Geospatial Health最新文献

While the relationship between socioeconomic status and Early Childhood Development (ECD) is well-documented, less is known about how developmental outcomes and child malnutrition cluster and interact across geographically proximate areas. This study applies spatial analysis to examine regional disparities in ECD in Pakistan and to assess the extent of spatial dependency in these outcomes. Using cross-sectional data from multiple indicator cluster survey (120,151 children across 144 districts) covering 2017- 2018, Moran's I statistics revealed significant positive spatial autocorrelation, consistent with Tobler's First Law of Geography. Districts with high (or low) ECD outcomes tended to be surrounded by similar districts. A distinct core periphery pattern emerged, with Punjab and Gilgit-Baltistan forming high-high clusters and Sindh, Khyber Pakhtunkhwa and Balochistan forming low-low clus- ters. Ordinary Least Squares (OLS) and Spatial Error Models (SEM) confirmed that stunting, underweight and overweight negative- ly affect ECD, while female literacy, access to mass media and child engagement in playing activities influence development posi- tively. Wasting showed no significant relationship. Results reveal that unobserved regional factors contribute to child development across districts, indicating that developmental deficits often cluster geographically. These findings extend spatial dependency theory to the ECD context in South Asia, underscoring the need for geographically coordinated interventions that address both local deter- minants and regionally shared underlying influences on child development.

Climatic variability plays a critical role in shaping dengue transmission dynamics, yet empirical findings remain inconsistent across studies. Divergent conclusions regarding the associations of temperature, relative humidity, wind speed, air pressure, precipitation, number of rainy days, and sunshine duration with dengue incidence often stem from unmodelled interactions and methodological limitations. To address these challenges, this study applies a Bayesian modelling framework to examine the associations between climatic drivers and dengue incidence in Bandung City, Indonesia, using monthly data from 2016 to 2024. We compared fixedeffects, nonlinear and dynamic modelling approaches to evaluate both the direction and magnitude of these associations while addressing overdispersion and potential multicollinearity among predictors. Our findings highlight temperature and relative humidity as the primary climatic variables associated with temporal variations in dengue incidence, with effects manifesting most strongly at a two-month lag. These results underscore the importance of adopting robust Bayesian modeling frameworks to support early warning systems and inform evidence-based public health interventions for dengue control.

In urban planning practice, urban basic public service facilities are essential spatial carriers for advancing social fairness and public wellbeing. Traditional methods of resource allocation that are primarily motivated by supply efficiency are facing new difficulties as China's urban spatial structure shifts from incremental expansion to stock optimization. As fundamental tenets of modern urban planning, equity and justice demand a more inclusive reevaluation of facility allocation, especially with regard to service accessibility and equitable for vulnerable populations. Beijing was chosen as the study region for this investigation, and information on public service facilities for people with disabilities was gathered from 6,080 residential neighborhoods. Accessibility was assessed using an integrated GIS-based analytical framework that combined kernel density analysis, surface-based hotspot detection, network analysis, and inverse distance weighting. This framework was based on the 15-minute living-circle concept and the actual walking speed of people with disabilities. A gap in previous research, which frequently depends on aggregated administrative units and ignores fine-scale spatial inequalities, is filled by the inclusion of surface-based hotspot detection, which enables accurate identification of high-accessibility clusters and peak areas. With high-value clusters concentrated in particular districts and obvious spatial mismatches between facility layouts and anticipated service needs, the results show notable differences in accessibility and facility distribution across service categories. This study suggests methods to increase facility coverage, optimize spatial organization, and improve street-level accessibility in order to overcome unequal facility distribution and insufficient street-network support. The results highlight a change from basic coverage to user-centered service quality and structural adaptability, which supports inclusive urban growth and the sustainable use of land resources.

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.