International Journal of Health Geographics最新文献

{"title":"Optimizing the maximum reported cluster size for the multinomial-based spatial scan statistic.","authors":"Jisu Moon, Minseok Kim, Inkyung Jung","doi":"10.1186/s12942-023-00353-4","DOIUrl":"10.1186/s12942-023-00353-4","url":null,"abstract":"<p><strong>Background: </strong>Correctly identifying spatial disease cluster is a fundamental concern in public health and epidemiology. The spatial scan statistic is widely used for detecting spatial disease clusters in spatial epidemiology and disease surveillance. Many studies default to a maximum reported cluster size (MRCS) set at 50% of the total population when searching for spatial clusters. However, this default setting can sometimes report clusters larger than true clusters, which include less relevant regions. For the Poisson, Bernoulli, ordinal, normal, and exponential models, a Gini coefficient has been developed to optimize the MRCS. Yet, no measure is available for the multinomial model.</p><p><strong>Results: </strong>We propose two versions of a spatial cluster information criterion (SCIC) for selecting the optimal MRCS value for the multinomial-based spatial scan statistic. Our simulation study suggests that SCIC improves the accuracy of reporting true clusters. Analysis of the Korea Community Health Survey (KCHS) data further demonstrates that our method identifies more meaningful small clusters compared to the default setting.</p><p><strong>Conclusions: </strong>Our method focuses on improving the performance of the spatial scan statistic by optimizing the MRCS value when using the multinomial model. In public health and disease surveillance, the proposed method can be used to provide more accurate and meaningful spatial cluster detection for multinomial data, such as disease subtypes.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"22 1","pages":"30"},"PeriodicalIF":4.9,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71522995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global mosquito observations dashboard (GMOD): creating a user-friendly web interface fueled by citizen science to monitor invasive and vector mosquitoes.","authors":"Johnny A Uelmen, Andrew Clark, John Palmer, Jared Kohler, Landon C Van Dyke, Russanne Low, Connor D Mapes, Ryan M Carney","doi":"10.1186/s12942-023-00350-7","DOIUrl":"10.1186/s12942-023-00350-7","url":null,"abstract":"<p><strong>Background: </strong>Mosquitoes and the diseases they transmit pose a significant public health threat worldwide, causing more fatalities than any other animal. To effectively combat this issue, there is a need for increased public awareness and mosquito control. However, traditional surveillance programs are time-consuming, expensive, and lack scalability. Fortunately, the widespread availability of mobile devices with high-resolution cameras presents a unique opportunity for mosquito surveillance. In response to this, the Global Mosquito Observations Dashboard (GMOD) was developed as a free, public platform to improve the detection and monitoring of invasive and vector mosquitoes through citizen science participation worldwide.</p><p><strong>Methods: </strong>GMOD is an interactive web interface that collects and displays mosquito observation and habitat data supplied by four datastreams with data generated by citizen scientists worldwide. By providing information on the locations and times of observations, the platform enables the visualization of mosquito population trends and ranges. It also serves as an educational resource, encouraging collaboration and data sharing. The data acquired and displayed on GMOD is freely available in multiple formats and can be accessed from any device with an internet connection.</p><p><strong>Results: </strong>Since its launch less than a year ago, GMOD has already proven its value. It has successfully integrated and processed large volumes of real-time data (~ 300,000 observations), offering valuable and actionable insights into mosquito species prevalence, abundance, and potential distributions, as well as engaging citizens in community-based surveillance programs.</p><p><strong>Conclusions: </strong>GMOD is a cloud-based platform that provides open access to mosquito vector data obtained from citizen science programs. Its user-friendly interface and data filters make it valuable for researchers, mosquito control personnel, and other stakeholders. With its expanding data resources and the potential for machine learning integration, GMOD is poised to support public health initiatives aimed at reducing the spread of mosquito-borne diseases in a cost-effective manner, particularly in regions where traditional surveillance methods are limited. GMOD is continually evolving, with ongoing development of powerful artificial intelligence algorithms to identify mosquito species and other features from submitted data. The future of citizen science holds great promise, and GMOD stands as an exciting initiative in this field.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"22 1","pages":"28"},"PeriodicalIF":3.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10612222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66784458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short-term exposure sequences and anxiety symptoms: a time series clustering of smartphone-based mobility trajectories.","authors":"Yuliang Lan, Marco Helbich","doi":"10.1186/s12942-023-00348-1","DOIUrl":"10.1186/s12942-023-00348-1","url":null,"abstract":"<p><strong>Background: </strong>Short-term environmental exposures, including green space, air pollution, and noise, have been suggested to affect health. However, the evidence is limited to aggregated exposure estimates which do not allow the capture of daily spatiotemporal exposure sequences. We aimed to (1) determine individuals' sequential exposure patterns along their daily mobility paths and (2) examine whether and to what extent these exposure patterns were associated with anxiety symptoms.</p><p><strong>Methods: </strong>We cross-sectionally tracked 141 participants aged 18-65 using their global positioning system (GPS) enabled smartphones for up to 7 days in the Netherlands. We estimated their location-dependent exposures for green space, fine particulate matter, and noise along their moving trajectories at 10-min intervals. The resulting time-resolved exposure sequences were then partitioned using multivariate time series clustering with dynamic time warping as the similarity measure. Respondents' anxiety symptoms were assessed with the Generalized Anxiety Disorders-7 questionnaire. We fitted linear regressions to assess the associations between sequential exposure patterns and anxiety symptoms.</p><p><strong>Results: </strong>We found four distinctive daily sequential exposure patterns across the participants. Exposure patterns differed in terms of exposure levels and daily variations. Regression results revealed that participants with a \"moderately health-threatening\" exposure pattern were significantly associated with fewer anxiety symptoms than participants with a \"strongly health-threatening\" exposure pattern.</p><p><strong>Conclusions: </strong>Our findings support that environmental exposures' daily sequence and short-term magnitudes may be associated with mental health. We urge more time-resolved mobility-based assessments in future analyses of environmental health effects in daily life.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"22 1","pages":"27"},"PeriodicalIF":4.9,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10563352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41216748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical environment features that predict outdoor active play can be measured using Google Street View images.","authors":"Randy Boyes, William Pickett, Ian Janssen, David Swanlund, Nadine Schuurman, Louise Masse, Christina Han, Mariana Brussoni","doi":"10.1186/s12942-023-00346-3","DOIUrl":"10.1186/s12942-023-00346-3","url":null,"abstract":"<p><strong>Background: </strong>Childrens' outdoor active play is an important part of their development. Play behaviour can be predicted by a variety of physical and social environmental features. Some of these features are difficult to measure with traditional data sources.</p><p><strong>Methods: </strong>This study investigated the viability of a machine learning method using Google Street View images for measurement of these environmental features. Models to measure natural features, pedestrian traffic, vehicle traffic, bicycle traffic, traffic signals, and sidewalks were developed in one city and tested in another.</p><p><strong>Results: </strong>The models performed well for features that are time invariant, but poorly for features that change over time, especially when tested outside of the context where they were initially trained.</p><p><strong>Conclusion: </strong>This method provides a potential automated data source for the development of prediction models for a variety of physical and social environment features using publicly accessible street view images.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"22 1","pages":"26"},"PeriodicalIF":4.9,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10536757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41174127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capturing emergency dispatch address points as geocoding candidates to quantify delimited confidence in residential geolocation.","authors":"Christian A Klaus, Kevin A Henry, Dora Il'yasova","doi":"10.1186/s12942-023-00347-2","DOIUrl":"10.1186/s12942-023-00347-2","url":null,"abstract":"<p><strong>Background: </strong>In response to citizens' concerns about elevated cancer incidence in their locales, US CDC proposed publishing cancer incidence at sub-county scales. At these scales, confidence in patients' residential geolocation becomes a key constraint of geospatial analysis. To support monitoring cancer incidence in sub-county areas, we presented summary metrics to numerically delimit confidence in residential geolocation.</p><p><strong>Results: </strong>We defined a concept of Residential Address Discriminant Power (RADP) as theoretically perfect within all residential addresses and its practical application, i.e., using Emergency Dispatch (ED) Address Point Candidates of Equivalent Likelihood (CEL) to quantify Residential Geolocation Discriminant Power (RGDP) to approximate RADP. Leveraging different productivity of probabilistic, deterministic, and interactive geocoding record linkage, we simultaneously detected CEL for 5,807 cancer cases reported to North Carolina Central Cancer Registry (NC CCR)- in January 2022. Batch-match probabilistic and deterministic algorithms matched 86.0% cases to their unique ED address point candidates or a CEL, 4.4% to parcel site address, and 1.4% to street centerline. Interactively geocoded cases were 8.2%. To demonstrate differences in residential geolocation confidence between enumeration areas, we calculated sRGDP for cancer cases by county and assessed the existing uncertainty within the ED data, i.e., identified duplicate addresses (as CEL) for each ED address point in the 2014 version of the NC ED data and calculated ED_sRGDP by county. Both summary RGDP (sRGDP) (0.62-1.00) and ED_sRGDP (0.36-1.00) varied across counties and were lower in rural counties (p < 0.05); sRGDP correlated with ED_sRGDP (r = 0.42, p < 0.001). The discussion covered multiple conceptual and economic issues attendant to quantifying confidence in residential geolocation and presented a set of organizing principles for future work.</p><p><strong>Conclusions: </strong>Our methodology produces simple metrics - sRGDP - to capture confidence in residential geolocation via leveraging ED address points as CEL. Two facts demonstrate the usefulness of sRGDP as area-based summary metrics: sRGDP variability between counties and the overall lower quality of residential geolocation in rural vs. urban counties. Low sRGDP for the cancer cases within the area of interest helps manage expectations for the uncertainty in cancer incidence data. By supplementing cancer incidence data with sRGDP and ED_sRGDP, CCRs can demonstrate transparency in geocoding success, which may help win citizen trust.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"22 1","pages":"25"},"PeriodicalIF":3.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10523746/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41152570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Small-area estimation and analysis of HIV/AIDS indicators for precise geographical targeting of health interventions in Nigeria. a spatial microsimulation approach.","authors":"Eleojo Oluwaseun Abubakar, Niall Cunningham","doi":"10.1186/s12942-023-00341-8","DOIUrl":"10.1186/s12942-023-00341-8","url":null,"abstract":"<p><strong>Background: </strong>Precise geographical targeting is well recognised as an indispensable intervention strategy for achieving many Sustainable Development Goals (SDGs). This is more cogent for health-related goals such as the reduction of the HIV/AIDS pandemic, which exhibits substantial spatial heterogeneity at various spatial scales (including at microscale levels). Despite the dire data limitations in Low and Middle Income Countries (LMICs), it is essential to produce fine-scale estimates of health-related indicators such as HIV/AIDS. Existing small-area estimates (SAEs) incorporate limited synthesis of the spatial and socio-behavioural aspects of the HIV/AIDS pandemic and/or are not adequately grounded in international indicator frameworks for sustainable development initiatives. They are, therefore, of limited policy-relevance, not least because of their inability to provide necessary fine-scale socio-spatial disaggregation of relevant indicators.</p><p><strong>Methods: </strong>The current study attempts to overcome these challenges through innovative utilisation of gridded demographic datasets for SAEs as well as the mapping of standard HIV/AIDS indicators in LMICs using spatial microsimulation (SMS).</p><p><strong>Results: </strong>The result is a spatially enriched synthetic individual-level population of the study area as well as microscale estimates of four standard HIV/AIDS and sexual behaviour indicators. The analysis of these indicators follows similar studies with the added advantage of mapping fine-grained spatial patterns to facilitate precise geographical targeting of relevant interventions. In doing so, the need to explicate socio-spatial variations through proper socioeconomic disaggregation of data is reiterated.</p><p><strong>Conclusions: </strong>In addition to creating SAEs of standard health-related indicators from disparate multivariate data, the outputs make it possible to establish more robust links (even at individual levels) with other mesoscale models, thereby enabling spatial analytics to be more responsive to evidence-based policymaking in LMICs. It is hoped that international organisations concerned with producing SDG-related indicators for LMICs move towards SAEs of such metrics using methods like SMS.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"22 1","pages":"23"},"PeriodicalIF":4.9,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10510115/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41169974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the association between food environment and dietary inflammation by community type: a cross-sectional REGARDS study.","authors":"Yasemin Algur, Pasquale E Rummo, Tara P McAlexander, S Shanika A De Silva, Gina S Lovasi, Suzanne E Judd, Victoria Ryan, Gargya Malla, Alain K Koyama, David C Lee, Lorna E Thorpe, Leslie A McClure","doi":"10.1186/s12942-023-00345-4","DOIUrl":"10.1186/s12942-023-00345-4","url":null,"abstract":"<p><strong>Background: </strong>Communities in the United States (US) exist on a continuum of urbanicity, which may inform how individuals interact with their food environment, and thus modify the relationship between food access and dietary behaviors.</p><p><strong>Objective: </strong>This cross-sectional study aims to examine the modifying effect of community type in the association between the relative availability of food outlets and dietary inflammation across the US.</p><p><strong>Methods: </strong>Using baseline data from the REasons for Geographic and Racial Differences in Stroke study (2003-2007), we calculated participants' dietary inflammation score (DIS). Higher DIS indicates greater pro-inflammatory exposure. We defined our exposures as the relative availability of supermarkets and fast-food restaurants (percentage of food outlet type out of all food stores or restaurants, respectively) using street-network buffers around the population-weighted centroid of each participant's census tract. We used 1-, 2-, 6-, and 10-mile (~ 2-, 3-, 10-, and 16 km) buffer sizes for higher density urban, lower density urban, suburban/small town, and rural community types, respectively. Using generalized estimating equations, we estimated the association between relative food outlet availability and DIS, controlling for individual and neighborhood socio-demographics and total food outlets. The percentage of supermarkets and fast-food restaurants were modeled together.</p><p><strong>Results: </strong>Participants (n = 20,322) were distributed across all community types: higher density urban (16.7%), lower density urban (39.8%), suburban/small town (19.3%), and rural (24.2%). Across all community types, mean DIS was - 0.004 (SD = 2.5; min = - 14.2, max = 9.9). DIS was associated with relative availability of fast-food restaurants, but not supermarkets. Association between fast-food restaurants and DIS varied by community type (P for interaction = 0.02). Increases in the relative availability of fast-food restaurants were associated with higher DIS in suburban/small towns and lower density urban areas (p-values < 0.01); no significant associations were present in higher density urban or rural areas.</p><p><strong>Conclusions: </strong>The relative availability of fast-food restaurants was associated with higher DIS among participants residing in suburban/small town and lower density urban community types, suggesting that these communities might benefit most from interventions and policies that either promote restaurant diversity or expand healthier food options.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"22 1","pages":"24"},"PeriodicalIF":3.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10510199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41149301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowering health geography research with location-based social media data: innovative food word expansion and energy density prediction via word embedding and machine learning.","authors":"Jue Wang, Gyoorie Kim, Kevin Chen-Chuan Chang","doi":"10.1186/s12942-023-00344-5","DOIUrl":"10.1186/s12942-023-00344-5","url":null,"abstract":"<p><strong>Background: </strong>The exponential growth of location-based social media (LBSM) data has ushered in novel prospects for investigating the urban food environment in health geography research. However, previous studies have primarily relied on word dictionaries with a limited number of food words and employed common-sense categorizations to determine the healthiness of those words. To enhance the analysis of the urban food environment using LBSM data, it is crucial to develop a more comprehensive list of food-related words. Within the context, this study delves into the exploration of expanding food-related words along with their associated energy densities.</p><p><strong>Methods: </strong>This study addresses the aforementioned research gap by introducing a novel methodology for expanding the food-related word dictionary and predicting energy densities. Seed words are generated from official and crowdsourced food composition databases, and new food words are discovered by clustering food words within the word embedding space using the Gaussian mixture model. Machine learning models are employed to predict the energy density classifications of these food words based on their feature vectors. To ensure a thorough exploration of the prediction problem, ten widely used machine learning models are evaluated.</p><p><strong>Results: </strong>The approach successfully expands the food-related word dictionary and accurately predicts food energy density (reaching 91.62%.). Through a comparison of the newly expanded dictionary with the initial seed words and an analysis of Yelp reviews in the city of Toronto, we observe significant improvements in identifying food words and gaining a deeper understanding of the food environment.</p><p><strong>Conclusions: </strong>This study proposes a novel method to expand food-related vocabulary and predict the food energy density based on machine learning and word embedding. This method makes a valuable contribution to building a more comprehensive list of food words that can be used in geography and public health studies by mining geotagged social media data.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"22 1","pages":"22"},"PeriodicalIF":3.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10505329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10654442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recreational walking and perceived environmental qualities: a national map-based survey in Denmark.","authors":"Lars Breum Christiansen, Trine Top Klein-Wengel, Sofie Koch, Jens Høyer-Kruse, Jasper Schipperijn","doi":"10.1186/s12942-023-00339-2","DOIUrl":"10.1186/s12942-023-00339-2","url":null,"abstract":"<p><strong>Background: </strong>The aim of the study is to explore the diversity in recreational walking motives across groups with different sociodemographic characteristics, and to use a dynamic and person-centered approach to geographically assess recreational walking behavior, and preferences for place quality related to recreational walking.</p><p><strong>Methods: </strong>A total of 1838 adult respondents (age 15-90 years), who engage in recreational walking, participated in the map-based survey. We used the online platform Maptionnaire to collect georeferenced information on the respondents' home location, other start locations for walking trips, and point of interest on their trips. Distance between home location and other start locations as well as point of interest were computed using a Geographic Information System (GIS). Additional information on recreational walking behavior and motives were collected using the traditional questionnaire function in Maptionnaire.</p><p><strong>Results: </strong>The most prevalent motives for walking were mental well-being and physical health, together with enjoyment and experiences related to walking. Having a tertiary education was positively associated with mental well-being motive, experiences, and taking the dog and the children outside. Income was also positively associated with experiences and walking the dog together with enjoyment of walking and spending time with others. Using the map-based approach, we found that recreational walking often starts at a location away from home and is not limited to the nearest neighborhood. A total of 4598 points of interest were mapped, and the most frequently reported place qualities were greenery, water, wildlife, good views, and tranquility.</p><p><strong>Conclusion: </strong>We used a dynamic and person-centered approach and thereby giving the respondents the opportunity to point to relevant locations for their walking behavior independently of their residential neighborhood. Recreational walking often starts away from home or is not limit to the nearest neighborhod. The median distance from home to the mapped points of interests was between 1.0 and 1.6 km for home-based trips and between 9.4 and 30.6 km for trips with other start locations. The most popular place quality related to the mapped points were greenery, water, wildlife, good views, and tranquility.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"22 1","pages":"21"},"PeriodicalIF":4.9,"publicationDate":"2023-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476396/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10165928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial and temporal trends of overweight/obesity and tobacco use in East Africa: subnational insights into cardiovascular disease risk factors.","authors":"Barbara Chebet Keino, Margaret Carrel","doi":"10.1186/s12942-023-00342-7","DOIUrl":"10.1186/s12942-023-00342-7","url":null,"abstract":"<p><strong>Background: </strong>Cardiovascular disease (CVD) is increasing in Sub-Saharan Africa (SSA). Overweight/obesity and tobacco use are modifiable CVD risk factors, however literature about the spatiotemporal dynamics of these risk factors in the region at subnational or local scales is lacking. We describe the spatiotemporal trends of overweight/obesity and tobacco use at subnational levels over a 13-year period (2003 to 2016) in five East African nations.</p><p><strong>Methods: </strong>Cross-sectional, nationally representative Demographic and Health Surveys (DHS) were used to explore the subnational spatiotemporal patterns of overweight/obesity and tobacco use in Burundi, Kenya, Rwanda, Tanzania, and Uganda, five East African Community (EAC) nations with unique cultural landscapes influencing CVD risk factors. Adaptive kernel density estimation and logistic regression were used to determine the spatial distribution and change over time of CVD risk factors on a subnational and subpopulation (rural/urban) scale.</p><p><strong>Results: </strong>Subnational analysis shows that regional and national level analysis masks important trends in CVD risk factor prevalence. Overweight/obesity and tobacco use trends were not similar: overweight/obesity prevalence increased across most nations included in the study and the inverse was true for tobacco use prevalence. Urban populations in each nation were more likely to be overweight/obese than rural populations, but the magnitude of difference varied widely between nations. Spatial analysis revealed that although the prevalence of overweight/obesity increased over time in both urban and rural populations, the rate of change differed between urban and rural areas. Rural populations were more likely to use tobacco than urban populations, though the likelihood of use varied substantially between nations. Additionally, spatial analysis showed that tobacco use was not evenly distributed across the landscape: tobacco use increased in and around major cities and urban centers but declined in rural areas.</p><p><strong>Conclusions: </strong>We highlight the importance of de-homogenizing CVD risk factor research in SSA. Studies of national or regional prevalence trends mask important information about subpopulation and place-specific behavior and drivers of risk factor prevalence. Spatially explicit studies should be considered as a vital tool to understand local drivers of health, disease, and associated risk factor trends, especially in highly diverse yet low-resourced, marginalized, and often homogenized regions.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"22 1","pages":"20"},"PeriodicalIF":4.9,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10109889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}