International Journal of Health Geographics最新文献

{"title":"Role of mammography accessibility, deprivation and spatial effect in breast cancer screening participation in France: an observational ecological study.","authors":"Nirmala Prajapati,&nbsp;Patricia Soler-Michel,&nbsp;Verónica M Vieira,&nbsp;Cindy M Padilla","doi":"10.1186/s12942-022-00320-5","DOIUrl":"https://doi.org/10.1186/s12942-022-00320-5","url":null,"abstract":"<p><strong>Background: </strong>The detection of cancer in its early latent stages can improve patients' chances of recovery and thereby reduce the overall burden of the disease. Our objectives were to investigate factors (geographic accessibility and deprivation level) affecting mammography screening participation variation and to determine how much geographic variation in participation rates can be explained by spillover effects between adjacent areas, while controlling for covariates.</p><p><strong>Methods: </strong>Mammography screening participation rates between 2015 and 2016 were calculated by census blocks (CB), for women aged 50-74 years, residing in Lyon metropolitan area. Global spatial autocorrelation tests were applied to identify the geographic variation of participation. Spatial regression models were used to incorporate spatial structure to estimate associations between mammography participation rate and the combined effect (geographic accessibility and deprivation level) adjusting for modes of travel and social cohesion.</p><p><strong>Results: </strong>The mammography participation rate was found to have a statistically significant and positive spatial correlation. The participation rate of one CB was significantly and positively associated with the participation rates of neighbouring CB. The participation was 53.2% in residential and rural areas and 46.6% in urban areas, p < 0.001. Using Spatial Lag models, whereas the population living in most deprived CBs have statistically significantly lower mammography participation rates than lower deprived ones, significant interaction demonstrates that the relation differs according to the degree of urbanization.</p><p><strong>Conclusions: </strong>This study makes an important methodological contribution in measuring geographical access and understanding better the combined effect of deprivation and the degree of urbanization on mammography participation and other contextual factors that affect the decision of using mammography screening services -which is a critical component of healthcare planning and equity.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"21 1","pages":"21"},"PeriodicalIF":4.9,"publicationDate":"2022-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9789573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10497286","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":"The effect of sampling health facilities on estimates of effective coverage: a simulation study.","authors":"Emily D Carter,&nbsp;Abdoulaye Maiga,&nbsp;Mai Do,&nbsp;Glebelho Lazare Sika,&nbsp;Rosine Mosso,&nbsp;Abdul Dosso,&nbsp;Melinda K Munos","doi":"10.1186/s12942-022-00307-2","DOIUrl":"https://doi.org/10.1186/s12942-022-00307-2","url":null,"abstract":"<p><strong>Background: </strong>Most existing facility assessments collect data on a sample of health facilities. Sampling of health facilities may introduce bias into estimates of effective coverage generated by ecologically linking individuals to health providers based on geographic proximity or administrative catchment.</p><p><strong>Methods: </strong>We assessed the bias introduced to effective coverage estimates produced through two ecological linking approaches (administrative unit and Euclidean distance) applied to a sample of health facilities. Our analysis linked MICS household survey data on care-seeking for child illness and childbirth care with data on service quality collected from a census of health facilities in the Savanes region of Cote d'Ivoire. To assess the bias introduced by sampling, we drew 20 random samples of three different sample sizes from our census of health facilities. We calculated effective coverage of sick child and childbirth care using both ecological linking methods applied to each sampled facility data set. We compared the sampled effective coverage estimates to ecologically linked census-based estimates and estimates based on true source of care. We performed sensitivity analyses with simulated preferential care-seeking from higher-quality providers and randomly generated provider quality scores.</p><p><strong>Results: </strong>Sampling of health facilities did not significantly bias effective coverage compared to either the ecologically linked estimates derived from a census of facilities or true effective coverage estimates using the original data or simulated random quality sensitivity analysis. However, a few estimates based on sampling in a setting where individuals preferentially sought care from higher-quality providers fell outside of the estimate bounds of true effective coverage. Those cases predominantly occurred using smaller sample sizes and the Euclidean distance linking method. None of the sample-based estimates fell outside the bounds of the ecologically linked census-derived estimates.</p><p><strong>Conclusions: </strong>Our analyses suggest that current health facility sampling approaches do not significantly bias estimates of effective coverage produced through ecological linking. Choice of ecological linking methods is a greater source of bias from true effective coverage estimates, although facility sampling can exacerbate this bias in certain scenarios. Careful selection of ecological linking methods is essential to minimize the potential effect of both ecological linking and sampling error.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"21 1","pages":"20"},"PeriodicalIF":4.9,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9758803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10496826","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":"Application of machine learning to predict transport modes from GPS, accelerometer, and heart rate data.","authors":"Santosh Giri,&nbsp;Ruben Brondeel,&nbsp;Tarik El Aarbaoui,&nbsp;Basile Chaix","doi":"10.1186/s12942-022-00319-y","DOIUrl":"https://doi.org/10.1186/s12942-022-00319-y","url":null,"abstract":"<p><strong>Background: </strong>There has been an increased focus on active transport, but the measurement of active transport is still difficult and error-prone. Sensor data have been used to predict active transport. While heart rate data have very rarely been considered before, this study used random forests (RF) to predict transport modes using Global Positioning System (GPS), accelerometer, and heart rate data and paid attention to methodological issues related to the prediction strategy and post-processing.</p><p><strong>Methods: </strong>The RECORD MultiSensor study collected GPS, accelerometer, and heart rate data over seven days from 126 participants living in the Ile-de-France region. RF models were built to predict transport modes for every minute (ground truth information on modes is from a GPS-based mobility survey), splitting observations between a Training dataset and a Test dataset at the participant level instead at the minute level. Moreover, several window sizes were tested for the post-processing moving average of the predicted transport mode.</p><p><strong>Results: </strong>The minute-level prediction rate of being on trips vs. at a visited location was 90%. Final prediction rates of transport modes ranged from 65% for public transport to 95% for biking. Using minute-level observations from the same participants in the Training and Test sets (as RF spontaneously does) upwardly biases prediction rates. The inclusion of heart rate data improved prediction rates only for biking. A 3 to 5-min bandwidth moving average was optimum for a posteriori homogenization.</p><p><strong>Conclusion: </strong>Heart rate only very slightly contributed to better predictions for specific transport modes. Moreover, our study shows that Training and Test sets must be carefully defined in RF models and that post-processing with carefully chosen moving average windows can improve predictions.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"21 1","pages":"19"},"PeriodicalIF":4.9,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9667683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10496291","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 accessibility to health facilities in Sub-Saharan Africa: comparing existing models with survey-based perceived accessibility.","authors":"Jérémie Bihin,&nbsp;Florence De Longueville,&nbsp;Catherine Linard","doi":"10.1186/s12942-022-00318-z","DOIUrl":"https://doi.org/10.1186/s12942-022-00318-z","url":null,"abstract":"<p><strong>Background: </strong>Mapping geographical accessibility to health services is essential to improve access to public health in sub-Saharan Africa. Different methods exist to estimate geographical accessibility, but little is known about the ability of these methods to represent the experienced accessibility of the population, and about the added-value of sophisticated and data-demanding methods over simpler ones. Here we compare the most commonly used methods to survey-based perceived accessibility in different geographical settings.</p><p><strong>Methods: </strong>Modelled accessibility maps are computed for 12 selected sub-Saharan African countries using four methods: Euclidean distance, cost-distance considering walking and motorized speed, and Kernel density. All methods are based on open and large-scale datasets to allow replication. Correlation coefficients are computed between the four modelled accessibility indexes and the perceived accessibility index extracted from Demographic and Health Surveys (DHS), and compared across different socio-geographical contexts (rural and urban, population with or without access to motorized transports, per country).</p><p><strong>Results: </strong>Our analysis suggests that, at medium spatial resolution and using globally-consistent input datasets, the use of sophisticated and data-demanding methods is difficult to justify as their added value over a simple Euclidian distance method is not clear. We also highlight that all modelled accessibilities are better correlated with perceived accessibility in rural than urban contexts and for population who do not have access to motorized transportation.</p><p><strong>Conclusions: </strong>This paper should guide researchers in the public health domain for knowing strengths and limits of different methods to evaluate disparities in health services accessibility. We suggest that using cost-distance accessibility maps over Euclidean distance is not always relevant, especially when based on low resolution and/or non-exhaustive geographical datasets, which is often the case in low- and middle-income countries.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":"18"},"PeriodicalIF":4.9,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40683854","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":"Participatory mapping to address neighborhood level data deficiencies for food security assessment in Southeastern Virginia, USA.","authors":"Nicole S Hutton,&nbsp;George McLeod,&nbsp;Thomas R Allen,&nbsp;Christopher Davis,&nbsp;Alexandra Garnand,&nbsp;Heather Richter,&nbsp;Prachi P Chavan,&nbsp;Leslie Hoglund,&nbsp;Jill Comess,&nbsp;Matthew Herman,&nbsp;Brian Martin,&nbsp;Cynthia Romero","doi":"10.1186/s12942-022-00314-3","DOIUrl":"https://doi.org/10.1186/s12942-022-00314-3","url":null,"abstract":"<p><strong>Background: </strong>Food is not equitably available. Deficiencies and generalizations limit national datasets, food security assessments, and interventions. Additional neighborhood level studies are needed to develop a scalable and transferable process to complement national and internationally comparative data sets with timely, granular, nuanced data. Participatory geographic information systems (PGIS) offer a means to address these issues by digitizing local knowledge.</p><p><strong>Methods: </strong>The objectives of this study were two-fold: (i) identify granular locations missing from food source and risk datasets and (ii) examine the relation between the spatial, socio-economic, and agency contributors to food security. Twenty-nine subject matter experts from three cities in Southeastern Virginia with backgrounds in food distribution, nutrition management, human services, and associated research engaged in a participatory mapping process.</p><p><strong>Results: </strong>Results show that publicly available and other national datasets are not inclusive of non-traditional food sources or updated frequently enough to reflect changes associated with closures, expansion, or new programs. Almost 6 percent of food sources were missing from publicly available and national datasets. Food pantries, community gardens and fridges, farmers markets, child and adult care programs, and meals served in community centers and homeless shelters were not well represented. Over 24 km² of participant identified need was outside United States Department of Agriculture low income, low access areas. Economic, physical, and social barriers to food security were interconnected with transportation limitations. Recommendations address an international call from development agencies, countries, and world regions for intervention methods that include systemic and generational issues with poverty, incorporate non-traditional spaces into food distribution systems, incentivize or regulate healthy food options in stores, improve educational opportunities, increase data sharing.</p><p><strong>Conclusions: </strong>Leveraging city and regional agency as appropriate to capitalize upon synergistic activities was seen as critical to achieve these goals, particularly for non-traditional partnership building. To address neighborhood scale food security needs in Southeastern Virginia, data collection and assessment should address both environment and utilization issues from consumer and producer perspectives including availability, proximity, accessibility, awareness, affordability, cooking capacity, and preference. The PGIS process utilized to facilitate information sharing about neighborhood level contributors to food insecurity and translate those contributors to intervention strategies through discussion with local subject matter experts and contextualization within larger scale food systems dynamics is transferable.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"21 1","pages":"17"},"PeriodicalIF":4.9,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10761658","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":"Climate-driven mosquito-borne viral suitability index: measuring risk transmission of dengue, chikungunya and Zika in Mexico.","authors":"Constantino Carreto,&nbsp;Roxana Gutiérrez-Romero,&nbsp;Tania Rodríguez","doi":"10.1186/s12942-022-00317-0","DOIUrl":"https://doi.org/10.1186/s12942-022-00317-0","url":null,"abstract":"<p><strong>Background: </strong>Climate variability influences the population dynamics of the Aedes aegypti mosquito that transmits the viruses that cause dengue, chikungunya and Zika. In recent years these diseases have grown considerably. Dengue is now the fastest-growing mosquito-transmitted disease worldwide, putting 40 per cent of the global population at risk. With no effective antiviral treatments or vaccines widely available, controlling mosquito population remains one of the most effective ways to prevent epidemics. This paper analyses the temporal and spatial dynamics of dengue in Mexico during 2000-2020 and that of chikungunya and Zika since they first appeared in the country in 2014 and 2015, respectively. This study aims to evaluate how seasonal climatological variability affects the potential risk of transmission of these mosquito-borne diseases. Mexico is among the world's most endemic countries in terms of dengue. Given its high incidence of other mosquito-borne diseases and its size and wide range of climates, it is a good case study.</p><p><strong>Methods: </strong>We estimate the recently proposed mosquito-borne viral suitability index P, which measures the transmission potential of mosquito-borne pathogens. This index mathematically models how humidity, temperature and precipitation affect the number of new infections generated by a single infected adult female mosquito in a host population. We estimate this suitability index across all Mexico, at small-area level, on a daily basis during 2000-2020.</p><p><strong>Results: </strong>We find that the index P predicted risk transmission is strongly correlated with the areas and seasons with a high incidence of dengue within the country. This correlation is also high enough for chikungunya and Zika in Mexico. We also show the index P is sensitive to seasonal climatological variability, including extreme weather shocks.</p><p><strong>Conclusions: </strong>The paper shows the dynamics of dengue, chikungunya and Zika in Mexico are strongly associated with seasonal climatological variability and the index P. This potential risk of transmission index, therefore, is a valuable tool for surveillance for mosquito-borne diseases, particularly in settings with varied climates and limited entomological capacity.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":"15"},"PeriodicalIF":4.9,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9610358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40652693","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 road criticality and loss of healthcare accessibility during floods: the case of Cyclone Idai, Mozambique 2019.","authors":"Sami Petricola,&nbsp;Marcel Reinmuth,&nbsp;Sven Lautenbach,&nbsp;Charles Hatfield,&nbsp;Alexander Zipf","doi":"10.1186/s12942-022-00315-2","DOIUrl":"https://doi.org/10.1186/s12942-022-00315-2","url":null,"abstract":"<p><strong>Background: </strong>The ability of disaster response, preparedness, and mitigation efforts to assess the loss of physical accessibility to health facilities and to identify impacted populations is key in reducing the humanitarian consequences of disasters. Recent studies use either network- or raster-based approaches to measure accessibility in respect to travel time. Our analysis compares a raster- and a network- based approach that both build on open data with respect to their ability to assess the loss of accessibility due to a severe flood event. As our analysis uses open access data, the approach should be transferable to other flood-prone sites to support decision-makers in the preparation of disaster mitigation and preparedness plans.</p><p><strong>Methods: </strong>Our study is based on the flood events following Cyclone Idai in Mozambique in 2019 and uses both raster- and network-based approaches to compare accessibility to health sites under normal conditions to the aftermath of the cyclone to assess the loss of accessibility. Part of the assessment is a modified centrality indicator, which identifies the specific use of the road network for the population to reach health facilities.</p><p><strong>Results: </strong>Results for the raster- and the network-based approaches differed by about 300,000 inhabitants (~ 800,000 to ~ 500,000) losing accessibility to healthcare sites. The discrepancy was related to the incomplete mapping of road networks and affected the network-based approach to a higher degree. The modified centrality indicator allowed us to identify road segments that were most likely to suffer from flooding and to highlight potential backup roads in disaster settings.</p><p><strong>Conclusions: </strong>The different results obtained between the raster- and network-based methods indicate the importance of data quality assessments in addition to accessibility assessments as well as the importance of fostering mapping campaigns in large parts of the Global South. Data quality is therefore a key parameter when deciding which method is best suited for local conditions. Another important aspect is the required spatial resolution of the results. Identification of critical segments of the road network provides essential information to prepare for potential disasters.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"21 1","pages":"14"},"PeriodicalIF":4.9,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9559768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10640347","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":"Evidence of transgenerational effects on autism spectrum disorder using multigenerational space-time cluster detection.","authors":"Rebecca Richards Steed,&nbsp;Amanda V Bakian,&nbsp;Ken Robert Smith,&nbsp;Neng Wan,&nbsp;Simon Brewer,&nbsp;Richard Medina,&nbsp;James VanDerslice","doi":"10.1186/s12942-022-00313-4","DOIUrl":"https://doi.org/10.1186/s12942-022-00313-4","url":null,"abstract":"<p><strong>Background: </strong>Transgenerational epigenetic risks associated with complex health outcomes, such as autism spectrum disorder (ASD), have attracted increasing attention. Transgenerational environmental risk exposures with potential for epigenetic effects can be effectively identified using space-time clustering. Specifically applied to ancestors of individuals with disease outcomes, space-time clustering characterized for vulnerable developmental stages of growth can provide a measure of relative risk for disease outcomes in descendants.</p><p><strong>Objectives: </strong>(1) Identify space-time clusters of ancestors with a descendent with a clinical ASD diagnosis and matched controls. (2) Identify developmental windows of ancestors with the highest relative risk for ASD in descendants. (3) Identify how the relative risk may vary through the maternal or paternal line.</p><p><strong>Methods: </strong>Family pedigrees linked to residential locations of ASD cases in Utah have been used to identify space-time clusters of ancestors. Control family pedigrees of none-cases based on age and sex have been matched to cases 2:1. The data have been categorized by maternal or paternal lineage at birth, childhood, and adolescence. A total of 3957 children, both parents, and maternal and paternal grandparents were identified. Bernoulli space-time binomial relative risk (RR) scan statistic was used to identify clusters. Monte Carlo simulation was used for statistical significance testing.</p><p><strong>Results: </strong>Twenty statistically significant clusters were identified. Thirteen increased RR (> 1.0) space-time clusters were identified from the maternal and paternal lines at a p-value < 0.05. The paternal grandparents carry the greatest RR (2.86-2.96) during birth and childhood in the 1950's-1960, which represent the smallest size clusters, and occur in urban areas. Additionally, seven statistically significant clusters with RR < 1 were relatively large in area, covering more rural areas of the state.</p><p><strong>Conclusion: </strong>This study has identified statistically significant space-time clusters during critical developmental windows that are associated with ASD risk in descendants. The geographic space and time clusters family pedigrees with over 3 + generations, which we refer to as a person's geographic legacy, is a powerful tool for studying transgenerational effects that may be epigenetic in nature. Our novel use of space-time clustering can be applied to any disease where family pedigree data is available.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":"13"},"PeriodicalIF":4.9,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33485987","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":"A simulation study for geographic cluster detection analysis on population-based health survey data using spatial scan statistics.","authors":"Jisu Moon,&nbsp;Inkyung Jung","doi":"10.1186/s12942-022-00311-6","DOIUrl":"https://doi.org/10.1186/s12942-022-00311-6","url":null,"abstract":"<p><strong>Background: </strong>In public health and epidemiology, spatial scan statistics can be used to identify spatial cluster patterns of health-related outcomes from population-based health survey data. Although it is appropriate to consider the complex sample design and sampling weight when analyzing complex sample survey data, the observed survey responses without these considerations are often used in many studies related to spatial cluster detection.</p><p><strong>Methods: </strong>We conducted a simulation study to investigate which data type from complex survey data is more suitable for use by comparing the spatial cluster detection results of three approaches: (1) individual-level data, (2) weighted individual-level data, and (3) aggregated data.</p><p><strong>Results: </strong>The results of the spatial cluster detection varied depending on the data type. To compare the performance of spatial cluster detection, sensitivity and positive predictive value (PPV) were evaluated over 100 iterations. The average sensitivity was high for all three approaches, but the average PPV was higher when using aggregated data than when using individual-level data with or without sampling weights.</p><p><strong>Conclusions: </strong>Through the simulation study, we found that use of aggregate-level data is more appropriate than other types of data, when searching for spatial clusters using spatial scan statistics on population-based health survey data.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":"11"},"PeriodicalIF":4.9,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9463844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33460719","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":"Development of a method for walking step observation based on large-scale GPS data.","authors":"Shohei Nagata,&nbsp;Tomoki Nakaya,&nbsp;Tomoya Hanibuchi,&nbsp;Naoki Nakaya,&nbsp;Atsushi Hozawa","doi":"10.1186/s12942-022-00312-5","DOIUrl":"https://doi.org/10.1186/s12942-022-00312-5","url":null,"abstract":"<p><strong>Background: </strong>Widespread use of smartphones has enabled the continuous monitoring of people's movements and physical activity. Linking global positioning systems (GPS) data obtained via smartphone applications to physical activity data may allow for large-scale and retrospective evaluation of where and how much physical activity has increased or decreased due to environmental, social, or individual changes caused by policy interventions, disasters, and infectious disease outbreaks. However, little attention has been paid to the use of large-scale commercial GPS data for physical activity research due to limitations in data specifications, including limited personal attribute and physical activity information. Using GPS logs with step counts measured by a smartphone application, we developed a simple method for daily walking step estimation based on large-scale GPS data.</p><p><strong>Methods: </strong>The samples of this study were users whose GPS logs were obtained in Sendai City, Miyagi Prefecture, Japan, during October 2019 (37,460 users, 36,059,000 logs), and some logs included information on daily step counts (731 users, 450,307 logs). The relationship between land use exposure and daily step counts in the activity space was modeled using the small-scale GPS logs with daily step counts. Furthermore, we visualized the geographic distribution of estimated step counts using a large set of GPS logs with no step count information.</p><p><strong>Results: </strong>The estimated model showed positive relationships between visiting high-rise buildings, parks and public spaces, and railway areas and step counts, and negative relationships between low-rise buildings and factory areas and daily step counts. The estimated daily step counts tended to be higher in urban areas than in suburban areas. Decreased step counts were mitigated in areas close to train stations. In addition, a clear temporal drop in step counts was observed in the suburbs during heavy rainfall.</p><p><strong>Conclusions: </strong>The relationship between land use exposure and step counts observed in this study was consistent with previous findings, suggesting that the assessment of walking steps based on large-scale GPS logs is feasible. The methodology of this study can contribute to future policy interventions and public health measures by enabling the retrospective and large-scale observation of physical activity by walking.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":"10"},"PeriodicalIF":4.9,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9449285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33447638","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}