Geospatial Health最新文献

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.

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.

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.

This study is a quantitative, ecological, descriptive, retrospective, cross-sectional study on dengue in the state of Paraíba in north-eastern Brazil aimed to compare the performance of spatial clustering methods based on epidemiological data. The population consisted of all people residing in the state, and the sample was all dengue fever cases reported annually between 2018 and 2022. The residence localization of people suffering from dengue fever was used to identify the spatial distribution of this infection in the Paraíba State. Scan Statistics, Besag-Newell, Getis-Ord, MStatistics and Tango were used and it was observed that the methods Getis-Ord, M-Statistic and Tango showed large spatial clusters, which included municipalities with high and low values. Scan Statistics and Besag-Newell's method also showed most of these clusters, with Scan Statistic providing better agreement with the high Standardized Incidence Ratio (SIR) than Besag-Newell's method. In conclusion, Scan statistic outperformed the other methods by identifying significant clusters in greater proportion in all study periods when mapping using Rigorous Impact Evaluation (RIE) was applied. However, it is necessary to consider each method's assumptions to select the most appropriate method for each application. Thus, this study provides relevant elements to help decision makers manage and prevent diseases, such as dengue fever and other vector-borne diseases.

The infant mortality rate in Turkey declined from 13.9 deaths per 1,000 live births in 2009 to 9.3 deaths per 1,000 live births in 2017. This study explored the role of spatio-temporal Bayesian models in explaining this decline. Parametric, nonparametric spatio- temporal Bayesian models, and a Bayesian generalized linear model without space, time, and space-time interaction were applied using the Integrated Nested Laplace Approximation (INLA) method. Exceedance probabilities were used for detecting significant risk clusters. The unstructured spatial and structured temporal interaction random effect of the best-fitting spatio-temporal Bayesian model contributed more to explaining variation in the relative risk of infant mortality than the other random effects. From 2009 to 2017, in each year, significant risk clusters were consistently detected in the eastern and south-eastern Anatolia regions. An increase of 1,000 USD in the Gross Domestic Product (GDP) per capita reduced the relative risk of infant mortality by 2.8%. When determining the factors that may affect infant mortality in Turkey, it is also essential to consider the effects of space, time, and space-time interaction. In addition, decision-makers should consider the increase in GDP per capita as a factor in reducing infant mortality in Turkey by focusing on these significant risk clusters in the eastern and south-eastern Anatolia regions.

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.

Healthcare accessibility is vital for sustainable urban development, ensuring timely diagnosis, chronic disease management, and emergency response. However, in many developing countries, the uneven distribution of advanced healthcare services exacerbates health disparities. Taking Tianjin in China as an example, this study aims to evaluate the spatial accessibility of tertiary hospitals and optimize hospital placement to improve healthcare coverage. Using the Gaussian Two-Step Floating Catchment Area (G2SFCA) method, the study integrated high-resolution spatial data on hospital locations, population density, and transportation networks, assessing the accessibility of higher-level healthcare services citywide. The results indicate that central urban districts exhibited high accessibility, where all demand points were within the 1-hour service range. In contrast, suburban districts had an average accessibility of 0.194, and outer suburban districts had the lowest citywide mean of 0.005, with less than 20% of the area covered. Despite its economic significance, Binhai New Area's healthcare accessibility remained inadequate, with a mean score of 0.010. The application of a location-allocation model to optimize the placement of 24 planned new hospitals, prioritizing areas with high population density and low accessibility resulted in an increased population coverage from 73.31% to 95.05%, significantly reducing non-accessible points. This study aligns with the United Nations' Sustainable Development Goals 3 and 11, advocating a hierarchical healthcare system, telemedicine, and improved transportation to minimize time costs and reduce inequities.

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.

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.

This review assessed the combined impact of poultry production, climate variability, and agricultural environments on human salmonellosis risk across the United States. It considers whether regions with both high poultry production and notable climate variability show amplified infection patterns and whether environmental transmission pathways are becoming more prominent alongside direct poultry exposure. A comprehensive systematic literature review in PubMed was conducted following PRISMA guidelines for studies published between 2011 and 2025 addressing Salmonella in relation to human incidence, poultry processing and environmental exposure. Our search yielded 22 studies that met the inclusion criteria and it included a range of methods such as surveillance, epidemiological modeling, and intervention research across different U.S. regions. The key analytical variables included were serotype diversity, seasonal and regional distribution, antimicrobial resistance, and climate-related environmental transmission. The findings revealed significant geographic overlap between areas of intensive poultry production and high salmonellosis rates, especially in the southern states. A rise in multidrug-resistant serovars, such as S. infantis in poultry products, was found. Seasonal contamination patterns showed chicken cuts peaking in contamination during late winter, in contrast to the summer peak of human cases. We also observed that temperature extremes and heavy precipitation were linked to increased environmental contamination, particularly of water sources, and higher human exposure risk. These conditions also influenced serotype prevalence and the distribution of resistance genes. As a result, there is a need for integrated One Health strategies that should include adaptive poultry management, climate-responsive environmental monitoring with a focus on serotype-specific risk assessment to reduce the overall public health impact of Salmonella.