TY - JOUR AU - Wei, Lan AU - Wu, Yongsheng AU - Chen, Lin AU - Cheng, Jinquan AU - Zhao, Jin PY - 2025/3/20 TI - 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 JO - J Med Internet Res SP - e69569 VL - 27 KW - men who have sex with men KW - geosocial networking apps KW - distribution KW - heterogeneity KW - mobility KW - HIV testing KW - risk behavior KW - big data N2 - 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. UR - https://www.jmir.org/2025/1/e69569 UR - http://dx.doi.org/10.2196/69569 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/69569 ER - TY - JOUR AU - Pullano, Giulia AU - Alvarez-Zuzek, Gisele Lucila AU - Colizza, Vittoria AU - Bansal, Shweta PY - 2025/2/18 TI - Characterizing US Spatial Connectivity and Implications for Geographical Disease Dynamics and Metapopulation Modeling: Longitudinal Observational Study JO - JMIR Public Health Surveill SP - e64914 VL - 11 KW - geographical disease dynamics KW - spatial connectivity KW - mobility data KW - metapopulation modeling KW - COVID-19 KW - human mobility KW - infectious diseases KW - social distancing KW - epidemic KW - mobile apps KW - SafeGraph KW - SARS-CoV-2 KW - coronavirus KW - pandemic KW - spatio-temporal KW - US KW - public health KW - mobile health KW - mHealth KW - digital health KW - health informatics N2 - 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. UR - https://publichealth.jmir.org/2025/1/e64914 UR - http://dx.doi.org/10.2196/64914 ID - info:doi/10.2196/64914 ER - TY - JOUR AU - Zhang, Kehe AU - Hunyadi, V. Jocelyn AU - de Oliveira Otto, C. Marcia AU - Lee, Miryoung AU - Zhang, Zitong AU - Ramphul, Ryan AU - Yamal, Jose-Miguel AU - Yaseen, Ashraf AU - Morrison, C. Alanna AU - Sharma, Shreela AU - Rahbar, Hossein Mohammad AU - Zhang, Xu AU - Linder, Stephen AU - Marko, Dritana AU - Roy, White Rachel AU - Banerjee, Deborah AU - Guajardo, Esmeralda AU - Crum, Michelle AU - Reininger, Belinda AU - Fernandez, E. Maria AU - Bauer, Cici PY - 2025/2/11 TI - Increasing COVID-19 Testing and Vaccination Uptake in the Take Care Texas Community-Based Randomized Trial: Adaptive Geospatial Analysis JO - JMIR Form Res SP - e62802 VL - 9 KW - COVID-19 testing KW - COVID-19 vaccination KW - study design KW - community-based interventions KW - geospatial analysis KW - public health KW - social determinants of health KW - data dashboard N2 - Background: Geospatial data science can be a powerful tool to aid the design, reach, efficiency, and impact of community-based intervention trials. The project titled Take Care Texas aims to develop and test an adaptive, multilevel, community-based intervention to increase COVID-19 testing and vaccination uptake among vulnerable populations in 3 Texas regions: Harris County, Cameron County, and Northeast Texas. Objective: We aimed to develop a novel procedure for adaptive selections of census block groups (CBGs) to include in the community-based randomized trial for the Take Care Texas project. Methods: CBG selection was conducted across 3 Texas regions over a 17-month period (May 2021 to October 2022). We developed persistent and recent COVID-19 burden metrics, using real-time SARS-CoV-2 monitoring data to capture dynamic infection patterns. To identify vulnerable populations, we also developed a CBG-level community disparity index, using 12 contextual social determinants of health (SDOH) measures from US census data. In each adaptive round, we determined the priority CBGs based on their COVID-19 burden and disparity index, ensuring geographic separation to minimize intervention ?spillover.? Community input and feedback from local partners and health workers further refined the selection. The selected CBGs were then randomized into 2 intervention arms?multilevel intervention and just-in-time adaptive intervention?and 1 control arm, using covariate adaptive randomization, at a 1:1:1 ratio. We developed interactive data dashboards, which included maps displaying the locations of selected CBGs and community-level information, to inform the selection process and guide intervention delivery. Selection and randomization occurred across 10 adaptive rounds. Results: A total of 120 CBGs were selected and followed the stepped planning and interventions, with 60 in Harris County, 30 in Cameron County, and 30 in Northeast Texas counties. COVID-19 burden presented substantial temporal changes and local variations across CBGs. COVID-19 burden and community disparity exhibited some common geographical patterns but also displayed distinct variations, particularly at different time points throughout this study. This underscores the importance of incorporating both real-time monitoring data and contextual SDOH in the selection process. Conclusions: The novel procedure integrated real-time monitoring data and geospatial data science to enhance the design and adaptive delivery of a community-based randomized trial. Adaptive selection effectively prioritized the most in-need communities and allowed for a rigorous evaluation of community-based interventions in a multilevel trial. This methodology has broad applicability and can be adapted to other public health intervention and prevention programs, providing a powerful tool for improving population health and addressing health disparities. UR - https://formative.jmir.org/2025/1/e62802 UR - http://dx.doi.org/10.2196/62802 ID - info:doi/10.2196/62802 ER - TY - JOUR AU - Dahu, M. Butros AU - Khan, Solaiman AU - Toubal, Eddine Imad AU - Alshehri, Mariam AU - Martinez-Villar, I. Carlos AU - Ogundele, B. Olabode AU - Sheets, R. Lincoln AU - Scott, J. Grant PY - 2024/12/17 TI - Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study JO - JMIR AI SP - e64362 VL - 3 KW - geospatial modeling KW - deep convolutional neural network KW - DCNN KW - Residual Network-50 KW - ResNet-50 KW - satellite imagery KW - Moran I KW - local indicators of spatial association KW - LISA KW - spatial lag model KW - obesity rate KW - artificial intelligence KW - AI N2 - Background: The global obesity epidemic demands innovative approaches to understand its complex environmental and social determinants. Spatial technologies, such as geographic information systems, remote sensing, and spatial machine learning, offer new insights into this health issue. This study uses deep learning and spatial modeling to predict obesity rates for census tracts in Missouri. Objective: This study aims to develop a scalable method for predicting obesity prevalence using deep convolutional neural networks applied to satellite imagery and geospatial analysis, focusing on 1052 census tracts in Missouri. Methods: Our analysis followed 3 steps. First, Sentinel-2 satellite images were processed using the Residual Network-50 model to extract environmental features from 63,592 image chips (224×224 pixels). Second, these features were merged with obesity rate data from the Centers for Disease Control and Prevention for Missouri census tracts. Third, a spatial lag model was used to predict obesity rates and analyze the association between deep neural visual features and obesity prevalence. Spatial autocorrelation was used to identify clusters of obesity rates. Results: Substantial spatial clustering of obesity rates was found across Missouri, with a Moran I value of 0.68, indicating similar obesity rates among neighboring census tracts. The spatial lag model demonstrated strong predictive performance, with an R2 of 0.93 and a spatial pseudo R2 of 0.92, explaining 93% of the variation in obesity rates. Local indicators from a spatial association analysis revealed regions with distinct high and low clusters of obesity, which were visualized through choropleth maps. Conclusions: This study highlights the effectiveness of integrating deep convolutional neural networks and spatial modeling to predict obesity prevalence based on environmental features from satellite imagery. The model?s high accuracy and ability to capture spatial patterns offer valuable insights for public health interventions. Future work should expand the geographical scope and include socioeconomic data to further refine the model for broader applications in obesity research. UR - https://ai.jmir.org/2024/1/e64362 UR - http://dx.doi.org/10.2196/64362 UR - http://www.ncbi.nlm.nih.gov/pubmed/39688897 ID - info:doi/10.2196/64362 ER - TY - JOUR AU - Zhou, Shanyu AU - Huang, Yongshun AU - Chen, Lin AU - Wen, Xianzhong AU - Wang, Shu AU - Huang, Lang AU - Li, Xudong PY - 2024/11/29 TI - Epidemiological Characteristics and Spatiotemporal Analysis of Occupational Noise?Induced Deafness From 2006 to 2022 in Guangdong, China: Surveillance Study JO - JMIR Public Health Surveill SP - e57851 VL - 10 KW - occupational noise-induced deafness KW - epidemiological characteristics KW - joinpoint regression KW - spatial autocorrelation KW - Guangdong KW - noise-induced KW - deafness KW - hearing loss KW - hearing impairment KW - occupational noise KW - noise KW - ONID KW - China KW - epidemiology KW - spatiotemporal analysis KW - comprehensive analysis KW - occupational diseases KW - policy formulation KW - health resource KW - surveillance KW - Moran's I KW - spatial KW - clustering KW - public health KW - teleaudiology KW - audiology N2 - Background: Occupational noise?induced deafness (ONID) has replaced occupational poisoning as the second most common occupational disease in China since 2015. However, there is a limited number of articles on epidemiological characteristics of legally diagnosed ONID. Objective: We conducted a comprehensive analysis of the epidemiological and spatiotemporal characteristics of ONID in Guangdong Province from 2006 to 2022, with the aim of providing a scientific foundation for policy formulation and health resource allocation. Methods: Surveillance data of ONID cases in Guangdong Province from 2006 to 2022 were obtained from the ?Occupational Diseases and Health Hazard Factors Monitoring Information System.? Joinpoint regression analysis was applied to assess the long-term trends in cases of ONID from 2006 to 2022. Global spatial autocorrelation analysis was performed to measure the overall degree of similarity of the attribute values of spatially adjacent or neighboring regional units. The local indicators of spatial autocorrelation (LISA) plots were then used to identify the local clusters of ONID in Guangdong. Results: There were 3761 ONID cases in Guangdong Province from 2006 to 2022, showing a significantly increased trend in cases across the entire study period (average annual percentage change 21.9, 95% CI 18.7-35.1). The Moran?s I values for the period of 2006 to 2022 ranged from 0.202 to 0.649 (all P<.001), indicating a positive spatial correlation of ONID across regions each year in Guangdong Province. A total of 15 high-high clusters were notably concentrated in specific counties within the Pearl River Delta. Conclusions: Significant spatiotemporal patterns of ONID in Guangdong Province from 2006 to 2022 were identified, characterized by a dramatic increase followed by stabilization in case numbers. ONID predominantly occur in manufacturing industries, domestically funded enterprises, among males, individuals aged 40?49 years, and those with 5+ years of occupational noise exposure. Spatial analysis demonstrated significant clustering in the Pearl River Delta region, with consistent positive spatial autocorrelation across years. These results could help prioritize the allocation of resources for targeted prevention and control measures for ONID. UR - https://publichealth.jmir.org/2024/1/e57851 UR - http://dx.doi.org/10.2196/57851 ID - info:doi/10.2196/57851 ER - TY - JOUR AU - Swartzendruber, Andrea AU - Luisi, Nicole AU - Johnson, R. Erin AU - Lambert, N. Danielle PY - 2024/11/6 TI - Spatial Analyses of Crisis Pregnancy Centers and Abortion Facilities in the United States, 2021 (Pre-Dobbs): Cross-Sectional Study JO - JMIR Public Health Surveill SP - e60001 VL - 10 KW - crisis pregnancy center KW - abortion, induced KW - reproductive health KW - policy KW - access to information KW - internet KW - directory KW - geographic information system KW - spatial analyses N2 - Background: Crisis pregnancy centers (CPCs) are religious nonprofit organizations with a primary mission of diverting people from having abortions. One CPC tactic has been to locate near abortion facilities. Despite medical groups? warnings that CPCs do not adhere to medical and ethical standards and pose risks, government support for CPCs has significantly increased. Objective: This study aims to map CPCs, abortion facilities, and geographical areas in the United States into 4 zones based on their proximity to CPCs and abortion facilities. We sought to describe the number and percentage of reproductive-aged women living in each zone and the proximity of CPCs to abortion facilities. Methods: Using 2021 data from CPC Map and the Advancing New Standards in Reproductive Health Abortion Facility Database, we determined the ratio of CPCs to abortion facilities. Along with census data, we categorized and mapped US block groups into 4 distinct zones based on locations of block group centroids within 15-mile (1 mile is approximately 1.609 km) radii of CPCs and abortion facilities, namely ?no presence,? ?CPC only,? ?abortion facility only,? and ?dual presence.? We calculated the number and percentage of block groups and reproductive-aged (15-49 years) women living in each zone. We calculated driving distances and drive times from abortion facilities to the nearest CPC and mapped abortion facilities with CPCs in close proximity. All analyses were conducted nationally and by region, division, and state. Results: Nationally, the ratio of CPCs to abortion facilities was 3.4, and 54.9% (131,410/239,462) of block groups were categorized in the ?dual presence? zone, 26.6% (63,679/239,462) as ?CPC only,? and 0.8% (63,679/239,462) as ?abortion facility only.? Most reproductive-aged women (45,150,110/75,582,028, 59.7%) lived in a ?dual presence? zone, 26.1% (19,696,572/75,582,028) in a ?CPC only? zone, and 0.8% (625,403/75,582,028) in an ?abortion facility only? zone. The number of block groups and women classified as living in each zone varied by region, division, and state. Nationally, the median distance from abortion facilities to the nearest CPC was 2 miles, and the median drive time was 5.5 minutes. Minimum drive times were <1 minute in all but 11 states. The percentages of abortion facilities with a CPC within 0.25, 0.5, 1, and 3 miles were 14.1% (107/757), 22.6% (171/757), 36.1% (273/757), and 66.3% (502/757), respectively. Conclusions: The findings suggest that CPCs? tactic of locating near abortion facilities was largely realized before the 2022 US Supreme Court decision that overturned the federal right to abortion. Research on CPCs? locations and tactics should continue given the dynamic abortion policy landscape and risks posed by CPCs. Tailored programming to raise awareness about CPCs and help people identify and access safe sources of health care may mitigate harm. Increased regulation of CPCs and government divestment may also mitigate CPC harms. UR - https://publichealth.jmir.org/2024/1/e60001 UR - http://dx.doi.org/10.2196/60001 UR - http://www.ncbi.nlm.nih.gov/pubmed/39504544 ID - info:doi/10.2196/60001 ER - TY - JOUR AU - Mollalo, Abolfazl AU - Hamidi, Bashir AU - Lenert, A. Leslie AU - Alekseyenko, V. Alexander PY - 2024/10/15 TI - Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review JO - JMIR Med Inform SP - e56343 VL - 12 KW - clinical phenotypes KW - electronic health records KW - geocoding KW - geographic information systems KW - patient phenotypes KW - spatial analysis N2 - Background: Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes. Objective: This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes. Methods: We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains. Results: A substantial proportion of studies (>85%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited. Conclusions: This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support. UR - https://medinform.jmir.org/2024/1/e56343 UR - http://dx.doi.org/10.2196/56343 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/56343 ER - TY - JOUR AU - Kong, Deliang AU - Wu, Chengguo AU - Cui, Yimin AU - Fan, Jun AU - Zhang, Ting AU - Zhong, Jiyuan AU - Pu, Chuan PY - 2024/9/24 TI - Epidemiological Characteristics and Spatiotemporal Clustering of Pulmonary Tuberculosis Among Students in Southwest China From 2016 to 2022: Analysis of Population-Based Surveillance Data JO - JMIR Public Health Surveill SP - e64286 VL - 10 KW - student PTB KW - Southwest China KW - epidemiology KW - visualizing incidence map KW - spatial autocorrelation analysis KW - spatiotemporal clusters KW - pulmonary tuberculosis N2 - Background: Pulmonary tuberculosis (PTB), as a respiratory infectious disease, poses significant risks of covert transmission and dissemination. The high aggregation and close contact among students in Chinese schools exacerbate the transmission risk of PTB outbreaks. Objective: This study investigated the epidemiological characteristics, geographic distribution, and spatiotemporal evolution of student PTB in Chongqing, Southwest China, aiming to delineate the incidence risks and clustering patterns of PTB among students. Methods: PTB case data from students monitored and reported in the Tuberculosis Information Management System within the China Information System for Disease Control and Prevention were used for this study. Descriptive analyses were conducted to characterize the epidemiological features of student PTB. Spatial trend surface analysis, global and local spatial autocorrelation analyses, and disease rate mapping were performed using ArcGIS 10.3. SaTScan 9.6 software was used to identify spatiotemporal clusters of PTB cases. Results: From 2016 to 2022, a total of 9920 student TB cases were reported in Chongqing, Southwest China, with an average incidence rate of 24.89/100,000. The incidence of student TB showed an initial increase followed by a decline, yet it remained relatively high. High school students (age: 13?18 years; 6649/9920, 67.03%) and college students (age: ?19 years; 2921/9920, 29.45%) accounted for the majority of student PTB cases. Patient identification primarily relied on passive detection, with a high proportion of delayed diagnosis and positive etiological results. COVID-19 prevention measures have had some impact on reducing incidence levels, but the primary factor appears to be the implementation of screening measures, which facilitated earlier case detection. Global spatial autocorrelation analysis indicated Moran I values of >0 for all years except 2018, ranging from 0.1908 to 0.4645 (all P values were <.05), suggesting strong positive spatial clustering of student PTB cases across Chongqing. Local spatial autocorrelation identified 7 high-high clusters, 13 low-low clusters, 5 high-low clusters, and 4 low-high clusters. High-high clusters were predominantly located in the southeast and northeast parts of Chongqing, consistent with spatial trend surface analysis and spatiotemporal clustering results. Spatiotemporal scan analysis revealed 4 statistically significant spatiotemporal clusters, with the most likely cluster in the southeast (relative risk [RR]=2.87, log likelihood ratio [LLR]=574.29, P<.001) and a secondary cluster in the northeast (RR=1.99, LLR=234.67, P<.001), indicating higher reported student TB cases and elevated risks of epidemic spread within these regions. Conclusions: Future efforts should comprehensively enhance prevention and control measures in high-risk areas of PTB in Chongqing to mitigate the incidence risk among students. Additionally, implementing proactive screening strategies and enhancing screening measures are crucial for early identification of student patients to prevent PTB outbreaks in schools. UR - https://publichealth.jmir.org/2024/1/e64286 UR - http://dx.doi.org/10.2196/64286 ID - info:doi/10.2196/64286 ER - TY - JOUR AU - Ohsawa, Yukio AU - Sun, Yi AU - Sekiguchi, Kaira AU - Kondo, Sae AU - Maekawa, Tomohide AU - Takita, Morihito AU - Tanimoto, Tetsuya AU - Kami, Masahiro PY - 2024/8/21 TI - Risk Index of Regional Infection Expansion of COVID-19: Moving Direction Entropy Study Using Mobility Data and Its Application to Tokyo JO - JMIR Public Health Surveill SP - e57742 VL - 10 KW - suppressing the spread of infection KW - index for risk assessment KW - local regions KW - diversity of mobility KW - mobility data KW - moving direction entropy KW - MDE KW - social network model KW - COVID-19 KW - influenza KW - sexually transmitted diseases N2 - Background: Policies, such as stay home, bubbling, and stay with your community, recommending that individuals reduce contact with diverse communities, including families and schools, have been introduced to mitigate the spread of the COVID-19 pandemic. However, these policies are violated if individuals from various communities gather, which is a latent risk in a real society where people move among various unreported communities. Objective: We aimed to create a physical index to assess the possibility of contact between individuals from diverse communities, which serves as an indicator of the potential risk of SARS-CoV-2 spread when considered and combined with existing indices. Methods: Moving direction entropy (MDE), which quantifies the diversity of moving directions of individuals in each local region, is proposed as an index to evaluate a region?s risk of contact of individuals from diverse communities. MDE was computed for each inland municipality in Tokyo using mobility data collected from smartphones before and during the COVID-19 pandemic. To validate the hypothesis that the impact of intercommunity contact on infection expansion becomes larger for a virus with larger infectivity, we compared the correlations of the expansion of infectious diseases with indices, including MDE and the densities of supermarkets, restaurants, etc. In addition, we analyzed the temporal changes in MDE in municipalities. Results: This study had 4 important findings. First, the MDE values for local regions showed significant invariance between different periods according to the Spearman rank correlation coefficient (>0.9). Second, MDE was found to correlate with the rate of infection cases of COVID-19 among local populations in 53 inland regions (average of 0.76 during the period of expansion). The density of restaurants had a similar correlation with COVID-19. The correlation between MDE and the rate of infection was smaller for influenza than for COVID-19, and tended to be even smaller for sexually transmitted diseases (order of infectivity). These findings support the hypothesis. Third, the spread of COVID-19 was accelerated in regions with high-rank MDE values compared to those with high-rank restaurant densities during and after the period of the governmental declaration of emergency (P<.001). Fourth, the MDE values tended to be high and increased during the pandemic period in regions where influx or daytime movement was present. A possible explanation for the third and fourth findings is that policymakers and living people have been overlooking MDE. Conclusions: We recommend monitoring the regional values of MDE to reduce the risk of infection spread. To aid in this monitoring, we present a method to create a heatmap of MDE values, thereby drawing public attention to behaviors that facilitate contact between communities during a highly infectious disease pandemic. UR - https://publichealth.jmir.org/2024/1/e57742 UR - http://dx.doi.org/10.2196/57742 UR - http://www.ncbi.nlm.nih.gov/pubmed/39037745 ID - info:doi/10.2196/57742 ER - TY - JOUR AU - Gan, Ting AU - Liu, Yunning AU - Bambrick, Hilary AU - Zhou, Maigeng AU - Hu, Wenbiao PY - 2024/8/8 TI - Liver Cancer Mortality Disparities at a Fine Scale Among Subpopulations in China: Nationwide Analysis of Spatial and Temporal Trends JO - JMIR Public Health Surveill SP - e54967 VL - 10 KW - liver cancer KW - mortality KW - year of life lost KW - spatial distribution KW - temporal trend N2 - Background: China has the highest number of liver cancers worldwide, and liver cancer is at the forefront of all cancers in China. However, current research on liver cancer in China primarily relies on extrapolated data or relatively lagging data, with limited focus on subregions and specific population groups. Objective: The purpose of this study is to identify geographic disparities in liver cancer by exploring the spatial and temporal trends of liver cancer mortality and the years of life lost (YLL) caused by it within distinct geographical regions, climate zones, and population groups in China. Methods: Data from the National Death Surveillance System between 2013 and 2020 were used to calculate the age-standardized mortality rate of liver cancer (LASMR) and YLL from liver cancer in China. The spatial distribution and temporal trends of liver cancer were analyzed in subgroups by sex, age, region, and climate classification. Estimated annual percentage change was used to describe liver cancer trends in various regions, and partial correlation was applied to explore associations between LASMR and latitude. Results: In China, the average LASMR decreased from 28.79 in 2013 to 26.38 per 100,000 in 2020 among men and 11.09 to 9.83 per 100,000 among women. This decline in mortality was consistent across all age groups. Geographically, Guangxi had the highest LASMR for men in China, with a rate of 50.15 per 100,000, while for women, it was Heilongjiang, with a rate of 16.64 per 100,000. Within these regions, the LASMR among men in most parts of Guangxi ranged from 32.32 to 74.98 per 100,000, whereas the LASMR among women in the majority of Heilongjiang ranged from 13.72 to 21.86 per 100,000. The trend of LASMR varied among regions. For both men and women, Guizhou showed an increasing trend in LASMR from 2013 to 2020, with estimated annual percentage changes ranging from 10.05% to 29.07% and from 10.09% to 21.71%, respectively. Both men and women observed an increase in LASMR with increasing latitude below the 40th parallel. However, overall, LASMR in men was positively correlated with latitude (R=0.225; P<.001), while in women, it showed a negative correlation (R=0.083; P=.04). High LASMR areas among men aligned with subtropical zones, like Cwa and Cfa. The age group 65 years and older, the southern region, and the Cwa climate zone had the highest YLL rates at 4850.50, 495.50, and 440.17 per 100,000, respectively. However, the overall trends in these groups showed a decline over the period. Conclusions: Despite the declining overall trend of liver cancer in China, there are still marked disparities between regions and populations. Future prevention and control should focus on high-risk regions and populations to further reduce the burden of liver cancer in China. UR - https://publichealth.jmir.org/2024/1/e54967 UR - http://dx.doi.org/10.2196/54967 ID - info:doi/10.2196/54967 ER - TY - JOUR AU - Liu, Jiaojiao AU - Wang, Hui AU - Zhong, Siyi AU - Zhang, Xiao AU - Wu, Qilin AU - Luo, Haipeng AU - Luo, Lei AU - Zhang, Zhoubin PY - 2024/8/2 TI - Spatiotemporal Changes and Influencing Factors of Hand, Foot, and Mouth Disease in Guangzhou, China, From 2013 to 2022: Retrospective Analysis JO - JMIR Public Health Surveill SP - e58821 VL - 10 KW - hand, foot, and mouth disease KW - spatial analysis KW - space-time scan statistics KW - geographically and temporally weighted regression KW - infectious disease N2 - Background: In the past 10 years, the number of hand, foot, and mouth disease (HFMD) cases reported in Guangzhou, China, has averaged about 60,000 per year. It is necessary to conduct an in-depth analysis to understand the epidemiological pattern and related influencing factors of HFMD in this region. Objective: This study aims to describe the epidemiological characteristics and spatiotemporal distribution of HFMD cases in Guangzhou from 2013 to 2022 and explore the relationship between sociodemographic factors and HFMD incidence. Methods: The data of HFMD cases in Guangzhou come from the Infectious Disease Information Management System of the Guangzhou Center for Disease Control and Prevention. Spatial analysis and space-time scan statistics were used to visualize the spatiotemporal distribution of HFMD cases. Multifactor ordinary minimum regression model, geographically weighted regression, and geographically and temporally weighted regression were used to analyze the influencing factors, including population, economy, education, and medical care. Results: From 2013 to 2022, a total of 599,353 HFMD cases were reported in Guangzhou, with an average annual incidence rate of 403.62/100,000. Children aged 5 years and younger accounted for 93.64% (561,218/599,353) of all cases. HFMD cases showed obvious bimodal distribution characteristics, with the peak period from May to July and the secondary peak period from August to October. HFMDs in Guangzhou exhibited a spatial aggregation trend, with the central urban area showing a pattern of low-low aggregation and the peripheral urban area demonstrating high-high aggregation. High-risk areas showed a dynamic trend of shifting from the west to the east of peripheral urban areas, with coverage first increasing and then decreasing. The geographically and temporally weighted regression model results indicated that population density (?=?0.016) and average annual income of employees (?=?0.007) were protective factors for HFMD incidence, while the average number of students in each primary school (?=1.416) and kindergarten (?=0.412) was a risk factor. Conclusions: HFMD cases in Guangzhou were mainly infants and young children, and there were obvious differences in time and space. HFMD is highly prevalent in summer and autumn, and peripheral urban areas were identified as high-risk areas. Improving the economic level of peripheral urban areas and reducing the number of students in preschool education institutions are key strategies to controlling HFMD. UR - https://publichealth.jmir.org/2024/1/e58821 UR - http://dx.doi.org/10.2196/58821 ID - info:doi/10.2196/58821 ER - TY - JOUR AU - Tao, Mingyong AU - Liu, Ying AU - Ling, Feng AU - Ren, Jiangping AU - Zhang, Rong AU - Shi, Xuguang AU - Guo, Song AU - Jiang, Jianmin AU - Sun, Jimin PY - 2024/8/2 TI - Factors Associated With the Spatial Distribution of Severe Fever With Thrombocytopenia Syndrome in Zhejiang Province, China: Risk Analysis Based on Maximum Entropy JO - JMIR Public Health Surveill SP - e46070 VL - 10 KW - severe fever with thrombocytopenia syndrome KW - MaxEnt KW - maximum entropy KW - tick density KW - spatial distribution KW - risk factor KW - China N2 - Background: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease that was first identified in mainland China in 2009 and has been reported in Zhejiang Province, China, since 2011. However, few studies have focused on the association between ticks, host animals, and SFTS. Objective: In this study, we analyzed the influence of meteorological and environmental factors as well as the influence of ticks and host animals on SFTS. This can serve as a foundational basis for the development of strategic policies aimed at the prevention and control of SFTS. Methods: Data on SFTS incidence, tick density, cattle density, and meteorological and environmental factors were collected and analyzed using a maximum entropy?based model. Results: As of December 2019, 463 laboratory-confirmed SFTS cases were reported in Zhejiang Province. We found that the density of ticks, precipitation in the wettest month, average temperature, elevation, and the normalized difference vegetation index were significantly associated with SFTS spatial distribution. The niche model fitted accurately with good performance in predicting the potential risk areas of SFTS (the average test area under the receiver operating characteristic curve for the replicate runs was 0.803 and the SD was 0.013). The risk of SFTS occurrence increased with an increase in tick density, and the response curve indicated that the risk was greater than 0.5 when tick density exceeded 1.4. The risk of SFTS occurrence decreased with increased precipitation in the wettest month, and the risk was less than 0.5 when precipitation exceeded 224.4 mm. The relationship between elevation and SFTS occurrence showed a reverse V shape, and the risk peaked at approximately 400 m. Conclusions: Tick density, precipitation, and elevation were dominant influencing factors for SFTS, and comprehensive intervention measures should be adjusted according to these factors to reduce SFTS incidence in Zhejiang Province. UR - https://publichealth.jmir.org/2024/1/e46070 UR - http://dx.doi.org/10.2196/46070 ID - info:doi/10.2196/46070 ER - TY - JOUR AU - Al-Aboosi, Mustafa Ahmad AU - Sheikh Abdullah, Huda Siti Norul AU - Ismail, Rozmi AU - Abdul Maulud, Nizam Khairul AU - Nahar, Lutfun AU - Zainol Ariffin, Akram Khairul AU - Lam, Chun Meng AU - bin Talib, Lazim Muhamad AU - Wahab, Suzaily AU - Elias, Mahadzir PY - 2024/7/30 TI - A Geospatial Drug Abuse Risk Assessment and Monitoring Dashboard Tailored for School Students: Development Study With Requirement Analysis and Acceptance Evaluation JO - JMIR Hum Factors SP - e48139 VL - 11 KW - geospatial KW - statistics KW - map KW - youth KW - drugs KW - dashboard KW - evaluation KW - drug abuse KW - monitoring KW - risk assessment N2 - Background: The enormous consequences of drugs include suicides, traffic accidents, and violence, affecting the individual, family, society, and country. Therefore, it is necessary to constantly identify and monitor the drug abuse rate among school-going youth. A geospatial dashboard is vital for the monitoring of drug abuse and related crime incidence in a decision support system. Objective: This paper mainly focuses on developing MyAsriGeo, a geospatial drug abuse risk assessment and monitoring dashboard tailored for school students. It introduces innovative functionality, seamlessly orchestrating the assessment of drug abuse usage patterns and risks using multivariate student data. Methods: A geospatial drug abuse dashboard for monitoring and analysis was designed and developed in this study based on agile methodology and prototyping. Using focus group and interviews, we first examined and gathered the requirements, feedback, and user approval of the MyAsriGeo dashboard. Experts and stakeholders such as the National Anti-Drugs Agency, police, the Federal Department of Town and Country Planning, school instructors, students, and researchers were among those who responded. A total of 20 specialists were involved in the requirement analysis and acceptance evaluation of the pilot and final version of the dashboard. The evaluation sought to identify various user acceptance aspects, such as ease of use and usefulness, for both the pilot and final versions, and 2 additional factors based on the Post-Study System Usability Questionnaire and Task-Technology Fit models were enlisted to assess the interface quality and dashboard sufficiency for the final version. Results: The MyAsriGeo geospatial dashboard was designed to meet the needs of all user types, as identified through a requirement gathering process. It includes several key functions, such as a geospatial map that shows the locations of high-risk areas for drug abuse, data on drug abuse among students, tools for assessing the risk of drug abuse in different areas, demographic information, and a self-problem test. It also includes the Alcohol, Smoking, and Substance Involvement Screening Test and its risk assessment to help users understand and interpret the results of student risk. The initial prototype and final version of the dashboard were evaluated by 20 experts, which revealed a significant improvement in the ease of use (P=.047) and usefulness (P=.02) factors and showed a high acceptance mean scores for ease of use (4.2), usefulness (4.46), interface quality (4.29), and sufficiency (4.13). Conclusions: The MyAsriGeo geospatial dashboard is useful for monitoring and analyzing drug abuse among school-going youth in Malaysia. It was developed based on the needs of various stakeholders and includes a range of functions. The dashboard was evaluated by a group of experts. Overall, the MyAsriGeo geospatial dashboard is a valuable resource for helping stakeholders understand and respond to the issue of drug abuse among youth. UR - https://humanfactors.jmir.org/2024/1/e48139 UR - http://dx.doi.org/10.2196/48139 UR - http://www.ncbi.nlm.nih.gov/pubmed/39078685 ID - info:doi/10.2196/48139 ER - TY - JOUR AU - Sawires, Rana AU - Clothier, J. Hazel AU - Burgner, David AU - Fahey, Collingwood Michael AU - Buttery, Jim PY - 2024/7/25 TI - Kawasaki Disease and Respiratory Viruses: Ecological Spatiotemporal Analysis JO - JMIR Public Health Surveill SP - e49648 VL - 10 KW - Kawasaki disease KW - pediatric KW - infection KW - RSV KW - human metapneumovirus KW - respiratory virus KW - virology KW - community KW - viral infection KW - respiratory disease KW - respiratory diseases KW - children KW - epidemiology KW - respiratory syncytial virus N2 - 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. UR - https://publichealth.jmir.org/2024/1/e49648 UR - http://dx.doi.org/10.2196/49648 ID - info:doi/10.2196/49648 ER - TY - JOUR AU - Chen, Yang AU - Zhou, Lidan AU - Zha, Yuanyi AU - Wang, Yujin AU - Wang, Kai AU - Lu, Lvliang AU - Guo, Pi AU - Zhang, Qingying PY - 2024/7/23 TI - Impact of Ambient Temperature on Mortality Burden and Spatial Heterogeneity in 16 Prefecture-Level Cities of a Low-Latitude Plateau Area in Yunnan Province: Time-Series Study JO - JMIR Public Health Surveill SP - e51883 VL - 10 KW - mortality burden KW - nonaccidental deaths KW - multivariate meta-analysis KW - distributed lagged nonlinear mode KW - attributable risk KW - climate change KW - human health KW - association KW - temperature KW - mortality KW - nonaccidental death KW - spatial heterogeneity KW - meteorological data KW - temperature esposure KW - heterogeneous KW - spatial planning KW - environmental temperature KW - prefecture-level KW - resource allocation N2 - Background: The relation between climate change and human health has become one of the major worldwide public health issues. However, the evidence for low-latitude plateau regions is limited, where the climate is unique and diverse with a complex geography and topography. Objectives: This study aimed to evaluate the effect of ambient temperature on the mortality burden of nonaccidental deaths in Yunnan Province and to further explore its spatial heterogeneity among different regions. Methods: We collected mortality and meteorological data from all 129 counties in Yunnan Province from 2014 to 2020, and 16 prefecture-level cities were analyzed as units. A distributed lagged nonlinear model was used to estimate the effect of temperature exposure on years of life lost (YLL) for nonaccidental deaths in each prefecture-level city. The attributable fraction of YLL due to ambient temperature was calculated. A multivariate meta-analysis was used to obtain an overall aggregated estimate of effects, and spatial heterogeneity among 16 prefecture-level cities was evaluated by adjusting the city-specific geographical characteristics, demographic characteristics, economic factors, and health resources factors. Results: The temperature-YLL association was nonlinear and followed slide-shaped curves in all regions. The cumulative cold and heat effect estimates along lag 0?21 days on YLL for nonaccidental deaths were 403.16 (95% empirical confidence interval [eCI] 148.14?615.18) and 247.83 (95% eCI 45.73?418.85), respectively. The attributable fraction for nonaccidental mortality due to daily mean temperature was 7.45% (95% eCI 3.73%?10.38%). Cold temperature was responsible for most of the mortality burden (4.61%, 95% eCI 1.70?7.04), whereas the burden due to heat was 2.84% (95% eCI 0.58?4.83). The vulnerable subpopulations include male individuals, people aged <75 years, people with education below junior college level, farmers, nonmarried individuals, and ethnic minorities. In the cause-specific subgroup analysis, the total attributable fraction (%) for mean temperature was 13.97% (95% eCI 6.70?14.02) for heart disease, 11.12% (95% eCI 2.52?16.82) for respiratory disease, 10.85% (95% eCI 6.70?14.02) for cardiovascular disease, and 10.13% (95% eCI 6.03?13.18) for stroke. The attributable risk of cold effect for cardiovascular disease was higher than that for respiratory disease cause of death (9.71% vs 4.54%). Furthermore, we found 48.2% heterogeneity in the effect of mean temperature on YLL after considering the inherent characteristics of the 16 prefecture-level cities, with urbanization rate accounting for the highest proportion of heterogeneity (15.7%) among urban characteristics. Conclusions: This study suggests that the cold effect dominated the total effect of temperature on mortality burden in Yunnan Province, and its effect was heterogeneous among different regions, which provides a basis for spatial planning and health policy formulation for disease prevention. UR - https://publichealth.jmir.org/2024/1/e51883 UR - http://dx.doi.org/10.2196/51883 ID - info:doi/10.2196/51883 ER - TY - JOUR AU - Mustafa, Hazim Ali AU - Khaleel, Abdulghafoor Hanan AU - Lami, Faris PY - 2024/7/3 TI - Human Brucellosis in Iraq: Spatiotemporal Data Analysis From 2007-2018 JO - JMIRx Med SP - e54611 VL - 5 KW - human brucellosis KW - livestock KW - clustering KW - spatial KW - temporal KW - Iraq N2 - 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. UR - https://xmed.jmir.org/2024/1/e54611 UR - http://dx.doi.org/10.2196/54611 ID - info:doi/10.2196/54611 ER - TY - JOUR AU - Babona Nshuti, Aimee Marie AU - Touray, Kebba AU - Muluh, Johnson Ticha AU - Ubong, Akpan Godwin AU - Ngofa, Opara Reuben AU - Mohammed, Isa Bello AU - Roselyne, Ishimwe AU - Oviaesu, David AU - Bakata, Oliver Evans Mawa AU - Lau, Fiona AU - Kipterer, John AU - Green, W. Hugh Henry AU - Seaman, Vincent AU - Ahmed, A. Jamal AU - Ndoutabe, Modjirom PY - 2024/6/21 TI - Development of a Consolidated Health Facility Masterlist Using Data From Polio Electronic Surveillance in the World Health Organization African Region JO - JMIR Public Health Surveill SP - e54250 VL - 10 KW - African region KW - electronic surveillance KW - geographic information systems KW - Global Polio Eradication Initiative KW - integrated supportive supervision KW - polio UR - https://publichealth.jmir.org/2024/1/e54250 UR - http://dx.doi.org/10.2196/54250 UR - http://www.ncbi.nlm.nih.gov/pubmed/38904997 ID - info:doi/10.2196/54250 ER - TY - JOUR AU - Ma, Shuli AU - Ge, Jie AU - Qin, Lei AU - Chen, Xiaoting AU - Du, Linlin AU - Qi, Yanbo AU - Bai, Li AU - Han, Yunfeng AU - Xie, Zhiping AU - Chen, Jiaxin AU - Jia, Yuehui PY - 2024/6/19 TI - Spatiotemporal Epidemiological Trends of Mpox in Mainland China: Spatiotemporal Ecological Comparison Study JO - JMIR Public Health Surveill SP - e57807 VL - 10 KW - mpox KW - spatiotemporal analysis KW - emergencies KW - prevention and control KW - public health N2 - Background: The World Health Organization declared mpox an international public health emergency. Since January 1, 2022, China has been ranked among the top 10 countries most affected by the mpox outbreak globally. However, there is a lack of spatial epidemiological studies on mpox, which are crucial for accurately mapping the spatial distribution and clustering of the disease. Objective: This study aims to provide geographically accurate visual evidence to determine priority areas for mpox prevention and control. Methods: Locally confirmed mpox cases were collected between June and November 2023 from 31 provinces of mainland China excluding Taiwan, Macao, and Hong Kong. Spatiotemporal epidemiological analyses, including spatial autocorrelation and regression analyses, were conducted to identify the spatiotemporal characteristics and clustering patterns of mpox attack rate and its spatial relationship with sociodemographic and socioeconomic factors. Results: From June to November 2023, a total of 1610 locally confirmed mpox cases were reported in 30 provinces in mainland China, resulting in an attack rate of 11.40 per 10 million people. Global spatial autocorrelation analysis showed that in July (Moran I=0.0938; P=.08), August (Moran I=0.1276; P=.08), and September (Moran I=0.0934; P=.07), the attack rates of mpox exhibited a clustered pattern and positive spatial autocorrelation. The Getis-Ord Gi* statistics identified hot spots of mpox attack rates in Beijing, Tianjin, Shanghai, Jiangsu, and Hainan. Beijing and Tianjin were consistent hot spots from June to October. No cold spots with low mpox attack rates were detected by the Getis-Ord Gi* statistics. Local Moran I statistics identified a high-high (HH) clustering of mpox attack rates in Guangdong, Beijing, and Tianjin. Guangdong province consistently exhibited HH clustering from June to November, while Beijing and Tianjin were identified as HH clusters from July to September. Low-low clusters were mainly located in Inner Mongolia, Xinjiang, Xizang, Qinghai, and Gansu. Ordinary least squares regression models showed that the cumulative mpox attack rates were significantly and positively associated with the proportion of the urban population (t0.05/2,1=2.4041 P=.02), per capita gross domestic product (t0.05/2,1=2.6955; P=.01), per capita disposable income (t0.05/2,1=2.8303; P=.008), per capita consumption expenditure (PCCE; t0.05/2,1=2.7452; P=.01), and PCCE for health care (t0.05/2,1=2.5924; P=.01). The geographically weighted regression models indicated a positive association and spatial heterogeneity between cumulative mpox attack rates and the proportion of the urban population, per capita gross domestic product, per capita disposable income, and PCCE, with high R2 values in north and northeast China. Conclusions: Hot spots and HH clustering of mpox attack rates identified by local spatial autocorrelation analysis should be considered key areas for precision prevention and control of mpox. Specifically, Guangdong, Beijing, and Tianjin provinces should be prioritized for mpox prevention and control. These findings provide geographically precise and visualized evidence to assist in identifying key areas for targeted prevention and control. UR - https://publichealth.jmir.org/2024/1/e57807 UR - http://dx.doi.org/10.2196/57807 UR - http://www.ncbi.nlm.nih.gov/pubmed/38896444 ID - info:doi/10.2196/57807 ER - TY - JOUR AU - Miao, Huazhang AU - He, Hui AU - Nie, Chuan AU - Ren, Jianbing AU - Luo, Xianqiong PY - 2024/6/18 TI - Spatiotemporal Characteristics and Risk Factors for All and Severity-Specific Preterm Births in Southern China, 2014-2021: Large Population-Based Study JO - JMIR Public Health Surveill SP - e48815 VL - 10 KW - preterm birth KW - spatiotemporal KW - incidence KW - risk KW - neonatal KW - infant KW - pregnancy health KW - pregnancy complication KW - pregnancy KW - birth defect KW - birth defects KW - obstetric labor KW - premature N2 - Background: The worldwide incidence of preterm births is increasing, and the risks of adverse outcomes for preterm infants significantly increase with shorter gestation, resulting in a substantial socioeconomic burden. Limited epidemiological studies have been conducted in China regarding the incidence and spatiotemporal trends of preterm births. Seasonal variations in risk indicate the presence of possible modifiable factors. Gender influences the risk of preterm birth. Objective: This study aims to assess the incidence rates of preterm birth, very preterm birth, and extremely preterm birth; elucidate their spatiotemporal distribution; and investigate the risk factors associated with preterm birth. Methods: We obtained data from the Guangdong Provincial Maternal and Child Health Information System, spanning from January 1, 2014, to December 31, 2021, pertaining to neonates with gestational ages ranging from 24 weeks to 42 weeks. The primary outcome measures assessed variations in the rates of different preterm birth subtypes over the course of the study, such as by year, region, and season. Furthermore, we examined the relationship between preterm birth incidence and per capita gross domestic product (GDP), simultaneously analyzing the contributing risk factors. Results: The analysis incorporated data from 13,256,743 live births. We identified 754,268 preterm infants and 12,502,475 full-term infants. The incidences of preterm birth, very preterm birth, and extremely preterm birth were 5.69 per 100 births, 4.46 per 1000 births, and 4.83 per 10,000 births, respectively. The overall incidence of preterm birth increased from 5.12% in 2014 to 6.38% in 2021. The incidence of extremely preterm birth increased from 4.10 per 10,000 births in 2014 to 8.09 per 10,000 births in 2021. There was a positive correlation between the incidence of preterm infants and GDP per capita. In more developed economic regions, the incidence of preterm births was higher. Furthermore, adjusted odds ratios revealed that advanced maternal age, multiple pregnancies, and male infants were associated with an increased risk of preterm birth, whereas childbirth in the autumn season was associated with a protective effect against preterm birth. Conclusions: The incidence of preterm birth in southern China exhibited an upward trend, closely linked to enhancements in the care capabilities for high-risk pregnant women and critically ill newborns. With the recent relaxation of China's 3-child policy, coupled with a temporary surge in advanced maternal age and multiple pregnancies, the risk of preterm birth has risen. Consequently, there is a pressing need to augment public health investments aimed at mitigating the risk factors associated with preterm birth, thereby alleviating the socioeconomic burden it imposes. UR - https://publichealth.jmir.org/2024/1/e48815 UR - http://dx.doi.org/10.2196/48815 UR - http://www.ncbi.nlm.nih.gov/pubmed/38888944 ID - info:doi/10.2196/48815 ER - TY - JOUR AU - Lai, Peixuan AU - Cai, Weicong AU - Qu, Lin AU - Hong, Chuangyue AU - Lin, Kaihao AU - Tan, Weiguo AU - Zhao, Zhiguang PY - 2024/6/14 TI - Pulmonary Tuberculosis Notification Rate Within Shenzhen, China, 2010-2019: Spatial-Temporal Analysis JO - JMIR Public Health Surveill SP - e57209 VL - 10 KW - tuberculosis KW - spatial analysis KW - spatial-temporal cluster KW - Shenzhen KW - China N2 - Background: Pulmonary tuberculosis (PTB) is a chronic communicable disease of major public health and social concern. Although spatial-temporal analysis has been widely used to describe distribution characteristics and transmission patterns, few studies have revealed the changes in the small-scale clustering of PTB at the street level. Objective: The aim of this study was to analyze the temporal and spatial distribution characteristics and clusters of PTB at the street level in the Shenzhen municipality of China to provide a reference for PTB prevention and control. Methods: Data of reported PTB cases in Shenzhen from January 2010 to December 2019 were extracted from the China Information System for Disease Control and Prevention to describe the epidemiological characteristics. Time-series, spatial-autocorrelation, and spatial-temporal scanning analyses were performed to identify the spatial and temporal patterns and high-risk areas at the street level. Results: A total of 58,122 PTB cases from 2010 to 2019 were notified in Shenzhen. The annual notification rate of PTB decreased significantly from 64.97 per 100,000 population in 2010 to 43.43 per 100,000 population in 2019. PTB cases exhibited seasonal variations with peaks in late spring and summer each year. The PTB notification rate was nonrandomly distributed and spatially clustered with a Moran I value of 0.134 (P=.02). One most-likely cluster and 10 secondary clusters were detected, and the most-likely clustering area was centered at Nanshan Street of Nanshan District covering 6 streets, with the clustering time spanning from January 2010 to November 2012. Conclusions: This study identified seasonal patterns and spatial-temporal clusters of PTB cases at the street level in the Shenzhen municipality of China. Resources should be prioritized to the identified high-risk areas for PTB prevention and control. UR - https://publichealth.jmir.org/2024/1/e57209 UR - http://dx.doi.org/10.2196/57209 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875687 ID - info:doi/10.2196/57209 ER - TY - JOUR 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" } TY - JOUR AU - Levin-Rector, Alison AU - Kulldorff, Martin AU - Peterson, R. Eric AU - Hostovich, Scott AU - Greene, K. Sharon PY - 2024/6/11 TI - Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks JO - JMIR Public Health Surveill SP - e50653 VL - 10 KW - communicable diseases KW - disease outbreaks KW - disease surveillance KW - epidemiology KW - infectious disease KW - outbreak detection KW - public health practice KW - SaTScan KW - spatiotemporal KW - urban health UR - https://publichealth.jmir.org/2024/1/e50653 UR - http://dx.doi.org/10.2196/50653 UR - http://www.ncbi.nlm.nih.gov/pubmed/38861711 ID - info:doi/10.2196/50653 ER - TY - JOUR AU - Khalifa, Aleya AU - Beres, K. Laura AU - Anok, Aggrey AU - Mbabali, Ismail AU - Katabalwa, Charles AU - Mulamba, Jeremiah AU - Thomas, G. Alvin AU - Bugos, Eva AU - Nakigozi, Gertrude AU - Chang, W. Larry AU - Grabowski, Kate M. PY - 2024/6/10 TI - Leveraging Ecological Momentary Assessment Data to Characterize Individual Mobility: Exploratory Pilot Study in Rural Uganda JO - JMIR Form Res SP - e54207 VL - 8 KW - ecological momentary assessment KW - spatial analysis KW - geographic mobility KW - global positioning system KW - health behaviors KW - Uganda KW - mobility KW - pilot study KW - smartphone KW - alcohol KW - cigarette KW - smoking KW - promoting KW - promotion KW - alcohol use KW - cigarette smoking KW - mobile phone N2 - Background: The geographical environments within which individuals conduct their daily activities may influence health behaviors, yet little is known about individual-level geographic mobility and specific, linked behaviors in rural low- and middle-income settings. Objective: Nested in a 3-month ecological momentary assessment intervention pilot trial, this study aims to leverage mobile health app user GPS data to examine activity space through individual spatial mobility and locations of reported health behaviors in relation to their homes. Methods: Pilot trial participants were recruited from the Rakai Community Cohort Study?an ongoing population-based cohort study in rural south-central Uganda. Participants used a smartphone app that logged their GPS coordinates every 1-2 hours for approximately 90 days. They also reported specific health behaviors (alcohol use, cigarette smoking, and having condomless sex with a non?long-term partner) via the app that were both location and time stamped. In this substudy, we characterized participant mobility using 3 measures: average distance (kilometers) traveled per week, number of unique locations visited (deduplicated points within 25 m of one another), and the percentage of GPS points recorded away from home. The latter measure was calculated using home buffer regions of 100 m, 400 m, and 800 m. We also evaluated the number of unique locations visited for each specific health behavior, and whether those locations were within or outside the home buffer regions. Sociodemographic information, mobility measures, and locations of health behaviors were summarized across the sample using descriptive statistics. Results: Of the 46 participants with complete GPS data, 24 (52%) participants were men, 30 (65%) participants were younger than 35 years, and 33 (72%) participants were in the top 2 socioeconomic status quartiles. On median, participants traveled 303 (IQR 152-585) km per week. Over the study period, participants on median recorded 1292 (IQR 963-2137) GPS points?76% (IQR 58%-86%) of which were outside their 400-m home buffer regions. Of the participants reporting drinking alcohol, cigarette smoking, and engaging in condomless sex, respectively, 19 (83%), 8 (89%), and 12 (86%) reported that behavior at least once outside their 400-m home neighborhood and across a median of 3.0 (IQR 1.5-5.5), 3.0 (IQR 1.0-3.0), and 3.5 (IQR 1.0-7.0) unique locations, respectively. Conclusions: Among residents in rural Uganda, an ecological momentary assessment app successfully captured high mobility and health-related behaviors across multiple locations. Our findings suggest that future mobile health interventions in similar settings can benefit from integrating spatial data collection using the GPS technology in mobile phones. Leveraging such individual-level GPS data can inform place-based strategies within these interventions for promoting healthy behavior change. UR - https://formative.jmir.org/2024/1/e54207 UR - http://dx.doi.org/10.2196/54207 UR - http://www.ncbi.nlm.nih.gov/pubmed/38857493 ID - info:doi/10.2196/54207 ER - TY - JOUR AU - Xie, Ziyi AU - Chen, Bowen AU - Duan, Zhizhuang PY - 2024/6/7 TI - Spatiotemporal Analysis of HIV/AIDS Incidence in China From 2009 to 2019 and Its Association With Socioeconomic Factors: Geospatial Study JO - JMIR Public Health Surveill SP - e56229 VL - 10 KW - HIV/AIDS KW - spatiotemporal distribution KW - cluster analysis KW - socioeconomic factors KW - China N2 - 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. UR - https://publichealth.jmir.org/2024/1/e56229 UR - http://dx.doi.org/10.2196/56229 UR - http://www.ncbi.nlm.nih.gov/pubmed/38848123 ID - info:doi/10.2196/56229 ER - TY - JOUR AU - Jain, Lovely AU - Pradhan, Sreya AU - Aggarwal, Arun AU - Padhi, Kumar Bijaya AU - Itumalla, Ramaiah AU - Khatib, Nazli Mahalaqua AU - Gaidhane, Shilpa AU - Zahiruddin, Syed Quazi AU - Santos, Guimarães Celso Augusto AU - AL-Mugheed, Khalid AU - Alrahbeni, Tahani AU - Kukreti, Neelima AU - Satapathy, Prakasini AU - Rustagi, Sarvesh AU - Heidler, Petra AU - Marzo, Rillera Roy PY - 2024/5/24 TI - Association of Child Growth Failure Indicators With Household Sanitation Practices in India (1998-2021): Spatiotemporal Observational Study JO - JMIR Public Health Surveill SP - e41567 VL - 10 KW - undernutrition KW - malnutrition KW - stunting KW - wasting KW - underweight KW - sanitation KW - WaSH KW - LISA KW - NFHS KW - DHS KW - spatial epidemiology KW - epidemiology KW - children KW - child KW - India KW - intervention N2 - Background: Undernutrition among children younger than 5 years is a subtle indicator of a country?s health and economic status. Despite substantial macroeconomic progress in India, undernutrition remains a significant burden with geographical variations, compounded by poor access to water, sanitation, and hygiene services. Objective: This study aimed to explore the spatial trends of child growth failure (CGF) indicators and their association with household sanitation practices in India. Methods: We used data from the Indian Demographic and Health Surveys spanning 1998-2021. District-level CGF indicators (stunting, wasting, and underweight) were cross-referenced with sanitation and sociodemographic characteristics. Global Moran I and Local Indicator of Spatial Association were used to detect spatial clustering of the indicators. Spatial regression models were used to evaluate the significant determinants of CGF indicators. Results: Our study showed a decreasing trend in stunting (44.9%-38.4%) and underweight (46.7%-35.7%) but an increasing prevalence of wasting (15.7%-21.0%) over 15 years. The positive values of Moran I between 1998 and 2021 indicate the presence of spatial autocorrelation. Geographic clustering was consistently observed in the states of Madhya Pradesh, Jharkhand, Odisha, Uttar Pradesh, Chhattisgarh, West Bengal, Rajasthan, Bihar, and Gujarat. Improved sanitation facilities, a higher wealth index, and advanced maternal education status showed a significant association in reducing stunting. Relative risk maps identified hotspots of CGF health outcomes, which could be targeted for future interventions. Conclusions: Despite numerous policies and programs, malnutrition remains a concern. Its multifaceted causes demand coordinated and sustained interventions that go above and beyond the usual. Identifying hotspot locations will aid in developing control methods for achieving objectives in target areas. UR - https://publichealth.jmir.org/2024/1/e41567 UR - http://dx.doi.org/10.2196/41567 UR - http://www.ncbi.nlm.nih.gov/pubmed/38787607 ID - info:doi/10.2196/41567 ER - TY - JOUR AU - Xuan, Kun AU - Zhang, Ning AU - Li, Tao AU - Pang, Xingya AU - Li, Qingru AU - Zhao, Tianming AU - Wang, Binbing AU - Zha, Zhenqiu AU - Tang, Jihai PY - 2024/4/5 TI - Epidemiological Characteristics of Varicella in Anhui Province, China, 2012-2021: Surveillance Study JO - JMIR Public Health Surveill SP - e50673 VL - 10 KW - varicella KW - incidence KW - epidemiology KW - spatial autocorrelation KW - contagious disease KW - chicken pox KW - varicella zoster virus KW - China N2 - Background: Varicella is a mild, self-limited disease caused by varicella-zoster virus (VZV) infection. Recently, the disease burden of varicella has been gradually increasing in China; however, the epidemiological characteristics of varicella have not been reported for Anhui Province. Objective: The aim of this study was to analyze the epidemiology of varicella in Anhui from 2012 to 2021, which can provide a basis for the future study and formulation of varicella prevention and control policies in the province. Methods: Surveillance data were used to characterize the epidemiology of varicella in Anhui from 2012 to 2021 in terms of population, time, and space. Spatial autocorrelation of varicella was explored using the Moran index (Moran I). The Kulldorff space-time scan statistic was used to analyze the spatiotemporal aggregation of varicella. Results: A total of 276,115 cases of varicella were reported from 2012 to 2021 in Anhui, with an average annual incidence of 44.8 per 100,000, and the highest incidence was 81.2 per 100,000 in 2019. The male-to-female ratio of cases was approximately 1.26, which has been gradually decreasing in recent years. The population aged 5-14 years comprised the high-incidence group, although the incidence in the population 30 years and older has gradually increased. Students accounted for the majority of cases, and the proportion of cases in both home-reared children (aged 0-7 years who are not sent to nurseries, daycare centers, or school) and kindergarten children (aged 3-6 years) has changed slightly in recent years. There were two peaks of varicella incidence annually, except for 2020, and the incidence was typically higher in the winter peak than in summer. The incidence of varicella in southern Anhui was higher than that in northern Anhui. The average annual incidence at the county level ranged from 6.61 to 152.14 per 100,000, and the varicella epidemics in 2018-2021 were relatively severe. The spatial and temporal distribution of varicella in Anhui was not random, with a positive spatial autocorrelation found at the county level (Moran I=0.412). There were 11 districts or counties with high-high clusters, mainly distributed in the south of Anhui, and 3 districts or counties with high-low or low-high clusters. Space-time scan analysis identified five possible clusters of areas, and the most likely cluster was distributed in the southeastern region of Anhui. Conclusions: This study comprehensively describes the epidemiology and changing trend of varicella in Anhui from 2012 to 2021. In the future, preventive and control measures should be strengthened for the key populations and regions of varicella. UR - https://publichealth.jmir.org/2024/1/e50673 UR - http://dx.doi.org/10.2196/50673 UR - http://www.ncbi.nlm.nih.gov/pubmed/38579276 ID - info:doi/10.2196/50673 ER - TY - JOUR AU - Hashtarkhani, Soheil AU - Schwartz, L. David AU - Shaban-Nejad, Arash PY - 2024/2/21 TI - Enhancing Health Care Accessibility and Equity Through a Geoprocessing Toolbox for Spatial Accessibility Analysis: Development and Case Study JO - JMIR Form Res SP - e51727 VL - 8 KW - geographical information system KW - geoprocessing tool KW - health disparities KW - health equity KW - health services management KW - hemodialysis services KW - spatial accessibility N2 - Background: Access to health care services is a critical determinant of population health and well-being. Measuring spatial accessibility to health services is essential for understanding health care distribution and addressing potential inequities. Objective: In this study, we developed a geoprocessing toolbox including Python script tools for the ArcGIS Pro environment to measure the spatial accessibility of health services using both classic and enhanced versions of the 2-step floating catchment area method. Methods: Each of our tools incorporated both distance buffers and travel time catchments to calculate accessibility scores based on users? choices. Additionally, we developed a separate tool to create travel time catchments that is compatible with both locally available network data sets and ArcGIS Online data sources. We conducted a case study focusing on the accessibility of hemodialysis services in the state of Tennessee using the 4 versions of the accessibility tools. Notably, the calculation of the target population considered age as a significant nonspatial factor influencing hemodialysis service accessibility. Weighted populations were calculated using end-stage renal disease incidence rates in different age groups. Results: The implemented tools are made accessible through ArcGIS Online for free use by the research community. The case study revealed disparities in the accessibility of hemodialysis services, with urban areas demonstrating higher scores compared to rural and suburban regions. Conclusions: These geoprocessing tools can serve as valuable decision-support resources for health care providers, organizations, and policy makers to improve equitable access to health care services. This comprehensive approach to measuring spatial accessibility can empower health care stakeholders to address health care distribution challenges effectively. UR - https://formative.jmir.org/2024/1/e51727 UR - http://dx.doi.org/10.2196/51727 UR - http://www.ncbi.nlm.nih.gov/pubmed/38381503 ID - info:doi/10.2196/51727 ER - TY - JOUR AU - Huang, Gang AU - Cheng, Wei AU - Xu, Yun AU - Yang, Jiezhe AU - Jiang, Jun AU - Pan, Xiaohong AU - Zhou, Xin AU - Jiang, Jianmin AU - Chai, Chengliang PY - 2024/2/13 TI - Spatiotemporal Pattern and Its Determinants for Newly Reported HIV/AIDS Among Older Adults in Eastern China From 2004 to 2021: Retrospective Analysis Study JO - JMIR Public Health Surveill SP - e51172 VL - 10 KW - HIV/AIDS KW - men who have sex with men KW - newly reported infections KW - older adults KW - spatiotemporal analysis N2 - Background: In recent years, the number and proportion of newly reported HIV/AIDS cases among older adults have increased dramatically. However, research on the pattern of temporal and spatial changes in newly reported HIV/AIDS among older adults remains limited. Objective: This study analyzed the spatial and temporal distribution of HIV/AIDS cases and its influencing factors among older adults in Eastern China from 2004 to 2021, with the goal of improving HIV/AIDS prevention and intervention. Methods: We extracted data on newly reported HIV/AIDS cases between 2004 and 2021 from a case-reporting system and used a Joinpoint regression model and an age-period-cohort model to analyze the temporal trends in HIV/AIDS prevalence. Spatial autocorrelation and geographically weighted regression models were used for spatial aggregation and influence factor analysis. Results: A total of 12,376 participants with HIV/AIDS were included in the study. The newly reported HIV infections among older adults increased from 0.13 cases per 100,000 people in 2004 to 7.00 cases per 100,000 people in 2021. The average annual percent change in newly reported HIV infections was 28.0% (95% CI ?21.6% to 34.8%). The results of the age-period-cohort model showed that age, period, and cohort factors affected the newly reported HIV infections among older adults. The newly reported HIV/AIDS cases among men who have sex with men (MSM) had spatial clustering, and the hotspots were mainly concentrated in Hangzhou. The disposable income of urban residents, illiteracy rate among people aged 15 years or older, and number of hospital beds per 1000 residents showed a positive association with the newly reported HIV infections among older MSM in the Zhejiang province. Conclusions: HIV/AIDS among older adults showed an increasing trend and was influenced by age, period, and cohort effects. Older MSM with HIV/AIDS showed regional clustering and was associated with factors such as the disposable income of urban residents, the illiteracy rate among people aged 15 years or older, and the number of hospital beds per 1000 people. Targeted prevention and control measures are needed to reduce HIV infection among those at higher risk. UR - https://publichealth.jmir.org/2024/1/e51172 UR - http://dx.doi.org/10.2196/51172 UR - http://www.ncbi.nlm.nih.gov/pubmed/38349727 ID - info:doi/10.2196/51172 ER - TY - JOUR AU - De La Cerda, Isela AU - Bauer, X. Cici AU - Zhang, Kehe AU - Lee, Miryoung AU - Jones, Michelle AU - Rodriguez, Arturo AU - McCormick, B. Joseph AU - Fisher-Hoch, P. Susan PY - 2023/12/20 TI - Evaluation of a Targeted COVID-19 Community Outreach Intervention: Case Report for Precision Public Health JO - JMIR Public Health Surveill SP - e47981 VL - 9 KW - community interventions KW - emergency preparedness KW - health disparities KW - intervention evaluation KW - precision public health KW - public health informatics KW - public health intervention KW - public health KW - spatial epidemiology KW - surveillance N2 - Background: Cameron County, a low-income south Texas-Mexico border county marked by severe health disparities, was consistently among the top counties with the highest COVID-19 mortality in Texas at the onset of the pandemic. The disparity in COVID-19 burden within Texas counties revealed the need for effective interventions to address the specific needs of local health departments and their communities. Publicly available COVID-19 surveillance data were not sufficiently timely or granular to deliver such targeted interventions. An agency-academic collaboration in Cameron used novel geographic information science methods to produce granular COVID-19 surveillance data. These data were used to strategically target an educational outreach intervention named ?Boots on the Ground? (BOG) in the City of Brownsville (COB). Objective: This study aimed to evaluate the impact of a spatially targeted community intervention on daily COVID-19 test counts. Methods: The agency-academic collaboration between the COB and UTHealth Houston led to the creation of weekly COVID-19 epidemiological reports at the census tract level. These reports guided the selection of census tracts to deliver targeted BOG between April 21 and June 8, 2020. Recordkeeping of the targeted BOG tracts and the intervention dates, along with COVID-19 daily testing counts per census tract, provided data for intervention evaluation. An interrupted time series design was used to evaluate the impact on COVID-19 test counts 2 weeks before and after targeted BOG. A piecewise Poisson regression analysis was used to quantify the slope (sustained) and intercept (immediate) change between pre- and post-BOG COVID-19 daily test count trends. Additional analysis of COB tracts that did not receive targeted BOG was conducted for comparison purposes. Results: During the intervention period, 18 of the 48 COB census tracts received targeted BOG. Among these, a significant change in the slope between pre- and post-BOG daily test counts was observed in 5 tracts, 80% (n=4) of which had a positive slope change. A positive slope change implied a significant increase in daily COVID-19 test counts 2 weeks after targeted BOG compared to the testing trend observed 2 weeks before intervention. In an additional analysis of the 30 census tracts that did not receive targeted BOG, significant slope changes were observed in 10 tracts, of which positive slope changes were only observed in 20% (n=2). In summary, we found that BOG-targeted tracts had mostly positive daily COVID-19 test count slope changes, whereas untargeted tracts had mostly negative daily COVID-19 test count slope changes. Conclusions: Evaluation of spatially targeted community interventions is necessary to strengthen the evidence base of this important approach for local emergency preparedness. This report highlights how an academic-agency collaboration established and evaluated the impact of a real-time, targeted intervention delivering precision public health to a small community. UR - https://publichealth.jmir.org/2023/1/e47981 UR - http://dx.doi.org/10.2196/47981 UR - http://www.ncbi.nlm.nih.gov/pubmed/38117549 ID - info:doi/10.2196/47981 ER - TY - JOUR AU - Lu, Yixiao AU - Zhu, Hansong AU - Hu, Zhijian AU - He, Fei AU - Chen, Guangmin PY - 2023/11/30 TI - Epidemic Characteristics, Spatiotemporal Pattern, and Risk Factors of Other Infectious Diarrhea in Fujian Province From 2005 to 2021: Retrospective Analysis JO - JMIR Public Health Surveill SP - e45870 VL - 9 KW - other infectious diarrhea KW - spatiotemporal pattern KW - disease cluster KW - epidemiological trends KW - spatial autocorrelation KW - meteorological factors KW - environmental factors N2 - Background: Other infectious diarrhea (OID) continues to pose a significant public health threat to all age groups in Fujian Province. There is a need for an in-depth analysis to understand the epidemiological pattern of OID and its associated risk factors in the region. Objective: In this study, we aimed to describe the overall epidemic characteristics and spatiotemporal pattern of OID in Fujian Province from 2005 to 2021 and explore the linkage between sociodemographic and environmental factors and the occurrence of OID within the study area. Methods: Notification data for OID in Fujian were extracted from the China Information System for Disease Control and Prevention. The spatiotemporal pattern of OID was analyzed using Moran index and Kulldorff scan statistics. The seasonality of and short-term impact of meteorological factors on OID were examined using an additive decomposition model and a generalized additive model. Geographical weighted regression and generalized linear mixed model were used to identify potential risk factors. Results: A total of 388,636 OID cases were recorded in Fujian Province from January 2005 to December 2021, with an average annual incidence of 60.3 (SD 16.7) per 100,000 population. Children aged <2 years accounted for 50.7% (196,905/388,636) of all cases. There was a steady increase in OID from 2005 to 2017 and a clear seasonal shift in OID cases from autumn to winter and spring between 2005 and 2020. Higher maximum temperature, atmospheric pressure, humidity, and precipitation were linked to a higher number of deseasonalized OID cases. The spatial and temporal aggregations were concentrated in Zhangzhou City and Xiamen City for 17 study years. Furthermore, the clustered areas exhibited a dynamic spreading trend, expanding from the southernmost Fujian to the southeast and then southward over time. Factors such as densely populated areas with a large <1-year-old population, less economically developed areas, and higher pollution levels contributed to OID cases in Fujian Province. Conclusions: This study revealed a distinct distribution of OID incidence across different population groups, seasons, and regions in Fujian Province. Zhangzhou City and Xiamen City were identified as the major hot spots for OID. Therefore, prevention and control efforts should prioritize these specific hot spots and highly susceptible groups. UR - https://publichealth.jmir.org/2023/1/e45870 UR - http://dx.doi.org/10.2196/45870 UR - http://www.ncbi.nlm.nih.gov/pubmed/38032713 ID - info:doi/10.2196/45870 ER - TY - JOUR AU - Zhang, Qi AU - Ding, Huan AU - Gao, Song AU - Zhang, Shipeng AU - Shen, Shiya AU - Chen, Xiaoyan AU - Xu, Zhuping PY - 2023/3/8 TI - Spatiotemporal Changes in Pulmonary Tuberculosis Incidence in a Low-Epidemic Area of China in 2005-2020: Retrospective Spatiotemporal Analysis JO - JMIR Public Health Surveill SP - e42425 VL - 9 KW - pulmonary tuberculosis KW - spatial analysis KW - temporal analysis KW - epidemiology KW - China N2 - 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. UR - https://publichealth.jmir.org/2023/1/e42425 UR - http://dx.doi.org/10.2196/42425 UR - http://www.ncbi.nlm.nih.gov/pubmed/36884278 ID - info:doi/10.2196/42425 ER - TY - JOUR AU - Spang, P. Robert AU - Haeger, Christine AU - Mümken, A. Sandra AU - Brauer, Max AU - Voigt-Antons, Jan-Niklas AU - Gellert, Paul PY - 2023/3/2 TI - Smartphone Global Positioning System?Based System to Assess Mobility in Health Research: Development, Accuracy, and Usability Study JO - JMIR Rehabil Assist Technol SP - e42258 VL - 10 KW - geographic information system KW - rehabilitation KW - prevention medicine KW - geoinformatics KW - out-of-home mobility N2 - 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 UR - https://rehab.jmir.org/2023/1/e42258 UR - http://dx.doi.org/10.2196/42258 UR - http://www.ncbi.nlm.nih.gov/pubmed/36862498 ID - info:doi/10.2196/42258 ER - TY - JOUR AU - Brakefield, S. Whitney AU - Olusanya, A. Olufunto AU - Shaban-Nejad, Arash PY - 2022/8/9 TI - Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach JO - JMIR Public Health Surveill SP - e37039 VL - 8 IS - 8 KW - obesity KW - obesity surveillance KW - disease surveillance KW - machine learning KW - geographic information systems KW - social determinants of health KW - SDOH KW - disparities N2 - 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. UR - https://publichealth.jmir.org/2022/8/e37039 UR - http://dx.doi.org/10.2196/37039 UR - http://www.ncbi.nlm.nih.gov/pubmed/35943795 ID - info:doi/10.2196/37039 ER - TY - JOUR AU - Krzyzanowski, Brittany AU - Manson, M. Steven PY - 2022/8/3 TI - Twenty Years of the Health Insurance Portability and Accountability Act Safe Harbor Provision: Unsolved Challenges and Ways Forward JO - JMIR Med Inform SP - e37756 VL - 10 IS - 8 KW - Health Insurance Portability and Accountability Act KW - HIPAA KW - data privacy KW - health KW - maps KW - safe harbor KW - visualization KW - patient privacy UR - https://medinform.jmir.org/2022/8/e37756 UR - http://dx.doi.org/10.2196/37756 UR - http://www.ncbi.nlm.nih.gov/pubmed/35921140 ID - info:doi/10.2196/37756 ER - TY - JOUR AU - Abdullah, Md Abu Yousuf AU - Law, Jane AU - Perlman, M. Christopher AU - Butt, A. Zahid PY - 2022/7/28 TI - Age- and Sex-Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study JO - JMIR Public Health Surveill SP - e34782 VL - 8 IS - 7 KW - mental health disorders KW - vegetation cover KW - age- and sex- specific association KW - Enhanced Vegetation Index KW - Bayesian KW - spatial KW - hierarchical modeling KW - marginalization KW - latent covariates N2 - 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. UR - https://publichealth.jmir.org/2022/7/e34782 UR - http://dx.doi.org/10.2196/34782 UR - http://www.ncbi.nlm.nih.gov/pubmed/35900816 ID - info:doi/10.2196/34782 ER - TY - JOUR AU - Sigalo, Nekabari AU - St Jean, Beth AU - Frias-Martinez, Vanessa PY - 2022/7/5 TI - Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets JO - JMIR Public Health Surveill SP - e34285 VL - 8 IS - 7 KW - social media KW - Twitter KW - food deserts KW - food insecurity N2 - 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. UR - https://publichealth.jmir.org/2022/7/e34285 UR - http://dx.doi.org/10.2196/34285 UR - http://www.ncbi.nlm.nih.gov/pubmed/35788108 ID - info:doi/10.2196/34285 ER - TY - JOUR AU - Shi, Qiming AU - Herbert, Carly AU - Ward, V. Doyle AU - Simin, Karl AU - McCormick, A. Beth AU - Ellison III, T. Richard AU - Zai, H. Adrian PY - 2022/6/13 TI - COVID-19 Variant Surveillance and Social Determinants in Central Massachusetts: Development Study JO - JMIR Form Res SP - e37858 VL - 6 IS - 6 KW - geographic information science KW - GIS KW - COVID-19 KW - SARS-CoV-2 KW - variants KW - surveillance KW - dashboard KW - web mapping KW - public health KW - web-based information KW - digital health KW - epidemiology N2 - 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. UR - https://formative.jmir.org/2022/6/e37858 UR - http://dx.doi.org/10.2196/37858 UR - http://www.ncbi.nlm.nih.gov/pubmed/35658093 ID - info:doi/10.2196/37858 ER - TY - JOUR AU - Abell-Hart, Kayley AU - Rashidian, Sina AU - Teng, Dejun AU - Rosenthal, N. Richard AU - Wang, Fusheng PY - 2022/4/12 TI - Where Opioid Overdose Patients Live Far From Treatment: Geospatial Analysis of Underserved Populations in New York State JO - JMIR Public Health Surveill SP - e32133 VL - 8 IS - 4 KW - opioid use disorder KW - opioid overdose KW - buprenorphine KW - naloxone KW - geospatial analysis KW - epidemiology KW - opioid pandemic KW - public health N2 - 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. UR - https://publichealth.jmir.org/2022/4/e32133 UR - http://dx.doi.org/10.2196/32133 UR - http://www.ncbi.nlm.nih.gov/pubmed/35412467 ID - info:doi/10.2196/32133 ER - TY - JOUR AU - Haithcoat, Timothy AU - Liu, Danlu AU - Young, Tiffany AU - Shyu, Chi-Ren PY - 2022/4/6 TI - Investigating Health Context Using a Spatial Data Analytical Tool: Development of a Geospatial Big Data Ecosystem JO - JMIR Med Inform SP - e35073 VL - 10 IS - 4 KW - context KW - Geographic Information System KW - big data KW - equity KW - population health KW - public health KW - digital health KW - eHealth KW - location KW - geospatial KW - data analytics KW - analytical framework KW - medical informatics KW - research knowledgebase N2 - 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. UR - https://medinform.jmir.org/2022/4/e35073 UR - http://dx.doi.org/10.2196/35073 UR - http://www.ncbi.nlm.nih.gov/pubmed/35311683 ID - info:doi/10.2196/35073 ER - TY - JOUR AU - Wang, Alex AU - McCarron, Robert AU - Azzam, Daniel AU - Stehli, Annamarie AU - Xiong, Glen AU - DeMartini, Jeremy PY - 2022/3/31 TI - Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study JO - JMIR Ment Health SP - e35253 VL - 9 IS - 3 KW - depression KW - epidemiology KW - internet KW - google trends KW - big data KW - mental health N2 - 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. UR - https://mental.jmir.org/2022/3/e35253 UR - http://dx.doi.org/10.2196/35253 UR - http://www.ncbi.nlm.nih.gov/pubmed/35357320 ID - info:doi/10.2196/35253 ER - TY - JOUR AU - Lyu, Zeyu AU - Takikawa, Hiroki PY - 2022/3/22 TI - The Disparity and Dynamics of Social Distancing Behaviors in Japan: Investigation of Mobile Phone Mobility Data JO - JMIR Med Inform SP - e31557 VL - 10 IS - 3 KW - COVID-19 KW - social distancing KW - mobility KW - time series KW - tracking KW - policy N2 - 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. UR - https://medinform.jmir.org/2022/3/e31557 UR - http://dx.doi.org/10.2196/31557 UR - http://www.ncbi.nlm.nih.gov/pubmed/35297764 ID - info:doi/10.2196/31557 ER - TY - JOUR AU - Beukenhorst, L. Anna AU - Sergeant, C. Jamie AU - Schultz, M. David AU - McBeth, John AU - Yimer, B. Belay AU - Dixon, G. Will PY - 2021/11/16 TI - Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study JO - JMIR Mhealth Uhealth SP - e28857 VL - 9 IS - 11 KW - geolocation KW - global positioning system KW - smartphones KW - mobile phone KW - mobile health KW - environmental exposures KW - data analysis KW - digital epidemiology KW - missing data KW - location data KW - mobile application N2 - 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. UR - https://mhealth.jmir.org/2021/11/e28857 UR - http://dx.doi.org/10.2196/28857 UR - http://www.ncbi.nlm.nih.gov/pubmed/34783661 ID - info:doi/10.2196/28857 ER - TY - JOUR AU - Bos, C. Véronique L. L. AU - Jansen, Tessa AU - Klazinga, S. Niek AU - Kringos, S. Dionne PY - 2021/10/12 TI - Development and Actionability of the Dutch COVID-19 Dashboard: Descriptive Assessment and Expert Appraisal Study JO - JMIR Public Health Surveill SP - e31161 VL - 7 IS - 10 KW - COVID-19 KW - dashboard KW - performance intelligence KW - Netherlands KW - actionability KW - communication KW - government KW - pandemic KW - public health N2 - 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. UR - https://publichealth.jmir.org/2021/10/e31161 UR - http://dx.doi.org/10.2196/31161 UR - http://www.ncbi.nlm.nih.gov/pubmed/34543229 ID - info:doi/10.2196/31161 ER - TY - JOUR AU - Jo, Youngji AU - Barthel, Nathan AU - Stierman, Elizabeth AU - Clifton, Kathryn AU - Pak, Semee Esther AU - Ezeiru, Sonachi AU - Ekweremadu, Diwe AU - Onugu, Nnaemeka AU - Ali, Zainab AU - Egwu, Elijah AU - Akoh, Ochayi AU - Uzunyayla, Orkan AU - Van Hulle, Suzanne PY - 2021/10/8 TI - The Potential of Digital Data Collection Tools for Long-lasting Insecticide-Treated Net Mass Campaigns in Nigeria: Formative Study JO - JMIR Form Res SP - e23648 VL - 5 IS - 10 KW - long-lasting insecticide-treated nets KW - malaria KW - Nigeria KW - information communication technology KW - geographic information system KW - supply chain management N2 - 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. UR - https://formative.jmir.org/2021/10/e23648 UR - http://dx.doi.org/10.2196/23648 UR - http://www.ncbi.nlm.nih.gov/pubmed/34623310 ID - info:doi/10.2196/23648 ER - TY - JOUR AU - De Ridder, David AU - Loizeau, Jutta Andrea AU - Sandoval, Luis José AU - Ehrler, Frédéric AU - Perrier, Myriam AU - Ritch, Albert AU - Violot, Guillemette AU - Santolini, Marc AU - Greshake Tzovaras, Bastian AU - Stringhini, Silvia AU - Kaiser, Laurent AU - Pradeau, Jean-François AU - Joost, Stéphane AU - Guessous, Idris PY - 2021/10/6 TI - Detection of Spatiotemporal Clusters of COVID-19?Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study JO - JMIR Res Protoc SP - e30444 VL - 10 IS - 10 KW - participatory surveillance KW - infectious disease KW - COVID-19 KW - SARS-CoV-2 KW - space-time clustering KW - digital health KW - mobile app KW - mHealth KW - epidemiology KW - surveillance KW - digital surveillance KW - public health N2 - 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 UR - https://www.researchprotocols.org/2021/10/e30444 UR - http://dx.doi.org/10.2196/30444 UR - http://www.ncbi.nlm.nih.gov/pubmed/34449403 ID - info:doi/10.2196/30444 ER - TY - JOUR AU - Hu, Tao AU - Wang, Siqin AU - Luo, Wei AU - Zhang, Mengxi AU - Huang, Xiao AU - Yan, Yingwei AU - Liu, Regina AU - Ly, Kelly AU - Kacker, Viraj AU - She, Bing AU - Li, Zhenlong PY - 2021/9/10 TI - Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective JO - J Med Internet Res SP - e30854 VL - 23 IS - 9 KW - Twitter KW - public opinion KW - COVID-19 vaccines KW - sentiment analysis KW - emotion analysis KW - topic modeling KW - COVID-19 N2 - 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. UR - https://www.jmir.org/2021/9/e30854 UR - http://dx.doi.org/10.2196/30854 UR - http://www.ncbi.nlm.nih.gov/pubmed/34346888 ID - info:doi/10.2196/30854 ER - TY - JOUR AU - Liu, Lei AU - Ni, Yizhao AU - Beck, F. Andrew AU - Brokamp, Cole AU - Ramphul, C. Ryan AU - Highfield, D. Linda AU - Kanjia, Karkera Megha AU - Pratap, ?Nick? J. PY - 2021/9/10 TI - Understanding Pediatric Surgery Cancellation: Geospatial Analysis JO - J Med Internet Res SP - e26231 VL - 23 IS - 9 KW - surgery cancellation KW - socioeconomic factors KW - spatial regression models KW - machine learning N2 - 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. UR - https://www.jmir.org/2021/9/e26231 UR - http://dx.doi.org/10.2196/26231 UR - http://www.ncbi.nlm.nih.gov/pubmed/34505837 ID - info:doi/10.2196/26231 ER - TY - JOUR AU - Trotter II, Robert AU - Baldwin, Julie AU - Buck, Loren Charles AU - Remiker, Mark AU - Aguirre, Amanda AU - Milner, Trudie AU - Torres, Emma AU - von Hippel, Arthur Frank PY - 2021/8/11 TI - Health Impacts of Perchlorate and Pesticide Exposure: Protocol for Community-Engaged Research to Evaluate Environmental Toxicants in a US Border Community JO - JMIR Res Protoc SP - e15864 VL - 10 IS - 8 KW - community-engaged research KW - endocrine disruption KW - environmental contaminants KW - health disparities KW - toxic metal contamination KW - perchlorates KW - pesticides KW - population health KW - thyroid disease N2 - 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 UR - https://www.researchprotocols.org/2021/8/e15864 UR - http://dx.doi.org/10.2196/15864 UR - http://www.ncbi.nlm.nih.gov/pubmed/34383679 ID - info:doi/10.2196/15864 ER - TY - JOUR AU - Chaney, Cunard Sarah AU - Mechael, Patricia AU - Thu, Myo Nay AU - Diallo, S. Mamadou AU - Gachen, Carine PY - 2021/8/3 TI - Every Child on the Map: A Theory of Change Framework for Improving Childhood Immunization Coverage and Equity Using Geospatial Data and Technologies JO - J Med Internet Res SP - e29759 VL - 23 IS - 8 KW - geospatial data KW - immunization KW - health information systems KW - service delivery KW - equity mapping KW - theory KW - framework KW - children KW - vaccine KW - equity KW - geospatial KW - data KW - outcome KW - coverage KW - low- and middle-income KW - LMIC UR - https://www.jmir.org/2021/8/e29759 UR - http://dx.doi.org/10.2196/29759 UR - http://www.ncbi.nlm.nih.gov/pubmed/34342584 ID - info:doi/10.2196/29759 ER - TY - JOUR AU - Zheng, Shuai AU - Edwards, R. Jonathan AU - Dudeck, A. Margaret AU - Patel, R. Prachi AU - Wattenmaker, Lauren AU - Mirza, Muzna AU - Tejedor, Chernetsky Sheri AU - Lemoine, Kent AU - Benin, L. Andrea AU - Pollock, A. Daniel PY - 2021/7/30 TI - Building an Interactive Geospatial Visualization Application for National Health Care?Associated Infection Surveillance: Development Study JO - JMIR Public Health Surveill SP - e23528 VL - 7 IS - 7 KW - data visualization KW - geospatial information system KW - health care?associated infection N2 - 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. UR - https://publichealth.jmir.org/2021/7/e23528 UR - http://dx.doi.org/10.2196/23528 UR - http://www.ncbi.nlm.nih.gov/pubmed/34328436 ID - info:doi/10.2196/23528 ER - TY - JOUR AU - Tamura, Kosuke AU - Curlin, Kaveri AU - Neally, J. Sam AU - Vijayakumar, P. Nithya AU - Mitchell, M. Valerie AU - Collins, S. Billy AU - Gutierrez-Huerta, Cristhian AU - Troendle, F. James AU - Baumer, Yvonne AU - Osei Baah, Foster AU - Turner, S. Briana AU - Gray, Veronica AU - Tirado, A. Brian AU - Ortiz-Chaparro, Erika AU - Berrigan, David AU - Mehta, N. Nehal AU - Vaccarino, Viola AU - Zenk, N. Shannon AU - Powell-Wiley, M. Tiffany PY - 2021/7/22 TI - Geospatial Analysis of Neighborhood Environmental Stress in Relation to Biological Markers of Cardiovascular Health and Health Behaviors in Women: Protocol for a Pilot Study JO - JMIR Res Protoc SP - e29191 VL - 10 IS - 7 KW - wearables KW - global positioning system KW - ecological momentary assessment KW - accelerometer KW - biomarkers of stress KW - mobile phone N2 - 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 UR - https://www.researchprotocols.org/2021/7/e29191 UR - http://dx.doi.org/10.2196/29191 UR - http://www.ncbi.nlm.nih.gov/pubmed/34292168 ID - info:doi/10.2196/29191 ER - TY - JOUR AU - Onovo, Amobi AU - Kalaiwo, Abiye AU - Katbi, Moses AU - Ogorry, Otse AU - Jaquet, Antoine AU - Keiser, Olivia PY - 2021/5/24 TI - Geographical Disparities in HIV Seroprevalence Among Men Who Have Sex with Men and People Who Inject Drugs in Nigeria: Exploratory Spatial Data Analysis JO - JMIR Public Health Surveill SP - e19587 VL - 7 IS - 5 KW - key population KW - MSM KW - PWID KW - HIV seroprevalence KW - HIV testing modality KW - hotspots KW - geospatial KW - Getis-Ord-Gi* KW - IBBSS KW - Nigeria N2 - 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. UR - https://publichealth.jmir.org/2021/5/e19587 UR - http://dx.doi.org/10.2196/19587 UR - http://www.ncbi.nlm.nih.gov/pubmed/34028360 ID - info:doi/10.2196/19587 ER - TY - JOUR AU - Lima, Yuri AU - Pinheiro, Wallace AU - Barbosa, Eduardo Carlos AU - Magalhães, Matheus AU - Chaves, Miriam AU - de Souza, Moreira Jano AU - Rodrigues, Sérgio AU - Xexéo, Geraldo PY - 2021/5/10 TI - Development of an Index for the Inspection of Aedes aegypti Breeding Sites in Brazil: Multi-criteria Analysis JO - JMIR Public Health Surveill SP - e19502 VL - 7 IS - 5 KW - multi-criteria analysis KW - public health KW - human sensors KW - vector surveillance KW - tropical diseases N2 - 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. UR - https://publichealth.jmir.org/2021/5/e19502 UR - http://dx.doi.org/10.2196/19502 UR - http://www.ncbi.nlm.nih.gov/pubmed/33970118 ID - info:doi/10.2196/19502 ER - TY - JOUR AU - Xiang, Anthony AU - Hou, Wei AU - Rashidian, Sina AU - Rosenthal, N. Richard AU - Abell-Hart, Kayley AU - Zhao, Xia AU - Wang, Fusheng PY - 2021/4/21 TI - Association of Opioid Use Disorder With 2016 Presidential Voting Patterns: Cross-sectional Study in New York State at Census Tract Level JO - JMIR Public Health Surveill SP - e23426 VL - 7 IS - 4 KW - opioid use disorder KW - opioid poisoning KW - racial and ethnic disparities KW - geographic variance KW - sociodemographic factors KW - presidential election N2 - 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. UR - https://publichealth.jmir.org/2021/4/e23426 UR - http://dx.doi.org/10.2196/23426 UR - http://www.ncbi.nlm.nih.gov/pubmed/33881409 ID - info:doi/10.2196/23426 ER - TY - JOUR AU - Ivankovi?, Damir AU - Barbazza, Erica AU - Bos, Véronique AU - Brito Fernandes, Óscar AU - Jamieson Gilmore, Kendall AU - Jansen, Tessa AU - Kara, Pinar AU - Larrain, Nicolas AU - Lu, Shan AU - Meza-Torres, Bernardo AU - Mulyanto, Joko AU - Poldrugovac, Mircha AU - Rotar, Alexandru AU - Wang, Sophie AU - Willmington, Claire AU - Yang, Yuanhang AU - Yelgezekova, Zhamin AU - Allin, Sara AU - Klazinga, Niek AU - Kringos, Dionne PY - 2021/2/24 TI - Features Constituting Actionable COVID-19 Dashboards: Descriptive Assessment and Expert Appraisal of 158 Public Web-Based COVID-19 Dashboards JO - J Med Internet Res SP - e25682 VL - 23 IS - 2 KW - COVID-19 KW - pandemic KW - internet KW - performance measures KW - public reporting of health care data KW - public health KW - surveillance KW - health information management KW - dashboard KW - accessibility KW - online tool KW - communication KW - feature KW - expert N2 - 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. UR - https://www.jmir.org/2021/2/e25682 UR - http://dx.doi.org/10.2196/25682 UR - http://www.ncbi.nlm.nih.gov/pubmed/33577467 ID - info:doi/10.2196/25682 ER - TY - JOUR AU - Ali, H. Shahmir AU - Imbruce, M. Valerie AU - Russo, G. Rienna AU - Kaplan, Samuel AU - Stevenson, Kaye AU - Mezzacca, Adjoian Tamar AU - Foster, Victoria AU - Radee, Ashley AU - Chong, Stella AU - Tsui, Felice AU - Kranick, Julie AU - Yi, S. Stella PY - 2021/2/18 TI - Evaluating Closures of Fresh Fruit and Vegetable Vendors During the COVID-19 Pandemic: Methodology and Preliminary Results Using Omnidirectional Street View Imagery JO - JMIR Form Res SP - e23870 VL - 5 IS - 2 KW - built environment KW - Google Street View KW - food retail environment KW - COVID-19 KW - geographic surveillance KW - food KW - longitudinal KW - supply chain KW - economy KW - demand KW - service KW - vendor KW - surveillance N2 - 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. UR - http://formative.jmir.org/2021/2/e23870/ UR - http://dx.doi.org/10.2196/23870 UR - http://www.ncbi.nlm.nih.gov/pubmed/33539310 ID - info:doi/10.2196/23870 ER - TY - JOUR AU - Gu, Dayong AU - He, Jianan AU - Sun, Jie AU - Shi, Xin AU - Ye, Ying AU - Zhang, Zishuai AU - Wang, Xiangjun AU - Su, Qun AU - Yu, Wenjin AU - Yuan, Xiaopeng AU - Dong, Ruiling PY - 2021/2/16 TI - The Global Infectious Diseases Epidemic Information Monitoring System: Development and Usability Study of an Effective Tool for Travel Health Management in China JO - JMIR Public Health Surveill SP - e24204 VL - 7 IS - 2 KW - infectious disease KW - epidemic information KW - travel health KW - global KW - surveillance N2 - 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. UR - http://publichealth.jmir.org/2021/2/e24204/ UR - http://dx.doi.org/10.2196/24204 UR - http://www.ncbi.nlm.nih.gov/pubmed/33591286 ID - info:doi/10.2196/24204 ER - TY - JOUR AU - Alves, Domingos AU - Yamada, Bettiol Diego AU - Bernardi, Andrade Filipe AU - Carvalho, Isabelle AU - Filho, Colombo Márcio Eloi AU - Neiva, Barros Mariane AU - Lima, Costa Vinícius AU - Félix, Maria Têmis PY - 2021/1/22 TI - Mapping, Infrastructure, and Data Analysis for the Brazilian Network of Rare Diseases: Protocol for the RARASnet Observational Cohort Study JO - JMIR Res Protoc SP - e24826 VL - 10 IS - 1 KW - rare disease KW - digital health KW - health observatory KW - data science KW - health network N2 - 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 UR - http://www.researchprotocols.org/2021/1/e24826/ UR - http://dx.doi.org/10.2196/24826 UR - http://www.ncbi.nlm.nih.gov/pubmed/33480849 ID - info:doi/10.2196/24826 ER - TY - JOUR AU - Parikh, Nidhi AU - Daughton, R. Ashlynn AU - Rosenberger, Earl William AU - Aberle, Jacob Derek AU - Chitanvis, Elizabeth Maneesha AU - Altherr, Michael Forest AU - Velappan, Nileena AU - Fairchild, Geoffrey AU - Deshpande, Alina PY - 2021/1/7 TI - Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study JO - JMIR Public Health Surveill SP - e24132 VL - 7 IS - 1 KW - disease re-emergence KW - infectious disease KW - supervised learning KW - random forest KW - visual analytics KW - surveillance N2 - 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. UR - http://publichealth.jmir.org/2021/1/e24132/ UR - http://dx.doi.org/10.2196/24132 UR - http://www.ncbi.nlm.nih.gov/pubmed/33316766 ID - info:doi/10.2196/24132 ER - TY - JOUR AU - Do, Quan AU - Marc, David AU - Plotkin, Marat AU - Pickering, Brian AU - Herasevich, Vitaly PY - 2020/12/24 TI - Starter Kit for Geotagging and Geovisualization in Health Care: Resource Paper JO - JMIR Form Res SP - e23379 VL - 4 IS - 12 KW - geographic mapping KW - medicalGIS guidelines KW - information storage and retrieval KW - mapping KW - geotagging KW - data visualization KW - population KW - public health N2 - 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. UR - http://formative.jmir.org/2020/12/e23379/ UR - http://dx.doi.org/10.2196/23379 UR - http://www.ncbi.nlm.nih.gov/pubmed/33361054 ID - info:doi/10.2196/23379 ER - TY - JOUR AU - Sambaturu, Prathyush AU - Bhattacharya, Parantapa AU - Chen, Jiangzhuo AU - Lewis, Bryan AU - Marathe, Madhav AU - Venkatramanan, Srinivasan AU - Vullikanti, Anil PY - 2020/9/4 TI - An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study JO - JMIR Public Health Surveill SP - e12842 VL - 6 IS - 3 KW - epidemic data analysis KW - summarization KW - spatio-temporal patterns KW - transactional data mining N2 - 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. UR - http://publichealth.jmir.org/2020/3/e12842/ UR - http://dx.doi.org/10.2196/12842 UR - http://www.ncbi.nlm.nih.gov/pubmed/32701458 ID - info:doi/10.2196/12842 ER - TY - JOUR AU - Liu, Dianbo AU - Clemente, Leonardo AU - Poirier, Canelle AU - Ding, Xiyu AU - Chinazzi, Matteo AU - Davis, Jessica AU - Vespignani, Alessandro AU - Santillana, Mauricio PY - 2020/8/17 TI - Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models JO - J Med Internet Res SP - e20285 VL - 22 IS - 8 KW - COVID-19 KW - coronavirus KW - digital epidemiology KW - modeling KW - modeling disease outbreaks KW - emerging outbreak KW - machine learning KW - precision public health KW - machine learning in public health KW - forecasting KW - digital data KW - mechanistic model KW - hybrid simulation KW - hybrid model KW - simulation N2 - 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. UR - http://www.jmir.org/2020/8/e20285/ UR - http://dx.doi.org/10.2196/20285 UR - http://www.ncbi.nlm.nih.gov/pubmed/32730217 ID - info:doi/10.2196/20285 ER - TY - JOUR AU - Elliston, G. Katherine AU - Schüz, Benjamin AU - Albion, Tim AU - Ferguson, G. Stuart PY - 2020/7/22 TI - Comparison of Geographic Information System and Subjective Assessments of Momentary Food Environments as Predictors of Food Intake: An Ecological Momentary Assessment Study JO - JMIR Mhealth Uhealth SP - e15948 VL - 8 IS - 7 KW - ecological momentary assessment KW - mHealth KW - geographic information systems KW - food intake KW - mobile phone N2 - 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. UR - https://mhealth.jmir.org/2020/7/e15948 UR - http://dx.doi.org/10.2196/15948 UR - http://www.ncbi.nlm.nih.gov/pubmed/32706728 ID - info:doi/10.2196/15948 ER - TY - JOUR AU - Swartzendruber, Andrea AU - Lambert, N. Danielle PY - 2020/3/27 TI - 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 JO - JMIR Public Health Surveill SP - e16726 VL - 6 IS - 1 KW - directory KW - crisis pregnancy center KW - abortion, induced KW - reproductive health KW - policy KW - access to information N2 - 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. UR - http://publichealth.jmir.org/2020/1/e16726/ UR - http://dx.doi.org/10.2196/16726 UR - http://www.ncbi.nlm.nih.gov/pubmed/32217502 ID - info:doi/10.2196/16726 ER - TY - JOUR AU - Pedersen, R. Eric AU - Firth, Caislin AU - Parker, Jennifer AU - Shih, A. Regina AU - Davenport, Steven AU - Rodriguez, Anthony AU - Dunbar, S. Michael AU - Kraus, Lisa AU - Tucker, S. Joan AU - D'Amico, J. Elizabeth PY - 2020/2/26 TI - Locating Medical and Recreational Cannabis Outlets for Research Purposes: Online Methods and Observational Study JO - J Med Internet Res SP - e16853 VL - 22 IS - 2 KW - marijuana KW - cannabis KW - dispensaries KW - retailers KW - Los Angeles KW - tobacco N2 - 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. UR - https://www.jmir.org/2020/2/e16853 UR - http://dx.doi.org/10.2196/16853 UR - http://www.ncbi.nlm.nih.gov/pubmed/32130141 ID - info:doi/10.2196/16853 ER - TY - JOUR AU - Poelman, P. Maartje AU - van Lenthe, J. Frank AU - Scheider, Simon AU - Kamphuis, BM Carlijn PY - 2020/1/28 TI - 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 JO - JMIR Res Protoc SP - e15283 VL - 9 IS - 1 KW - ecological momentary assessment KW - eating behavior KW - environmental exposure KW - mobile apps KW - smartphone KW - geographic information systems KW - food preferences KW - diet records N2 - 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 UR - http://www.researchprotocols.org/2020/1/e15283/ UR - http://dx.doi.org/10.2196/15283 UR - http://www.ncbi.nlm.nih.gov/pubmed/32012100 ID - info:doi/10.2196/15283 ER - TY - JOUR AU - Fulton, Lawrence AU - Kruse, Scott Clemens PY - 2019/10/29 TI - Hospital-Based Back Surgery: Geospatial-Temporal, Explanatory, and Predictive Models JO - J Med Internet Res SP - e14609 VL - 21 IS - 10 KW - back surgery KW - neurosurgeon KW - elastic net KW - lasso KW - ridge KW - random forest KW - geospatial mapping KW - health economics KW - obesity KW - practice variation N2 - 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. UR - http://www.jmir.org/2019/10/e14609/ UR - http://dx.doi.org/10.2196/14609 UR - http://www.ncbi.nlm.nih.gov/pubmed/31663856 ID - info:doi/10.2196/14609 ER - TY - JOUR AU - Cooper, Fenimore Hannah Luke AU - Crawford, D. Natalie AU - Haardörfer, Regine AU - Prood, Nadya AU - Jones-Harrell, Carla AU - Ibragimov, Umedjon AU - Ballard, M. April AU - Young, M. April PY - 2019/10/18 TI - Using Web-Based Pin-Drop Maps to Capture Activity Spaces Among Young Adults Who Use Drugs in Rural Areas: Cross-Sectional Survey JO - JMIR Public Health Surveill SP - e13593 VL - 5 IS - 4 KW - rural KW - substance use disorder KW - Web-based data collection KW - geospatial methods KW - risk environment KW - activity spaces N2 - 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. UR - https://publichealth.jmir.org/2019/4/e13593 UR - http://dx.doi.org/10.2196/13593 UR - http://www.ncbi.nlm.nih.gov/pubmed/31628787 ID - info:doi/10.2196/13593 ER - TY - JOUR AU - Basak, Arinjoy AU - Cadena, Jose AU - Marathe, Achla AU - Vullikanti, Anil PY - 2019/06/17 TI - Detection of Spatiotemporal Prescription Opioid Hot Spots With Network Scan Statistics: Multistate Analysis JO - JMIR Public Health Surveill SP - e12110 VL - 5 IS - 2 KW - opiate dependence KW - spatial temporal analysis KW - network scan statistics KW - medical specialty N2 - 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. UR - http://publichealth.jmir.org/2019/2/e12110/ UR - http://dx.doi.org/10.2196/12110 UR - http://www.ncbi.nlm.nih.gov/pubmed/31210142 ID - info:doi/10.2196/12110 ER - TY - JOUR AU - Concannon, David AU - Herbst, Kobus AU - Manley, Ed PY - 2019/04/04 TI - Developing a Data Dashboard Framework for Population Health Surveillance: Widening Access to Clinical Trial Findings JO - JMIR Form Res SP - e11342 VL - 3 IS - 2 KW - data visualization KW - data dashboards KW - health and demographic surveillance KW - sub-Saharan Africa KW - treatment as prevention KW - clinical trials KW - demographics KW - real-time KW - data literacy N2 - 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. UR - https://formative.jmir.org/2019/2/e11342/ UR - http://dx.doi.org/10.2196/11342 UR - http://www.ncbi.nlm.nih.gov/pubmed/30946016 ID - info:doi/10.2196/11342 ER - TY - JOUR AU - Zanetti, Michele AU - Campi, Rita AU - Olivieri, Paola AU - Campiotti, Marta AU - Faggianelli, Alice AU - Bonati, Maurizio PY - 2019/03/22 TI - A Web-Based Form With Interactive Charts Used to Collect and Analyze Data on Home Births in Italy JO - J Med Internet Res SP - e10335 VL - 21 IS - 3 KW - Web-based form KW - home birth KW - interactive charts KW - internet KW - survey methods N2 - 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. UR - https://www.jmir.org/2019/3/e10335/ UR - http://dx.doi.org/10.2196/10335 UR - http://www.ncbi.nlm.nih.gov/pubmed/30900993 ID - info:doi/10.2196/10335 ER - TY - JOUR AU - Velappan, Nileena AU - Daughton, Rae Ashlynn AU - Fairchild, Geoffrey AU - Rosenberger, Earl William AU - Generous, Nicholas AU - Chitanvis, Elizabeth Maneesha AU - Altherr, Michael Forest AU - Castro, A. Lauren AU - Priedhorsky, Reid AU - Abeyta, Luis Esteban AU - Naranjo, A. Leslie AU - Hollander, Dawn Attelia AU - Vuyisich, Grace AU - Lillo, Maria Antonietta AU - Cloyd, Kathryn Emily AU - Vaidya, Rajendra Ashvini AU - Deshpande, Alina PY - 2019/02/25 TI - Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks JO - JMIR Public Health Surveill SP - e12032 VL - 5 IS - 1 KW - epidemiology KW - infectious diseases KW - algorithm KW - public health informatics KW - web browser N2 - 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. UR - http://publichealth.jmir.org/2019/1/e12032/ UR - http://dx.doi.org/10.2196/12032 UR - http://www.ncbi.nlm.nih.gov/pubmed/30801254 ID - info:doi/10.2196/12032 ER - TY - JOUR AU - DeJohn, D. Amber AU - Schulz, English Emily AU - Pearson, L. Amber AU - Lachmar, Megan E. AU - Wittenborn, K. Andrea PY - 2018/11/05 TI - Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study JO - JMIR Ment Health SP - e61 VL - 5 IS - 4 KW - depression KW - Web-based KW - social connection KW - Twitter KW - tweet KW - online communities N2 - 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. UR - http://mental.jmir.org/2018/4/e61/ UR - http://dx.doi.org/10.2196/mental.9533 UR - http://www.ncbi.nlm.nih.gov/pubmed/30401662 ID - info:doi/10.2196/mental.9533 ER - TY - JOUR AU - Amato, S. Michael AU - Graham, L. Amanda PY - 2018/10/24 TI - Geographic Representativeness of a Web-Based Smoking Cessation Intervention: Reach Equity Analysis JO - J Med Internet Res SP - e11668 VL - 20 IS - 10 KW - smoking cessation KW - health behavior KW - internet KW - population health KW - rural health KW - urban health KW - health equity KW - telemedicine N2 - 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. UR - http://www.jmir.org/2018/10/e11668/ UR - http://dx.doi.org/10.2196/11668 UR - http://www.ncbi.nlm.nih.gov/pubmed/30355557 ID - info:doi/10.2196/11668 ER - TY - JOUR AU - Goodspeed, Robert AU - Yan, Xiang AU - Hardy, Jean AU - Vydiswaran, Vinod V. G. AU - Berrocal, J. Veronica AU - Clarke, Philippa AU - Romero, M. Daniel AU - Gomez-Lopez, N. Iris AU - Veinot, Tiffany PY - 2018/08/13 TI - Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study JO - JMIR Mhealth Uhealth SP - e168 VL - 6 IS - 8 KW - urban population KW - spatial behavior KW - mobile phone KW - environment and public health KW - data accuracy N2 - 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. UR - http://mhealth.jmir.org/2018/8/e168/ UR - http://dx.doi.org/10.2196/mhealth.9771 UR - http://www.ncbi.nlm.nih.gov/pubmed/30104185 ID - info:doi/10.2196/mhealth.9771 ER - TY - JOUR AU - Card, George Kiffer AU - Gibbs, Jeremy AU - Lachowsky, John Nathan AU - Hawkins, W. Blake AU - Compton, Miranda AU - Edward, Joshua AU - Salway, Travis AU - Gislason, K. Maya AU - Hogg, S. Robert PY - 2018/08/08 TI - Using Geosocial Networking Apps to Understand the Spatial Distribution of Gay and Bisexual Men: Pilot Study JO - JMIR Public Health Surveill SP - e61 VL - 4 IS - 3 KW - service access KW - geosocial networking apps KW - gay and bisexual men KW - spatial distribution KW - gay neighborhoods N2 - 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. UR - http://publichealth.jmir.org/2018/3/e61/ UR - http://dx.doi.org/10.2196/publichealth.8931 UR - http://www.ncbi.nlm.nih.gov/pubmed/30089609 ID - info:doi/10.2196/publichealth.8931 ER - TY - JOUR AU - Nsabimana, Placide Alain AU - Uzabakiriho, Bernard AU - Kagabo, M. Daniel AU - Nduwayo, Jerome AU - Fu, Qinyouen AU - Eng, Allison AU - Hughes, Joshua AU - Sia, K. Samuel PY - 2018/08/07 TI - Bringing Real-Time Geospatial Precision to HIV Surveillance Through Smartphones: Feasibility Study JO - JMIR Public Health Surveill SP - e11203 VL - 4 IS - 3 KW - HIV surveillance KW - smartphones KW - mobile phones KW - geospatial data N2 - 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. UR - http://publichealth.jmir.org/2018/3/e11203/ UR - http://dx.doi.org/10.2196/11203 UR - http://www.ncbi.nlm.nih.gov/pubmed/30087088 ID - info:doi/10.2196/11203 ER - TY - JOUR AU - Jones, Helen Kerina AU - Daniels, Helen AU - Heys, Sharon AU - Ford, Vincent David PY - 2018/07/19 TI - Challenges and Potential Opportunities of Mobile Phone Call Detail Records in Health Research: Review JO - JMIR Mhealth Uhealth SP - e161 VL - 6 IS - 7 KW - call detail records KW - mobile phone data KW - health research N2 - 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. UR - http://mhealth.jmir.org/2018/7/e161/ UR - http://dx.doi.org/10.2196/mhealth.9974 UR - http://www.ncbi.nlm.nih.gov/pubmed/30026176 ID - info:doi/10.2196/mhealth.9974 ER - TY - JOUR AU - Algarin, B. Angel AU - Ward, J. Patrick AU - Christian, Jay W. AU - Rudolph, E. Abby AU - Holloway, W. Ian AU - Young, M. April PY - 2018/05/31 TI - Spatial Distribution of Partner-Seeking Men Who Have Sex With Men Using Geosocial Networking Apps: Epidemiologic Study JO - J Med Internet Res SP - e173 VL - 20 IS - 5 KW - men who have sex with men KW - public health KW - mobile phone KW - social environment KW - HIV KW - sexually transmitted diseases N2 - 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. UR - http://www.jmir.org/2018/5/e173/ UR - http://dx.doi.org/10.2196/jmir.9919 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/jmir.9919 ER - TY - JOUR AU - Cartwright, F. Alice AU - Karunaratne, Mihiri AU - Barr-Walker, Jill AU - Johns, E. Nicole AU - Upadhyay, D. Ushma PY - 2018/05/14 TI - Identifying National Availability of Abortion Care and Distance From Major US Cities: Systematic Online Search JO - J Med Internet Res SP - e186 VL - 20 IS - 5 KW - abortion seekers KW - reproductive health KW - internet KW - access to information KW - information seeking KW - abortion patients KW - reproductive health services KW - information seeking behavior N2 - 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. UR - http://www.jmir.org/2018/5/e186/ UR - http://dx.doi.org/10.2196/jmir.9717 UR - http://www.ncbi.nlm.nih.gov/pubmed/29759954 ID - info:doi/10.2196/jmir.9717 ER - TY - JOUR AU - Ben Ramadan, Ahmed Awatef AU - Jackson-Thompson, Jeannette AU - Schmaltz, Lee Chester PY - 2018/05/03 TI - Improving Visualization of Female Breast Cancer Survival Estimates: Analysis Using Interactive Mapping Reports JO - JMIR Public Health Surveill SP - e42 VL - 4 IS - 2 KW - survival KW - female breast cancer KW - Missouri KW - cancer registry N2 - 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. UR - http://publichealth.jmir.org/2018/2/e42/ UR - http://dx.doi.org/10.2196/publichealth.8163 UR - http://www.ncbi.nlm.nih.gov/pubmed/29724710 ID - info:doi/10.2196/publichealth.8163 ER - TY - JOUR AU - Stopka, J. Thomas AU - Brinkley-Rubinstein, Lauren AU - Johnson, Kendra AU - Chan, A. Philip AU - Hutcheson, Marga AU - Crosby, Richard AU - Burke, Deirdre AU - Mena, Leandro AU - Nunn, Amy PY - 2018/04/03 TI - HIV Clustering in Mississippi: Spatial Epidemiological Study to Inform Implementation Science in the Deep South JO - JMIR Public Health Surveill SP - e35 VL - 4 IS - 2 KW - hotspots KW - HIV KW - racial disparities KW - social determinants of health KW - HIV treatment KW - HIV screening N2 - 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. UR - http://publichealth.jmir.org/2018/2/e35/ UR - http://dx.doi.org/10.2196/publichealth.8773 UR - http://www.ncbi.nlm.nih.gov/pubmed/29615383 ID - info:doi/10.2196/publichealth.8773 ER - TY - JOUR AU - Rudolph, Abby AU - Tobin, Karin AU - Rudolph, Jonathan AU - Latkin, Carl PY - 2018/01/19 TI - Web-Based Survey Application to Collect Contextually Relevant Geographic Data With Exposure Times: Application Development and Feasibility Testing JO - JMIR Public Health Surveill SP - e12 VL - 4 IS - 1 KW - spatial analysis KW - geographic mapping KW - substance-related disorder N2 - 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. UR - http://publichealth.jmir.org/2018/1/e12/ UR - http://dx.doi.org/10.2196/publichealth.8581 UR - http://www.ncbi.nlm.nih.gov/pubmed/29351899 ID - info:doi/10.2196/publichealth.8581 ER - TY - JOUR AU - Saeb, Sohrab AU - Lattie, G. Emily AU - Kording, P. Konrad AU - Mohr, C. David PY - 2017/08/10 TI - Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety JO - JMIR Mhealth Uhealth SP - e112 VL - 5 IS - 8 KW - semantic location KW - geographic positioning systems KW - mobile phone KW - classification KW - decision tree ensembles KW - extreme gradient boosting KW - depression KW - anxiety N2 - 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. UR - http://mhealth.jmir.org/2017/8/e112/ UR - http://dx.doi.org/10.2196/mhealth.7297 UR - http://www.ncbi.nlm.nih.gov/pubmed/28798010 ID - info:doi/10.2196/mhealth.7297 ER - TY - JOUR AU - Ben Ramadan, Ahmed Awatef AU - Jackson-Thompson, Jeannette AU - Schmaltz, Lee Chester PY - 2017/08/04 TI - Usability Assessment of the Missouri Cancer Registry?s Published Interactive Mapping Reports: Round One JO - JMIR Hum Factors SP - e19 VL - 4 IS - 3 KW - geographic information systems KW - health professionals KW - interactive maps KW - Missouri Cancer Registry KW - usability N2 - 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. UR - http://humanfactors.jmir.org/2017/3/e19/ UR - http://dx.doi.org/10.2196/humanfactors.7899 UR - http://www.ncbi.nlm.nih.gov/pubmed/28778842 ID - info:doi/10.2196/humanfactors.7899 ER - TY - JOUR AU - Nasrinpour, Reza Hamid AU - Reimer, A. Alexander AU - Friesen, R. Marcia AU - McLeod, D. Robert PY - 2017/07/17 TI - Data Preparation for West Nile Virus Agent-Based Modelling: Protocol for Processing Bird Population Estimates and Incorporating ArcMap in AnyLogic JO - JMIR Res Protoc SP - e138 VL - 6 IS - 7 KW - AnyLogic KW - shapefiles KW - ArcMap KW - West Nile Virus KW - land cover KW - bird roosts KW - bird home range KW - Manitoba N2 - 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. UR - http://www.researchprotocols.org/2017/7/e138/ UR - http://dx.doi.org/10.2196/resprot.6213 UR - http://www.ncbi.nlm.nih.gov/pubmed/28716770 ID - info:doi/10.2196/resprot.6213 ER - TY - JOUR AU - Albert, Nikhila AU - Daniels, Jena AU - Schwartz, Jessey AU - Du, Michael AU - Wall, P. Dennis PY - 2017/05/04 TI - GapMap: Enabling Comprehensive Autism Resource Epidemiology JO - JMIR Public Health Surveill SP - e27 VL - 3 IS - 2 KW - autism KW - autism spectrum disorder KW - crowdsourcing KW - prevalence KW - resources KW - epidemiology N2 - Background: For individuals with autism spectrum disorder (ASD), finding resources can be a lengthy and difficult process. The difficulty in obtaining global, fine-grained autism epidemiological data hinders researchers from quickly and efficiently studying large-scale correlations among ASD, environmental factors, and geographical and cultural factors. Objective: The objective of this study was to define resource load and resource availability for families affected by autism and subsequently create a platform to enable a more accurate representation of prevalence rates and resource epidemiology. Methods: We created a mobile application, GapMap, to collect locational, diagnostic, and resource use information from individuals with autism to compute accurate prevalence rates and better understand autism resource epidemiology. GapMap is hosted on AWS S3, running on a React and Redux front-end framework. The backend framework is comprised of an AWS API Gateway and Lambda Function setup, with secure and scalable end points for retrieving prevalence and resource data, and for submitting participant data. Measures of autism resource scarcity, including resource load, resource availability, and resource gaps were defined and preliminarily computed using simulated or scraped data. Results: The average distance from an individual in the United States to the nearest diagnostic center is approximately 182 km (50 miles), with a standard deviation of 235 km (146 miles). The average distance from an individual with ASD to the nearest diagnostic center, however, is only 32 km (20 miles), suggesting that individuals who live closer to diagnostic services are more likely to be diagnosed. Conclusions: This study confirmed that individuals closer to diagnostic services are more likely to be diagnosed and proposes GapMap, a means to measure and enable the alleviation of increasingly overburdened diagnostic centers and resource-poor areas where parents are unable to diagnose their children as quickly and easily as needed. GapMap will collect information that will provide more accurate data for computing resource loads and availability, uncovering the impact of resource epidemiology on age and likelihood of diagnosis, and gathering localized autism prevalence rates. UR - http://publichealth.jmir.org/2017/2/e27/ UR - http://dx.doi.org/10.2196/publichealth.7150 UR - http://www.ncbi.nlm.nih.gov/pubmed/28473303 ID - info:doi/10.2196/publichealth.7150 ER - TY - JOUR AU - Stefanidis, Anthony AU - Vraga, Emily AU - Lamprianidis, Georgios AU - Radzikowski, Jacek AU - Delamater, L. Paul AU - Jacobsen, H. Kathryn AU - Pfoser, Dieter AU - Croitoru, Arie AU - Crooks, Andrew PY - 2017/04/20 TI - Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts JO - JMIR Public Health Surveill SP - e22 VL - 3 IS - 2 KW - Zika virus KW - social media KW - Twitter messaging KW - geographic information systems N2 - 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. UR - http://publichealth.jmir.org/2017/2/e22/ UR - http://dx.doi.org/10.2196/publichealth.6925 UR - http://www.ncbi.nlm.nih.gov/pubmed/28428164 ID - info:doi/10.2196/publichealth.6925 ER - TY - JOUR AU - Cleary, Galkina Ekaterina AU - Patton, P. Allison AU - Wu, Hsin-Ching AU - Xie, Alan AU - Stubblefield, Joseph AU - Mass, William AU - Grinstein, Georges AU - Koch-Weser, Susan AU - Brugge, Doug AU - Wong, Carolyn PY - 2017/04/12 TI - Making Air Pollution Visible: A Tool for Promoting Environmental Health Literacy JO - JMIR Public Health Surveill SP - e16 VL - 3 IS - 2 KW - computer visualization KW - digital cartography KW - environmental health literacy KW - health communication KW - environmental health KW - computer-based education KW - air pollution KW - ultrafine particles KW - immigrant education N2 - Background: Digital maps are instrumental in conveying information about environmental hazards geographically. For laypersons, computer-based maps can serve as tools to promote environmental health literacy about invisible traffic-related air pollution and ultrafine particles. Concentrations of these pollutants are higher near major roadways and increasingly linked to adverse health effects. Interactive computer maps provide visualizations that can allow users to build mental models of the spatial distribution of ultrafine particles in a community and learn about the risk of exposure in a geographic context. Objective: The objective of this work was to develop a new software tool appropriate for educating members of the Boston Chinatown community (Boston, MA, USA) about the nature and potential health risks of traffic-related air pollution. The tool, the Interactive Map of Chinatown Traffic Pollution (?Air Pollution Map? hereafter), is a prototype that can be adapted for the purpose of educating community members across a range of socioeconomic contexts. Methods: We built the educational visualization tool on the open source Weave software platform. We designed the tool as the centerpiece of a multimodal and intergenerational educational intervention about the health risk of traffic-related air pollution. We used a previously published fine resolution (20 m) hourly land-use regression model of ultrafine particles as the algorithm for predicting pollution levels and applied it to one neighborhood, Boston Chinatown. In designing the map, we consulted community experts to help customize the user interface to communication styles prevalent in the target community. Results: The product is a map that displays ultrafine particulate concentrations averaged across census blocks using a color gradation from white to dark red. The interactive features allow users to explore and learn how changing meteorological conditions and traffic volume influence ultrafine particle concentrations. Users can also select from multiple map layers, such as a street map or satellite view. The map legends and labels are available in both Chinese and English, and are thus accessible to immigrants and residents with proficiency in either language. The map can be either Web or desktop based. Conclusions: The Air Pollution Map incorporates relevant language and landmarks to make complex scientific information about ultrafine particles accessible to members of the Boston Chinatown community. In future work, we will test the map in an educational intervention that features intergenerational colearning and the use of supplementary multimedia presentations. UR - http://publichealth.jmir.org/2017/2/e16/ UR - http://dx.doi.org/10.2196/publichealth.7492 UR - http://www.ncbi.nlm.nih.gov/pubmed/28404541 ID - info:doi/10.2196/publichealth.7492 ER - TY - JOUR AU - Maheswaran, Ravi AU - Holmes, John AU - Green, Mark AU - Strong, Mark AU - Pearson, Tim AU - Meier, Petra PY - 2016/12/16 TI - Investigation of the Association Between Alcohol Outlet Density and Alcohol-Related Hospital Admission Rates in England: Study Protocol JO - JMIR Res Protoc SP - e243 VL - 5 IS - 4 KW - alcohol KW - outlets KW - hospital KW - admissions KW - geography KW - epidemiology N2 - Background: Availability of alcohol is a major policy issue for governments, and one of the availability factors is the density of alcohol outlets within geographic areas. Objective: The aim of this study is to investigate the association between alcohol outlet density and hospital admissions for alcohol-related conditions in a national (English) small area level ecological study. Methods: This project will employ ecological correlation and cross-sectional time series study designs to examine spatial and temporal relationships between alcohol outlet density and hospital admissions. Census units to be used in the analysis will include all Lower and Middle Super-Output Areas (LSOAs and MSOAs) in England (53 million total population; 32,482 LSOAs and 6781 MSOAs). LSOAs (approximately 1500 people per LSOA) will support investigation at a fine spatial resolution. Spatio-temporal associations will be investigated using MSOAs (approximately 7500 people per MSOA). The project will use comprehensive coverage data on alcohol outlets in England (from 2003, 2007, 2010, and 2013) from a commercial source, which has estimated that the database includes 98% of all alcohol outlets in England. Alcohol outlets may be classified into two broad groups: on-trade outlets, comprising outlets from which alcohol can be purchased and consumed on the premises (eg, pubs); and off-trade outlets, in which alcohol can be purchased but not consumed on the premises (eg, off-licenses). In the 2010 dataset, there are 132,989 on-trade and 51,975 off-trade outlets. The longitudinal data series will allow us to examine associations between changes in outlet density and changes in hospital admission rates. The project will use anonymized data on alcohol-related hospital admissions in England from 2003 to 2013 and investigate associations with acute (eg, admissions for injuries) and chronic (eg, admissions for alcoholic liver disease) harms. The investigation will include the examination of conditions that are wholly and partially attributable to alcohol, using internationally standardized alcohol-attributable fractions. Results: The project is currently in progress. Results are expected in 2017. Conclusions: The results of this study will provide a national evidence base to inform policy decisions regarding the licensing of alcohol sales outlets. UR - http://www.researchprotocols.org/2016/4/e243/ UR - http://dx.doi.org/10.2196/resprot.6300 UR - http://www.ncbi.nlm.nih.gov/pubmed/27986646 ID - info:doi/10.2196/resprot.6300 ER - TY - JOUR AU - Nguyen, C. Quynh AU - Li, Dapeng AU - Meng, Hsien-Wen AU - Kath, Suraj AU - Nsoesie, Elaine AU - Li, Feifei AU - Wen, Ming PY - 2016/10/17 TI - Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity JO - JMIR Public Health Surveill SP - e158 VL - 2 IS - 2 KW - social media KW - Twitter messaging KW - health behavior KW - happiness KW - food KW - physical activity N2 - Background: Studies suggest that where people live, play, and work can influence health and well-being. However, the dearth of neighborhood data, especially data that is timely and consistent across geographies, hinders understanding of the effects of neighborhoods on health. Social media data represents a possible new data resource for neighborhood research. Objective: The aim of this study was to build, from geotagged Twitter data, a national neighborhood database with area-level indicators of well-being and health behaviors. Methods: We utilized Twitter?s streaming application programming interface to continuously collect a random 1% subset of publicly available geolocated tweets for 1 year (April 2015 to March 2016). We collected 80 million geotagged tweets from 603,363 unique Twitter users across the contiguous United States. We validated our machine learning algorithms for constructing indicators of happiness, food, and physical activity by comparing predicted values to those generated by human labelers. Geotagged tweets were spatially mapped to the 2010 census tract and zip code areas they fall within, which enabled further assessment of the associations between Twitter-derived neighborhood variables and neighborhood demographic, economic, business, and health characteristics. Results: Machine labeled and manually labeled tweets had a high level of accuracy: 78% for happiness, 83% for food, and 85% for physical activity for dichotomized labels with the F scores 0.54, 0.86, and 0.90, respectively. About 20% of tweets were classified as happy. Relatively few terms (less than 25) were necessary to characterize the majority of tweets on food and physical activity. Data from over 70,000 census tracts from the United States suggest that census tract factors like percentage African American and economic disadvantage were associated with lower census tract happiness. Urbanicity was related to higher frequency of fast food tweets. Greater numbers of fast food restaurants predicted higher frequency of fast food mentions. Surprisingly, fitness centers and nature parks were only modestly associated with higher frequency of physical activity tweets. Greater state-level happiness, positivity toward physical activity, and positivity toward healthy foods, assessed via tweets, were associated with lower all-cause mortality and prevalence of chronic conditions such as obesity and diabetes and lower physical inactivity and smoking, controlling for state median income, median age, and percentage white non-Hispanic. Conclusions: Machine learning algorithms can be built with relatively high accuracy to characterize sentiment, food, and physical activity mentions on social media. Such data can be utilized to construct neighborhood indicators consistently and cost effectively. Access to neighborhood data, in turn, can be leveraged to better understand neighborhood effects and address social determinants of health. We found that neighborhoods with social and economic disadvantage, high urbanicity, and more fast food restaurants may exhibit lower happiness and fewer healthy behaviors. UR - http://publichealth.jmir.org/2016/2/e158/ UR - http://dx.doi.org/10.2196/publichealth.5869 UR - http://www.ncbi.nlm.nih.gov/pubmed/27751984 ID - info:doi/10.2196/publichealth.5869 ER - TY - JOUR AU - Laranjo, Liliana AU - Rodrigues, David AU - Pereira, Marta Ana AU - Ribeiro, T. Rogério AU - Boavida, Manuel José PY - 2016/03/17 TI - Use of Electronic Health Records and Geographic Information Systems in Public Health Surveillance of Type 2 Diabetes: A Feasibility Study JO - JMIR Public Health Surveill SP - e12 VL - 2 IS - 1 KW - electronic health records KW - diabetes mellitus KW - geographic information systems KW - primary health care KW - health records KW - personal N2 - Background: Data routinely collected in electronic health records (EHRs) offer a unique opportunity to monitor chronic health conditions in real-time. Geographic information systems (GIS) may be an important complement in the analysis of those data. Objective: The aim of this study was to explore the feasibility of using primary care EHRs and GIS for population care management and public health surveillance of chronic conditions, in Portugal. Specifically, type 2 diabetes was chosen as a case study, and we aimed to map its prevalence and the presence of comorbidities, as well as to identify possible populations at risk for cardiovascular complications. Methods: Cross-sectional study using individual-level data from 514 primary care centers, collected from three different types of EHRs. Data were obtained on adult patients with type 2 diabetes (identified by the International Classification of Primary Care [ICPC-2] code, T90, in the problems list). GISs were used for mapping the prevalence of diabetes and comorbidities (hypertension, dyslipidemia, and obesity) by parish, in the region of Lisbon and Tagus Valley. Descriptive statistics and multivariate logistic regression were used for data analysis. Results: We identified 205,068 individuals with the diagnosis of type 2 diabetes, corresponding to a prevalence of 5.6% (205,068/3,659,868) in the study population. The mean age of these patients was 67.5 years, and hypertension was present in 71% (144,938/205,068) of all individuals. There was considerable variation in diagnosed comorbidities across parishes. Diabetes patients with concomitant hypertension or dyslipidemia showed higher odds of having been diagnosed with cardiovascular complications, when adjusting for age and gender (hypertension odds ratio [OR] 2.16, confidence interval [CI] 2.10-2.22; dyslipidemia OR 1.57, CI 1.54-1.60). Conclusions: Individual-level data from EHRs may play an important role in chronic disease surveillance, namely through the use of GIS. Promoting the quality and comprehensiveness of data, namely through patient involvement in their medical records, is crucial to enhance the feasibility and usefulness of this approach. UR - http://publichealth.jmir.org/2016/1/e12/ UR - http://dx.doi.org/10.2196/publichealth.4319 UR - http://www.ncbi.nlm.nih.gov/pubmed/27227147 ID - info:doi/10.2196/publichealth.4319 ER - TY - JOUR AU - Ramos Herrera, Martin Igor AU - Gonzalez Castañeda, Miguel AU - Robles, Juan AU - Fonseca León, Joel PY - 2016/03/16 TI - Development of the Health Atlas of Jalisco: A New Web-Based Service for the Ministry of Health and the Community in Mexico JO - JMIR Public Health Surveill SP - e11 VL - 2 IS - 1 KW - Public health KW - health atlas KW - geographic information systems KW - geographic mapping KW - online systems KW - health information systems N2 - Background: Maps have been widely used to provide a visual representation of information of a geographic area. Health atlases are collections of maps related to conditions, infrastructure or services provided. Various countries have put resources towards producing health atlases that support health decision makers to enhance their services to the communities. Latin America, as well as Spain, have produced several atlases of importance such as the interactive mortality atlas of Andalucía, which is very similar to the one that is presented in this paper. In Mexico, the National Institute of Public Health produced the only health atlas found that is of relevance. It was published online in 2003 and is currently still active. Objective: The objective of this work is to describe the methods used to develop the Health Atlas of Jalisco (HAJ), and show its characteristics and how it interactively works with the user as a Web-based service. Methods: This work has an ecological design in which the analysis units are the 125 municipalities (counties) of the state of Jalisco, Mexico. We created and published online a geographic health atlas displaying a system based on input from official health database of the Health Ministry of Jalisco (HMJ), and some databases from the National Institute of Statistics and Geography (NISGI). The atlas displays 256 different variables as health-direct or health-related indicators. Instant Atlas software was used to generate the online application. The atlas was developed using these procedures: (1) datasheet processing and base maps generation, (2) software arrangements, and (3) website creation. Results: The HAJ is a Web-based service that allows users to interact with health and general data, regions, and categories according to their information needs and generates thematic maps (eg, the total population of the state or of a single municipality grouped by age or sex). The atlas is capable of displaying more than 32,000 different maps by combining categories, indicators, municipalities, and regions. Users can select the entire province, one or several municipalities, and the indicator they require. The atlas then generates and displays the requested map. Conclusions: This atlas is a Web-based service that interactively allows users to review health indicators such as structure, supplies, processes, and the impact on public health and related sectors in Jalisco, Mexico. One of the main interests is to reduce the number of information requests that the Ministry of Health receives every week from the general public, media reporters, and other government sectors. The atlas will support transparency, information diffusion, health decision-making, and the formulation of new public policies. Furthermore, the research team intends to promote research and education in public health. UR - http://publichealth.jmir.org/2016/1/e11/ UR - http://dx.doi.org/10.2196/publichealth.5255 UR - http://www.ncbi.nlm.nih.gov/pubmed/27227146 ID - info:doi/10.2196/publichealth.5255 ER -