Spatial and Spatio-Temporal Epidemiology最新文献_第3页

Mapping gentrification, segregation, rental cost burden and sexually transmitted infections in Atlanta, Georgia, 2005–2018 绘制 2005-2018 年佐治亚州亚特兰大市的绅士化、种族隔离、房租成本负担和性传播感染地图

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-08-08 DOI: 10.1016/j.sste.2024.100680

Sabriya L. Linton , Anne E. Corrigan , Laura Nicole Sisson , Hannah L.F. Cooper , Michael R. Kramer , Frank C. Curriero

{"title":"Mapping gentrification, segregation, rental cost burden and sexually transmitted infections in Atlanta, Georgia, 2005–2018","authors":"Sabriya L. Linton , Anne E. Corrigan , Laura Nicole Sisson , Hannah L.F. Cooper , Michael R. Kramer , Frank C. Curriero","doi":"10.1016/j.sste.2024.100680","DOIUrl":"10.1016/j.sste.2024.100680","url":null,"abstract":"<div><p>Racial disparities in sexually transmitted infections (STIs) in the United States have been linked to social inequities. Gentrification instigates population-level shifts in housing markets and neighborhood racial/ethnic composition in ways that may impact the spatial distribution of STIs. This study assessed overlap in clusters of STIs, gentrification, social and economic disadvantage, and rental cost burden in Atlanta, Georgia, between 2005 and 2018. Overlap between gentrification and STIs among Black people was greater than that observed for the overlap between gentrification and STIs among White people. Overlap of STIs with social disadvantage and rental cost burden was more prominent among White people than Black people over time. Additional investigation into the factors behind the spatial dynamics observed in this study, and explanations for their variation by race, are necessary to inform where place-based efforts are targeted to reduce racial disparities in STI transmission in gentrifying cities.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"51 ","pages":"Article 100680"},"PeriodicalIF":2.1,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241569","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}

引用次数: 0

Edge effects in spatial infectious disease models 空间传染病模型中的边缘效应

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-08-01 DOI: 10.1016/j.sste.2024.100673

Emil Hodzic-Santor , Rob Deardon

引用次数: 0

Bayesian group testing regression models for spatial data 空间数据的贝叶斯分组测试回归模型

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-08-01 DOI: 10.1016/j.sste.2024.100677

Rongjie Huang , Alexander C. McLain , Brian H. Herrin , Melissa Nolan , Bo Cai , Stella Self

引用次数: 0

Impact of deforestation and climate on spatio-temporal spread of dengue fever in Mexico 森林砍伐和气候对墨西哥登革热时空传播的影响

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-08-01 DOI: 10.1016/j.sste.2024.100679

José Mauricio Galeana-Pizaña , Gustavo Manuel Cruz-Bello , Camilo Alberto Caudillo-Cos , Aldo Daniel Jiménez-Ortega

{"title":"Impact of deforestation and climate on spatio-temporal spread of dengue fever in Mexico","authors":"José Mauricio Galeana-Pizaña , Gustavo Manuel Cruz-Bello , Camilo Alberto Caudillo-Cos , Aldo Daniel Jiménez-Ortega","doi":"10.1016/j.sste.2024.100679","DOIUrl":"10.1016/j.sste.2024.100679","url":null,"abstract":"<div><p>Dengue prevalence results from the interaction of multiple socio-environmental variables which influence its spread. This study investigates the impact of forest loss, precipitation, and temperature on dengue incidence in Mexico from 2010 to 2020 using a Bayesian hierarchical spatial model. Three temporal structures—AR1, RW1, and RW2—were compared, with RW2 showing superior performance. Findings indicate that a 1 % loss of municipal forest cover correlates with a 16.9 % increase in dengue risk. Temperature also significantly affects the vectors' ability to initiate and maintain outbreaks, highlighting the significant role of environmental factors. The research emphasizes the importance of multilevel modeling, finer temporal data resolution, and understanding deforestation causes to enhance the predictability and effectiveness of public health interventions. As dengue continues affecting global populations, particularly in tropical and subtropical regions, this study contributes insights, advocating for an integrated approach to health and environmental policy to mitigate the impact of vector-borne diseases.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"50 ","pages":"Article 100679"},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929729","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}

引用次数: 0

Spatial and spatio-temporal statistical implications for measuring structural racism: A review of three widely used residential segregation measures 衡量结构性种族主义的空间和时空统计影响：对三种广泛使用的住宅隔离测量方法的回顾

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-08-01 DOI: 10.1016/j.sste.2024.100678

Loni Philip Tabb, Ruby Bayliss, Yang Xu

{"title":"Spatial and spatio-temporal statistical implications for measuring structural racism: A review of three widely used residential segregation measures","authors":"Loni Philip Tabb, Ruby Bayliss, Yang Xu","doi":"10.1016/j.sste.2024.100678","DOIUrl":"10.1016/j.sste.2024.100678","url":null,"abstract":"<div><p>Social determinants of health are the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning and quality of life outcomes and risks – these social determinants of health often aid in explaining the racial and ethnic health inequities present in the United States (US). The root cause of these social determinants of health has been tied to structural racism, and residential segregation is one such domain of structural racism that allows for the operationalization of the geography of structural racism. This review focuses on three residential segregation measures that are often utilized to capture segregation as a function of race/ethnicity, income, and simultaneously race/ethnicity and income. Empirical findings related to the spatial and spatio-temporal heterogeneity of these residential segregation measures are presented. We also discuss some of the implications of utilizing these three residential segregation measures.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"50 ","pages":"Article 100678"},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000455/pdfft?md5=10f2a5768e6d0e4da5e6546a6e581ddd&pid=1-s2.0-S1877584524000455-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141839971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The effect of spatio-temporal sample imbalance in epidemiologic surveillance using opportunistic samples: An ecological study using real and simulated self-reported COVID-19 symptom data 使用机会性样本进行流行病学监测时，时空样本不平衡的影响：一项使用真实和模拟自我报告的 COVID-19 症状数据进行的生态研究

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-08-01 DOI: 10.1016/j.sste.2024.100676

Alejandro Rozo Posada , Christel Faes , Philippe Beutels , Koen Pepermans , Niel Hens , Pierre Van Damme , Thomas Neyens

{"title":"The effect of spatio-temporal sample imbalance in epidemiologic surveillance using opportunistic samples: An ecological study using real and simulated self-reported COVID-19 symptom data","authors":"Alejandro Rozo Posada , Christel Faes , Philippe Beutels , Koen Pepermans , Niel Hens , Pierre Van Damme , Thomas Neyens","doi":"10.1016/j.sste.2024.100676","DOIUrl":"10.1016/j.sste.2024.100676","url":null,"abstract":"<div><p>Open surveys complementing surveillance programs often yield opportunistically sampled data characterised by spatio-temporal imbalance. We set up our study to understand to what extent spatio-temporal statistical models using such data achieve in describing epidemiological trends. We used self-reported symptomatic COVID-19 data from two Belgian regions, Flanders and the Brussels-Capital Region. These data were collected in a large-scale open survey with spatio-temporally imbalanced participation rates. We compared incidence estimates of both self-reported symptoms and test-confirmed COVID-19 cases obtained through generalised linear mixed models correcting for spatio-temporal correlation. We additionally simulated symptom incidences under different sampling strategies to explore the impact of sample imbalance, sample size and disease incidence, on trend detection. Our study shows that spatio-temporal sample imbalance generally does not lead to bad model performances in spatio-temporal trend estimation and high-risk area detection. Except for low-incidence diseases, collecting large samples will often be more essential than ensuring spatio-temporally sample balance.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"50 ","pages":"Article 100676"},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848756","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}

引用次数: 0

Bayesian hierarchical modeling for bivariate multiscale spatial data with application to blood test monitoring 应用于血液检测监测的双变量多尺度空间数据贝叶斯分层模型

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-07-10 DOI: 10.1016/j.sste.2024.100661

Shijie Zhou, Jonathan R. Bradley

{"title":"Bayesian hierarchical modeling for bivariate multiscale spatial data with application to blood test monitoring","authors":"Shijie Zhou, Jonathan R. Bradley","doi":"10.1016/j.sste.2024.100661","DOIUrl":"10.1016/j.sste.2024.100661","url":null,"abstract":"<div><p>Public health spatial data are often recorded at different spatial scales (or geographic regions/divisions) and over different correlated variables. Motivated by data from the Dartmouth Atlas Project, we consider jointly analyzing average annual percentages of diabetic Medicare enrollees who have taken the hemoglobin A1c and blood lipid tests, observed at the hospital service area (HSA) and county levels, respectively. Capitalizing on bivariate relationships between these two scales is not immediate as counties are not nested within HSAs. It is well known that one can improve predictions by leveraging correlations across both variables and scales. There are very few methods available that simultaneously model multivariate and multiscale correlations. We propose three new hierarchical Bayesian models for bivariate multiscale spatial data, extending spatial random effects, multivariate conditional autoregressive (MCAR), and ordered hierarchical models through a multiscale spatial approach. We simulated data from each of the three models and compared the corresponding predictions, and found the computationally intensive multiscale MCAR model is more robust to model misspecification. In an analysis of 2015 Texas Dartmouth Atlas Project data, we produced finer resolution predictions (partitioning of HSAs and counties) than univariate analyses, determined that the novel multiscale MCAR and OH models were preferable via out-of-sample metrics, and determined the HSA with the highest within-HSA variability of hemoglobin A1c blood testing. Additionally, we compare the univariate multiscale models to the bivariate multiscale models and see clear improvements in prediction over univariate analyses.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"50 ","pages":"Article 100661"},"PeriodicalIF":2.1,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141715862","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}

引用次数: 0

Spatial dynamics of COVID-19 in São Paulo: A cellular automata and GIS approach 圣保罗 COVID-19 的空间动态：细胞自动机和地理信息系统方法

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-07-08 DOI: 10.1016/j.sste.2024.100674

W.L. Barreto, F.H. Pereira, Y. Perez, P.H.T. Schimit

{"title":"Spatial dynamics of COVID-19 in São Paulo: A cellular automata and GIS approach","authors":"W.L. Barreto, F.H. Pereira, Y. Perez, P.H.T. Schimit","doi":"10.1016/j.sste.2024.100674","DOIUrl":"10.1016/j.sste.2024.100674","url":null,"abstract":"<div><p>This study examines the spread of COVID-19 in São Paulo, Brazil, using a combination of cellular automata and geographic information systems to model the epidemic’s spatial dynamics. By integrating epidemiological models with georeferenced data and social indicators, we analyse how the virus propagates in a complex urban setting, characterized by significant social and economic disparities. The research highlights the role of various factors, including mobility patterns, neighbourhood configurations, and local inequalities, in the spatial spreading of COVID-19 throughout São Paulo. We simulate disease transmission across the city’s 96 districts, offering insights into the impact of network topology and district-specific variables on the spread of infections. The study seeks to fine-tune the model to extract epidemiological parameters for further use in a statistical analysis of social variables. Our findings underline the critical importance of spatial analysis in public health strategies and emphasize the necessity for targeted interventions in vulnerable communities. Additionally, the study explores the potential of mathematical modelling in understanding and mitigating the effects of pandemics in urban environments.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"50 ","pages":"Article 100674"},"PeriodicalIF":2.1,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141622660","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}

引用次数: 0

Exploring the feasibility of linking historical air pollution data to the Christchurch Health and Development study: A birth cohort study in Aotearoa, New Zealand 探索将历史空气污染数据与基督城健康与发展研究联系起来的可行性：新西兰奥特亚罗亚出生队列研究

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-06-29 DOI: 10.1016/j.sste.2024.100675

M. Hobbs , L. Marek , G.F.H. McLeod , L.J. Woodward , A. Sturman , S. Kingham , A. Ahuriri-Driscoll , M. Epton , P. Eggleton , B. Deng , M. Campbell , J. Boden

{"title":"Exploring the feasibility of linking historical air pollution data to the Christchurch Health and Development study: A birth cohort study in Aotearoa, New Zealand","authors":"M. Hobbs , L. Marek , G.F.H. McLeod , L.J. Woodward , A. Sturman , S. Kingham , A. Ahuriri-Driscoll , M. Epton , P. Eggleton , B. Deng , M. Campbell , J. Boden","doi":"10.1016/j.sste.2024.100675","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100675","url":null,"abstract":"<div><p>Spatial life course epidemiological approaches offer promise for prospectively examining the impacts of air pollution exposure on longer-term health outcomes, but existing research is limited. An essential aspect, often overlooked is the comprehensiveness of exposure data across the lifecourse. The primary objective was to meticulously reconstruct historical estimates of air pollution exposure to include prenatal exposure as well as annual exposure from birth to 10 years (1977–1987) for each cohort member. We linked these data from a birth cohort of 1,265 individuals, born in Aotearoa/New Zealand in mid-1977 and studied to age 40, to historical air pollution data to create estimates of exposure from birth to 10 years (1977–1987). Improvements in air quality over time were found. However, outcomes varied by demographic and socioeconomic factors. Future research should examine how inequitable air pollution exposure is related to health outcomes over the life course.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"50 ","pages":"Article 100675"},"PeriodicalIF":2.1,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S187758452400042X/pdfft?md5=ab1c0a776e40f02c0c0afb281945ef92&pid=1-s2.0-S187758452400042X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Individual-level models of disease transmission incorporating piecewise spatial risk functions 包含片断空间风险函数的疾病传播个体水平模型

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-06-13 DOI: 10.1016/j.sste.2024.100664

Chinmoy Roy Rahul , Rob Deardon

{"title":"Individual-level models of disease transmission incorporating piecewise spatial risk functions","authors":"Chinmoy Roy Rahul , Rob Deardon","doi":"10.1016/j.sste.2024.100664","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100664","url":null,"abstract":"<div><p>Modelling epidemics is crucial for understanding the emergence, transmission, impact and control of diseases. Spatial individual-level models (ILMs) that account for population heterogeneity are a useful tool, accounting for factors such as location, vaccination status and genetic information.</p><p>Parametric forms for spatial risk functions, or kernels, are often used, but rely on strong assumptions about underlying transmission mechanisms. Here, we propose a class of non-parametric spatial disease transmission model, fitted within a Bayesian Markov chain Monte Carlo (MCMC) framework, allowing for more flexible assumptions when estimating the effect on spatial distance and infection risk.</p><p>We focus upon two specific forms of non-parametric spatial infection kernel: piecewise constant and piecewise linear. Although these are relatively simple forms, we find them to produce results in line with, or superior to, parametric spatial ILMs. The performance of these models is examined using simulated data, including under circumstances of model misspecification, and then applied to data from the UK 2001 foot-and-mouth disease.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"50 ","pages":"Article 100664"},"PeriodicalIF":3.4,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141429621","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}

引用次数: 0