Geospatial Health最新文献

{"title":"Spatial risk of hypertension in rural China: modelling the effect of dietary, behavioural and climate factors.","authors":"Jie Wang, Enwei Zhang, Kun Yang","doi":"10.4081/gh.2026.1498","DOIUrl":"https://doi.org/10.4081/gh.2026.1498","url":null,"abstract":"<p><p>Hypertension poses a major health challenge for the rural elderly in China, but evidence on its spatial patterns and key influencing factors is still limited. This study examined the spatial distribution of hypertension prevalence among rural older adults assessing its associations with dietary, behavioural, socioeconomic and environmental variables. Pearson correlation and maximum entropy (MaxEnt) were used to analyze correlations and identify spatial risk levels. The results showed that hypertension prevalence was negatively correlated with grain intake (r=-0.600, p=0.002) and poultry consumption (r=-0.504,p=0.010) and alcohol use showed the highest contribution among behavioural variables (39.2%) in the MaxEnt model. Climate zones were also associated with prevalence (r=-0.260,p=0.010), with higher risk in temperate and some subtropical regions. The MaxEnt model showed good discriminatory ability (area under the curve (AUC)=0.871) in identifying hypertension high-risk areas mainly in northern and north-eastern rural China. This study provides spatial epidemiological evidence on hypertension among the rural elderly, suggesting that dietary patterns, alcohol consumption and climate conditions may be associated with spatial risk variation. These findings support the use of MaxEnt as an exploratory tool for chronic disease spatial analysis and may provide spatial reference for public health strategies and resource allocation.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"21 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563888","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":"Synergistic drivers of Lyme disease emergence in North America: an eco-syndemic review.","authors":"Michael F Ojo, Korine N Kolivras","doi":"10.4081/gh.2026.1479","DOIUrl":"https://doi.org/10.4081/gh.2026.1479","url":null,"abstract":"<p><p>Lyme disease is a growing public health threat driven by interplay of climatic, ecological and social factors. This review adopts an eco-syndemic framework, viewing Lyme disease emergence as the confluence of multiple synergistic drivers rather than a single- cause phenomenon. We synthesize evidence that climate conditions, ecological circumstances and human behavioural patterns inter- sect to amplify Lyme disease risk. By integrating these seemingly separate domains, our review highlights novel insights; for exam- ple, habitat fragmentation and predator loss may magnify the effects of a warming climate on reservoir or host populations, and human activities in suburban green spaces may mediate infection cycles. This holistic perspective is a departure from reductionist explanations and emphasizes the importance of coupled human-environment systems in studies of disease emergence. We discuss how an eco-syndemic frame can improve surveillance and prevention emphasizing One Health approaches that unite environmental management with animal health and public health outreach. Understanding the interactive and non-linear dynamics of Lyme disease drivers is crucial for developing effective interventions and preparing for future tick-borne disease challenges. While selected com- parative evidence from Europe is used where relevant, the review's primary empirical focus is North America.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"21 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802237","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":"Bayesian hierarchical spatial models for disease mapping in the presence of missing covariates.","authors":"Sami Ullah, Tianfa Xie","doi":"10.4081/gh.2026.1511","DOIUrl":"https://doi.org/10.4081/gh.2026.1511","url":null,"abstract":"<p><p>Bayesian spatial models for disease mapping, such as Besag-York-Mollié (BYM) models, are widely used to model disease counts while accounting for spatial dependence. However, these models are not equipped to handle missing covariate values. Covariates are often partially observed, yet these models require separate imputation that ignores imputation uncertainty. We extend this Bayesian framework for disease mapping to accommodate missing covariates while modeling the disease counts. Missing covariate values are treated as unknown parameters that are estimated simultaneously with the other model's parameters within the same Bayesian model. Evaluation on the benchmark Scottish lip cancer dataset demonstrates that the proposed model is effective in recovering the parameters of interest, compared with the complete-data BYM2 model under low-to-moderate missingness in a covariate.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"21 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802267","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":"Ten common pitfalls in spatial epidemiology and how to avoid them.","authors":"Behzad Kiani, Nima Kianfar, Munazza Fatima, Robert Bergquist","doi":"10.4081/gh.2026.1544","DOIUrl":"10.4081/gh.2026.1544","url":null,"abstract":"<p><p>Spatial epidemiology provides powerful tools for understanding geographic patterns in health and disease, but methodological and interpretative pitfalls can undermine the validity of spatial analyses. This editorial highlights ten common pitfalls spanning spatial dependence, scale, ecological inference, small-area estimation, spatial confounding, hotspot interpretation, model validation, measurement and statistical uncertainty, and causal interpretation. For each, we provide practical guidance to support more rigorous and reliable spatial epidemiological research. As geospatial data, artificial intelligence, and analytical methods continue to advance, careful spatial reasoning remains essential to ensure that methodological sophistication translates into valid, interpretable, and meaningful public health evidence.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"21 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867902","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":"Prostate cancer burden and attributable risk factors in East Asia from 1990 to 2021: a systematic analysis.","authors":"Wen-Tong Ji, Run-Hua Tang, Yu-Sun Guo, Xiang-Ze Wang, Jian-Long Wang","doi":"10.4081/gh.2026.1487","DOIUrl":"https://doi.org/10.4081/gh.2026.1487","url":null,"abstract":"<p><p>Prostate cancer is a significant health concern in East Asia, where distinctive characteristics of disease presentation and outcomes warrant focused epidemiological investigation. This study evaluated the current status, future trends and risk factors across six East Asian regions. Mortality, incidence, Disability-Adjusted Life-Years (DALYs), and age-standardized rates were estimated using data from the Global Burden of Disease Study 2021. Join point, an age-period-cohort model and decomposition analyses were applied to reflect epidemiological trends. The Autoregressive Integrated Moving Average (ARIMA) model was used to predict the burden of prostate cancer 2022-2050. Taiwan had the highest Age-Standardized Mortality Rate (ASMR: 10.83 [95% uncertainty interval [UI]: 9.48-12.18]), Age-Standardized Incidence Rate (ASIR: 37.68 [95% UI: 32.54-43.34]), and DALY rate (ASDR: 192.07 [95% UI: 170.35-214.13]) in 2021. Mortality increased with age across all regions, while the cohort effect on mortality remained stable. The effects of age, period and cohort on mortality and incidence and the percentage of aging, population and epidemiological changes in the total trend differed across regions. ARIMA projections predicted a \"U\"-shaped ASMR trends in Japan. The Democratic People's Republic of Korea had the highest DALYs attributable to low in milk and calcium intake, whereas smoking-related ASDR were highest in Taiwan. The prostate cancer burden in East Asia remains substantial, particularly among older populations. Although overall trends appear more favourable than global averages, several regions face concerning future prospects.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"21 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119227","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":"Household-level spatial modelling of child height-for-age in Northern Province, Rwanda: comparison of geographically weighted and neural network weighted regression models.","authors":"Clarisse Kagoyire, Gilbert Nduwayezu, Rachid Oucheikh, Jean Pierre Bizimana, Petter Pilesjö, Ali Mansourian","doi":"10.4081/gh.2026.1491","DOIUrl":"https://doi.org/10.4081/gh.2026.1491","url":null,"abstract":"<p><p>Childhood stunting remains a major public health concern in low- and middle-income countries, with persistent sub-national disparities. This study examined spatially varying associations between Height-for-Age z-scores (HAZ) and selected child, maternal, and household factors, and compared Geographically Weighted Regression (GWR), Multi-scale GWR (MGWR) and Geographically Neural Network Weighted Regression (GNNWR) in their ability to capture spatial heterogeneity and non-linearity. We analysed data from a cross-sectional survey conducted in December 2021 in the Northern Province of Rwanda. The survey covered 615 households, with analyses performed for 601 children aged 1-36 months. After imputation, multicollinearity screening, and feature selection, HAZ was modelled using Ordinary Least Squares (OLS), GWR, MGWR and GNNWR. Model performance was assessed using coefficient of determination (R2), Root Mean Square Error (RMSE), Akaike Information Criterion/AIC Corrected (AIC/AICc), and Moran's I of residuals. Overall stunting prevalence was 27.1%. Spatially varying associations were observed for child age, sex, birthweight, underweight status, selected childcare practices, maternal support, and household living conditions. GNNWR achieved the best training performance (R2 = 0.66; RMSE=0.74) followed by MGWR (R2 =0.51; RMSE=0.88) and GWR (R2 =0.43; RMSE=0.95). Validation performance declined for all models, although GNNWR retained a higher validation (R2=0.28) with negligible residual spatial autocorrelation (Moran's I = -0.005). GNNWR provided the strongest overall fit among the compared models and highlighted local spatial variation in associations with HAZ. However, the modest validation performance indicates that the findings should be interpreted cautiously. The workflow offers a portable framework for analysing DHS-like geocoded household datasets, but broader applicability should be assessed using spatial cross-validation, and sensitivity analyses.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"21 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148963829","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":"Epidemiological and spatial analysis of breast cancer mortality and survival among women in San Luis Potosi, Mexico 2009-2019.","authors":"Carlos Daniel Coronado-Ruis, Enrique Ibarra-Zapata, Darío Gaytán-Hernández, Sergio Zarazúa-Guzmán, Thuluz Meza-Menchaca, Marisol Gallegos-García, Omar Medina-de la Cruz, Verónica Gallegos-Garcìa","doi":"10.4081/gh.2026.1490","DOIUrl":"https://doi.org/10.4081/gh.2026.1490","url":null,"abstract":"<p><p>This study investigated variations in breast cancer mortality among women in San Luis Potosí, Mexico using an epidemiologicalspatial approach covering the period 2009-2019. Additionally, global and specific survival and its associations with clinical stage, age at diagnosis, marginalization index, recurrence and arsenic concentration in water were evaluated. We conducted a retrospective ecological, study involving1, 626 confirmed breast cancer cases and 264 related deaths registered at a public hospital. Survival analysis was performed using the Kaplan-Meier method and Log-Rank tests. Spatial distribution patterns of municipal average mortality were assessed using Moran's I and visualized in ArcMap 10.1. At the state-level the mean age at diagnosis among deceased patients was 53.49 years, with 84% presenting at advanced stages (IIB-IV). A progressively increased mortality, particularly in Health Jurisdiction IV and the municipality of Cerro de San Pedro, was observed, with peaks in 2016. The 5-year global survival rate was 83.78%. Factors significantly associated with reduced survival included advanced stage, older age, higher marginalization index and recurrence. Although arsenic exposure was not statistically significant, patients exposed to levels exceeding 0.025 mg/L exhibited longer survival times. The integration of spatial epidemiology with survival analysis underscored critical geographic and sociodemographic disparities and offer valuable insights for targeted healthcare planning and resource allocation, particularly in high-priority regions.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"21 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563902","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":"Geospatial assessment of dengue fever risk and spatial patterns in Phayao Province, Thailand.","authors":"Phaisarn Jeefoo, Watcharaporn Preedapirom Jeefoo, Sakorn Mekruksavanich, Kanchana Nakhapakorn","doi":"10.4081/gh.2026.1497","DOIUrl":"https://doi.org/10.4081/gh.2026.1497","url":null,"abstract":"<p><p>This study employed geospatial approaches to assess the risk and spatial distribution of Dengue Fever (DF) and Dengue Hemorrhagic Fever (DHF) in Phayao Province, Thailand. Epidemiological data from 2016 to 2024, comprising 3,600 reported cases, were analysed alongside demographic and climatic variables. Temporal analysis revealed a major epidemic in 2023 lasting 20 weeks, coinciding with peak rainfall, with adolescents and young adults (13-24 years old) being the most affected group. Spatial autocorrelation (Moran's I) indicated significant clustering of dengue morbidity rates, with hotspots concentrated in urbanised districts such as Mueang Phayao, Dok Khamtai and Chiang Kham, while Kernel Density Estimation (KDE) highlighted shifts of hotspots toward eastern districts in later years. Local Indicators of Spatial Association (LISA) identified 24 high-high clusters in 2019, predominantly in Mae Chai District. Case-control analysis further revealed that socio-economic conditions, housing environments and inconsistent preventive behaviours influenced dengue incidence, with strong community participation linked to more effective prevention. These findings underscore the spatial heterogeneity of dengue transmission and provide geospatial evidence to guide targeted vector control, strengthen community-based interventions, and support evidence-based public health strategies in northern Thailand.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"21 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563833","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":"Spatio-temporal epidemiology of intestinal infectious diseases in Blantyre District, Malawi.","authors":"Richard Mvula, Emmanuel Chinkaka, Vincent Katonda, Wilson Tchongwe, Lemson Kachedwa, John Njalam'mano, Wilfred Kadewa, Fasil Ejigu Eregno","doi":"10.4081/gh.2026.1427","DOIUrl":"https://doi.org/10.4081/gh.2026.1427","url":null,"abstract":"<p><p>Intestinal infectious diseases remain among the leading causes of death, mainly in sub-Saharan Africa. In Malawi's commercial hub, Blantyre, the spatiotemporal dynamics of these diseases have not been extensively studied, despite their significant public health burden. We studied high-risk areas and analyze the spatial and temporal patterns of intestinal infectious diseases, including cholera, schistosomiasis, and Other Diarrheal Diseases (ODDs) in Blantyre district from 2018 to 2023. Descriptive statistics were used to evaluate the epidemic characteristics of the intestinal infectious diseases. Spatial autocorrelation with Local Moran's I was employed to detect spatial clusters, and the Kulldorf retrospective method for space-time analysis. ODDs had the highest incidence rates, ranging from 2714/100,000 to 3631/100,000, while cholera had the lowest, ranging from 0 to 320/100,000. Temporal characteristics showed that ODDs and cholera peaked during the rainy season (October-March), while schistosomiasis occurred from April to June. Spatial clustering of schistosomiasis declined from two High-High (HH) clusters in Blantyre's northern and western parts (2018- 2020) to one (2021-2023). Cholera exhibited High-High (HH) clusters only in the northern part from 2021 to 2023. ODDs expanded from two to three HH clusters extending southward between 2018-2020 and 2021-2023. Space-time analysis identified nine most likely clusters for schistosomiasis and ODDs and two for cholera. Most likely, clusters were in the city, with secondary clusters in the northern and western parts. Given their high incidence, focused public health strategies for schistosomiasis and ODDs should be prioritized in future planning.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"21 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148972280","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":"Geospatial approaches to analysing spatial patterns of stroke incidence and mortality: a systematic review.","authors":"Min-Jung Kim","doi":"10.4081/gh.2026.1486","DOIUrl":"https://doi.org/10.4081/gh.2026.1486","url":null,"abstract":"<p><p>Stroke is a leading cause of mortality and disability worldwide; however, its burden is unevenly distributed across geographical areas. This systematic review aimed to synthesise studies examining the spatial distribution of stroke incidence, prevalence and mortality, with a review of geospatial analytical approaches used to identify these patterns. A systematic literature search identified 29 studies that applied spatial analysis or spatial modelling techniques to stroke-related outcomes. The included studies demonstrated heterogeneity in geographical scale, spatial units, outcome measures and covariate selection. Methodological quality was assessed using a modified Joanna Briggs Institute (JBI) critical appraisal tool. Overall, stroke incidence and mortality were consistently found to be non-randomly distributed in space, with significant spatial autocorrelation and clustering observed across multiple spatial scales. These spatial patterns were closely associated with area-level factors, including population ageing, socioeconomic conditions and urban-rural characteristics. Environmental exposures and disparities in the spatial distribution of healthcare resources and access to care were also reported to contribute to geographical differences in stroke outcomes. Geospatial methods commonly employed included GIS-based mapping, spatial autocorrelation analyses, spatial regression models and Bayesian disease mapping. Although most studies demonstrated moderate to high methodological quality, variation remained in the justification of spatial unit selection and the control of covariates. In conclusion, the burden of stroke exhibits pronounced spatial heterogeneity shaped by structural, environmental and healthcare accessibility factors. Future geospatial research should adopt evidence-based spatial units and integrated analytical approaches to support place-based stroke prevention strategies and inform health policy development.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"21 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119258","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}