Geospatial Health最新文献

{"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":"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}

{"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":"Advanced analysis of depression tendency in China: an investigation of environmental and social factors based on geographical and temporal weighted regression.","authors":"Yanhong Xu, Zhilin Hong, Huimei Lin, Xiaofeng Huang","doi":"10.4081/gh.2025.1385","DOIUrl":"https://doi.org/10.4081/gh.2025.1385","url":null,"abstract":"<p><p>The spatiotemporal distribution of depressive tendencies across China from 2011 to 2022 was investigated using the Baidu Depression Search Index (BDSI). We examined key influencing natural factors, such as water pollution, air pollution, and deforestation, along with economic indicators, such as gross domestic product per capita, disposable income per capita, and health professionals per 10,000 population. Geographical and Temporal Weighted Regression (GTWR) was applied to capture the spatiotemporal heterogeneity of the BDSI determinants. The results revealed significant regional disparities, with the China's eastern region consistently exhibiting the highest values reflecting heightened mental health concerns, while the western region were found to have the lowest. The BDSI trends followed different trajectories, all of which peaked in 2019 before a sharp decline in 2020. Water pollution transitioned from negative to positive influence in the East, while deforestation exhibited regionally variable effects. Air pollution, peaking in 2019 and 2022, demonstrated the highest impact variability. The economic indicators showed complex regional and temporal patterns underscoring the need for tailored interventions. Together, these findings provided critical insights into the intricate interplay between environmental, economic, and healthcare factors in shaping mental health that highlighted the necessity of region-specific policies to mitigate depressive tendencies and enhance public mental well-being. These research results offer targeted recommendations for regionally adaptive mental health strategies across China.</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":"144676679","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":"Associations between rocky mountain spotted fever and veterinary care access, climatic factors and landscape in the State of Arizona, USA.","authors":"Yan Lin, Al Ekram Elahee Hridoy, Meifang Li, Zhe Wang, Li Luo, Xiaogang Ma, Zhuoming Liu, Murphy John, Chao Fan, Irene Ruberto, Xi Gong, Xun Shi","doi":"10.4081/gh.2025.1390","DOIUrl":"https://doi.org/10.4081/gh.2025.1390","url":null,"abstract":"<p><p>Rocky Mountain Spotted Fever (RMSF) is a potentially fatal tick-borne disease historically prevalent in the eastern and southeastern U.S. Since the early 2000s, there has been a notable rise in RMSF cases in the south-western U.S. Despite the documented role of dogs in tick-borne disease transmission, research on the influence of other factors, such as veterinary care access, climatic conditions and landscape characteristics on RMSF incidence is limited. This study investigated the combined impact of these factors on RMSF using county-level temperature, relative humidity, precipitation, land cover, dog populations and veterinary care access in Arizona from 2006 to 2021. Employing a spatial negative binomial regression model, the study revealed significant associations between veterinary care access, precipitation, relative humidity, shrubland, and RMSF incidence across three models incorporating lagged effects (0-month, 1-month, and 2-month) for climatic variables. A key finding was that counties experiencing higher veterinary care access were more likely to report lower RMSF case counts (incidence rate ratio (IRR): 0.9237). The mean precipitation consistently showed the highest positive IRR (1.8137) across all models, indicating its strong influence. In contrast, relative humidity (IRR: 0.9413) and shrubland presence (IRR: 0.9265) demonstrated significant negative associations with RMSF incidence. These findings underscore the importance of veterinary care access, climatic factors, and land cover in shaping RMSF dynamics, particularly in regions with increasing incidence rates.</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":"144661125","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 Bayesian semi-parametric Cox-Leroux modelling of stroke patient hospitalization: aspects on survival.","authors":"Aswi Aswi, Bobby Poerwanto, Nurussyariah Hammado, Nurwan Nurwan, Oktaviana Oktaviana, Siti Djawijah, Susanna Cramb","doi":"10.4081/gh.2025.1380","DOIUrl":"https://doi.org/10.4081/gh.2025.1380","url":null,"abstract":"<p><p>Survival analysis consists of a set of statistical methods used to analyse data where the outcome variable is the time until an event occurs. When such data are collected across distinct spatial regions, incorporating spatial information into survival models can be beneficial. A common approach is to apply an intrinsic Conditional Autoregressive (CAR) prior to an area-level frailty term to account for spatial correlation between regions. We extend the Bayesian Cox semi-parametric model by incorporating a spatial frailty term using the Leroux CAR prior. The aim was to improve the model's ability to describe stroke hospitalisations at the Stroke Centre Hospital in Makassar, Indonesia with a focus on understanding the geographic distribution of hospitalisations, Length of Stay (LOS) and factors influencing patient outcomes. The dataset was obtained from medical records of stroke patients admitted to this hospital (April 2021-June 2024). Variables included LOS, discharge outcomes, sex, age, stroke type, uric acid levels, hypertension, hypercholesterolemia, and diabetes mellitus. Our findings indicate that diabetes, stroke type and the presence of hypercholesterolemia significantly influence recovery rates in stroke patients. Specifically, patients with diabetes had lower recovery, while those with hypercholesterolemia and ischemic stroke patients had faster recovery compared to those with haemorrhagic strokes.</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":"144676680","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":"Integrating agent-based disease, mobility and wastewater models for the study of the spread of communicable diseases.","authors":"Néstor DelaPaz-Ruíz, Ellen-Wien Augustijn, Mahdi Farnaghi, Sheheen A Abdulkareem, Raul Zurita Milla","doi":"10.4081/gh.2025.1326","DOIUrl":"10.4081/gh.2025.1326","url":null,"abstract":"<p><p>Wastewater-based epidemiology was utilized during the COVID-19 outbreak to monitor the circulation of SARS-CoV-2, the virus causing this disease. However, this approach is limited by the need for additional methods to accurately translate virus concentrations in wastewater to disease-positive human counts. Combined modelling of COVID-19 disease cases and the concentration of its causative virus, SARS-CoV-2, in wastewater will necessarily deepen our understanding. However, this requires addressing the technical differences between disease, population mobility and wastewater models. To that end, we developed an integrated Agent-Based Model (ABM) that facilitates analysis in space and time at various temporal resolutions, including disease spread, population mobility and wastewater production, while also being sufficiently generic for different types of infectious diseases or pathogens. The integrated model replicates the epidemic curve for COVID-19 and can estimate the daily infections at the household level, enabling the monitoring of the spatial patterns of infection intensity. Additionally, the model allows monitoring the estimated production of infected wastewater over time and spatially across the sewage and treatment plant. The model addresses differences between resolutions and can potentially support Early Warning Systems (EWS) for future pandemics.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400935","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 livestock systems, bovine and caprine diseases in Mayo-Kebbi Ouest Province, Chad.","authors":"Kella Douzouné, Joseph Oloukoi, Ismaila Toko Imorou, Toure Gorgui Ba, Derrick Chefor Ymele Demeveng","doi":"10.4081/gh.2025.1365","DOIUrl":"10.4081/gh.2025.1365","url":null,"abstract":"<p><p>This study aimed to compile an inventory of the main diseases affecting these species in Mayo-Kebbi Ouest Province in Chad. A survey was conducted between 6 May and 7 August 2024 using a cascade data collection method identifying 310 farmers and 19 veterinarians with an average of 10 to 12 years of experience in advising and supporting livestock practices The data collected included socio-professional characteristics of participants, livestock practices, and geospatial information. These data were managed in Excel and analysed with R. The analysis involved descriptive and inferential statistical techniques including binary logistic regression resulting in maps illustrating disease hotspots and livestock systems. Thematic maps, tables and charts with a 5% significance threshold visualised risk areas and associated livestock practices. The results show a predominance of male farmers (91.9%) from 20 different ethnic groups. The livestock systems identified include data on farming divided into extensive (14.8%), mixed (0.3%) and semi-intensive farming (84.8%). On average, farms have 41 cattle and 25 goats. Animal diseases were found to cause 29.5% reduction in herd productivity. Transhumance (p=0.000356) and animal disease incidence (p=0.03) were observed as significant risk factors associated with the abandonment of livestock farming. The main diseases recorded in cattle include contagious bovine pleuropneumonia (11.3%), bovine tuberculosis (2.5%), foot-and-mouth disease (45.0%), bluetongue (1.7%) and disease with symptoms reminiscent of rinderpest (2.5%). For goats, notable diseases include brucellosis (3.8%), lumpy skin disease (19.2%), goat plague (7.9%) and Rift Valley fever (6.3%). These findings confirm the importance of a geospatial epidemiological surveillance tool for monitoring animal diseases in this region.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124265","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 association of socioeconomic and health service factors with antibiotic self-medication in Thailand.","authors":"Worrayot Darasawang, Wongsa Laohasiriwong, Kittipong Sornlorm, Warangkana Sungsitthisawad, Roshan Kumar Mahato","doi":"10.4081/gh.2025.1329","DOIUrl":"10.4081/gh.2025.1329","url":null,"abstract":"<p><p>Antibiotic Self-Medication (ASM) is a major contributing factor to Antimicrobial Resistance (AMR) that can lead to both mortality and long-term hospitalizations. High provincial ASM proportions associated with mortality due to AMR have been observed in Thailand but there is a lack of studies on geographic factors contributing to ASM. The present study aimed to quantify the distribution of ASM in Thailand and its correlated factors. Socioeconomic and health services factors were included in the spatial analysis. Moran's I was performed to identify global autocorrelation with the significance level set at p=0.05 and spatial regression were applied to identify the factors associated with ASM, the proportion of which is predominant in the north-eastern, central and eastern regions with Phitsanulok Province reporting the highest proportion of Thailand's 77 provinces. Autocorrelation between Night-Time Light (NTL) and the proportion of ASM was observed to be statistically significant at p=0.030. The Spatial Lag Model (SLM) and the Spatial Error Model (SEM) were used with the latter providing both the lowest R2 and Akaike Information Criterion (AIC). It was demonstrated that the proportion of alcohol consumption significantly increased the proportion of ASM. The annual number of outpatient department visits and the average NTL decreased the proportion of ASM by 1.5% and 0.4%, respectively. Average monthly household expenditures also decreased the ASM proportion. Policies to control alcohol consumption while promoting healthcare visits are essential strategies to mitigate the burden of AMR in Thailand.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048825","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":"Socio-spatial vulnerability index of type 2 diabetes mellitus in Mexico in 2020.","authors":"Enríque Ibarra-Zapata, Darío Gaytán-Hernández, Yolanda Terán-Figueroa, Verónica Gallegos-García, Carmen Del Pilar Suárez-Rodríguez, Sergio Zarazúa Guzmán, Omar Parra Rodríguez","doi":"10.4081/gh.2025.1348","DOIUrl":"10.4081/gh.2025.1348","url":null,"abstract":"<p><p>This study aimed to estimate a socio-spatial vulnerability index for type 2 diabetes mellitus (T2DM) at the municipal level in Mexico for 2020. It incorporated factors such as poverty, social backwardness, marginalization index, and human development index. This retrospective ecological study analyzed 317,011 incident cases of T2DM in 2020. Utilizing multi-criteria decision analysis, weighted values were assigned to each vulnerability criterion. A multiple linear regression model was developed, complemented by cluster and outlier analyses using Moran I's and the high-low clustering method. A clustered spatial autocorrelation of high values was found across 17.65% of Mexico, which was statistically significant (p < 0.001). Conversely, 37.78% of the territory showed a pattern of low values without significant evidence of groupings. The analysis revealed 117 nodes of very high vulnerability forming six focal areas, 172 nodes with high vulnerability across five areas, 168 nodes with medium vulnerability in two areas, 112 nodes with low vulnerability across 16 areas, and 152 nodes with very low vulnerability in 24 focal areas. This method proves to be robust and offers a technical-scientific basis for guiding T2DM prevention strategies and actions using a spatial/epidemiological approach. It is recommended that future strategies take into account factors such as poverty, social backwardness, marginalization index, and human development index to be effective.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048822","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}