pubmed/33970118"
}


@Article{info:doi/10.2196/23426,
author="Xiang, Anthony
and Hou, Wei
and Rashidian, Sina
and Rosenthal, N. Richard
and Abell-Hart, Kayley
and Zhao, Xia
and Wang, Fusheng",
title="Association of Opioid Use Disorder With 2016 Presidential Voting Patterns: Cross-sectional Study in New York State at Census Tract Level",
journal="JMIR Public Health Surveill",
year="2021",
month="Apr",
day="21",
volume="7",
number="4",
pages="e23426",
keywords="opioid use disorder",
keywords="opioid poisoning",
keywords="racial and ethnic disparities",
keywords="geographic variance",
keywords="sociodemographic factors",
keywords="presidential election",
abstract="Background: Opioid overdose-related deaths have increased dramatically in recent years. Combating the opioid epidemic requires better understanding of the epidemiology of opioid poisoning (OP) and opioid use disorder (OUD). Objective: We aimed to discover geospatial patterns in nonmedical opioid use and its correlations with demographic features related to despair and economic hardship, most notably the US presidential voting patterns in 2016 at census tract level in New York State. Methods: This cross-sectional analysis used data from New York Statewide Planning and Research Cooperative System claims data and the presidential voting results of 2016 in New York State from the Harvard Election Data Archive. We included 63,958 patients who had at least one OUD diagnosis between 2010 and 2016 and 36,004 patients with at least one OP diagnosis between 2012 and 2016. Geospatial mappings were created to compare areas of New York in OUD rates and presidential voting patterns. A multiple regression model examines the extent that certain factors explain OUD rate variation. Results: Several areas shared similar patterns of OUD rates and Republican vote: census tracts in western New York, central New York, and Suffolk County. The correlation between OUD rates and the Republican vote was .38 (P<.001). The regression model with census tract level of demographic and socioeconomic factors explains 30\% of the variance in OUD rates, with disability and Republican vote as the most significant predictors. Conclusions: At the census tract level, OUD rates were positively correlated with Republican support in the 2016 presidential election, disability, unemployment, and unmarried status. Socioeconomic and demographic despair-related features explain a large portion of the association between the Republican vote and OUD. Together, these findings underscore the importance of socioeconomic interventions in combating the opioid epidemic. ",
doi="10.2196/23426",
url="https://publichealth.jmir.org/2021/4/e23426",
url="http://www.ncbi.nlm.nih.gov/pubmed/33881409"
}


@Article{info:doi/10.2196/25682,
author="Ivankovi{\'c}, Damir
and Barbazza, Erica
and Bos, V{\'e}ronique
and Brito Fernandes, {\'O}scar
and Jamieson Gilmore, Kendall
and Jansen, Tessa
and Kara, Pinar
and Larrain, Nicolas
and Lu, Shan
and Meza-Torres, Bernardo
and Mulyanto, Joko
and Poldrugovac, Mircha
and Rotar, Alexandru
and Wang, Sophie
and Willmington, Claire
and Yang, Yuanhang
and Yelgezekova, Zhamin
and Allin, Sara
and Klazinga, Niek
and Kringos, Dionne",
title="Features Constituting Actionable COVID-19 Dashboards: Descriptive Assessment and Expert Appraisal of 158 Public Web-Based COVID-19 Dashboards",
journal="J Med Internet Res",
year="2021",
month="Feb",
day="24",
volume="23",
number="2",
pages="e25682",
keywords="COVID-19",
keywords="pandemic",
keywords="internet",
keywords="performance measures",
keywords="public reporting of health care data",
keywords="public health",
keywords="surveillance",
keywords="health information management",
keywords="dashboard",
keywords="accessibility",
keywords="online tool",
keywords="communication",
keywords="feature",
keywords="expert",
abstract="Background: Since the outbreak of COVID-19, the development of dashboards as dynamic, visual tools for communicating COVID-19 data has surged worldwide. Dashboards can inform decision-making and support behavior change. To do so, they must be actionable. The features that constitute an actionable dashboard in the context of the COVID-19 pandemic have not been rigorously assessed. Objective: The aim of this study is to explore the characteristics of public web-based COVID-19 dashboards by assessing their purpose and users (``why''), content and data (``what''), and analyses and displays (``how'' they communicate COVID-19 data), and ultimately to appraise the common features of highly actionable dashboards. Methods: We conducted a descriptive assessment and scoring using nominal group technique with an international panel of experts (n=17) on a global sample of COVID-19 dashboards in July 2020. The sequence of steps included multimethod sampling of dashboards; development and piloting of an assessment tool; data extraction and an initial round of actionability scoring; a workshop based on a preliminary analysis of the results; and reconsideration of actionability scores followed by joint determination of common features of highly actionable dashboards. We used descriptive statistics and thematic analysis to explore the findings by research question. Results: A total of 158 dashboards from 53 countries were assessed. Dashboards were predominately developed by government authorities (100/158, 63.0\%) and were national (93/158, 58.9\%) in scope. We found that only 20 of the 158 dashboards (12.7\%) stated both their primary purpose and intended audience. Nearly all dashboards reported epidemiological indicators (155/158, 98.1\%), followed by health system management indicators (85/158, 53.8\%), whereas indicators on social and economic impact and behavioral insights were the least reported (7/158, 4.4\% and 2/158, 1.3\%, respectively). Approximately a quarter of the dashboards (39/158, 24.7\%) did not report their data sources. The dashboards predominately reported time trends and disaggregated data by two geographic levels and by age and sex. The dashboards used an average of 2.2 types of displays (SD 0.86); these were mostly graphs and maps, followed by tables. To support data interpretation, color-coding was common (93/158, 89.4\%), although only one-fifth of the dashboards (31/158, 19.6\%) included text explaining the quality and meaning of the data. In total, 20/158 dashboards (12.7\%) were appraised as highly actionable, and seven common features were identified between them. Actionable COVID-19 dashboards (1) know their audience and information needs; (2) manage the type, volume, and flow of displayed information; (3) report data sources and methods clearly; (4) link time trends to policy decisions; (5) provide data that are ``close to home''; (6) break down the population into relevant subgroups; and (7) use storytelling and visual cues. Conclusions: COVID-19 dashboards are diverse in the why, what, and how by which they communicate insights on the pandemic and support data-driven decision-making. To leverage their full potential, dashboard developers should consider adopting the seven actionability features identified. ",
doi="10.2196/25682",
url="https://www.jmir.org/2021/2/e25682",
url="http://www.ncbi.nlm.nih.gov/pubmed/33577467"
}


@Article{info:doi/10.2196/23870,
author="Ali, H. Shahmir
and Imbruce, M. Valerie
and Russo, G. Rienna
and Kaplan, Samuel
and Stevenson, Kaye
and Mezzacca, Adjoian Tamar
and Foster, Victoria
and Radee, Ashley
and Chong, Stella
and Tsui, Felice
and Kranick, Julie
and Yi, S. Stella",
title="Evaluating Closures of Fresh Fruit and Vegetable Vendors During the COVID-19 Pandemic: Methodology and Preliminary Results Using Omnidirectional Street View Imagery",
journal="JMIR Form Res",
year="2021",
month="Feb",
day="18",
volume="5",
number="2",
pages="e23870",
keywords="built environment",
keywords="Google Street View",
keywords="food retail environment",
keywords="COVID-19",
keywords="geographic surveillance",
keywords="food",
keywords="longitudinal",
keywords="supply chain",
keywords="economy",
keywords="demand",
keywords="service",
keywords="vendor",
keywords="surveillance",
abstract="Background: The COVID-19 pandemic has significantly disrupted the food retail environment. However, its impact on fresh fruit and vegetable vendors remains unclear; these are often smaller, more community centered, and may lack the financial infrastructure to withstand supply and demand changes induced by such crises. Objective: This study documents the methodology used to assess fresh fruit and vegetable vendor closures in New York City (NYC) following the start of the COVID-19 pandemic by using Google Street View, the new Apple Look Around database, and in-person checks. Methods: In total, 6 NYC neighborhoods (in Manhattan and Brooklyn) were selected for analysis; these included two socioeconomically advantaged neighborhoods (Upper East Side, Park Slope), two socioeconomically disadvantaged neighborhoods (East Harlem, Brownsville), and two Chinese ethnic neighborhoods (Chinatown, Sunset Park). For each neighborhood, Google Street View was used to virtually walk down each street and identify vendors (stores, storefronts, street vendors, or wholesalers) that were open and active in 2019 (ie, both produce and vendor personnel were present at a location). Past vendor surveillance (when available) was used to guide these virtual walks. Each identified vendor was geotagged as a Google Maps pinpoint that research assistants then physically visited. Using the ``notes'' feature of Google Maps as a data collection tool, notes were made on which of three categories best described each vendor: (1) open, (2) open with a more limited setup (eg, certain sections of the vendor unit that were open and active in 2019 were missing or closed during in-person checks), or (3) closed/absent. Results: Of the 135 open vendors identified in 2019 imagery data, 35\% (n=47) were absent/closed and 10\% (n=13) were open with more limited setups following the beginning of the COVID-19 pandemic. When comparing boroughs, 35\% (28/80) of vendors in Manhattan were absent/closed, as were 35\% (19/55) of vendors in Brooklyn. Although Google Street View was able to provide 2019 street view imagery data for most neighborhoods, Apple Look Around was required for 2019 imagery data for some areas of Park Slope. Past surveillance data helped to identify 3 additional established vendors in Chinatown that had been missed in street view imagery. The Google Maps ``notes'' feature was used by multiple research assistants simultaneously to rapidly collect observational data on mobile devices. Conclusions: The methodology employed enabled the identification of closures in the fresh fruit and vegetable retail environment and can be used to assess closures in other contexts. The use of past baseline surveillance data to aid vendor identification was valuable for identifying vendors that may have been absent or visually obstructed in the street view imagery data. Data collection using Google Maps likewise has the potential to enhance the efficiency of fieldwork in future studies. ",
doi="10.2196/23870",
url="http://formative.jmir.org/2021/2/e23870/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33539310"
}


@Article{info:doi/10.2196/24204,
author="Gu, Dayong
and He, Jianan
and Sun, Jie
and Shi, Xin
and Ye, Ying
and Zhang, Zishuai
and Wang, Xiangjun
and Su, Qun
and Yu, Wenjin
and Yuan, Xiaopeng
and Dong, Ruiling",
title="The Global Infectious Diseases Epidemic Information Monitoring System: Development and Usability Study of an Effective Tool for Travel Health Management in China",
journal="JMIR Public Health Surveill",
year="2021",
month="Feb",
day="16",
volume="7",
number="2",
pages="e24204",
keywords="infectious disease",
keywords="epidemic information",
keywords="travel health",
keywords="global",
keywords="surveillance",
abstract="Background: Obtaining comprehensive epidemic information for specific global infectious diseases is crucial to travel health. However, different infectious disease information websites may have different purposes, which may lead to misunderstanding by travelers and travel health staff when making accurate epidemic control and management decisions. Objective: The objective of this study was to develop a Global Infectious Diseases Epidemic Information Monitoring System (GIDEIMS) in order to provide comprehensive and timely global epidemic information. Methods: Distributed web crawler and cloud agent acceleration technologies were used to automatically collect epidemic information about more than 200 infectious diseases from 26 established epidemic websites and Baidu News. Natural language processing and in-depth learning technologies have been utilized to intelligently process epidemic information collected in 28 languages. Currently, the GIDEIMS presents world epidemic information using a geographical map, including date, disease name, reported cases in different countries, and the epidemic situation in China. In order to make a practical assessment of the GIDEIMS, we compared infectious disease data collected from the GIDEIMS and other websites on July 16, 2019. Results: Compared with the Global Incident Map and Outbreak News Today, the GIDEIMS provided more comprehensive information on human infectious diseases. The GIDEIMS is currently used in the Health Quarantine Department of Shenzhen Customs District (Shenzhen, China) and was recommended to the Health Quarantine Administrative Department of the General Administration of Customs (China) and travel health--related departments. Conclusions: The GIDEIMS is one of the most intelligent tools that contributes to safeguarding the health of travelers, controlling infectious disease epidemics, and effectively managing public health in China. ",
doi="10.2196/24204",
url="http://publichealth.jmir.org/2021/2/e24204/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33591286"
}


@Article{info:doi/10.2196/24826,
author="Alves, Domingos
and Yamada, Bettiol Diego
and Bernardi, Andrade Filipe
and Carvalho, Isabelle
and Filho, Colombo M{\'a}rcio Eloi
and Neiva, Barros Mariane
and Lima, Costa Vin{\'i}cius
and F{\'e}lix, Maria T{\^e}mis",
title="Mapping, Infrastructure, and Data Analysis for the Brazilian Network of Rare Diseases: Protocol for the RARASnet Observational Cohort Study",
journal="JMIR Res Protoc",
year="2021",
month="Jan",
day="22",
volume="10",
number="1",
pages="e24826",
keywords="rare disease",
keywords="digital health",
keywords="health observatory",
keywords="data science",
keywords="health network",
abstract="Background: A rare disease is a medical condition with low prevalence in the general population, but these can collectively affect up to 10\% of the population. Thus, rare diseases have a significant impact on the health care system, and health professionals must be familiar with their diagnosis, management, and treatment. Objective: This paper aims to provide health indicators regarding the rare diseases in Brazil and to create a network of reference centers with health professionals from different regions of the country. RARASnet proposes to map, analyze, and communicate all the data regarding the infrastructure of the centers and the patients' progress or needs. The focus of the proposed study is to provide all the technical infrastructure and analysis, following the World Health Organization and the Brazilian Ministry of Health guidelines. Methods: To build this digitized system, we will provide a security framework to assure the privacy and protection of each patient when collecting data. Systems development life cycle methodologies will also be applied to align software development, infrastructure operation, and quality assurance. After data collection of all information designed by the specialists, the computational analysis, modeling, and results will be communicated in scientific research papers and a digital health observatory. Results: The project has several activities, and it is in an initial stage. Initially, a survey was given to all health care centers to understand the technical aspects of each network member, such as the existence of computers, technical support staff, and digitized systems. In this survey, we detected that 59\% (23/39) of participating health units have electronic medical records, while 41\% (16/39) have paper records. Therefore, we will have different strategies to access the data from each center in the data collection phase. Later, we will standardize and analyze the clinical and epidemiological data and use these data to develop a national network for monitoring rare diseases and a digital health observatory to make the information available. The project had its financing approved in December 2019. Retrospective data collection started in October 2020, and we expect to finish in January 2021. During the third quarter of 2020, we enrolled 40 health institutions from all regions of Brazil. Conclusions: The nature of rare disease diagnosis is complex and diverse, and many problems will be faced in the evolution of the project. However, decisions based on data analysis are the best option for the improvement of the rare disease network in Brazil. The creation of RARASnet, along with all the digitized infrastructure, can improve the accessibility of information and standardization of rare diseases in the country. International Registered Report Identifier (IRRID): DERR1-10.2196/24826 ",
doi="10.2196/24826",
url="http://www.researchprotocols.org/2021/1/e24826/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33480849"
}


@Article{info:doi/10.2196/24132,
author="Parikh, Nidhi
and Daughton, R. Ashlynn
and Rosenberger, Earl William
and Aberle, Jacob Derek
and Chitanvis, Elizabeth Maneesha
and Altherr, Michael Forest
and Velappan, Nileena
and Fairchild, Geoffrey
and Deshpande, Alina",
title="Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study",
journal="JMIR Public Health Surveill",
year="2021",
month="Jan",
day="7",
volume="7",
number="1",
pages="e24132",
keywords="disease re-emergence",
keywords="infectious disease",
keywords="supervised learning",
keywords="random forest",
keywords="visual analytics",
keywords="surveillance",
abstract="Background: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. Objective: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? Methods: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies. Results: Our supervised learning models were able to identify 82\%-90\% of the local re-emergence events, although with 18\%-31\% (except 46\% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence. Conclusions: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above. ",
doi="10.2196/24132",
url="http://publichealth.jmir.org/2021/1/e24132/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33316766"
}


@Article{info:doi/10.2196/23379,
author="Do, Quan
and Marc, David
and Plotkin, Marat
and Pickering, Brian
and Herasevich, Vitaly",
title="Starter Kit for Geotagging and Geovisualization in Health Care: Resource Paper",
journal="JMIR Form Res",
year="2020",
month="Dec",
day="24",
volume="4",
number="12",
pages="e23379",
keywords="geographic mapping",
keywords="medicalGIS guidelines",
keywords="information storage and retrieval",
keywords="mapping",
keywords="geotagging",
keywords="data visualization",
keywords="population",
keywords="public health",
abstract="Background: Geotagging is the process of attaching geospatial tags to various media data types. In health care, the goal of geotagging is to gain a better understanding of health-related questions applied to populations. Although there has been a prevalence of geographic information in public health, in order to effectively use and expand geotagging across health care there is a requirement to understand other factors such as the disposition, standardization, data sources, technologies, and limitations. Objective: The objective of this document is to serve as a resource for new researchers in the field. This report aims to be comprehensive but easy for beginners to understand and adopt in practice. The optimal geocodes, their sources, and a rationale for use are suggested. Geotagging's issues and limitations are also discussed. Methods: A comprehensive review of technical instructions and articles was conducted to evaluate guidelines for geotagging, and online resources were curated to support the implementation of geotagging practices. Summary tables were developed to describe the available geotagging resources (free and for fee) that can be leveraged by researchers and quality improvement personnel to effectively perform geospatial analyses primarily targeting US health care. Results: This paper demonstrated steps to develop an initial geotagging and geovisualization project with clear structure and instructions. The geotagging resources were summarized. These resources are essential for geotagging health care projects. The discussion section provides better understanding of geotagging's limitations and suggests suitable way to approach it. Conclusions: We explain how geotagging can be leveraged in health care and offer the necessary initial resources to obtain geocodes, adjustment data, and health-related measures. The resources outlined in this paper can support an individual and/or organization in initiating a geotagging health care project. ",
doi="10.2196/23379",
url="http://formative.jmir.org/2020/12/e23379/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33361054"
}


@Article{info:doi/10.2196/12842,
author="Sambaturu, Prathyush
and Bhattacharya, Parantapa
and Chen, Jiangzhuo
and Lewis, Bryan
and Marathe, Madhav
and Venkatramanan, Srinivasan
and Vullikanti, Anil",
title="An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study",
journal="JMIR Public Health Surveill",
year="2020",
month="Sep",
day="4",
volume="6",
number="3",
pages="e12842",
keywords="epidemic data analysis",
keywords="summarization",
keywords="spatio-temporal patterns",
keywords="transactional data mining",
abstract="Background: Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. Objective: Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons. Methods: We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio). Results: We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50\% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio. Conclusions: Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives. ",
doi="10.2196/12842",
url="http://publichealth.jmir.org/2020/3/e12842/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32701458"
}


@Article{info:doi/10.2196/20285,
author="Liu, Dianbo
and Clemente, Leonardo
and Poirier, Canelle
and Ding, Xiyu
and Chinazzi, Matteo
and Davis, Jessica
and Vespignani, Alessandro
and Santillana, Mauricio",
title="Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models",
journal="J Med Internet Res",
year="2020",
month="Aug",
day="17",
volume="22",
number="8",
pages="e20285",
keywords="COVID-19",
keywords="coronavirus",
keywords="digital epidemiology",
keywords="modeling",
keywords="modeling disease outbreaks",
keywords="emerging outbreak",
keywords="machine learning",
keywords="precision public health",
keywords="machine learning in public health",
keywords="forecasting",
keywords="digital data",
keywords="mechanistic model",
keywords="hybrid simulation",
keywords="hybrid model",
keywords="simulation",
abstract="Background: The inherent difficulty of identifying and monitoring emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing coronavirus disease (COVID-19) outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events. Objective: We present a timely and novel methodology that combines disease estimates from mechanistic models and digital traces, via interpretable machine learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real time. Methods: Our method uses the following as inputs: (a) official health reports, (b) COVID-19--related internet search activity, (c) news media activity, and (d) daily forecasts of COVID-19 activity from a metapopulation mechanistic model. Our machine learning methodology uses a clustering technique that enables the exploitation of geospatial synchronicities of COVID-19 activity across Chinese provinces and a data augmentation technique to deal with the small number of historical disease observations characteristic of emerging outbreaks. Results: Our model is able to produce stable and accurate forecasts 2 days ahead of the current time and outperforms a collection of baseline models in 27 out of 32 Chinese provinces. Conclusions: Our methodology could be easily extended to other geographies currently affected by COVID-19 to aid decision makers with monitoring and possibly prevention. ",
doi="10.2196/20285",
url="http://www.jmir.org/2020/8/e20285/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32730217"
}


@Article{info:doi/10.2196/15948,
author="Elliston, G. Katherine
and Sch{\"u}z, Benjamin
and Albion, Tim
and Ferguson, G. Stuart",
title="Comparison of Geographic Information System and Subjective Assessments of Momentary Food Environments as Predictors of Food Intake: An Ecological Momentary Assessment Study",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Jul",
day="22",
volume="8",
number="7",
pages="e15948",
keywords="ecological momentary assessment",
keywords="mHealth",
keywords="geographic information systems",
keywords="food intake",
keywords="mobile phone",
abstract="Background: It has been observed that eating is influenced by the presence and availability of food. Being aware of the presence of food in the environment may enable mobile health (mHealth) apps to use geofencing techniques to determine the most appropriate time to proactively deliver interventions. To date, however, studies on eating typically rely on self-reports of environmental contexts, which may not be accurate or feasible for issuing mHealth interventions. Objective: This study aimed to compare the subjective and geographic information system (GIS) assessments of the momentary food environment to explore the feasibility of using GIS data to predict eating behavior and inform geofenced interventions. Methods: In total, 72 participants recorded their food intake in real-time for 14 days using an ecological momentary assessment approach. Participants logged their food intake and responded to approximately 5 randomly timed assessments each day. During each assessment, the participants reported the number and type of food outlets nearby. Their electronic diaries simultaneously recorded their GPS coordinates. The GPS data were later overlaid with a GIS map of food outlets to produce an objective count of the number of food outlets within 50 m of the participant. Results: Correlations between self-reported and GIS counts of food outlets within 50 m were only of a small size (r=0.17; P<.001). Logistic regression analyses revealed that the GIS count significantly predicted eating similar to the self-reported counts (area under the curve for the receiver operating characteristic curve [AUC-ROC] self-report=0.53, SE 0.00 versus AUC-ROC 50 m GIS=0.53, SE 0.00; P=.41). However, there was a significant difference between the GIS-derived and self-reported counts of food outlets and the self-reported type of food outlets (AUC-ROC self-reported outlet type=0.56, SE 0.01; P<.001). Conclusions: The subjective food environment appears to predict eating better than objectively measured food environments via GIS. mHealth apps may need to consider the type of food outlets rather than the raw number of outlets in an individual's environment. ",
doi="10.2196/15948",
url="https://mhealth.jmir.org/2020/7/e15948",
url="http://www.ncbi.nlm.nih.gov/pubmed/32706728"
}


@Article{info:doi/10.2196/16726,
author="Swartzendruber, Andrea
and Lambert, N. Danielle",
title="A Web-Based Geolocated Directory of Crisis Pregnancy Centers (CPCs) in the United States: Description of CPC Map Methods and Design Features and Analysis of Baseline Data",
journal="JMIR Public Health Surveill",
year="2020",
month="Mar",
day="27",
volume="6",
number="1",
pages="e16726",
keywords="directory",
keywords="crisis pregnancy center",
keywords="abortion, induced",
keywords="reproductive health",
keywords="policy",
keywords="access to information",
abstract="Background: Crisis pregnancy centers (CPCs) are nonprofit organizations that aim to dissuade people considering abortion. The centers frequently advertise in misleading ways and provide inaccurate health information. CPCs in the United States are becoming more medicalized and gaining government funding and support. We created a CPC Map, a Web-based geolocated database of all CPCs currently operating in the United States, to help individuals seeking health services know which centers are CPCs and to facilitate academic research. Objective: This study aimed to describe the methods used to develop and maintain the CPC Map and baseline findings regarding the number and distribution of CPCs in the United States. We also examined associations between direct state funding and the number of CPCs and relationships between the number of CPCs and state legislation proposed in 2018-2019 to ban all or most abortions. Methods: In 2018, we used standard protocols to identify and verify the locations of and services offered by CPCs operating in the United States. The CPC Map was designed to be a publicly accessible, user-friendly searchable database that can be easily updated. We examined the number of CPCs and, using existing data, the ratios of women of reproductive age to CPCs and CPCs to abortion facilities nationally and by region, subregion, and state. We used unadjusted and adjusted negative binomial regression models to examine associations between direct state funding and the number of CPCs. We used unadjusted and adjusted logistic regression models to examine associations between the number of CPCs by state and legislation introduced in 2018-2019 to ban all or most abortions. Adjusted models controlled for the numbers of women of reproductive age and abortion facilities per state. Results: We identified 2527 operating CPCs. Of these, 66.17\% (1672/2527) offered limited medical services. Nationally, the ratio of women of reproductive age to CPCs was 29,304:1. The number of CPCs per abortion facility was 3.2. The South and Midwest had the greatest numbers of CPCs. The number of CPCs per state ranged from three (Rhode Island) to 203 (Texas). Direct funding was associated with a greater number of CPCs in unadjusted (coefficient: 0.87, 95\% CI 0.51-1.22) and adjusted (coefficient: 0.45, 95\% CI 0.33-0.57) analyses. The number of CPCs was associated with the state legislation introduced in 2018-2019 to ban all or most abortions in unadjusted (odds ratio [OR] 1.04, 95\% CI 1.01-1.06) and adjusted analyses (OR 1.11, 95\% CI 1.04-1.19). Conclusions: CPCs are located in every state and particularly prevalent in the South and Midwest. Distribution of CPCs in the United States is associated with state funding and extreme proposals to restrict abortion. Researchers should track CPCs over time and examine factors that influence their operations and impact on public health and policy. ",
doi="10.2196/16726",
url="http://publichealth.jmir.org/2020/1/e16726/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32217502"
}


@Article{info:doi/10.2196/16853,
author="Pedersen, R. Eric
and Firth, Caislin
and Parker, Jennifer
and Shih, A. Regina
and Davenport, Steven
and Rodriguez, Anthony
and Dunbar, S. Michael
and Kraus, Lisa
and Tucker, S. Joan
and D'Amico, J. Elizabeth",
title="Locating Medical and Recreational Cannabis Outlets for Research Purposes: Online Methods and Observational Study",
journal="J Med Internet Res",
year="2020",
month="Feb",
day="26",
volume="22",
number="2",
pages="e16853",
keywords="marijuana",
keywords="cannabis",
keywords="dispensaries",
keywords="retailers",
keywords="Los Angeles",
keywords="tobacco",
abstract="Background: An increasing number of states have laws for the legal sale of recreational and medical cannabis out of brick-and-mortar storefront locations. Given the proliferation of cannabis outlets and their potential for impact on local economies, neighborhood structures, and individual patterns of cannabis use, it is essential to create practical and thorough methods to capture the location of such outlets for research purposes. However, methods used by researchers vary greatly between studies and often do not include important information about the retailer's license status and storefront signage. Objective: The aim of this study was to find methods for locating and observing cannabis outlets in Los Angeles County after the period when recreational cannabis retailers were granted licenses and allowed to be open for business. Methods: The procedures included searches of online cannabis outlet databases, followed by methods to verify each outlet's name, address, license information, and open status. These procedures, conducted solely online, resulted in a database of 531 outlets. To further verify each outlet's information and collect signage data, we conducted direct observations of the 531 identified outlets. Results: We found that 80.9\% (430/531) of these outlets were open for business, of which 37.6\% (162/430) were licensed to sell cannabis. Unlicensed outlets were less likely to have signage indicating the store sold cannabis, such as a green cross, which was the most prevalent form of observed signage. Co-use of cannabis and tobacco/nicotine has been found to be a substantial health concern, and we observed that 40.6\% (175/430) of cannabis outlets had a tobacco/nicotine outlet within sight of the cannabis outlet. Most (350/430, 81.4\%) cannabis outlets were located within the City of Los Angeles, and these outlets were more likely to be licensed than outlets outside the city. Conclusions: The findings of this study suggest that online searches and observational methods are both necessary to best capture accurate and detailed information about cannabis outlets. The methods described here can be applied to other metropolitan areas to more accurately capture the availability of cannabis in an area. ",
doi="10.2196/16853",
url="https://www.jmir.org/2020/2/e16853",
url="http://www.ncbi.nlm.nih.gov/pubmed/32130141"
}


@Article{info:doi/10.2196/15283,
author="Poelman, P. Maartje
and van Lenthe, J. Frank
and Scheider, Simon
and Kamphuis, BM Carlijn",
title="A Smartphone App Combining Global Positioning System Data and Ecological Momentary Assessment to Track Individual Food Environment Exposure, Food Purchases, and Food Consumption: Protocol for the Observational FoodTrack Study",
journal="JMIR Res Protoc",
year="2020",
month="Jan",
day="28",
volume="9",
number="1",
pages="e15283",
keywords="ecological momentary assessment",
keywords="eating behavior",
keywords="environmental exposure",
keywords="mobile apps",
keywords="smartphone",
keywords="geographic information systems",
keywords="food preferences",
keywords="diet records",
abstract="Background: Our understanding of how food choices are affected by exposure to the food environment is limited, and there are important gaps in the literature. Recently developed smartphone-based technologies, including global positioning systems and ecological momentary assessment, enable these gaps to be filled. Objective: We present the FoodTrack study design and methods, as well as participants' compliance with the study protocol and their experiences with the app. We propose future analyses of the data to examine individual food environmental exposure taking into account the accessible food environment and individual time constraints; to assess people's food choices in relation to food environmental exposure; and to examine the moderating role of individual and contextual determinants of food purchases and consumption. Methods: We conducted a 7-day observational study among adults (25-45 years of age) living in urban areas in the Netherlands. Participants completed a baseline questionnaire, used an app (incorporating global positioning system tracking and ecological momentary assessment) for 7 days, and then completed a closing survey. The app automatically collected global positioning system tracking data, and participants uploaded information on all food purchases over the 7-day period into the app. Participants also answered questions on contextual or individual purchase-related determinants directly after each purchase. During the final 3 days of the study, the participants also uploaded data on fruit, vegetable, and snack consumption and answered similar ecological momentary assessment questions after each intake. Results: In total, 140 participants completed the study. More than half of the participants said they liked the app (81/140, 57.9\%) and found it easy to use (75/140, 53.6\%). Of the 140 participants, 126 (90.0\%) said that they had collected data on all or almost all purchases and intakes during the 7-day period. Most found the additional ecological momentary assessment questions ``easy to answer'' (113/140, 80.7\%) with ``no effort'' (99/140, 70.7\%). Of 106 participants who explored their trips in the app, 20 (18.8\%) had trouble with their smartphone's global positioning system tracking function. Therefore, we will not be able to include all participants in some of the proposed analyses, as we lack these data. We are analyzing data from the first study aim and we expect to publish the results in the spring of 2020. Conclusions: Participants perceived the FoodTrack app as a user-friendly tool. The app is particularly useful for observational studies that aim to gain insight into daily food environment exposure and food choices. Further analyses of the FoodTrack study data will provide novel insights into individual food environmental exposure, evidence on the individual food environment-diet interaction, and insights into the underlying individual and contextual mechanisms of food purchases and consumption. International Registered Report Identifier (IRRID): DERR1-10.2196/15283 ",
doi="10.2196/15283",
url="http://www.researchprotocols.org/2020/1/e15283/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32012100"
}


@Article{info:doi/10.2196/14609,
author="Fulton, Lawrence
and Kruse, Scott Clemens",
title="Hospital-Based Back Surgery: Geospatial-Temporal, Explanatory, and Predictive Models",
journal="J Med Internet Res",
year="2019",
month="Oct",
day="29",
volume="21",
number="10",
pages="e14609",
keywords="back surgery",
keywords="neurosurgeon",
keywords="elastic net",
keywords="lasso",
keywords="ridge",
keywords="random forest",
keywords="geospatial mapping",
keywords="health economics",
keywords="obesity",
keywords="practice variation",
abstract="Background: Hospital-based back surgery in the United States increased by 60\% from January 2012 to December 2017, yet the supply of neurosurgeons remained relatively constant. During this time, adult obesity grew by 5\%. Objective: This study aimed to evaluate the demand and associated costs for hospital-based back surgery by geolocation over time to evaluate provider practice variation. The study then leveraged hierarchical time series to generate tight demand forecasts on an unobserved test set. Finally, explanatory financial, technical, workload, geographical, and temporal factors as well as state-level obesity rates were investigated as predictors for the demand for hospital-based back surgery. Methods: Hospital data from January 2012 to December 2017 were used to generate geospatial-temporal maps and a video of the Current Procedural Terminology codes beginning with the digit 63 claims. Hierarchical time series modeling provided forecasts for each state, the census regions, and the nation for an unobserved test set and then again for the out-years of 2018 and 2019. Stepwise regression, lasso regression, ridge regression, elastic net, and gradient-boosted random forests were built on a training set and evaluated on a test set to evaluate variables important to explaining the demand for hospital-based back surgery. Results: Widespread, unexplained practice variation over time was seen using geographical information systems (GIS) multimedia mapping. Hierarchical time series provided accurate forecasts on a blind dataset and suggested a 6.52\% (from 497,325 procedures in 2017 to 529,777 in 2018) growth of hospital-based back surgery in 2018 (529,777 and up to 13.00\% by 2019 [from 497,325 procedures in 2017 to 563,023 procedures in 2019]). The increase in payments by 2019 are estimated to be US \$323.9 million. Extreme gradient-boosted random forests beat constrained and unconstrained regression models on a 20\% unobserved test set and suggested that obesity is one of the most important factors in explaining the increase in demand for hospital-based back surgery. Conclusions: Practice variation and obesity are factors to consider when estimating demand for hospital-based back surgery. Federal, state, and local planners should evaluate demand-side and supply-side interventions for this emerging problem. ",
doi="10.2196/14609",
url="http://www.jmir.org/2019/10/e14609/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31663856"
}


@Article{info:doi/10.2196/13593,
author="Cooper, Fenimore Hannah Luke
and Crawford, D. Natalie
and Haard{\"o}rfer, Regine
and Prood, Nadya
and Jones-Harrell, Carla
and Ibragimov, Umedjon
and Ballard, M. April
and Young, M. April",
title="Using Web-Based Pin-Drop Maps to Capture Activity Spaces Among Young Adults Who Use Drugs in Rural Areas: Cross-Sectional Survey",
journal="JMIR Public Health Surveill",
year="2019",
month="Oct",
day="18",
volume="5",
number="4",
pages="e13593",
keywords="rural",
keywords="substance use disorder",
keywords="Web-based data collection",
keywords="geospatial methods",
keywords="risk environment",
keywords="activity spaces",
abstract="Background: Epicenters of harmful drug use are expanding to US rural areas, with rural young adults bearing a disproportionate burden. A large body of work suggests that place characteristics (eg, spatial access to health services) shape vulnerability to drug-related harms among urban residents. Research on the role of place characteristics in shaping these harms among rural residents is nascent, as are methods of gathering place-based data. Objective: We (1) analyzed whether young rural adults who used drugs answered self-administered Web-based mapping items about locations where they engaged in risk behaviors and (2) determined the precision of mapped locations. Methods: Eligible individuals had to report recently using opioids to get high; be aged between 18 and 35 years; and live in the 5-county rural Appalachian Kentucky study area. We used targeted outreach and peer-referral methods to recruit participants. The survey asked participants to drop a pin in interactive maps to mark where they completed the survey, and where they had slept most; used drugs most; and had sex most in the past 6 months. Precision was assessed by (1) determining whether mapped locations were within 100 m of a structure and (2) calculating the Euclidean distance between the pin-drop home location and the street address where participants reported sleeping most often. Measures of central tendency and dispersion were calculated for all variables; distributions of missingness for mapping items and for the Euclidean distance variable were explored across participant characteristics. Results: Of the 151 participants, 88.7\% (134/151) completed all mapping items, and ?92.1\% (>139/151) dropped a pin at each of the 4 locations queried. Missingness did not vary across most participant characteristics, except that lower percentages of full-time workers and peer-recruited participants mapped some locations. Two-thirds of the pin-drop sex and drug use locations were less than 100 m from a structure, as were 92.1\% (139/151) of pin-drop home locations. The median distance between the pin-drop and street-address home locations was 2.0 miles (25th percentile=0.8 miles; 75th percentile=5.5 miles); distances were shorter for high-school graduates, staff-recruited participants, and participants reporting no technical difficulties completing the survey. Conclusions: Missingness for mapping items was low and unlikely to introduce bias, given that it varied across few participant characteristics. Precision results were mixed. In a rural study area of 1378 square miles, most pin-drop home addresses were near a structure; it is unsurprising that fewer drug and sex locations were near structures because most participants reported engaging in these activities outside at times. The error in pin-drop home locations, however, might be too large for some purposes. We offer several recommendations to strengthen future research, including gathering metadata on the extent to which participants zoom in on each map and recruiting participants via trusted staff. ",
doi="10.2196/13593",
url="https://publichealth.jmir.org/2019/4/e13593",
url="http://www.ncbi.nlm.nih.gov/pubmed/31628787"
}


@Article{info:doi/10.2196/12110,
author="Basak, Arinjoy
and Cadena, Jose
and Marathe, Achla
and Vullikanti, Anil",
title="Detection of Spatiotemporal Prescription Opioid Hot Spots With Network Scan Statistics: Multistate Analysis",
journal="JMIR Public Health Surveill",
year="2019",
month="Jun",
day="17",
volume="5",
number="2",
pages="e12110",
keywords="opiate dependence",
keywords="spatial temporal analysis",
keywords="network scan statistics",
keywords="medical specialty",
abstract="Background: Overuse and misuse of prescription opioids have become signi?cant public health burdens in the United States. About 11.5 million people are estimated to have misused prescription opioids for nonmedical purposes in 2016. This has led to a signi?cant number of drug overdose deaths in the United States. Previous studies have examined spatiotemporal clusters of opioid misuse, but they have been restricted to circular shaped regions. Objective: The goal of this study was to identify spatiotemporal hot spots of opioid users and opioid prescription claims using Medicare data. Methods: We examined spatiotemporal clusters with signi?cantly higher number of bene?ciaries and rate of prescriptions for opioids using Medicare payment data from the Centers for Medicare \& Medicaid Services. We used network scan statistics to detect signi?cant clusters with arbitrary shapes, the Kulldorff scan statistic to examine the signi?cant clusters for each year (2013, 2014, and 2015) and an expectation-based version to examine the signi?cant clusters relative to past years. Regression analysis was used to characterize the demographics of the counties that are a part of any signi?cant cluster, and data mining techniques were used to discover the specialties of the anomalous providers. Results: We examined anomalous spatial clusters with respect to opioid prescription claims and bene?ciary counts and found some common patterns across states: the counties in the most anomalous clusters were fairly stable in 2014 and 2015, but they have shrunk from 2013. In Virginia, a higher percentage of African Americans in a county lower the odds of the county being anomalous in terms of opioid beneficiary counts to about 0.96 in 2015. For opioid prescription claim counts, the odds were 0.92. This pattern was consistent across the 3 states and across the 3 years. A higher number of people in the county with access to Medicaid increased the odds of the county being in the anomalous cluster to 1.16 in both types of counts in Virginia. A higher number of people with access to direct purchase of insurance plans decreased the odds of a county being in an anomalous cluster to 0.85. The expectation-based scan statistic, which captures change over time, revealed different clusters than the Kulldorff statistic. Providers with an unusually high number of opioid beneficiaries and opioid claims include specialties such as physician's assistant, nurse practitioner, and family practice. Conclusions: Our analysis of the Medicare claims data provides characteristics of the counties and provider specialties that have higher odds of being anomalous. The empirical analysis identifies highly refined spatial hot spots that are likely to encounter prescription opioid misuse and overdose. The methodology is generic and can be applied to monitor providers and their prescription behaviors in regions that are at a high risk of abuse. ",
doi="10.2196/12110",
url="http://publichealth.jmir.org/2019/2/e12110/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31210142"
}


@Article{info:doi/10.2196/11342,
author="Concannon, David
and Herbst, Kobus
and Manley, Ed",
title="Developing a Data Dashboard Framework for Population Health Surveillance: Widening Access to Clinical Trial Findings",
journal="JMIR Form Res",
year="2019",
month="Apr",
day="04",
volume="3",
number="2",
pages="e11342",
keywords="data visualization",
keywords="data dashboards",
keywords="health and demographic surveillance",
keywords="sub-Saharan Africa",
keywords="treatment as prevention",
keywords="clinical trials",
keywords="demographics",
keywords="real-time",
keywords="data literacy",
abstract="Background: Population surveillance sites generate many datasets relevant to disease surveillance. However, there is a risk that these data are underutilized because of the volumes of data gathered and the lack of means to quickly disseminate analysis. Data visualization offers a means to quickly disseminate, understand, and interpret datasets, facilitating evidence-driven decision making through increased access to information. Objectives: This paper describes the development and evaluation of a framework for data dashboard design, to visualize datasets produced at a demographic health surveillance site. The aim of this research was to produce a comprehensive, reusable, and scalable dashboard design framework to fit the unique requirements of the context. Methods: The framework was developed and implemented at a demographic surveillance platform at the Africa Health Research Institute, in KwaZulu-Natal, South Africa. This context represents an exemplar implementation for the use of data dashboards within a population health-monitoring setting. Before the full launch, an evaluation study was undertaken to assess the effectiveness of the dashboard framework as a data communication and decision-making tool. The evaluation included a quantitative task evaluation to assess usability and a qualitative questionnaire exploring the attitudes to the use of dashboards. Results: The evaluation participants were drawn from a diverse group of users working at the site (n=20), comprising of community members, nurses, scientific and operational staff. Evaluation demonstrated high usability for the dashboard across user groups, with scientific and operational staff having minimal issues in completing tasks. There were notable differences in the efficiency of task completion among user groups, indicating varying familiarity with data visualization. The majority of users felt that the dashboards provided a clear understanding of the datasets presented and had a positive attitude to their increased use. Conclusions: Overall, this exploratory study indicates the viability of the data dashboard framework in communicating data trends within population surveillance setting. The usability differences among the user groups discovered during the evaluation demonstrate the need for the user-led design of dashboards in this context, addressing heterogeneous computer and visualization literacy present among the diverse potential users present in such settings. The questionnaire highlighted the enthusiasm for increased access to datasets from all stakeholders highlighting the potential of dashboards in this context. ",
doi="10.2196/11342",
url="https://formative.jmir.org/2019/2/e11342/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30946016"
}


@Article{info:doi/10.2196/10335,
author="Zanetti, Michele
and Campi, Rita
and Olivieri, Paola
and Campiotti, Marta
and Faggianelli, Alice
and Bonati, Maurizio",
title="A Web-Based Form With Interactive Charts Used to Collect and Analyze Data on Home Births in Italy",
journal="J Med Internet Res",
year="2019",
month="Mar",
day="22",
volume="21",
number="3",
pages="e10335",
keywords="Web-based form",
keywords="home birth",
keywords="interactive charts",
keywords="internet",
keywords="survey methods",
abstract="Background: The use of Web-based forms and data analysis can improve the collection and visualization of data in clinical research. In Italy, no register exists that collects clinical data concerning home births. Objective: The purpose of this study was (1) to develop a Web portal to collect, through a Web-based form, data on home births in Italy and (2) to provide those interested with a graphic visualization of the analyses and data collected. Methods: Following the World Health Organization's guidelines, and adding questions based on scientific evidence, the case report form (CRF) on the online form was drafted by midwives of the National Association of Out-of-Hospital Birth Midwives. During an initial phase, a group of midwives (n=10) tested the CRF, leading to improvements and adding the necessary questions to achieve a CRF that would allow a more complete collection of data. After the test phase, the entire group of midwives (n=166) registered themselves on the system and began filling out birth questionnaires. In a subsequent phase, the administrators of the portal were able to view the completed forms in a graphic format through the use of interactive maps and graphs. Results: From 2014 to 2016, 58 midwives included 599 birth questionnaires via the Web portal; of these, 443 were home-based, 76\% (321/424) of which were performed at home and 24\% (103/424) at a midwifery unit. Most of the births assisted (79\%, 335/424) were in northern Italy, and the average ages of the mother and father were 33.6 (SD 4.7) years and 37.0 (SD 5.6) years, respectively. Conclusions: We developed an innovative Web-based form that allows, for the first time in Italy, the collection of data on home births and births in the midwifery unit. Furthermore, the data collected are viewable online by the midwives through interactive maps and graphs that allow them to have a general and continuously updated view of the situation of out-of-hospital births performed by the National Association of Out-of-Hospital Birth Midwives. The future goal is to be able to expand this data collection to all out-of-hospital births throughout the national territory. With an increase in the number of enrolled midwives, it would be possible to use the portal as a Web-based form and also as a portal for sharing resources that would help midwives in their clinical practice. ",
doi="10.2196/10335",
url="https://www.jmir.org/2019/3/e10335/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30900993"
}


@Article{info:doi/10.2196/12032,
author="Velappan, Nileena
and Daughton, Rae Ashlynn
and Fairchild, Geoffrey
and Rosenberger, Earl William
and Generous, Nicholas
and Chitanvis, Elizabeth Maneesha
and Altherr, Michael Forest
and Castro, A. Lauren
and Priedhorsky, Reid
and Abeyta, Luis Esteban
and Naranjo, A. Leslie
and Hollander, Dawn Attelia
and Vuyisich, Grace
and Lillo, Maria Antonietta
and Cloyd, Kathryn Emily
and Vaidya, Rajendra Ashvini
and Deshpande, Alina",
title="Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks",
journal="JMIR Public Health Surveill",
year="2019",
month="Feb",
day="25",
volume="5",
number="1",
pages="e12032",
keywords="epidemiology",
keywords="infectious diseases",
keywords="algorithm",
keywords="public health informatics",
keywords="web browser",
abstract="Background: Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. Objective: This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. Methods: We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user's understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. Results: The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. Conclusions: AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak. ",
doi="10.2196/12032",
url="http://publichealth.jmir.org/2019/1/e12032/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30801254"
}


@Article{info:doi/10.2196/mental.9533,
author="DeJohn, D. Amber
and Schulz, English Emily
and Pearson, L. Amber
and Lachmar, Megan E.
and Wittenborn, K. Andrea",
title="Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study",
journal="JMIR Ment Health",
year="2018",
month="Nov",
day="05",
volume="5",
number="4",
pages="e61",
keywords="depression",
keywords="Web-based",
keywords="social connection",
keywords="Twitter",
keywords="tweet",
keywords="online communities",
abstract="Background: Depression is the leading cause of diseases globally and is often characterized by a lack of social connection. With the rise of social media, it is seen that Twitter users are seeking Web-based connections for depression. Objective: This study aimed to identify communities where Twitter users tweeted using the hashtag \#MyDepressionLooksLike to connect about depression. Once identified, we wanted to understand which community characteristics correlated to Twitter users turning to a Web-based community to connect about depression. Methods: Tweets were collected using NCapture software from May 25 to June 1, 2016 during the Mental Health Month (n=104) in the northeastern United States and Washington DC. After mapping tweets, we used a Poisson multilevel regression model to predict tweets per community (county) offset by the population and adjusted for percent female, percent population aged 15-44 years, percent white, percent below poverty, and percent single-person households. We then compared predicted versus observed counts and calculated tweeting index values (TIVs) to represent undertweeting and overtweeting. Last, we examined trends in community characteristics by TIV using Pearson correlation. Results: We found significant associations between tweet counts and area-level proportions of females, single-person households, and population aged 15-44 years. TIVs were lower than expected (TIV 1) in eastern, seaboard areas of the study region. There were communities tweeting as expected in the western, inland areas (TIV 2). Counties tweeting more than expected were generally scattered throughout the study region with a small cluster at the base of Maine. When examining community characteristics and overtweeting and undertweeting by county, we observed a clear upward gradient in several types of nonprofits and TIV values. However, we also observed U-shaped relationships for many community factors, suggesting that the same characteristics were correlated with both overtweeting and undertweeting. Conclusions: Our findings suggest that Web-based communities, rather than replacing physical connection, may complement or serve as proxies for offline social communities, as seen through the consistent correlations between higher levels of tweeting and abundant nonprofits. Future research could expand the spatiotemporal scope to confirm these findings. ",
doi="10.2196/mental.9533",
url="http://mental.jmir.org/2018/4/e61/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30401662"
}


@Article{info:doi/10.2196/11668,
author="Amato, S. Michael
and Graham, L. Amanda",
title="Geographic Representativeness of a Web-Based Smoking Cessation Intervention: Reach Equity Analysis",
journal="J Med Internet Res",
year="2018",
month="Oct",
day="24",
volume="20",
number="10",
pages="e11668",
keywords="smoking cessation",
keywords="health behavior",
keywords="internet",
keywords="population health",
keywords="rural health",
keywords="urban health",
keywords="health equity",
keywords="telemedicine",
abstract="Background: Cigarette smoking is the leading cause of preventable death and disease in the United States. Smoking prevalence is higher in rural areas than in metropolitan areas, due partly to differences in access to cessation treatment. With internet use at 89\% of all US adults, digital approaches could increase use of cessation treatment and reduce smoking. Objective: We investigated the extent to which smokers from rural areas use a digital cessation resource. We compared the geographic distribution of registered users of a free Web-based smoking cessation program with the geographic distribution of US smokers. Methods: We mapped user-provided ZIP codes to Rural-Urban Continuum Codes. A total of 59,050 of 118,574 users (49.80\%) provided valid ZIP codes from 2013 to 2017. We used US National Survey of Drug Use and Health data from 2013 to 2017 to compare the geographic distribution of our sample of Web-based cessation users with the geographic distribution of US smokers. Reach ratios and 95\% confidence intervals quantified the extent to which rural smokers' representation in the sample was proportionate to their representation in the national smoking population. Reach ratios less than 1 indicate underrepresentation. Results: Smokers from rural areas were significantly underrepresented in 2013 (reach ratio 0.89, 95\% CI 0.87-0.91) and 2014 (reach ratio 0.89, 95\% CI 0.86-0.92), proportionally represented in 2015 (reach ratio 1.08, 95\% CI 1.02-1.14) and 2016 (reach ratio 1.03, 95\% CI 0.94-1.14), and proportionally overrepresented in 2017 (reach ratio 1.16, 95\% CI 1.12-1.21). Smokers from Large Metro areas were proportionally represented in 2013 and 2014 but underrepresented in 2015 (reach ratio 0.97, 95\% CI 0.94-1.00), 2016 (reach ratio 0.89, 95\% CI 0.85-0.94), and 2017 (reach ratio 0.89, 95\% CI 0.86-0.91). Conclusions: Results suggest that smokers from rural areas are more than proportionally reached by a long-standing digital cessation intervention. The underrepresentation of smokers from Large Metro areas warrants further study. ",
doi="10.2196/11668",
url="http://www.jmir.org/2018/10/e11668/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30355557"
}


@Article{info:doi/10.2196/mhealth.9771,
author="Goodspeed, Robert
and Yan, Xiang
and Hardy, Jean
and Vydiswaran, Vinod V. G.
and Berrocal, J. Veronica
and Clarke, Philippa
and Romero, M. Daniel
and Gomez-Lopez, N. Iris
and Veinot, Tiffany",
title="Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study",
journal="JMIR Mhealth Uhealth",
year="2018",
month="Aug",
day="13",
volume="6",
number="8",
pages="e168",
keywords="urban population",
keywords="spatial behavior",
keywords="mobile phone",
keywords="environment and public health",
keywords="data accuracy",
abstract="Background: Mobile devices are increasingly used to collect location-based information from individuals about their physical activities, dietary intake, environmental exposures, and mental well-being. Such research, which typically uses wearable devices or mobile phones to track location, benefits from the growing availability of fine-grained data regarding human mobility. However, little is known about the comparative geospatial accuracy of such devices. Objective: In this study, we compared the data quality of location information collected from two mobile devices that determine location in different ways---a global positioning system (GPS) watch and a mobile phone with Google's Location History feature enabled. Methods: A total of 21 chronically ill participants carried both devices, which generated digital traces of locations, for 28 days. A mobile phone--based brief ecological momentary assessment (EMA) survey asked participants to manually report their location at 4 random times throughout each day. Participants also took part in qualitative interviews and completed surveys twice during the study period in which they reviewed recent mobile phone and watch trace data to compare the devices' trace data with their memory of their activities on those days. Trace data from the devices were compared on the basis of (1) missing data days, (2) reasons for missing data, (3) distance between the route data collected for matching day and the associated EMA survey locations, and (4) activity space total area and density surfaces. Results: The watch resulted in a much higher proportion of missing data days (P<.001), with missing data explained by technical differences between the devices as well as participant behaviors. The mobile phone was significantly more accurate in detecting home locations (P=.004) and marginally more accurate (P=.07) for all types of locations combined. The watch data resulted in a smaller activity space area and more accurately recorded outdoor travel and recreation. Conclusions: The most suitable mobile device for location-based health research depends on the particular study objectives. Furthermore, data generated from mobile devices, such as GPS phones and smartwatches, require careful analysis to ensure quality and completeness. Studies that seek precise measurement of outdoor activity and travel, such as measuring outdoor physical activity or exposure to localized environmental hazards, would benefit from the use of GPS devices. Conversely, studies that aim to account for time within buildings at home or work, or those that document visits to particular places (such as supermarkets, medical facilities, or fast food restaurants), would benefit from the greater precision demonstrated by the mobile phone in recording indoor activities. ",
doi="10.2196/mhealth.9771",
url="http://mhealth.jmir.org/2018/8/e168/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30104185"
}


@Article{info:doi/10.2196/publichealth.8931,
author="Card, George Kiffer
and Gibbs, Jeremy
and Lachowsky, John Nathan
and Hawkins, W. Blake
and Compton, Miranda
and Edward, Joshua
and Salway, Travis
and Gislason, K. Maya
and Hogg, S. Robert",
title="Using Geosocial Networking Apps to Understand the Spatial Distribution of Gay and Bisexual Men: Pilot Study",
journal="JMIR Public Health Surveill",
year="2018",
month="Aug",
day="08",
volume="4",
number="3",
pages="e61",
keywords="service access",
keywords="geosocial networking apps",
keywords="gay and bisexual men",
keywords="spatial distribution",
keywords="gay neighborhoods",
abstract="Background: While services tailored for gay, bisexual, and other men who have sex with men (gbMSM) may provide support for this vulnerable population, planning access to these services can be difficult due to the unknown spatial distribution of gbMSM outside of gay-centered neighborhoods. This is particularly true since the emergence of geosocial networking apps, which have become a widely used venue for meeting sexual partners. Objective: The goal of our research was to estimate the spatial density of app users across Metro Vancouver and identify the independent and adjusted neighborhood-level factors that predict app user density. Methods: This pilot study used a popular geosocial networking app to estimate the spatial density of app users across rural and urban Metro Vancouver. Multiple Poisson regression models were then constructed to model the relationship between app user density and areal population-weighted neighbourhood-level factors from the 2016 Canadian Census and National Household Survey. Results: A total of 2021 app user profiles were counted within 1 mile of 263 sampling locations. In a multivariate model controlling for time of day, app user density was associated with several dissemination area--level characteristics, including population density (per 100; incidence rate ratio [IRR] 1.03, 95\% CI 1.02-1.04), average household size (IRR 0.26, 95\% CI 0.11-0.62), average age of males (IRR 0.93, 95\% CI 0.88-0.98), median income of males (IRR 0.96, 95\% CI 0.92-0.99), proportion of males who were not married (IRR 1.08, 95\% CI 1.02-1.13), proportion of males with a postsecondary education (IRR 1.06, 95\% CI 1.03-1.10), proportion of males who are immigrants (IRR 1.04, 95\% CI 1.004-1.07), and proportion of males living below the low-income cutoff level (IRR 0.93, 95\% CI 0.89-0.98). Conclusions: This pilot study demonstrates how the combination of geosocial networking apps and administrative datasets might help care providers, planners, and community leaders target online and offline interventions for gbMSM who use apps. ",
doi="10.2196/publichealth.8931",
url="http://publichealth.jmir.org/2018/3/e61/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30089609"
}


@Article{info:doi/10.2196/11203,
author="Nsabimana, Placide Alain
and Uzabakiriho, Bernard
and Kagabo, M. Daniel
and Nduwayo, Jerome
and Fu, Qinyouen
and Eng, Allison
and Hughes, Joshua
and Sia, K. Samuel",
title="Bringing Real-Time Geospatial Precision to HIV Surveillance Through Smartphones: Feasibility Study",
journal="JMIR Public Health Surveill",
year="2018",
month="Aug",
day="07",
volume="4",
number="3",
pages="e11203",
keywords="HIV surveillance",
keywords="smartphones",
keywords="mobile phones",
keywords="geospatial data",
abstract="Background: Precise measurements of HIV incidences at community level can help mount a more effective public health response, but the most reliable methods currently require labor-intensive population surveys. Novel mobile phone technologies are being tested for adherence to medical appointments and antiretroviral therapy, but using them to track HIV test results with automatically generated geospatial coordinates has not been widely tested. Objective: We customized a portable reader for interpreting the results of HIV lateral flow tests and developed a mobile phone app to track HIV test results in urban and rural locations in Rwanda. The objective was to assess the feasibility of this technology to collect front line HIV test results in real time and with geospatial context to help measure HIV incidences and improve epidemiological surveillance. Methods: Twenty health care workers used the technology to track the test results of 2190 patients across 3 hospital sites (2 urban sites in Kigali and a rural site in the Western Province of Rwanda). Mobile phones for less than US \$70 each were used. The mobile phone app to record HIV test results could take place without internet connectivity with uploading of results to the cloud taking place later with internet. Results: A total of 91.51\% (2004/2190) of HIV test results could be tracked in real time on an online dashboard with geographical resolution down to street level. Out of the 20 health care workers, 14 (70\%) would recommend the lateral flow reader, and 100\% would recommend the mobile phone app. Conclusions: Smartphones have the potential to simplify the input of HIV test results with geospatial context and in real time to improve public health surveillance of HIV. ",
doi="10.2196/11203",
url="http://publichealth.jmir.org/2018/3/e11203/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30087088"
}


@Article{info:doi/10.2196/mhealth.9974,
author="Jones, Helen Kerina
and Daniels, Helen
and Heys, Sharon
and Ford, Vincent David",
title="Challenges and Potential Opportunities of Mobile Phone Call Detail Records in Health Research: Review",
journal="JMIR Mhealth Uhealth",
year="2018",
month="Jul",
day="19",
volume="6",
number="7",
pages="e161",
keywords="call detail records",
keywords="mobile phone data",
keywords="health research",
abstract="Background: Call detail records (CDRs) are collected by mobile network operators in the course of providing their service. CDRs are increasingly being used in research along with other forms of big data and represent an emerging data type with potential for public good. Many jurisdictions have infrastructures for health data research that could benefit from the integration of CDRs with health data. Objective: The objective of this study was to review how CDRs have been used in health research and to identify challenges and potential opportunities for their wider use in conjunction with health data. Methods: A literature review was conducted using structured search terms making use of major search engines. Initially, 4066 items were identified. Following screening, 46 full text articles were included in the qualitative synthesis. Information extracted included research topic area, population of study, datasets used, information governance and ethical considerations, study findings, and data limitations. Results: The majority of published studies were focused on low-income and middle-income countries. Making use of the location element in CDRs, studies often modeled the transmission of infectious diseases or estimated population movement following natural disasters with a view to implementing interventions. CDRs were used in anonymized or aggregated form, and the process of gaining regulatory approvals varied with data provider and by jurisdiction. None included public views on the use of CDRs in health research. Conclusions: Despite various challenges and limitations, anonymized mobile phone CDRs have been used successfully in health research. The use of aggregated data is a safeguard but also a further limitation. Greater opportunities could be gained if validated anonymized CDRs were integrated with routine health records at an individual level, provided that permissions and safeguards could be put in place. Further work is needed, including gaining public views, to develop an ethically founded framework for the use of CDRs in health research. ",
doi="10.2196/mhealth.9974",
url="http://mhealth.jmir.org/2018/7/e161/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30026176"
}


@Article{info:doi/10.2196/jmir.9919,
author="Algarin, B. Angel
and Ward, J. Patrick
and Christian, Jay W.
and Rudolph, E. Abby
and Holloway, W. Ian
and Young, M. April",
title="Spatial Distribution of Partner-Seeking Men Who Have Sex With Men Using Geosocial Networking Apps: Epidemiologic Study",
journal="J Med Internet Res",
year="2018",
month="May",
day="31",
volume="20",
number="5",
pages="e173",
keywords="men who have sex with men",
keywords="public health",
keywords="mobile phone",
keywords="social environment",
keywords="HIV",
keywords="sexually transmitted diseases",
abstract="Background: Geosocial networking apps have made sexual partner-seeking easier for men who have sex with men, raising both challenges and opportunities for human immunodeficiency virus and sexually transmitted infection prevention and research. Most studies on men who have sex with men geosocial networking app use have been conducted in large urban areas, despite research indicating similar patterns of online- and app-based sex-seeking among men who have sex with men in rural and midsize cities. Objective: The goal of our research was to examine the spatial distribution of geosocial networking app usage and characterize areas with increasing numbers of partner-seeking men who have sex with men in a midsize city in the South. Methods: Data collection points (n=62) were spaced in 2-mile increments along 9 routes (112 miles) covering the county encompassing the city. At each point, staff logged into 3 different geosocial networking apps to record the number of geosocial networking app users within a 1-mile radius. Data were collected separately during weekday daytime (9:00 AM to 4:00 PM) and weekend nighttime (8:00 PM to 12:00 AM) hours. Empirical Bayesian kriging was used to create a raster estimating the number of app users throughout the county. Raster values were summarized for each of the county's 208 Census block groups and used as the outcome measure (ie, geosocial networking app usage). Negative binomial regression and Wilcoxon signed rank sum tests were used to examine Census block group variables (eg, median income, median age) associated with geosocial networking app usage and temporal differences in app usage, respectively. Results: The number of geosocial networking app users within a 1-mile radius of the data collection points ranged from 0 to 36 during weekday daytime hours and 0 to 39 during weekend nighttime hours. In adjusted analyses, Census block group median income and percent Hispanic ethnicity were negatively associated with geosocial networking app usage for all 3 geosocial networking apps during weekday daytime and weekend nighttime hours. Population density and the presence of businesses were positively associated with geosocial networking app usage for all 3 geosocial networking apps during both times. Conclusions: In this midsize city, geosocial networking app usage was highest in areas that were more population-dense, were lower income, and had more businesses. This research is an example of how geosocial networking apps' geospatial capabilities can be used to better understand patterns of virtual partner-seeking among men who have sex with men. ",
doi="10.2196/jmir.9919",
url="http://www.jmir.org/2018/5/e173/"
}


@Article{info:doi/10.2196/jmir.9717,
author="Cartwright, F. Alice
and Karunaratne, Mihiri
and Barr-Walker, Jill
and Johns, E. Nicole
and Upadhyay, D. Ushma",
title="Identifying National Availability of Abortion Care and Distance From Major US Cities: Systematic Online Search",
journal="J Med Internet Res",
year="2018",
month="May",
day="14",
volume="20",
number="5",
pages="e186",
keywords="abortion seekers",
keywords="reproductive health",
keywords="internet",
keywords="access to information",
keywords="information seeking",
keywords="abortion patients",
keywords="reproductive health services",
keywords="information seeking behavior",
abstract="Background: Abortion is a common medical procedure, yet its availability has become more limited across the United States over the past decade. Women who do not know where to go for abortion care may use the internet to find abortion facility information, and there appears to be more online searches for abortion in states with more restrictive abortion laws. While previous studies have examined the distances women must travel to reach an abortion provider, to our knowledge no studies have used a systematic online search to document the geographic locations and services of abortion facilities. Objective: The objective of our study was to describe abortion facilities and services available in the United States from the perspective of a potential patient searching online and to identify US cities where people must travel the farthest to obtain abortion care. Methods: In early 2017, we conducted a systematic online search for abortion facilities in every state and the largest cities in each state. We recorded facility locations, types of abortion services available, and facility gestational limits. We then summarized the frequencies by region and state. If the online information was incomplete or unclear, we called the facility using a mystery shopper method, which simulates the perspective of patients calling for services. We also calculated distance to the closest abortion facility from all US cities with populations of 50,000 or more. Results: We identified 780 facilities through our online search, with the fewest in the Midwest and South. Over 30\% (236/780, 30.3\%) of all facilities advertised the provision of medication abortion services only; this proportion was close to 40\% in the Northeast (89/233, 38.2\%) and West (104/262, 39.7\%). The lowest gestational limit at which services were provided was 12 weeks in Wyoming; the highest was 28 weeks in New Mexico. People in 27 US cities must travel over 100 miles (160 km) to reach an abortion facility; the state with the largest number of such cities is Texas (n=10). Conclusions: Online searches can provide detailed information about the location of abortion facilities and the types of services they provide. However, these facilities are not evenly distributed geographically, and many large US cities do not have an abortion facility. Long distances can push women to seek abortion in later gestations when care is even more limited. ",
doi="10.2196/jmir.9717",
url="http://www.jmir.org/2018/5/e186/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29759954"
}


@Article{info:doi/10.2196/publichealth.8163,
author="Ben Ramadan, Ahmed Awatef
and Jackson-Thompson, Jeannette
and Schmaltz, Lee Chester",
title="Improving Visualization of Female Breast Cancer Survival Estimates: Analysis Using Interactive Mapping Reports",
journal="JMIR Public Health Surveill",
year="2018",
month="May",
day="03",
volume="4",
number="2",
pages="e42",
keywords="survival",
keywords="female breast cancer",
keywords="Missouri",
keywords="cancer registry",
abstract="Background: The Missouri Cancer Registry collects population-based cancer incidence data on Missouri residents diagnosed with reportable malignant neoplasms. The Missouri Cancer Registry wanted to produce data that would be of interest to lawmakers as well as public health officials at the legislative district level on breast cancer, the most common non-skin cancer among females. Objective: The aim was to measure and interactively visualize survival data of female breast cancer cases in the Missouri Cancer Registry. Methods: Female breast cancer data were linked to Missouri death records and the Social Security Death Index. Unlinked female breast cancer cases were crossmatched to the National Death Index. Female breast cancer cases in subcounty senate districts were geocoded using TIGER/Line shapefiles to identify their district. A database was created and analyzed in SEER*Stat. Senatorial district maps were created using US Census Bureau's cartographic boundary files. The results were loaded with the cartographic data into InstantAtlas software to produce interactive mapping reports. Results: Female breast cancer survival profiles of 5-year cause-specific survival percentages and 95\% confidence intervals, displayed in tables and interactive maps, were created for all 34 senatorial districts. The maps visualized survival data by age, race, stage, and grade at diagnosis for the period from 2004 through 2010. Conclusions: Linking cancer registry data to the National Death Index database improved accuracy of female breast cancer survival data in Missouri and this could positively impact cancer research and policy. The created survival mapping report could be very informative and usable by public health professionals, policy makers, at-risk women, and the public. ",
doi="10.2196/publichealth.8163",
url="http://publichealth.jmir.org/2018/2/e42/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29724710"
}


@Article{info:doi/10.2196/publichealth.8773,
author="Stopka, J. Thomas
and Brinkley-Rubinstein, Lauren
and Johnson, Kendra
and Chan, A. Philip
and Hutcheson, Marga
and Crosby, Richard
and Burke, Deirdre
and Mena, Leandro
and Nunn, Amy",
title="HIV Clustering in Mississippi: Spatial Epidemiological Study to Inform Implementation Science in the Deep South",
journal="JMIR Public Health Surveill",
year="2018",
month="Apr",
day="03",
volume="4",
number="2",
pages="e35",
keywords="hotspots",
keywords="HIV",
keywords="racial disparities",
keywords="social determinants of health",
keywords="HIV treatment",
keywords="HIV screening",
abstract="Background: In recent years, more than half of new HIV infections in the United States occur among African Americans in the Southeastern United States. Spatial epidemiological analyses can inform public health responses in the Deep South by identifying HIV hotspots and community-level factors associated with clustering. Objective: The goal of this study was to identify and characterize HIV clusters in Mississippi through analysis of state-level HIV surveillance data. Methods: We used a combination of spatial epidemiology and statistical modeling to identify and characterize HIV hotspots in Mississippi census tracts (n=658) from 2008 to 2014. We conducted spatial analyses of all HIV infections, infections among men who have sex with men (MSM), and infections among African Americans. Multivariable logistic regression analyses identified community-level sociodemographic factors associated with HIV hotspots considering all cases. Results: There were HIV hotspots for the entire population, MSM, and African American MSM identified in the Mississippi Delta region, Southern Mississippi, and in greater Jackson, including surrounding rural counties (P<.05). In multivariable models for all HIV cases, HIV hotspots were significantly more likely to include urban census tracts (adjusted odds ratio [AOR] 2.01, 95\% CI 1.20-3.37) and census tracts that had a higher proportion of African Americans (AOR 3.85, 95\% CI 2.23-6.65). The HIV hotspots were less likely to include census tracts with residents who had less than a high school education (AOR 0.95, 95\% CI 0.92-0.98), census tracts with residents belonging to two or more racial/ethnic groups (AOR 0.46, 95\% CI 0.30-0.70), and census tracts that had a higher percentage of the population living below the poverty level (AOR 0.51, 95\% CI 0.28-0.92). Conclusions: We used spatial epidemiology and statistical modeling to identify and characterize HIV hotspots for the general population, MSM, and African Americans. HIV clusters concentrated in Jackson and the Mississippi Delta. African American race and urban location were positively associated with clusters, whereas having less than a high school education and having a higher percentage of the population living below the poverty level were negatively associated with clusters. Spatial epidemiological analyses can inform implementation science and public health response strategies, including improved HIV testing, targeted prevention and risk reduction education, and tailored preexposure prophylaxis to address HIV disparities in the South. ",
doi="10.2196/publichealth.8773",
url="http://publichealth.jmir.org/2018/2/e35/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29615383"
}


@Article{info:doi/10.2196/publichealth.8581,
author="Rudolph, Abby
and Tobin, Karin
and Rudolph, Jonathan
and Latkin, Carl",
title="Web-Based Survey Application to Collect Contextually Relevant Geographic Data With Exposure Times: Application Development and Feasibility Testing",
journal="JMIR Public Health Surveill",
year="2018",
month="Jan",
day="19",
volume="4",
number="1",
pages="e12",
keywords="spatial analysis",
keywords="geographic mapping",
keywords="substance-related disorder",
abstract="Background: Although studies that characterize the risk environment by linking contextual factors with individual-level data have advanced infectious disease and substance use research, there are opportunities to refine how we define relevant neighborhood exposures; this can in turn reduce the potential for exposure misclassification. For example, for those who do not inject at home, injection risk behaviors may be more influenced by the environment where they inject than where they live. Similarly, among those who spend more time away from home, a measure that accounts for different neighborhood exposures by weighting each unique location proportional to the percentage of time spent there may be more correlated with health behaviors than one's residential environment. Objective: This study aimed to develop a Web-based application that interacts with Google Maps application program interfaces (APIs) to collect contextually relevant locations and the amount of time spent in each. Our analysis examined the extent of overlap across different location types and compared different approaches for classifying neighborhood exposure. Methods: Between May 2014 and March 2017, 547 participants enrolled in a Baltimore HIV care and prevention study completed an interviewer-administered Web-based survey that collected information about where participants were recruited, worked, lived, socialized, injected drugs, and spent most of their time. For each location, participants gave an address or intersection which they confirmed using Google Map and Street views. Geographic coordinates (and hours spent in each location) were joined to neighborhood indicators by Community Statistical Area (CSA). We computed a weighted exposure based on the proportion of time spent in each unique location. We compared neighborhood exposures based on each of the different location types with one another and the weighted exposure using analysis of variance with Bonferroni corrections to account for multiple comparisons. Results: Participants reported spending the most time at home, followed by the location where they injected drugs. Injection locations overlapped most frequently with locations where people reported socializing and living or sleeping. The least time was spent in the locations where participants reported earning money and being recruited for the study; these locations were also the least likely to overlap with other location types. We observed statistically significant differences in neighborhood exposures according to the approach used. Overall, people reported earning money in higher-income neighborhoods and being recruited for the study and injecting in neighborhoods with more violent crime, abandoned houses, and poverty. Conclusions: This analysis revealed statistically significant differences in neighborhood exposures when defined by different locations or weighted based on exposure time. Future analyses are needed to determine which exposure measures are most strongly associated with health and risk behaviors and to explore whether associations between individual-level behaviors and neighborhood exposures are modified by exposure times. ",
doi="10.2196/publichealth.8581",
url="http://publichealth.jmir.org/2018/1/e12/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29351899"
}


@Article{info:doi/10.2196/mhealth.7297,
author="Saeb, Sohrab
and Lattie, G. Emily
and Kording, P. Konrad
and Mohr, C. David",
title="Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety",
journal="JMIR Mhealth Uhealth",
year="2017",
month="Aug",
day="10",
volume="5",
number="8",
pages="e112",
keywords="semantic location",
keywords="geographic positioning systems",
keywords="mobile phone",
keywords="classification",
keywords="decision tree ensembles",
keywords="extreme gradient boosting",
keywords="depression",
keywords="anxiety",
abstract="Background: Is someone at home, at their friend's place, at a restaurant, or enjoying the outdoors? Knowing the semantic location of an individual matters for delivering medical interventions, recommendations, and other context-aware services. This knowledge is particularly useful in mental health care for monitoring relevant behavioral indicators to improve treatment delivery. Local search-and-discovery services such as Foursquare can be used to detect semantic locations based on the global positioning system (GPS) coordinates, but GPS alone is often inaccurate. Mobile phones can also sense other signals (such as movement, light, and sound), and the use of these signals promises to lead to a better estimation of an individual's semantic location. Objective: We aimed to examine the ability of mobile phone sensors to estimate semantic locations, and to evaluate the relationship between semantic location visit patterns and depression and anxiety. Methods: A total of 208 participants across the United States were asked to log the type of locations they visited daily, using their mobile phones for a period of 6 weeks, while their phone sensor data was recorded. Using the sensor data and Foursquare queries based on GPS coordinates, we trained models to predict these logged locations, and evaluated their prediction accuracy on participants that models had not seen during training. We also evaluated the relationship between the amount of time spent in each semantic location and depression and anxiety assessed at baseline, in the middle, and at the end of the study. Results: While Foursquare queries detected true semantic locations with an average area under the curve (AUC) of 0.62, using phone sensor data alone increased the AUC to 0.84. When we used Foursquare and sensor data together, the AUC further increased to 0.88. We found some significant relationships between the time spent in certain locations and depression and anxiety, although these relationships were not consistent. Conclusions: The accuracy of location services such as Foursquare can significantly benefit from using phone sensor data. However, our results suggest that the nature of the places people visit explains only a small part of the variation in their anxiety and depression symptoms. ",
doi="10.2196/mhealth.7297",
url="http://mhealth.jmir.org/2017/8/e112/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28798010"
}


@Article{info:doi/10.2196/humanfactors.7899,
author="Ben Ramadan, Ahmed Awatef
and Jackson-Thompson, Jeannette
and Schmaltz, Lee Chester",
title="Usability Assessment of the Missouri Cancer Registry's Published Interactive Mapping Reports: Round One",
journal="JMIR Hum Factors",
year="2017",
month="Aug",
day="04",
volume="4",
number="3",
pages="e19",
keywords="geographic information systems",
keywords="health professionals",
keywords="interactive maps",
keywords="Missouri Cancer Registry",
keywords="usability",
abstract="Background: ?Many users of spatial data have difficulty interpreting information in health-related spatial reports.?The Missouri Cancer Registry and Research Center (MCR-ARC) has produced interactive reports for several years. These reports have never been tested for usability. Objective: ?The aims of this study were to: (1) conduct a multi-approach usability testing study to understand ease of use (user friendliness) and user satisfaction; and (2) evaluate the usability of MCR-ARC's published InstantAtlas reports. Methods: ? An institutional review board (IRB) approved mixed methodology usability testing study using a convenience sample of health professionals. A recruiting email was sent to faculty in the Master of Public Health program and to faculty and staff in the Department of Health Management and Informatics at the University of Missouri-Columbia. The study included 7 participants.?The test included a pretest questionnaire, a multi-task usability test, and the System Usability Scale (SUS). Also, the researchers collected participants' comments about the tested maps immediately after every trial. Software was used to record the computer screen during the trial and the participants' spoken comments.?Several performance and usability metrics were measured to evaluate the usability of MCR-ARC's published mapping reports. Results: Of the 10 assigned tasks, 6 reached a 100\% completion success rate, and this outcome was relative to the complexity of the tasks. The simple tasks were handled more efficiently than the complicated tasks. The SUS score ranged between 20-100 points, with an average of 62.7 points and a median of 50.5 points. The tested maps' effectiveness outcomes were better than the efficiency and satisfaction outcomes. There was a statistically significant relationship between the subjects' performance on the study test and the users' previous experience with geographic information system (GIS) tools (P=.03). There were no statistically significant relationships between users' performance and satisfaction and their education level, work type, or previous experience in health care (P>.05). There were strong positive correlations between the three measured usability elements. Conclusions: The tested maps should undergo an extensive refining and updating to overcome all the discovered usability issues and meet the perspectives and needs of the tested maps' potential users. The study results might convey the perspectives of academic health professionals toward GIS health data. We need to conduct a second-round usability study with public health practitioners and cancer professionals who use GIS tools on a routine basis. Usability testing should be conducted before and after releasing MCR-ARC's maps in the future. ",
doi="10.2196/humanfactors.7899",
url="http://humanfactors.jmir.org/2017/3/e19/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28778842"
}


@Article{info:doi/10.2196/resprot.6213,
author="Nasrinpour, Reza Hamid
and Reimer, A. Alexander
and Friesen, R. Marcia
and McLeod, D. Robert",
title="Data Preparation for West Nile Virus Agent-Based Modelling: Protocol for Processing Bird Population Estimates and Incorporating ArcMap in AnyLogic",
journal="JMIR Res Protoc",
year="2017",
month="Jul",
day="17",
volume="6",
number="7",
pages="e138",
keywords="AnyLogic",
keywords="shapefiles",
keywords="ArcMap",
keywords="West Nile Virus",
keywords="land cover",
keywords="bird roosts",
keywords="bird home range",
keywords="Manitoba",
abstract="Background: West Nile Virus (WNV) was first isolated in 1937. Since the 1950s, many outbreaks have occurred in various countries. The first appearance of infected birds in Manitoba, Canada was in 2002. Objective: This paper describes the data preparation phase of setting up a geographic information system (GIS) simulation environment for WNV Agent-Based Modelling in Manitoba. Methods: The main technology used in this protocol is based on AnyLogic and ArcGIS software. A diverse variety of topics and techniques regarding the data collection phase are presented, as modelling WNV has many disparate attributes, including landscape and weather impacts on mosquito population dynamics and birds' roosting locations, population count, and movement patterns. Results: Different maps were combined to create a grid land cover map of Manitoba, Canada in a shapefile format compatible with AnyLogic, in order to modulate mosquito parameters. A significant amount of data regarding 152 bird species, along with their population estimates and locations in Manitoba, were gathered and assembled. Municipality shapefile maps were converted to built-in AnyLogic GIS regions for better compatibility with census data and initial placement of human agents. Accessing shapefiles and their databases in AnyLogic are also discussed. Conclusions: AnyLogic simulation software in combination with Esri ArcGIS provides a powerful toolbox for developers and modellers to simulate almost any GIS-based environment or process. This research should be useful to others working on a variety of mosquito-borne diseases (eg, Zika, dengue, and chikungunya) by demonstrating the importance of data relating to Manitoba and/or introducing procedures to compile such data. ",
doi="10.2196/resprot.6213",
url="http://www.researchprotocols.org/2017/7/e138/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28716770"
}



@Article{info:doi/10.2196/publichealth.6925,
author="Stefanidis, Anthony
and Vraga, Emily
and Lamprianidis, Georgios
and Radzikowski, Jacek
and Delamater, L. Paul
and Jacobsen, H. Kathryn
and Pfoser, Dieter
and Croitoru, Arie
and Crooks, Andrew",
title="Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts",
journal="JMIR Public Health Surveill",
year="2017",
month="Apr",
day="20",
volume="3",
number="2",
pages="e22",
keywords="Zika virus",
keywords="social media",
keywords="Twitter messaging",
keywords="geographic information systems",
abstract="Background: The recent Zika outbreak witnessed the disease evolving from a regional health concern to a global epidemic. During this process, different communities across the globe became involved in Twitter, discussing the disease and key issues associated with it. This paper presents a study of this discussion in Twitter, at the nexus of location, actors, and concepts. Objective: Our objective in this study was to demonstrate the significance of 3 types of events: location related, actor related, and concept related, for understanding how a public health emergency of international concern plays out in social media, and Twitter in particular. Accordingly, the study contributes to research efforts toward gaining insights on the mechanisms that drive participation, contributions, and interaction in this social media platform during a disease outbreak. Methods: We collected 6,249,626 tweets referring to the Zika outbreak over a period of 12 weeks early in the outbreak (December 2015 through March 2016). We analyzed this data corpus in terms of its geographical footprint, the actors participating in the discourse, and emerging concepts associated with the issue. Data were visualized and evaluated with spatiotemporal and network analysis tools to capture the evolution of interest on the topic and to reveal connections between locations, actors, and concepts in the form of interaction networks. Results: The spatiotemporal analysis of Twitter contributions reflects the spread of interest in Zika from its original hotspot in South America to North America and then across the globe. The Centers for Disease Control and World Health Organization had a prominent presence in social media discussions. Tweets about pregnancy and abortion increased as more information about this emerging infectious disease was presented to the public and public figures became involved in this. Conclusions: The results of this study show the utility of analyzing temporal variations in the analytic triad of locations, actors, and concepts. This contributes to advancing our understanding of social media discourse during a public health emergency of international concern. ",
doi="10.2196/publichealth.6925",
url="http://publichealth.jmir.org/2017/2/e22/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28428164"
}





@Article{info:doi/10.2196/jmir.5701,
author="Vaughan, S. Adam
and Kramer, R. Michael
and Cooper, LF Hannah
and Rosenberg, S. Eli
and Sullivan, S. Patrick",
title="Completeness and Reliability of Location Data Collected on the Web: Assessing the Quality of Self-Reported Locations in an Internet Sample of Men Who Have Sex With Men",
journal="J Med Internet Res",
year="2016",
month="Jun",
day="09",
volume="18",
number="6",
pages="e142",
keywords="HIV",
keywords="digital mapping",
keywords="geographic locations",
keywords="survey",
keywords="men who have sex with men",
abstract="Background: Place is critical to our understanding of human immunodeficiency virus (HIV) infections among men who have sex with men (MSM) in the United States. However, within the scientific literature, place is almost always represented by residential location, suggesting a fundamental assumption of equivalency between neighborhood of residence, place of risk, and place of prevention. However, the locations of behaviors among MSM show significant spatial variation, and theory has posited the importance of nonresidential contextual exposures. This focus on residential locations has been at least partially necessitated by the difficulties in collecting detailed geolocated data required to explore nonresidential locations. Objective: Using a Web-based map tool to collect locations, which may be relevant to the daily lives and health behaviors of MSM, this study examines the completeness and reliability of the collected data. Methods: MSM were recruited on the Web and completed a Web-based survey. Within this survey, men used a map tool embedded within a question to indicate their homes and multiple nonresidential locations, including those representing work, sex, socialization, physician, and others. We assessed data quality by examining data completeness and reliability. We used logistic regression to identify demographic, contextual, and location-specific predictors of answering all eligible map questions and answering specific map questions. We assessed data reliability by comparing selected locations with other participant-reported data. Results: Of 247 men completing the survey, 167 (67.6\%) answered the entire set of eligible map questions. Most participants (>80\%) answered specific map questions, with sex locations being the least reported (80.6\%). Participants with no college education were less likely than those with a college education to answer all map questions (prevalence ratio, 0.4; 95\% CI, 0.2-0.8). Participants who reported sex at their partner's home were less likely to indicate the location of that sex (prevalence ratio, 0.8; 95\% CI, 0.7-1.0). Overall, 83\% of participants placed their home's location within the boundaries of their reported residential ZIP code. Of locations having a specific text description, the median distance between the participant-selected location and the location determined using the specific text description was 0.29 miles (25th and 75th percentiles, 0.06-0.88). Conclusions: Using this Web-based map tool, this Web-based sample of MSM was generally willing and able to provide accurate data regarding both home and nonresidential locations. This tool provides a mechanism to collect data that can be used in more nuanced studies of place and sexual risk and preventive behaviors of MSM. ",
doi="10.2196/jmir.5701",
url="http://www.jmir.org/2016/6/e142/",
url="http://www.ncbi.nlm.nih.gov/pubmed/27283957"
}




@Article{info:doi/10.2196/publichealth.4191,
author="Cantrell, Jennifer
and Ganz, Ollie
and Ilakkuvan, Vinu
and Tacelosky, Michael
and Kreslake, Jennifer
and Moon-Howard, Joyce
and Aidala, Angela
and Vallone, Donna
and Anesetti-Rothermel, Andrew
and Kirchner, R. Thomas",
title="Implementation of a Multimodal Mobile System for Point-of-Sale Surveillance: Lessons Learned From Case Studies in Washington, DC, and New York City",
journal="JMIR Public Health Surveill",
year="2015",
month="Nov",
day="26",
volume="1",
number="2",
pages="e20",
keywords="mobile technology",
keywords="public health surveillance",
keywords="tobacco",
keywords="point-of-sale",
keywords="implementation",
keywords="tobacco industry advertising",
keywords="marketing",
abstract="Background: In tobacco control and other fields, point-of-sale surveillance of the retail environment is critical for understanding industry marketing of products and informing public health practice. Innovations in mobile technology can improve existing, paper-based surveillance methods, yet few studies describe in detail how to operationalize the use of technology in public health surveillance. Objective: The aims of this paper are to share implementation strategies and lessons learned from 2 tobacco, point-of-sale surveillance projects to inform and prepare public health researchers and practitioners to implement new mobile technologies in retail point-of-sale surveillance systems. Methods: From 2011 to 2013, 2 point-of-sale surveillance pilot projects were conducted in Washington, DC, and New York, New York, to capture information about the tobacco retail environment and test the feasibility of a multimodal mobile data collection system, which included capabilities for audio or video recording data, electronic photographs, electronic location data, and a centralized back-end server and dashboard. We established a preimplementation field testing process for both projects, which involved a series of rapid and iterative tests to inform decisions and establish protocols around key components of the project. Results: Important components of field testing included choosing a mobile phone that met project criteria, establishing an efficient workflow and accessible user interfaces for each component of the system, training and providing technical support to fieldworkers, and developing processes to integrate data from multiple sources into back-end systems that can be utilized in real-time. Conclusions: A well-planned implementation process is critical for successful use and performance of multimodal mobile surveillance systems. Guidelines for implementation include (1) the need to establish and allow time for an iterative testing framework for resolving technical and logistical challenges; (2) developing a streamlined workflow and user-friendly interfaces for data collection; (3) allowing for ongoing communication, feedback, and technology-related skill-building among all staff; and (4) supporting infrastructure for back-end data systems. Although mobile technologies are evolving rapidly, lessons learned from these case studies are essential for ensuring that the many benefits of new mobile systems for rapid point-of-sale surveillance are fully realized. ",
doi="10.2196/publichealth.4191",
url="http://publichealth.jmir.org/2015/2/e20/",
url="http://www.ncbi.nlm.nih.gov/pubmed/27227138"
}


@Article{info:doi/10.2196/publichealth.4809,
author="Cawkwell, B. Philip
and Lee, Lily
and Weitzman, Michael
and Sherman, E. Scott",
title="Tracking Hookah Bars in New York: Utilizing Yelp as a Powerful Public Health Tool",
journal="JMIR Public Health Surveill",
year="2015",
month="Nov",
day="20",
volume="1",
number="2",
pages="e19",
keywords="hookah",
keywords="hookah bar",
keywords="Internet",
keywords="public health",
keywords="Yelp",
abstract="Background: While cigarette use has seen a steady decline in recent years, hookah (water pipe) use has rapidly increased in popularity. While anecdotal reports have noted a rise in hookah bars, methodological difficulties have prevented researchers from drawing definitive conclusions about the number of hookah bars in any given location. There is no publicly available database that has been shown to reliably provide this information.  It is now possible to analyze Internet trends as a measure of population behavior and health-related phenomena. Objective: The objective of the study was to investigate whether Yelp can be used to accurately identify the number of hookah bars in New York State, assess the distribution and characteristics of hookah bars, and monitor temporal trends in their presence. Methods: Data were obtained from Yelp that captures a variety of parameters for every business listed in their database as of October 28, 2014, that was tagged as a ``hookah bar'' and operating in New York State.  Two algebraic models were created: one estimated the date of opening of a hookah bar based on the first Yelp review received and the other estimated whether the bar was open or closed based on the date of the most recent Yelp review. These findings were then compared with empirical data obtained by Internet searches. Results: From 2014 onward, the date of the first Yelp review predicts the opening date of new hookah bars to within 1 month.  Yelp data allow the estimate of such venues and demonstrate that new bars are not randomly distributed, but instead are clustered near colleges and in specific racial/ethnic neighborhoods. New York has seen substantially more new hookah bars in 2012-2014 compared with the number that existed prior to 2009. Conclusions: Yelp is a powerful public health tool that allows for the investigation of various trends and characteristics of hookah bars.  New York is experiencing tremendous growth in hookah bars, a worrying phenomenon that necessitates further investigation. ",
doi="10.2196/publichealth.4809",
url="http://publichealth.jmir.org/2015/2/e19/",
url="http://www.ncbi.nlm.nih.gov/pubmed/27227137"
}


@Article{info:doi/10.2196/publichealth.4525,
author="Dasgupta, Sharoda
and Kramer, R. Michael
and Rosenberg, S. Eli
and Sanchez, H. Travis
and Reed, Landon
and Sullivan, S. Patrick",
title="The Effect of Commuting Patterns on HIV Care Attendance Among Men Who Have Sex With Men (MSM) in Atlanta, Georgia",
journal="JMIR Public Health Surveill",
year="2015",
month="Aug",
day="24",
volume="1",
number="2",
pages="e10",
keywords="men who have sex with men",
keywords="HIV care",
keywords="commuting patterns",
keywords="public transportation",
abstract="Background: Travel-related barriers to human immunodeficiency virus (HIV) care, such as commute time and mode of transportation, have been reported in the United States. Objective: The objective of the study was to investigate the association between public transportation use and HIV care attendance among a convenience sample of Atlanta-based, HIV-positive men who have sex with men (MSM), evaluate differences across regions of residence, and estimate the relationship between travel distance and time by mode of transportation taken to attend appointments. Methods: We used Poisson regression to estimate the association between use of public transportation to attend HIV-related medical visits and frequency of care attendance over the previous 12 months.  The relationship between travel distance and commute time was estimated using linear regression.  Kriging was used to interpolate commute time to visually examine geographic differences in commuting patterns in relation to access to public transportation and population-based estimates of household vehicle ownership. Results: Using public transportation was associated with lower rates of HIV care attendance compared to using private transportation, but only in south Atlanta (south: aRR: 0.75, 95\% CI 0.56, 1.0, north: aRR: 0.90, 95\% CI 0.71, 1.1).  Participants living in south Atlanta were more likely to have longer commute times associated with attending HIV visits, have greater access to public transportation, and may live in areas with low vehicle ownership.  A majority of attended HIV providers were located in north and central Atlanta, despite there being participants living all across the city.  Estimated commute times per mile traveled were three times as high among public transit users compared to private transportation users. Conclusions: Improving local public transit and implementing use of mobile clinics could help address travel-related barriers to HIV care. ",
doi="10.2196/publichealth.4525",
url="http://publichealth.jmir.org/2015/2/e10/",
url="http://www.ncbi.nlm.nih.gov/pubmed/27227128"
}