Geospatial Health最新文献

{"title":"A Bayesian spatial ecological analysis of social conditions, infection burden and COVID-19 mortality across mainland Scottish councils 2020-2021.","authors":"Zhiyuan Luo, Yancheng Liu, Wenqi Xu","doi":"10.4081/gh.2026.1477","DOIUrl":"10.4081/gh.2026.1477","url":null,"abstract":"<p><p>The COVID-19 pandemic has generated substantial spatial and social inequalities in mortality, yet within-country council-level variations remain incompletely understood. Scotland offers a useful case, with a severe epidemic but rich administrative data and marked socio-economic gradients. We examined how infection burden, hospital-related indicators and selected area-level social indicators were associated with COVID-19 mortality across mainland Scotland 2020-2021. We assembled a two-year panel for 29 mainland councils, using deaths involving COVID-19, population denominators, COVID-19 testing and hospital activity, council-level indicators of marriage, unemployment, smoking cessation and migration. A Bayesian Poisson log-linear model with conditional autore- gressive random effects was used to estimate area-level relative risks and covariate associations. Given the short panel, the analysis is interpreted primarily as a spatial ecological analysis conducted over two successive pandemic years. Crude mortality showed consistently higher COVID-19 mortality in the central belt than in many northern and rural councils. After adjustment, spatial differences remained but were modest: no council in 2021 had a posterior probability above 0.10 of exceeding the national mean mortality risk. Positive test burden was positively associated with mortality and is interpreted primarily as a proximal epidemiological indicator of infection burden. Social determinants were also important. Higher unemployment was associated with increased risk, whereas higher marriage counts were linked to lower mortality. Smoking quit rates showed a positive association with deaths, likely reflecting residual confounding by underlying deprivation and historical smoking prevalence. Hospital utilisation and migration indicators showed weaker and more uncertain effects. Together, these findings indicate that crude spatial disparities in COVID-19 mortality across mainland Scottish councils became more moderate after adjustment. Given the ecological design and the two-year panel, the findings should be interpreted as area-level associations rather than as evidence of specific local social mechanisms.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"21 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148213468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collective efficacy and community readiness for dementia prevention: a spatial spillover analysis.","authors":"Duan-Rung Chen, Chun-Tung Kuo, Tzu-Chin Wu","doi":"10.4081/gh.2026.1476","DOIUrl":"10.4081/gh.2026.1476","url":null,"abstract":"<p><p>Dementia represents a growing public health challenge in Taiwan, particularly within its rapidly aging population. This study employed the Community Readiness Model (CRM) to systematically assess community readiness for dementia prevention across multiple domains. It further examined whether collective efficacy, defined as shared social cohesion and a community's capacity for collective action, is associated with greater readiness for dementia-related prevention efforts. A cross-sectional survey was conducted from March to June 2021 among 3,129 community leaders in 456 communities in Taipei City. A total of 447 valid responses were analyzed, representing 288 communities (63.2% of all communities). A spatial lag regression model was conducted, and spatial spillover effects were further assessed. Spatial lag regressions revealed that willingness to intervene (B=0.256, p<0.0001), social cohesion (B=0.375, p<0.0001), and prior dementia prevention programs (B=1.036, p=0.01) were significantly associated with higher community readiness for dementia prevention. Spill-over patterns - particularly for social cohesion and prior programs - appeared to play a potential, though not fully conclusive, role in shaping readiness across neighbouring communities. Those with higher proportions of residents aged 85+ showed lower readiness, while average income was not a significant predictor. Collective efficacy and prior dementia efforts were associated with higher community readiness. Tailored, community-based strategies that foster social cohesion and proactive engagement while accounting for spatial disparities are considered essential for effective dementia prevention.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"21 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148201074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Socioeconomic determinants of pandemics: a spatial methodological approach with evidence from COVID-19 in Nice, France.","authors":"Laurent Bailly, Rania Belgaied, Thomas Jobert, Benjamin Montmartin","doi":"10.4081/gh.2025.1383","DOIUrl":"10.4081/gh.2025.1383","url":null,"abstract":"<p><p>During the period 4 January 4 - 14 February 2021 the spread of the COVID-19 epidemic peaked in the city of Nice, France with a worrying number of infected cases. This article focuses on analyzing the explicit, spatial pattern of virus spread and assessing the geographical factors influencing this distribution. Spatial modelling was carried out to examine geographical disparities in terms of distribution, incidence and prevalence of the virus, while taking socio-economic factors into account. A multiple linear regression model was used to identify the key socio-economic variables. Global and local spatial autocorrelation were measured using Moran and LISA indices, followed by spatial autocorrelation analysis of the residuals. Similarly, we used the Geographically Weighted Regression (GWR) model and the Multiscale Geographically Weighted Regression (MGWR) model to assess the influence of socio-economic factors that vary on a global and local scale. Our results reveal a marked geographical polarization, with affluent areas in the Southeast of the city contrasting sharply with disadvantaged neighbourhoods in the Northwest. Neighbourhoods with low Localized Human Development Index (LHDI), low levels of education, social housing and immigrant populations all pointed to worrying values. On the other hand, people who use public transport were significantly more likely to be contaminated by the virus. These results underline the importance of geographically predicting COVID-19 distribution patterns to guide targeted interventions and health policies. Understanding these spatial patterns using models such as MGWR can help guide public health interventions and inform future health policies, particularly in the context of pandemics.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A post-pandemic analysis of air pollution over small-sized urban areas in southern Thailand following the COVID-19 lockdown.","authors":"Dimitris Stratoulias, Beomgeun Jang, Narissara Nuthammachot","doi":"10.4081/gh.2025.1354","DOIUrl":"10.4081/gh.2025.1354","url":null,"abstract":"<p><p>COVID-19 has been a pandemic with paramount effects on human health that brought about a noticeable improvement of air quality due to a reduction of anthropogenic activities. While studying this phenomenon in large cities has been a popular research topic, related research on smaller-sized urban areas has not been given the necessary attention. In the current study, we focus on the period during and after the COVID-19 pandemic over 8 small- and medium-sized urban areas in southern Thailand and present the effect of the lockdown on the air quality as quantified by the Sentinel-5P satellite and regulatory-grade surface stations over the years 2020, 2021 and 2022. Findings indicate that there is a noticeable reduction of -14%, -24% and -28% for NO2, PM2.5 and PM10 surface concentrations, respectively, for all the 8 urban areas cumulatively for the 2-month period following the lockdown, while results for O3 were inconclusive. An alignment between the ground and satellite observations is noticed, despite their difference in spatial scales and measuring different physical characteristics. Regression analysis between the single-pixel values over the ground station locations and the spatially-averaged pixels over the urban extent indicates an agreement between these two features, suggesting that single measurements can be representative of the air pollution status for relatively small-sized urban areas.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of geocoding techniques for epidemiological surveillance in the Federal District, Brazil: a case study using dengue.","authors":"Lucas Sanglard, Klauss K S Garcia, Walter Massa Ramalho","doi":"10.4081/gh.2025.1403","DOIUrl":"10.4081/gh.2025.1403","url":null,"abstract":"<p><p>This study aimed to compare different address geocoding services and their applicability to epidemiological surveillance using dengue as an example. We applied a cross-sectional, descriptive study based on case notifications in the Notifiable Diseases Information System (SINAN) for the Brazilian capital in 2014 that includes complete postal code (CEP) information identified in the National Address Database for Statistical Purposes (CNEFE), which is considered the 'gold standard' for accuracy analysis. For records without CEP, georeferencing was performed through linkage of the original database with four geocoding tools: Google Maps, CNEFE, OpenStreetMap (OSM) and ArcGIS. Variables used for georeferencing were 'street name', 'code for municipality/ city of residency' and 'State' using accuracy rate estimate and mean spatial error (MSE) of case locations. The two most accurate models were used for kernel density (KD) analysis which is valuable for identifying priority areas for intervention. There were 18,206 dengue cases, 109 (0.6%) of which had correct CEP information and geocoded using CNEFE bases. The linkage results showed that Google Maps application programming interface (API) had an accuracy of 17.6% (MSE: 178.89km), CNEFE 9.0% (MSE: 17.24km), OSM 7.1% (MSE: 564.19km), and ArcGIS 3.7% (MSE: 2001.33km). Although overall accuracy values were modest, the best two models proven to be effective for KD analysis revealed similar patterns between Google Maps and CNEFE results but choosing the preferable geocoding technique should also financial resources. This study recommends the use of Google Maps API for georeferencing, followed by CNEFE.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors associated with the spatial distribution of leprosy: a systematic review of the published literature.","authors":"Nathan Guilherme de Oliveira, Bruna Eduarda Bortolomai, Andréa Cristina Bogado, Ida Maria Foschiani Dias-Baptista","doi":"10.4081/gh.2025.1394","DOIUrl":"10.4081/gh.2025.1394","url":null,"abstract":"<p><p>This systematic review aimed to identify factors related to the spatial distribution of leprosy through studies utilising geographic information systems (GIS) techniques. PRISMA 2020 guidelines were adopted and the Population, Concept, Context (PCC) strategy employed to formulate the research question and define its scope: what factors associated with the spatial context of leprosy have been identified in studies utilising GIS techniques, and what are the key contributions of GIS in understanding the disease? The bibliographic databases consulted included PubMed, LILACS, EMBASE and Scopus. Only full original research articles in English, Spanish or Portuguese were included. Of the identified articles, 35 (23.8%) met the inclusion criteria, with the majority addressing socioeconomic factors (60.0%), followed by health indicators (17.1%). A smaller proportion of studies focused on logistics/distance (8.6%) or environmental aspects (2.9%). Although numerous studies utilise GIS techniques for understanding leprosy, few adopt robust methodologies to investigate the factors influencing its spatial features. There is a scarcity of studies employing GIS to examine environmental and logistical aspects related to the spatial distribution of leprosy. Addressing these gaps requires broader dissemination of the potential advantages of GIS in leprosy; the provision of reliable public data; and the capacity building of professionals committed to combating and controlling leprosy in endemic areas.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145081820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping healthcare resources and regional mortality in Europe: a spatial study of current service coverage.","authors":"Alicja Olejnik, Agata Żółtaszek","doi":"10.4081/gh.2025.1425","DOIUrl":"10.4081/gh.2025.1425","url":null,"abstract":"<p><p>The NUTS classification, established by Eurostat, divides the European territories into three levels: NUTS 1 (major regions), NUTS 2 (basic regions), and NUTS 3 (small regions). Our study investigated regional disparities in mortality across 232 NUTS 2 regions in Europe by analysing the function of their spatial health services. Using a spatial error model, we assessed the influence of healthcare expenditures and the number of hospital beds and medical doctors on death rates across eight major disease categories. We employed global and local spatial statistics to capture spatial disparities in resource allocation and death rates. Spatial clustering techniques revealed distinctive but differing patterns regarding mortality and resource allocation, with central and East Europe experiencing higher mortality from circulatory and digestive diseases, with mental and neurological conditions being more prevalent in the more affluent West. Our findings demonstrated decreasing returns at scale across all resources, with varied elasticities depending on disease type. Improved financial resources significantly reduced mortality for most illnesses except for mental or neurological disorders, while outcomes with respect to neoplasms depended on systemic factors beyond spending levels. The number of hospital beds often correlated positively with mortality, indicating system strain and reactive action rather than with preventive healthcare factors. Access to doctors reduced mortality only for mental and neurological conditions, highlighting the importance of specialised, continuous care. Regional affluence was found to consistently reduce mortality for several disease categories, underscoring the role of socioeconomic context in public health. These insights offer crucial guidance for more equitable and disease-specific resource allocation in health policy.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145679739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oncologic burden in Ukraine: regional inequalities and environmental risk factors.","authors":"Anatolii Kornus, Olesia Kornus, Yurii Liannoi, Olena Danylchenko, Serhii Lutsenko","doi":"10.4081/gh.2025.1418","DOIUrl":"10.4081/gh.2025.1418","url":null,"abstract":"<p><p>This study investigated regional inequalities in cancer incidence in Ukraine and their potential links to environmental pollution. Using data from 26 Ukrainian administrative regions, we analyzed 50 cancer indicators - covering incidence, prevalence and mortality across population subgroups - and 25 environmental variables reflecting air, water and soil contamination, including emissions of methane, sulphur dioxide, ammonia, suspended particulate matter and radioactive waste. A total of 1,250 pair-wise Pearson correlations were computed, revealing 69 moderate-to strong positive associations (r≥0.3), of which 23 were statistically significant at the 95% confidence level (p<0.05). The most consistent associations were observed for methane emissions, which showed significant correlations with six cancers, including breast, uterine, skin and non-Hodgkin lymphomas. Sulphur dioxide, suspended particulates and non-methane volatile organic compounds also demonstrated significant associations, particularly with hormonally mediated cancers and urban cancer prevalence. Geographic disparities were further shaped by demographic structure, healthcare access and underreporting in conflict-affected regions. Spatial visualizations and heatmaps supported the identification of recurrent pollutant-cancer associations, suggesting systemic environmental contributions to cancer burden. These findings underscore the multi-factorial nature of cancer risk in Ukraine and highlight the need for integrated environmental monitoring, strengthened diagnostic infrastructure, and regionally tailored public health strategies to reduce environmentally mediated cancer incidence.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145544140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lung cancer associated with natural vegetation cover: spatial analysis in the state of Pará, eastern Brazil.","authors":"Bruna Rafaela Leite Dias, Laura Maria Vidal Nogueira, Ivaneide Leal Ataíde Rodrigues, Bruna Puty, Maria Liracy Batista de Souza, Gracileide Maia Corrêa, Altem Nascimento Pontes","doi":"10.4081/gh.2025.1399","DOIUrl":"10.4081/gh.2025.1399","url":null,"abstract":"<p><p>Lung cancer represents the second-highest incidence of cancer worldwide and the leading cause of cancer-related deaths. Smoking is still the main risk factor, but other factors are also important, such as those associated with the large-scale exploitation of natural resources. This ecological study aimed to analyse the potential association between the spatial distribution of lung cancer and the natural vegetation cover in the state of Pará, Brazil. The study included 700 new cases of lung cancer taken from the Integrador Hospital Cancer Registries, a web-based system consolidating cancer data across Brazil. Spatial exploratory techniques were estimated by global and local spatial correlation coefficients and presented as thematic maps. The independent variables were socio-economic and environmental indicators. A significant variation was identified between different geographical areas and the distribution pattern of lung cancer incidence, with a negative correlation (I = - 0.12, p-value = < 0.001) between cancer rates and natural vegetation cover. The findings provide insights into the role of environmental factors that influence public health, ratifying the need for environmental conservation policies to promote health and prevent disease.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal variations and determinants of antenatal care utilization among adolescents in Bulawayo metropolitan area, Zimbabwe: an analysis of routine data, 2019-2024.","authors":"Tariroyashe Chivanganye, Maphios Siamuchembu, Alice Gumbo, Lenka Beňová, Peter M Macharia","doi":"10.4081/gh.2025.1434","DOIUrl":"10.4081/gh.2025.1434","url":null,"abstract":"<p><p>Adolescent pregnancy remains a major public health chal- lenge in low- and middle-income countries, contributing to mater- nal and neonatal morbidity and mortality. Antenatal Care (ANC) mitigates pregnancy-related risks through timely screening, edu- cation, and skilled care. However, adolescent ANC utilization remains low - even in urban areas with numerous health service providers. While national demographic and health surveys are used to estimate ANC utilization rates in urban areas, they lack the spatial detail needed to reveal intra-urban disparities for local level health planning. We modelled spatial and temporal varia- tions for at least one visit with a skilled provider (ANC1+) utiliza- tion among pregnant adolescents (10-19 years) within Bulawayo metropolitan province, Zimbabwe, 2019-2024. We extracted ANC utilization records from the District Health Information System and linked the data to a geocoded list of health facilities. Adolescent population denominators (pregnancies) were derived from three independent sources: WorldPop, national statistics agency and the US Census Bureau International Database (IDB). Health Facility Catchment Areas (HFCA) were estimated based on Thiessen polygons and linked with ANC use, pregnancies by population source and geospatial covariates (travel time to facili- ties, urbanization, maternal education, household wealth index, family planning, and vaccine coverage). A Bayesian spatial-tem- poral model was used to estimate ANC1+ coverage per HFCA by year and population. Provincial ANC1+ coverage ranged from 60.4% (WorldPop) to 70.6% (IDB) based on the population source. There was a high spatial heterogeneity in coverage across catchment areas, ranging from below 25% to over 80%. HFCAs located within core urban areas had higher coverage relative to the periphery. No clear temporal trend was observed. Higher wealth index and shorter travel time were significantly associated with ANC1+ utilization. The results are useful for local targeting of resources.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}