Spatial and Spatio-Temporal Epidemiology最新文献

{"title":"Ensemble Bayesian modelling with dynamic population to estimate excess deaths due to extreme temperatures","authors":"Garyfallos Konstantinoudis ,&nbsp;Anthony Hauser ,&nbsp;Julien Riou","doi":"10.1016/j.sste.2025.100760","DOIUrl":"10.1016/j.sste.2025.100760","url":null,"abstract":"<div><div>Extreme heat has been linked to increased mortality. Monitoring the mortality burden attributable to extreme heat is crucial to inform policies, such as heat warnings, and prevent heat-related deaths. In this study, we evaluate excess mortality during summer 2022 in Switzerland, identify vulnerable populations and estimate temperature thresholds relevant for heat adaptation policies. We use nationwide mortality and population data during 2011–2022 by age, sex, day and canton. We develop a Bayesian ensemble modelling approach with dynamic population to predict expected mortality in summer 2022 and calculate excess by comparing expected with observed mortality. We account for covariates associated with mortality such as national holidays, and spatiotemporal random effects to improve predictions. After accounting for the effect of the COVID-19 pandemic, we observed a total of 487 (95% Credible Interval: 10–935) excess deaths during summer 2022 in people older than 80 years. We illustrate that for periods of extreme heat longer than four days, the minimum excess mortality temperature threshold in the oldest age group is the 70th percentile of the temperature. Our approach highlights the importance of lower temperature thresholds during prolonged periods of extreme heat and advocates for integrating this insight in heat adaptation policies.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"55 ","pages":"Article 100760"},"PeriodicalIF":1.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal analysis of dengue fever in tourist destinations using a Time-Lagged DCCAC approach","authors":"Jéssica B. Oliveira ,&nbsp;Thiago B. Murari ,&nbsp;Hernane B. de B. Pereira ,&nbsp;Marcelo A. Moret ,&nbsp;Claudia Andrea L. Cardoso","doi":"10.1016/j.sste.2025.100730","DOIUrl":"10.1016/j.sste.2025.100730","url":null,"abstract":"<div><h3>Background:</h3><div>Dengue is one of the most important neglected tropical diseases in the world and is spread rapidly through human movement, especially throughout intermunicipal, national and international routes. The Pantanal is the largest wetland in the world and is a UNESCO World Heritage Site spanning the states of Mato Grosso do Sul and Mato Grosso. In addition, the Pantanal of Mato Grosso do Sul is an important tourist hub.</div></div><div><h3>Methods:</h3><div>This study addresses the spread of dengue in different regions, focusing on the Pantanal of Mato Grosso do Sul. The objective of our study is to evaluate the spread of dengue, using the time-lagged Detrended Cross-Correlation Analysis Coefficient (DCCAC) method to provide data that will help in discussions of protocols to combat dengue in the region.</div></div><div><h3>Results:</h3><div>Through the time-lagged DCCAC, it was possible to identify similar behaviors in the lagged DCCAC comovements in different regions, including Bolivia and Paraguay and states in Brazil, such as Mato Grosso do Sul, Mato Grosso and others.</div></div><div><h3>Conclusion:</h3><div>This study suggests the importance of cooperation between these regions to fight the disease in an integrated way and share information about the behavior of dengue cases in each area. Implementing a shared system across a network formed by these regions can effectively combat dengue and allow the regions to work together to identify and address the factors that affect the behavior of dengue cases.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100730"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating small-area estimation into mediation analyses with areal datasets","authors":"Melissa J. Smith ,&nbsp;Emily K. Roberts ,&nbsp;Mary E. Charlton ,&nbsp;Jacob J. Oleson","doi":"10.1016/j.sste.2025.100735","DOIUrl":"10.1016/j.sste.2025.100735","url":null,"abstract":"<div><div>Various methods have been employed in the medical literature to conduct mediation analyses with areal datasets. These analyses are typically performed to understand why age-adjusted incidence or mortality rates vary by county or ZIP code-level characteristics. Two primary approaches are commonly used: the “Calculation before mediation” (C-BM) approach, where age-adjusted rates are calculated from the raw data for each areal unit and used as the outcome in the mediation analysis, and the “Small-area estimation before mediation” (SAE-BM) approach, which uses pre-existing small-area estimates as the outcome in the mediation analysis. However, these approaches have significant limitations that can impact the inferences around mediation effects and the overall conclusions of a mediation analysis. In this paper, we propose a new method, the “Small-area estimation within mediation” (SAE-WM) approach, for conducting mediation analyses with areal datasets. This method integrates Bayesian small-area estimation techniques into the mediation analysis outcome model, allowing for precise estimation of mediation effects with areal datasets. We conduct a simulation study to demonstrate the advantages of the SAE-WM method for estimating mediation effects with areal datasets, while highlighting the pitfalls and potential problems with the C-BM and SAE-BM methods. We also illustrate an application of the SAE-WM method to assess whether healthcare access mediates the relationship between ZIP code-level socioeconomic environment and age-adjusted colorectal cancer incidence rates in Iowa.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100735"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal methods to handle missing data in syndromic surveillance with applications to health management information system data","authors":"Nicholas B. Link ,&nbsp;Anuraag Gopaluni ,&nbsp;Isabel Fulcher ,&nbsp;Emma Jean Boley ,&nbsp;Rachel C. Nethery ,&nbsp;Bethany Hedt-Gauthier","doi":"10.1016/j.sste.2025.100736","DOIUrl":"10.1016/j.sste.2025.100736","url":null,"abstract":"<div><div>Syndromic surveillance monitors infectious diseases, especially in situations where direct disease monitoring is unavailable. However, conventional syndromic surveillance methods face challenges in handling missing data, particularly when the missing completely at random (MCAR) assumption is violated. Additionally, these methods often do not leverage spatio-temporal techniques that can reduce bias and improve their performance. This study addresses both of these limitations by comparing a baseline syndromic surveillance model with a frequentist spatio-temporal model used in infectious diseases and a Bayesian spatio-temporal conditional autoregressive (CAR) model.</div><div>Drawing inspiration from COVID-19 symptom data collected via routine health systems in Liberia, we conduct simulations with various data generating processes, spatio-temporal correlation structures, and missing data mechanisms. Across the diverse simulations for outbreak detection, the baseline model and the Bayesian CAR model had high specificity, thus limiting outbreak false alarms. The findings underscore the importance of considering spatio-temporal models for syndromic surveillance.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100736"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting the occurrence of suicide clusters at city block scale: evidence from a 26-year data series","authors":"SA Estay ,&nbsp;G Rivera ,&nbsp;JC Olivares ,&nbsp;R Fuentes-Ferrada ,&nbsp;M Gotelli ,&nbsp;C Heskia ,&nbsp;C Rojas-Carvajal ,&nbsp;J Santander ,&nbsp;HC Lungenstrass ,&nbsp;T Baader","doi":"10.1016/j.sste.2025.100734","DOIUrl":"10.1016/j.sste.2025.100734","url":null,"abstract":"<div><div>Suicide clusters have profound negative impacts in the affected communities. Despite the fact that the occurrence of this phenomenon is a rare event, it has been observed across diverse cultural contexts. Suicide clusters are commonly described as a higher number of suicides than expected by chance, occurring at relatively small time/space scales. In particular, point clusters have been reported as occurring in areas ranging from hundreds of meters to several kilometers and lasting from a few days to several months or years. Nevertheless, effective prevention of suicide clusters requires robust estimation of their spatial and temporal scales, especially at short distances such as the city-block scale. This study analyzes data from the city of Valdivia, Chile (1996–2021) using a DBSCAN-based approach. We detected seven suicide clusters at very small scale in different years. In the clusters, the distances between suicides were &lt;300 m (two to three city blocks). For the entire period, 6 % of the suicides occurred in a cluster. These clusters contain between three and four suicides, each with higher prevalence of men and people over 30 years old. Our results provide important insights for implementing preventive actions at the neighborhood scale.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100734"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying hotspots of malaria incidence and mortality for tailored interventions in Cameroon using routine data from 2011 to 2021: A Bayesian space-time variability modeling","authors":"Fottsoh Fokam Arnold ,&nbsp;Fati Kirakoya-Samadoulougou ,&nbsp;Ateba Marcelin ,&nbsp;Fosso Jean ,&nbsp;Yazoume Ye ,&nbsp;Sekou Samadoulougou","doi":"10.1016/j.sste.2025.100733","DOIUrl":"10.1016/j.sste.2025.100733","url":null,"abstract":"<div><div>Malaria remains a health challenge globally, particularly in Cameroon. Despite the emphasis of recent studies to provide estimates of spatio-temporal variations in malaria in the country, these studies have overlooked an important aspect of disease surveillance, which is the monitoring and assessing of spatio-temporal risk in hotspots. This study addresses this gap by examining the ex-ante dynamics of malaria risk clustering. It identifies health districts in Cameroon with a high potential to be hotspots based on uncomplicated incidence, severe incidence, and deaths. The authors used the malaria routine data from 189 contiguous health districts in Cameroon from January 2011 - December 2021 to achieve this. By fitting a Bayesian spatiotemporal model, the classification of the spatial trend into hotspots, coldspots, and neutral-spots, and the classification of the differential time trends into increasing, decreasing, and stable trends were defined to assess the ex-ante risk of hotspot. As findings, 42.9 % (81), 44.9 % (85) and 28.6 % (54) districts show a decreasing trend for the uncomplicated, severe, and malaria deaths, respectively. However, 44.9 % (85), 37.6 % (71), and 32.8 % (62) districts show an increasing trend for the uncomplicated, severe, and malaria deaths, respectively, including 24.3 % (46), 23.3 % (44), and 21.7 % (41) identified with a high likelihood of becoming hotspots for uncomplicated, severe, and malaria deaths, respectively. This trend suggests that these neutral-spot and coldspots health districts, especially those with increasing trends, are at risk of becoming hotspots. Although aggregated health facility data may not accurately reflect individual-level risk and could be influenced by varying surveillance and case management practices, integrating a Bayesian space-time variability modeling into Cameroon's malaria surveillance system can help pinpoint ex-ante hotspots and facilitate a proactive and targeted interventions.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100733"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geostatistical and machine learning approaches for high-resolution mapping of vaccination coverage","authors":"C. Edson Utazi ,&nbsp;Ortis Yankey ,&nbsp;Somnath Chaudhuri ,&nbsp;Iyanuloluwa D. Olowe ,&nbsp;M. Carolina Danovaro-Holliday ,&nbsp;Attila N. Lazar ,&nbsp;Andrew J. Tatem","doi":"10.1016/j.sste.2025.100744","DOIUrl":"10.1016/j.sste.2025.100744","url":null,"abstract":"<div><div>Recently, there has been a growing interest in the production of high-resolution maps of vaccination coverage. These maps have been useful for uncovering geographic inequities in coverage and improving targeting of interventions to reach marginalized populations. Different methodological approaches have been developed for producing these maps using mostly geolocated household survey data and geospatial covariate information. However, it remains unclear how much the predicted coverage maps produced by the various methods differ, and which methods yield more reliable estimates. Here, we explore the predictive performance of these methods and resulting implications for spatial prioritization to fill this gap. Using Nigeria Demographic and Health Survey as a case study, we generate 1 × 1 km and district level maps of indicators of vaccination coverage using geostatistical, machine learning (ML) and hybrid methods and evaluate predictive performance via cross-validation. Our results show similar predictive performance for five of the seven methods investigated, although two geostatistical approaches are the best performing methods. The worst-performing methods are two ML approaches. We find marked differences in spatial prioritization using these methods, which could potentially result in missing important underserved populations, although broad similarities exist. Our study can help guide map production for other health and development metrics.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100744"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variable Screening Methods in Conditional Logistic Individual Level Models of Disease Spread","authors":"Tahmina Akter ,&nbsp;Rob Deardon","doi":"10.1016/j.sste.2025.100742","DOIUrl":"10.1016/j.sste.2025.100742","url":null,"abstract":"<div><div>The conditional logistic individual-level model is a recently developed infectious disease model, particularly suited for modeling spatial-based infection risk. It is designed to reduce computational complexity and expand the range of available statistical software for data analysis (Akter &amp; Deardon, 2025). This study aims to apply and evaluate different variable selection techniques for the newly introduced conditional logistic individual-level models (CL-ILMs). These variable selection methods include forward and backward stepwise Akaike information criterion (AIC), least absolute shrinkage and selection operator (Lasso), spike-and-slab prior (SS prior), and two-stage screening methods. The ultimate goal is to boost model performance and interpretability, and to reduce the risk of overfitting ultimately leading to more robust and effective models. We examine and compare the performance of these methods using simulated data and real-life data from the outbreak of foot-and-mouth disease in the UK in 2001.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100742"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The causal impact of urbanicity on neighbourhood psychosis prevalence","authors":"Peter Congdon","doi":"10.1016/j.sste.2025.100739","DOIUrl":"10.1016/j.sste.2025.100739","url":null,"abstract":"<div><div>There is considerable evidence of elevated psychosis rates in more urban settings. However, the urbanicity effect is confounded with other neighbourhood contextual effects, such as from deprivation and crime. To assess the nature of the underlying urbanicity effect, removing distorting effects of confounders, we consider a novel method to assessing causality in spatial applications: a propensity weight approach, with weights obtained by entropy optimization, and adjusting for the spatial overlap in the urbanicity effect via a bivariate exposure approach. The application is to the effect of urbanicity on psychosis prevalence in 6856 English neighbourhoods. We use a measure of urbanicity adapted to represent aspects of urban form, rather than simply population density or a binary indicator. The overlap effect in the psychosis outcome model is shown to outweigh the local effect, and we find a clear urbanicity gradient with a relative risk of 1.91 comparing the most and least urban areas, after adjustment for confounding through propensity weighting.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100739"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial clustering and sociodemographic factors impacting obesity and hypertension in Nepal: Analysis of a national demographic and health survey, 2022","authors":"Biraj Neupane ,&nbsp;Bikram Adhikari ,&nbsp;Niharika Jha ,&nbsp;Ian Brooks ,&nbsp;Csaba Varga","doi":"10.1016/j.sste.2025.100743","DOIUrl":"10.1016/j.sste.2025.100743","url":null,"abstract":"<div><h3>Background</h3><div>Overweight/obesity and hypertension pose significant global health challenges. This study examines the spatial distribution, sociodemographic determinants, and clustering patterns of these conditions in Nepal.</div></div><div><h3>Methods</h3><div>We conducted a comprehensive spatial-epidemiological analysis of 136,235 participants from the 2022 Nepal Demographic and Health Survey. Outcome variables in this study were overweight/obesity (present/absent) and hypertension (present/absent). A weighted descriptive and inferential analysis addressed the complex survey design and non-response rate. We used spatial scan statistics to identify areas with higher or lower-than-expected cases, and geospatial mapping to illustrate the distribution of cases and the significant spatial clusters. Multivariable logistic regression models determine the association between the outcome variables and respondents’ age, gender, marital status, education level, and wealth.</div></div><div><h3>Findings</h3><div>Overall, 42.5 % of respondents were obese, and 38.5 % had hypertension. Respondents who were women, middle-aged, married, educated, wealthy, and living in cities had higher odds of being overweight. Similarly, respondents who were male, older, single, poor, uneducated, and lived in cities had higher odds of having hypertension. A spatial scan statistic using the Bernoulli model identified twelve (seven low and five high rate) significant clusters for obesity and eleven (five low and six high rate) for hypertension.</div></div><div><h3>Conclusion</h3><div>This study showed the utility of health risk mapping across Nepal, emphasizing the complex interaction between sociodemographic and geographic factors impacting the prevalence of obesity and hypertension. The findings highlighted the need for targeted interventions in the high-risk regions of Nepal based on the identified risk factors to mitigate the impact.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100743"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}