Spatial and Spatio-Temporal Epidemiology最新文献_第4页

A Bayesian Interrupted Time Series framework for evaluating policy change on mental well-being: An application to England’s welfare reform 评估心理健康政策变化的贝叶斯中断时间序列框架：应用于英格兰的福利改革

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-06-11 DOI: 10.1016/j.sste.2024.100662

Connor Gascoigne , Annie Jeffery , Zejing Shao , Sara Geneletti , James B. Kirkbride , Gianluca Baio , Marta Blangiardo

{"title":"A Bayesian Interrupted Time Series framework for evaluating policy change on mental well-being: An application to England’s welfare reform","authors":"Connor Gascoigne , Annie Jeffery , Zejing Shao , Sara Geneletti , James B. Kirkbride , Gianluca Baio , Marta Blangiardo","doi":"10.1016/j.sste.2024.100662","DOIUrl":"10.1016/j.sste.2024.100662","url":null,"abstract":"<div><p>Factors contributing to social inequalities are associated with negative mental health outcomes and disparities in mental well-being. We propose a Bayesian hierarchical controlled interrupted time series to evaluate the impact of policies on population well-being whilst accounting for spatial and temporal patterns. Using data from the UKs Household Longitudinal Study, we apply this framework to evaluate the impact of the UKs welfare reform implemented in the 2010s on the mental health of the participants, measured using the GHQ-12 index. Our findings indicate that the reform led to a 2.36% (95% CrI: 0.57%–4.37%) increase in the national GHQ-12 index in the exposed group, after adjustment for the control group. Moreover, the geographical areas that experienced the largest increase in the GHQ-12 index are from more disadvantage backgrounds than affluent backgrounds.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"50 ","pages":"Article 100662"},"PeriodicalIF":2.1,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000297/pdfft?md5=b7f341be6b17bce90e7ae5dd046958c3&pid=1-s2.0-S1877584524000297-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141408121","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

Improving the spatial and temporal resolution of burden of disease measures with Bayesian models 利用贝叶斯模型提高疾病负担测量的时空分辨率

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-06-01 DOI: 10.1016/j.sste.2024.100663

James Hogg , Kerry Staples , Alisha Davis , Susanna Cramb , Candice Patterson , Laura Kirkland , Michelle Gourley , Jianguo Xiao , Wendy Sun

{"title":"Improving the spatial and temporal resolution of burden of disease measures with Bayesian models","authors":"James Hogg , Kerry Staples , Alisha Davis , Susanna Cramb , Candice Patterson , Laura Kirkland , Michelle Gourley , Jianguo Xiao , Wendy Sun","doi":"10.1016/j.sste.2024.100663","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100663","url":null,"abstract":"<div><p>This paper contributes to the field by addressing the critical issue of enhancing the spatial and temporal resolution of health data. Although Bayesian methods are frequently employed to address this challenge in various disciplines, the application of Bayesian spatio-temporal models to burden of disease (BOD) studies remains limited. Our novelty lies in the exploration of two existing Bayesian models that we show to be applicable to a wide range of BOD data, including mortality and prevalence, thereby providing evidence to support the adoption of Bayesian modeling in full BOD studies in the future. We illustrate the benefits of Bayesian modeling with an Australian case study involving asthma and coronary heart disease. Our results showcase the effectiveness of Bayesian approaches in increasing the number of small areas for which results are available and improving the reliability and stability of the results compared to using data directly from surveys or administrative sources.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100663"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000303/pdfft?md5=b3ba22d405646c7a9d07dd0ece1b27c6&pid=1-s2.0-S1877584524000303-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141250461","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

Geospatial patterns of excess mortality in Belgium: Insights from the first year of the COVID-19 pandemic 比利时超高死亡率的地理空间模式：COVID-19 大流行第一年的启示

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-05-20 DOI: 10.1016/j.sste.2024.100660

Yessika Adelwin Natalia , Geert Molenberghs , Christel Faes , Thomas Neyens

{"title":"Geospatial patterns of excess mortality in Belgium: Insights from the first year of the COVID-19 pandemic","authors":"Yessika Adelwin Natalia , Geert Molenberghs , Christel Faes , Thomas Neyens","doi":"10.1016/j.sste.2024.100660","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100660","url":null,"abstract":"<div><h3>Objectives:</h3><p>Belgium experienced multiple COVID-19 waves that hit various groups in the population, which changed the mortality pattern compared to periods before the pandemic. In this study, we investigated the geographical excess mortality trend in Belgium during the first year of the COVID-19 pandemic.</p></div><div><h3>Methods:</h3><p>We retrieved the number of deaths and population data in 2020 based on gender, age, and municipality of residence, and we made a comparison with the mortality data in 2017–2019 using a spatially discrete model.</p></div><div><h3>Results:</h3><p>Excess mortality was significantly associated with age, gender, and COVID-19 incidence, with larger effects in the second half of 2020. Most municipalities had higher risks of mortality with a number of exceptions in the northeastern part of Belgium. Some discrepancies in excess mortality were observed between the north and south regions.</p></div><div><h3>Conclusions:</h3><p>This study offers useful insight into excess mortality and will aid local and regional authorities in monitoring mortality trends.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100660"},"PeriodicalIF":3.4,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000273/pdfft?md5=7a33aa87415bc6024b01678a9da46fea&pid=1-s2.0-S1877584524000273-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091001","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

Bayesian hierarchical spatiotemporal models for prediction of (under)reporting rates and cases: COVID-19 infection among the older people in the United States during the 2020–2022 pandemic 预测（低）报告率和病例的贝叶斯分层时空模型：2020-2022 年大流行期间美国老年人感染 COVID-19 的情况

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-05-15 DOI: 10.1016/j.sste.2024.100658

Jingxin Lei, Ying MacNab

{"title":"Bayesian hierarchical spatiotemporal models for prediction of (under)reporting rates and cases: COVID-19 infection among the older people in the United States during the 2020–2022 pandemic","authors":"Jingxin Lei, Ying MacNab","doi":"10.1016/j.sste.2024.100658","DOIUrl":"10.1016/j.sste.2024.100658","url":null,"abstract":"<div><p>The gap between the reported and actual COVID-19 infection cases has been an issue of concern. Here, we present Bayesian hierarchical spatiotemporal disease mapping models for state-level predictions of COVID-19 infection risks and (under)reporting rates among people aged 65 and above during the first two years of the pandemic in the United States. With prior elicitation based on recent prevalence studies, the study suggests that the median state-level reporting rate of COVID-19 infection was 90% (interquartile range: [78%, 96%]). Our study uncovers spatiotemporal variations and dynamics in state-level infection risks and (under)reporting rates, suggesting time-varying associations between higher population density, higher percentage of minorities, and higher percentage of vaccination and increased risks of COVID-19 infection, as well as an association between more easily accessible tests and higher reporting rates. With sensitivity analyses, we highlight the impact and importance of incorporating covariates information and objective prior references for evaluating the issue of underreporting.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100658"},"PeriodicalIF":3.4,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S187758452400025X/pdfft?md5=40f3a0a9d691f686b96927ca9e548985&pid=1-s2.0-S187758452400025X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141040776","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

Assessing and attenuating the impact of selection bias on spatial cluster detection studies 评估和减轻选择偏差对空间聚类检测研究的影响

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-05-12 DOI: 10.1016/j.sste.2024.100659

Joseph Boyle , Mary H. Ward , James R. Cerhan , Nathaniel Rothman , David C. Wheeler

{"title":"Assessing and attenuating the impact of selection bias on spatial cluster detection studies","authors":"Joseph Boyle , Mary H. Ward , James R. Cerhan , Nathaniel Rothman , David C. Wheeler","doi":"10.1016/j.sste.2024.100659","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100659","url":null,"abstract":"<div><p>Spatial cluster analyses are commonly used in epidemiologic studies of case-control data to detect whether certain areas in a study region have an excess of disease risk. Case-control studies are susceptible to potential biases including selection bias, which can result from non-participation of eligible subjects in the study. However, there has been no systematic evaluation of the effects of non-participation on the findings of spatial cluster analyses. In this paper, we perform a simulation study assessing the effect of non-participation on spatial cluster analysis using the local spatial scan statistic under a variety of scenarios that vary the location and rates of study non-participation and the presence and intensity of a zone of elevated risk for disease for simulated case-control studies. We find that geographic areas of lower participation among controls than cases can greatly inflate false-positive rates for identification of artificial spatial clusters. Additionally, we find that even modest non-participation outside of a true zone of elevated risk can decrease spatial power to identify the true zone. We propose a spatial algorithm to correct for potentially spatially structured non-participation that compares the spatial distributions of the observed sample and underlying population. We demonstrate its ability to markedly decrease false positive rates in the absence of elevated risk and resist decreasing spatial sensitivity to detect true zones of elevated risk. We apply our method to a case-control study of non-Hodgkin lymphoma. Our findings suggest that greater attention should be paid to the potential effects of non-participation in spatial cluster studies.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100659"},"PeriodicalIF":3.4,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140948652","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

Mapping high probability area for the Bacillus anthracis occurrence in wildlife protected area, South Omo, Ethiopia 绘制埃塞俄比亚南奥莫野生动物保护区炭疽杆菌高发区地图

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-05-08 DOI: 10.1016/j.sste.2024.100657

Fekede Regassa Joka

引用次数: 0

Geographic accessibility to physiotherapy care in Aotearoa New Zealand 新西兰奥特亚罗瓦地区物理治疗护理的地理可达性

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-05-05 DOI: 10.1016/j.sste.2024.100656

Miranda Buhler , Tayyab Shah , Meredith Perry , Marc Tennant , Estie Kruger , Stephan Milosavljevic

{"title":"Geographic accessibility to physiotherapy care in Aotearoa New Zealand","authors":"Miranda Buhler , Tayyab Shah , Meredith Perry , Marc Tennant , Estie Kruger , Stephan Milosavljevic","doi":"10.1016/j.sste.2024.100656","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100656","url":null,"abstract":"<div><p>Disparities in care access for health conditions where physiotherapy can play a major role are abetting health inequities. Spatial analyses can contribute to illuminating inequities in health yet the geographic accessibility to physiotherapy care across New Zealand has not been examined. This population-based study evaluated the accessibility of the New Zealand physiotherapy workforce relative to the population at a local scale. The locations of 5,582 physiotherapists were geocoded and integrated with 2018 Census data to generate 'accessibility scores' for each Statistical Area 2 using the newer 3-step floating catchment area method. For examining the spatial distribution and mapping, accessibility scores were categorized into seven levels, centered around 0.5 SD above and below the mean. New Zealand has an above-average physiotherapy-to-population ratio compared with other OECD countries; however, this workforce is maldistributed. This study identified areas (and locations) where geographic accessibility to physiotherapy care is relatively low.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100656"},"PeriodicalIF":3.4,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000236/pdfft?md5=a6c3502d7a0a373be233e8f2bfe88175&pid=1-s2.0-S1877584524000236-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140947418","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

Model-based disease mapping using primary care registry data 利用初级保健登记数据绘制基于模型的疾病分布图

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-05-03 DOI: 10.1016/j.sste.2024.100654

Arne Janssens , Bert Vaes , Gijs Van Pottelbergh , Pieter J.K. Libin , Thomas Neyens

{"title":"Model-based disease mapping using primary care registry data","authors":"Arne Janssens , Bert Vaes , Gijs Van Pottelbergh , Pieter J.K. Libin , Thomas Neyens","doi":"10.1016/j.sste.2024.100654","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100654","url":null,"abstract":"<div><h3>Background:</h3><p>Spatial modeling of disease risk using primary care registry data is promising for public health surveillance. However, it remains unclear to which extent challenges such as spatially disproportionate sampling and practice-specific reporting variation affect statistical inference.</p></div><div><h3>Methods:</h3><p>Using lower respiratory tract infection data from the INTEGO registry, modeled with a logistic model incorporating patient characteristics, a spatially structured random effect at municipality level, and an unstructured random effect at practice level, we conducted a case and simulation study to assess the impact of these challenges on spatial trend estimation.</p></div><div><h3>Results:</h3><p>Even with spatial imbalance and practice-specific reporting variation, the model performed well. Performance improved with increasing spatial sample balance and decreasing practice-specific variation.</p></div><div><h3>Conclusion:</h3><p>Our findings indicate that, with correction for reporting efforts, primary care registries are valuable for spatial trend estimation. The diversity of patient locations within practice populations plays an important role.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100654"},"PeriodicalIF":3.4,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000212/pdfft?md5=2abc0e361764c74dc95a5694546fab63&pid=1-s2.0-S1877584524000212-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140947078","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

Analyzing the geographic influence of financial inclusion on illicit drug use in Nigeria 分析尼日利亚金融包容性对非法药物使用的地域影响

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-04-30 DOI: 10.1016/j.sste.2024.100655

Richard Adeleke , Ayodeji Emmanuel Iyanda

{"title":"Analyzing the geographic influence of financial inclusion on illicit drug use in Nigeria","authors":"Richard Adeleke , Ayodeji Emmanuel Iyanda","doi":"10.1016/j.sste.2024.100655","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100655","url":null,"abstract":"<div><p>Nigeria grapples with a formidable public health concern, as approximately 14 million individuals partake in illicit drug use (IDU). This predicament significantly impacts psychiatric disorders, suicides, disability, and mortality rates. Despite previous investigations into predictors and remedies, the role of financial inclusion (FI) remains inadequately explored. Leveraging existing literature on FI and population health, this study asserts that bolstering FI could be instrumental in mitigating IDU prevalence in Nigeria. We employ spatial analysis to scrutinize the influence of FI and other social factors on IDU, revealing a 14.4 % national prevalence with spatial variations ranging from 7 % in Jigawa state to 33 % in Lagos state. Significant IDU hotspots were identified in the southwest states, while cold spots were observed in the Federal Capital Territory and Nassarawa. Multivariate spatial analysis indicates that FI, income, unemployment, and the proportion of the young population are pivotal predictors of IDU nationwide, explaining approximately 67 % of the spatial variance. Given these findings, the study advocates heightened levels of FI and underscores the need for intensified government initiatives to prevent and address illicit drug use.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100655"},"PeriodicalIF":3.4,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140825893","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

A two–stage bayesian model for assessing the geography of racialized economic segregation and premature mortality across US counties 评估美国各县种族化经济隔离和过早死亡率的两阶段贝叶斯模型

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-04-25 DOI: 10.1016/j.sste.2024.100652

Yang Xu , Leslie A McClure , Harrison Quick , Jaquelyn L Jahn , Issa Zakeri , Irene Headen , Loni Philip Tabb

{"title":"A two–stage bayesian model for assessing the geography of racialized economic segregation and premature mortality across US counties","authors":"Yang Xu , Leslie A McClure , Harrison Quick , Jaquelyn L Jahn , Issa Zakeri , Irene Headen , Loni Philip Tabb","doi":"10.1016/j.sste.2024.100652","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100652","url":null,"abstract":"<div><p>Racialized economic segregation, a key metric that simultaneously accounts for spatial, social and income polarization in communities, has been linked to adverse health outcomes, including morbidity and mortality. Due to the spatial nature of this metric, the association between health outcomes and racialized economic segregation could also change with space. Most studies assessing the relationship between racialized economic segregation and health outcomes have always treated racialized economic segregation as a fixed effect and ignored the spatial nature of it. This paper proposes a two–stage Bayesian statistical framework that provides a broad, flexible approach to studying the spatially varying association between premature mortality and racialized economic segregation while accounting for neighborhood–level latent health factors across US counties. The two–stage framework reduces the dimensionality of spatially correlated data and highlights the importance of accounting for spatial autocorrelation in racialized economic segregation measures, in health equity focused settings.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100652"},"PeriodicalIF":3.4,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000194/pdfft?md5=f1a9e557d0e6fee157c99814b3f4bd6a&pid=1-s2.0-S1877584524000194-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140813628","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