Geospatial Health最新文献

{"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":"https://doi.org/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":1.0,"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":"https://doi.org/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":"https://doi.org/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":"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":"Spatial analysis of congenital heart disease in São Paulo State, Brazil 2012-2022: associations with air pollution, maternal factors and social vulnerability.","authors":"Amarílis Bahia Bezerra, Ligia Vizeu Barrozo, Alfredo Pereira de Queiroz","doi":"10.4081/gh.2025.1407","DOIUrl":"https://doi.org/10.4081/gh.2025.1407","url":null,"abstract":"<p><p>Congenital Heart Disease (CHD) is a major cause of neonatal and infant morbidity and mortality and it has a multifactorial aetiology. This study aimed to analyse the spatial association between exposure to air pollutants during the first trimester of pregnancy, social vulnerability, and maternal factors with the occurrence of CHD between 2012 and 2022 in the state of São Paulo, Brazil. Data were obtained from the live birth information system for maternal outcomes and characteristics, the São Paulo social vulnerability index as a contextual indicator, and concentrations of fine particulate matter (PM2.5), Carbon Monoxide (CO) and ozone, estimated using the Copernicus Atmosphere Monitoring Service (CAMS-EAC4) reanalysis dataset of environmental exposure. A Bayesian hierarchical spatial model with a Besag-York- Mollié 2 (BYM2) specification was applied using the INLA approach. The results showed that exposure to PM2.5 was significantly associated with an increased risk of CHD (RR = 1.022; 95% CrI: 1.005-1.040), as were advanced maternal age (>35 years) (RR = 1.649; 95% CrI: 1.587-1.715) and inadequate prenatal care (RR = 1.112; 95% CrI: 1.070-1.155). Conversely, municipalities classified as having medium (RR = 0.757; 95% CrI: 0.641-0.894) and high social vulnerability (RR = 0.643; 95% CrI: 0.492-0.844) showed a significantly lower adjusted risk compared to those with low vulnerability. No significant associations were identified for CO or ozone. Spatial analysis revealed persistently high risks in municipalities within the São Paulo Metropolitan Region, even after adjusting for environmental and socio-demographic variables, highlighting population profiles and priority areas for public health surveillance and targeted interventions.</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":"145082039","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":"Spatial autocorrelation patterns and factors associated with regular alcohol consumption behaviour among Thai men.","authors":"Naowarat Maneenin, Warangkana Sungsitthisawad, Chanwit Maneenin, Chananya Jirapornkul, Kittipong Sornlorm, Roshan Kumar Mahato, Wongsa Laohasiriwong","doi":"10.4081/gh.2025.1406","DOIUrl":"https://doi.org/10.4081/gh.2025.1406","url":null,"abstract":"<p><p>Alcohol consumption is a major health concern in Thailand contributing to addiction and disease. With 17 million Thai men regularly drinking alcohol, cultural norms and environmental factors influence consumption patterns. Geographic Information Systems (GIS) research has established connections between alcohol outlet density and increased drinking. Using Moran's I, Local Indicators of Spatial Association (LISA), and spatial regression models, spatial clusters of alcohol consumption were identified across Thai provinces, with Chonburi Province showing the highest rate at 72.2% and Yala the lowest at 28.6%. Regular alcohol consumption among Thai men exhibited a positive spatial correlation, with Moran's I equal to 0.477. Bivariate analysis found significant spatial autocorrelation between alcohol outlet density (0.301), population density (0.237) and access to medical facilities (0.290), showing high-high clusters in urbanized areas and low-low clusters in southern regions. Spatial regression using the Spatial Lag Model (SLM) demonstrated that alcohol outlet density, population density and the proportion of the population to medical facilities are significant factors influencing alcohol consumption, explaining 49.2% of the variation in alcohol consumption. The findings suggest the need for targeted public health interventions in high-risk areas, especially in regions with dense alcohol outlets and urban populations, alongside developing policies to promote healthier behaviours and limit alcohol access.</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":"145193992","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":"Evaluation of emergency medical service application from a geographical location perspective in Turkey.","authors":"Özgür Elmas, Rahmi Nurhan Çelik","doi":"10.4081/gh.2025.1408","DOIUrl":"https://doi.org/10.4081/gh.2025.1408","url":null,"abstract":"<p><p>An important area of use of the geographic information systems in health is the organization of Emergency Medical Services (EMS). In this study, the EMS application offered in Turkey's 81 provinces, in particular, Istanbul metropolis, which has the highest population in the country, was examined with a statistical approach. It was determined that the correlation level between the number of EMS stations and the population of the 39 districts of Istanbul was higher compared to the land area and population density; the number of EMS stations in the Fatih District was significantly greater than the median value of the number of EMS stations in all districts of Istanbul. It was determined that the number of EMS stations, ambulances, and hospitals in Istanbul is significantly greater than the median value of all provinces in Turkey; the population density per hospital and EMS station in Istanbul is significantly greater than the median value of all provinces, and the area value is smaller than the median value of all provinces. Ambulance response time, hospital transfer time and reasons for delays at these stages were questioned through a survey. The most common reasons for delay were traffic congestion, followed by the few and far distances of ambulance stations. Considering the problems arising from the geographical location of EMS stations and hospitals, it is expected that taking population density into account when planning EMS station distribution would contribute to increased efficiency in EMS and equality in access to services.</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":"145194028","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 Bayesian spatiotemporal Poisson conditional autoregressive model for dengue haemorrhagic fever in Indonesia integrating satellite-generated environmental data.","authors":"Sukarna Sukarna, Hari Wijayanto, Yenni Angraini, Anang Kurnia","doi":"10.4081/gh.2025.1379","DOIUrl":"https://doi.org/10.4081/gh.2025.1379","url":null,"abstract":"<p><p>In association with cases of Dengue Haemorrhagic Fever (DHF), Indonesia's Breteau Index has consistently fallen below the national standard of 95% over the past 12 years (2007-2019). Currently, the country relies on survey methods to map DHF spread, but these methods are costly and require substantial resource support since monitoring DHF cases necessitates considering both spatial and temporal aspects. As an alternative, we proposed a pilot study utilizing a localized version of the hierarchical Bayesian spatiotemporal conditional autoregressive model (LHBSTCARM) to predict the DHF cases in Makassar City, Indonesia. Using this approach, we examined the relationship between DHF and the normalized difference built-up index (NDBI), the Normalized Difference Vegetation Index (NDVI), and the Normalized Difference Water Index (NDWI) that were downloaded from the Sentinel-2 satellite. Based on these datasets, we identified an optimal LHBSTCARM model that classified areas in Makassar City into distinct spatial risk groups based on the likelihood of dengue occurrence. Specifically, the model identified four districts with low relative risk, one with high relative risk and the remaining districts with moderate relative risk. Incorporating covariates, the model also revealed that NDVI and NDWI were significant predictors for dengue outbreaks, whereas NDBI was not. Both significant covariates showed negative effects, with a one-unit increase in NDVI and NDWI associated with reductions in DHF cases by 84.5% and 81.5%, respectively. Thus, NDVI and NDWI are the environmental variables of choice for the prediction of DHF incidence.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 2","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661124","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":"Urban gentrification and infectious diseases: an interdisciplinary narrative review.","authors":"Behzad Kiani, Gabriel Parker, Senobar Naderian, Colleen L Lau, Benn Sartorius","doi":"10.4081/gh.2025.1388","DOIUrl":"https://doi.org/10.4081/gh.2025.1388","url":null,"abstract":"<p><p>Urban gentrification, the transformation of neighbourhoods by influx of new residential groups, leading to displacement of lowerincome communities, is a complex, multifaceted process with significant but generally unexplored public health implications. This study focused on the impact of this process on infectious disease dynamics investigating key factors such as sociodemographic disparities, economic conditions, housing and urban environmental changes. A systemic literature research was performed based on the search terms: gentrification and infectious disease in PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar, with additional references identified using the snowballing method. After screening the resulting 542 articles, 14 studies were selected based on relevance, with data were extracted through a consensusdriven process. This review identified the complex challenges posed by gentrification in the context of infectious disease dynamics and burdens providing valuable insights both to academic discourse and public health policy discussions. Gentrification may contribute to higher infection rates within specific urban neighbourhoods or among certain residents. For blood-borne and Sexually Transmitted Infections (STIs), gentrification leads to reduced access to essential healthcare services, including HIV and STI testing, particularly among marginalised populations, such as female sex workers and LGBTQ+ communities. For airborne diseases, gentrification can exacerbate health inequalities by increasing residential overcrowding and displacement from gentrified areas to more disadvantaged suburbs. Housing and urban planning associated with changes in the urban environment are primarily linked with vector-borne diseases, tick-borne diseases in particular, among displaced populations. We advocate the use of spatial epidemiology to examine the potential impact of gentrification on the risk for infectious diseases. Since many gentrification metrics are area-specific, mapping and visualising key indicator data can pre-emptively support practical decision-making. This approach also helps capture the complex dynamics of displacement and the within-place changes experienced by populations affected by gentrification, which might affect infectious disease dynamics. Finally, we outline key research priorities to bridge existing knowledge gaps in future multidisciplinary research on infectious diseases and gentrification.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 2","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585738","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}