Geospatial Health最新文献

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.

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.

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.

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.

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.

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.

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.

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.