Spatial and Spatio-Temporal Epidemiology最新文献

{"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":"2025-07-21","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":"Understanding the COVID-19 pandemic through bayesian spatio-temporal modeling of several outcomes","authors":"Lander Rodriguez-Idiazabal ,&nbsp;Miguel Angel Martinez-Beneito ,&nbsp;Jose M. Quintana ,&nbsp;Julia Garcia-Asensio ,&nbsp;Maria Jose Legarreta ,&nbsp;Nere Larrea ,&nbsp;Irantzu Barrio","doi":"10.1016/j.sste.2025.100737","DOIUrl":"10.1016/j.sste.2025.100737","url":null,"abstract":"<div><div>Understanding the spatio-temporal dynamics of past pandemics and the factors driving these patterns can enhance preparedness against future pandemics. This study aimed to investigate the COVID-19 pandemic by analyzing the spatio-temporal variations in infections, hospitalizations, deaths and reinfections.</div><div>We conducted a retrospective analysis of data from the adult population of the Basque Country at Primary Care Unit level from March 1, 2020 to January 9, 2022. Using a Bayesian hierarchical spatio-temporal model, we estimated relative risks for each outcome, accounting for the effects of a deprivation index, urbanicity, and COVID-19 testing rates.</div><div>SARS-CoV-2 infections and mortality followed similar risk patterns, with a strong clustering in highly populated areas. Hospitalization risks were influenced by proximity to hospitals, revealing potential access barriers in remote areas. High reinfection risks were predominantly localized in the northwest coast of our region. Increased testing rates were associated with higher risks across all outcomes. Urbanicity showed positive associations with hospitalizations (relative risk, [95 % credible interval]: 1.22, [1.12–1.33]) and infections (1.34, [1.15–1.57]). Similarly, deprivation was positively associated with hospitalization risks (1.09, [1.04–1.15]) and mortality risks (1.07, [1.02–1.12]), reflecting the increased vulnerability of socioeconomically disadvantaged populations.</div><div>This comprehensive analysis of various COVID-19 outcomes provides valuable insights into the pandemic’s spatio-temporal dynamics and highlights key improvement areas. Addressing healthcare access disparities in rural areas and focusing on the deprived populations could help mitigate the impact of future pandemics. This approach could be extended to other regions to inform specific public health strategies.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100737"},"PeriodicalIF":2.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703646","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":"2025-07-01","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":"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":"2025-06-19","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":"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":"2025-06-18","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":"Evaluation of spatial and non-spatial factors on tuberculosis using geospatial information system and fuzzy logic","authors":"Mahsa Rakhsh Khorshid ,&nbsp;Saeed Behzadi ,&nbsp;Alireza Sharifi ,&nbsp;Alireza Vafaeinejad ,&nbsp;Ziba Abbasian ,&nbsp;Hossein Naderi","doi":"10.1016/j.sste.2025.100729","DOIUrl":"10.1016/j.sste.2025.100729","url":null,"abstract":"<div><div>Tuberculosis is a deadly infectious disease that has not been eradicated yet. The prevalence of this disease is still very high in some parts of the world, so it is considered a deadly disease. Iran is one of the countries which has not yet achieved the ability to eliminate this disease. The prevalence of tuberculosis is relatively higher in some provinces than in the other ones. Sistan and Baluchestan is the province with high rates of tuberculosis. In this paper, the factors affecting tuberculosis are modeled in Sistan and Baluchestan province using Geospatial Information Systems (GIS) and FL. This research contains two general analyzes. In the first analysis, three different scenarios of FL rules are presented. The first two scenarios examine spatial and non-spatial factors respectively. The third scenario also examines the combination of spatial and non-spatial factors simultaneously. As a result, the effect of spatial and non-spatial factors on tuberculosis is obtained. In the second analysis, a spatial scatter density map of tuberculosis is produced according to spatial data. This research reveals that the effects of spatial and non-spatial factors on tuberculosis are 57 % and 43 %, respectively. By comparing the results with samplings, the scatter rate map of tuberculosis is obtained with an accuracy of 71 %.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100729"},"PeriodicalIF":2.1,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254152","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":"Disease mapping with individual level information; a case study of acute myocardial infarction mortality","authors":"Xavier Puig,&nbsp;Josep Ginebra","doi":"10.1016/j.sste.2025.100721","DOIUrl":"10.1016/j.sste.2025.100721","url":null,"abstract":"<div><div>When mapping relative mortality risk under specific causes of death in time, one can use small areas and single year mortality data to explore the space time variation in detail. To reduce the variability of the initial mortality risk estimates and help explain their differences, hierarchical Poisson models are typically used. Here we deal with the situation where besides aggregated small-area level data necessary for that, one also has complete individual level data about the presence of certain risk factors in the population, which is now rare but it should become routine in places with universal health coverage using a medical record sharing system. In particular, we consider the convenience of including individual level covariates in the models, and mapping relative mortality risk adjusted for them. That is illustrated by exploring how mortality due to acute myocardial infarction varies in space and in time in Catalonia between 2014 and 2019 using individual data on obesity, diabetes, dyslipidemia and smoking habits.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"53 ","pages":"Article 100721"},"PeriodicalIF":2.1,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888077","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":"Integrating data at multiple spatial scales to estimate the local burden of the opioid syndemic","authors":"Eva Murphy ,&nbsp;David Kline ,&nbsp;Erin McKnight ,&nbsp;Andrea Bonny ,&nbsp;William C. Miller ,&nbsp;Lance Waller ,&nbsp;Staci A. Hepler","doi":"10.1016/j.sste.2025.100720","DOIUrl":"10.1016/j.sste.2025.100720","url":null,"abstract":"<div><div>The opioid epidemic has been particularly severe in Ohio, prompting significant efforts to understand its spatial patterns, mainly using available data at the county level. However, relying solely on county-level analysis can overlook crucial information relevant to localized effects. To address this, we integrate spatially misaligned data observed at the county and ZIP code levels to explore the complex interaction of five opioid-related outcomes, providing a more detailed local understanding of the opioid epidemic. We demonstrate how to map ZIP-code level data to ZIP-code Tabulation Areas (ZCTAs) and relate the county-level and ZCTA-level outcomes to a spatially correlated latent factor. The latent factor is defined on the intersection of the misaligned areal units, which provides a more granular understanding of the opioid epidemic. Furthermore, this approach allows us to identify areas with varying levels of opioid burden and reveals local regions with relatively high burden that county-level analyses might miss. Finally, we highlight the need for careful consideration when relying solely on ZIP code level data for naloxone, as it may lead to misinterpretations, particularly in rural regions.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"53 ","pages":"Article 100720"},"PeriodicalIF":2.1,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863591","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":"Physician and healthcare partner engagement in the creation of healthfulness indices for West Michigan","authors":"Richard Casey Sadler ,&nbsp;Samantha Gailey ,&nbsp;Erin R. McNeely","doi":"10.1016/j.sste.2025.100722","DOIUrl":"10.1016/j.sste.2025.100722","url":null,"abstract":"<div><div>Community participatory mapping can direct health research, offering opportunity to build spatial awareness and generate future research. Here we establish healthfulness indices by consulting healthcare system partners for their expert opinions on characteristics they felt influenced health. Partners started from 36 variables and narrowed to 16 in 4 simplified categories. The analytic hierarchy process was used to identify variable and category weights. Opinions were consolidated for each partner sub-group and overall. Map layers were assigned calculated weights and indices were created from weighted layers. Areas with more amenities scored higher, including in and around downtown areas and smaller towns. Lower scores were found in suburban and lower-income urban areas. Variation in maps among subgroups reflect differing priorities in tackling health equity issues. This work increases healthcare partner engagement in built environment work and generates future research pathways. Partners now have a tool for interrogating and communicating the environment’s cumulative impact.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"53 ","pages":"Article 100722"},"PeriodicalIF":2.1,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860326","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":"A multivariate generalized logistic approach with spatially varying nonlinear components for modeling epidemic data","authors":"Marcos O. Prates ,&nbsp;Dani Gamerman ,&nbsp;Samuel F. Candido ,&nbsp;Luis M. Castro","doi":"10.1016/j.sste.2025.100718","DOIUrl":"10.1016/j.sste.2025.100718","url":null,"abstract":"<div><div>This work considers the joint analysis of time series for epidemiological count data of neighboring regions. The joint analysis involves parameter estimation and prediction of future outcomes. The literature concentrated on imposing similarities on components of the linear predictor for the mean. However, some hierarchical model specifications for the mean contain non-linear components with similar behavior over neighboring regions. This paper proposes the use of spatial specification for these components. Parametric forms based on a data-driven approach are assumed for the waves of epidemic counts, and multiple waves are considered. The resulting model is tested in simulation studies and applied to real data. Model evaluation is based on the fitting and prediction capabilities. An illustration is provided by the analysis of counts of COVID19 cases, and it compares favorably against alternative models. Finally, the paper concludes with a discussion of the proposed methodology.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"53 ","pages":"Article 100718"},"PeriodicalIF":2.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776818","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}