Spatial and Spatio-Temporal Epidemiology最新文献_第2页

Spatial pattern of all cause excess mortality in Swiss districts during the pandemic years 1890, 1918 and 2020 1890 年、1918 年和 2020 年大流行期间瑞士各地区各种原因超额死亡率的空间模式

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Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-11-01 DOI: 10.1016/j.sste.2024.100697

Katarina L Matthes , Joël Floris , Aziza Merzouki , Christoph Junker , Rolf Weitkunat , Frank Rühli , Olivia Keiser , Kaspar Staub

{"title":"Spatial pattern of all cause excess mortality in Swiss districts during the pandemic years 1890, 1918 and 2020","authors":"Katarina L Matthes , Joël Floris , Aziza Merzouki , Christoph Junker , Rolf Weitkunat , Frank Rühli , Olivia Keiser , Kaspar Staub","doi":"10.1016/j.sste.2024.100697","DOIUrl":"10.1016/j.sste.2024.100697","url":null,"abstract":"<div><div>Every pandemic is embedded in specific spatial and temporal context. However, spatial patterns have almost always only been considered in the context of one individual pandemic. Until now, there has been limited consideration of spatial similarities or differences between pandemics. In this study, Bayesian spatial models for disease mapping were used to estimate excess mortality for the pandemics of 1890, 1918 and 2020. A robust linear regression was used to assess the association between ecological determinants and excess mortality. Spatial variations of excess mortality across Switzerland were observed in each pandemic, but the spatial patterns differ between the pandemics. Different determinants contribute to excess mortality, and these factors vary between COVID-19 and the previous pandemics. Spatial excess mortality from COVID-19 is most likely due to cultural and SEP differences, whereas in historical pandemics, mobility, pre-existing tuberculosis or remote mountain living likely contributed to spatial excess mortality.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"51 ","pages":"Article 100697"},"PeriodicalIF":2.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572953","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

Multiple “spaces”: Using wildlife surveillance, climatic variables, and spatial statistics to identify and map a climatic niche for endemic plague in California, U.S.A. 多重 "空间"：利用野生动物监测、气候变量和空间统计来识别和绘制美国加利福尼亚州地方性鼠疫的气候生态位。

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-11-01 DOI: 10.1016/j.sste.2024.100696

Ian D. Buller , Gregory M. Hacker , Mark G. Novak , James R. Tucker , A. Townsend Peterson , Lance A. Waller

{"title":"Multiple “spaces”: Using wildlife surveillance, climatic variables, and spatial statistics to identify and map a climatic niche for endemic plague in California, U.S.A.","authors":"Ian D. Buller , Gregory M. Hacker , Mark G. Novak , James R. Tucker , A. Townsend Peterson , Lance A. Waller","doi":"10.1016/j.sste.2024.100696","DOIUrl":"10.1016/j.sste.2024.100696","url":null,"abstract":"<div><div>Regional climatic features in endemic areas can help inform surveillance for plague, a bacterial disease typically transmitted by fleas and maintained in mammals. We use 7,954 coyotes (<em>Canis latrans</em>), a sentinel species for plague, screened for plague exposure by the California Department of Public Health - Vector-Borne Disease Section (CDPH-VBDS; 1983-2015) to identify and map plague-suitable local climates within California to empirically inform ongoing sampling and surveillance plans. Using spatial point processes, we compare the distributions of seropositive and seronegative coyotes within the “space” defined by the first two principal components of PRISM Climate Group 30-year average climate variables (primarily temperature and moisture). The approach identifies both regions consistent with CDPH-VBDS mapping of plague-positive rodent and other carnivore samples over the same period and additional plague-suitable areas with climate profiles similar to seropositive samples elsewhere but little or no historical sampling, providing new data-informed insight for prioritizing limited surveillance resources.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"51 ","pages":"Article 100696"},"PeriodicalIF":2.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572954","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

Road traffic deaths caused by at-fault drivers and drinking-driving in China: A spatiotemporal analysis of the 2017–2020 period 中国过失司机和酒驾导致的道路交通死亡：2017-2020 年期间的时空分析

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Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-10-19 DOI: 10.1016/j.sste.2024.100695

Feng Li , Yu Cheng Hsu , Yunyu Xiao , Paul S.F. Yip , Feng Yang

引用次数: 0

Spatio-temporal modeling to identify factors associated with stunting in Indonesia using a Modified Generalized Lasso 利用改良广义套索建立时空模型，确定印度尼西亚发育迟缓的相关因素

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-09-27 DOI: 10.1016/j.sste.2024.100694

Septian Rahardiantoro , Alfidhia Rahman Nasa Juhanda , Anang Kurnia , Aswi Aswi , Bagus Sartono , Dian Handayani , Agus Mohamad Soleh , Yusma Yanti , Susanna Cramb

{"title":"Spatio-temporal modeling to identify factors associated with stunting in Indonesia using a Modified Generalized Lasso","authors":"Septian Rahardiantoro , Alfidhia Rahman Nasa Juhanda , Anang Kurnia , Aswi Aswi , Bagus Sartono , Dian Handayani , Agus Mohamad Soleh , Yusma Yanti , Susanna Cramb","doi":"10.1016/j.sste.2024.100694","DOIUrl":"10.1016/j.sste.2024.100694","url":null,"abstract":"<div><div>This study investigates the factors associated with stunting prevalence in Indonesia, utilizing a generalized lasso framework with modified penalty matrices to accommodate spatio-temporal data structures. Novel approaches are introduced to construct the penalty matrices, with particular focus on defining neighborhood structures. The proposed method is applied to data from 34 Indonesian provinces, covering the years 2019 to 2023. The primary outcome is stunting prevalence, modeled against nine predictor variables: poverty, exclusive breastfeeding, low birth weight (LBW), high school completion, access to proper sanitation, unmet health service needs, Gross Domestic Product (GDP), calorie consumption, and protein consumption. A total of nine spatio-temporal models were compared, including a modified generalized lasso with three distinct penalty matrices for each two tuning selection methods and a generalized ridge regression with three penalty matrices. Results indicate that the generalized lasso model with a 3-nearest neighbor adjacency matrix outperformed the alternatives. Temporal variations were observed in the effects of exclusive breastfeeding, LBW, high school completion, and unmet health service needs. Positive associations with stunting prevalence were identified for poverty, exclusive breastfeeding, LBW, and unmet health service needs, while negative associations were found for high school completion rates, access to proper sanitation, GDP, calorie intake, and protein consumption. The strongest associations were observed in parts of Sumatra, Sulawesi, and Jakarta. These findings suggest that government interventions aimed at improving education, healthcare access, and poverty reduction may help alleviate stunting in Indonesia, particularly in regions with the greatest need.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"51 ","pages":"Article 100694"},"PeriodicalIF":2.1,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427423","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

Multivariate skew-normal distribution for modelling skewed spatial data 为倾斜空间数据建模的多元倾斜正态分布

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-09-27 DOI: 10.1016/j.sste.2024.100692

Kassahun Abere Ayalew , Samuel Manda , Bo Cai

引用次数: 0

Spatial pattern of congenital toxoplasmosis incidence and its relationship with vulnerability and national health indicators in Brazil 巴西先天性弓形虫病发病率的空间模式及其与易感性和国家健康指标的关系

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-09-24 DOI: 10.1016/j.sste.2024.100693

Matheus Santos Melo , Lúcia Rolim Santana de Freitas , Francisco Edilson Ferreira Lima-Júnior , Alexander Vargas , Júlio dos Santos Pereira , Pedro de Alcântara Brito-Júnior , Renata Carla de Oliveira , Janaína de Sousa Menezes , Tarcilla Corrente Borghesan , Josivânia Arrais de Figueiredo , Rosalynd Vinicios da Rocha Moreira , Alda Maria da Cruz , Ana Ribeiro , Tainá Raiol , Shirley Verônica Melo Almeida Lima , Márcio Bezerra-Santos , Allan Dantas dos Santos , Caíque Jordan Nunes Ribeiro , Vitor Vieira Vasconcelos

{"title":"Spatial pattern of congenital toxoplasmosis incidence and its relationship with vulnerability and national health indicators in Brazil","authors":"Matheus Santos Melo , Lúcia Rolim Santana de Freitas , Francisco Edilson Ferreira Lima-Júnior , Alexander Vargas , Júlio dos Santos Pereira , Pedro de Alcântara Brito-Júnior , Renata Carla de Oliveira , Janaína de Sousa Menezes , Tarcilla Corrente Borghesan , Josivânia Arrais de Figueiredo , Rosalynd Vinicios da Rocha Moreira , Alda Maria da Cruz , Ana Ribeiro , Tainá Raiol , Shirley Verônica Melo Almeida Lima , Márcio Bezerra-Santos , Allan Dantas dos Santos , Caíque Jordan Nunes Ribeiro , Vitor Vieira Vasconcelos","doi":"10.1016/j.sste.2024.100693","DOIUrl":"10.1016/j.sste.2024.100693","url":null,"abstract":"<div><div>There is a gap in evidence regarding spatial clusters of the congenital toxoplasmosis (CT) and its association with social and health indicators in the Brazilian territory. Thus, we aimed herein to identify CT risk areas in Brazil and its association with social vulnerability and health indicators. An ecological and population-based study was conducted. The CT incidence coefficient was calculated and smoothed using the Local Empirical Bayesian method. Global regression models and local spatial regression model were applied. High-incidence clusters of the disease were identified throughout the country. Additionally, a positive association was observed between the incidence of congenital toxoplasmosis and the Social Vulnerability Index, coverage of community health agents, and the percentage of prenatal consultations. This association was stronger the further south in the country. Herewith, the implementation and strengthening of public strategies, with focus on priority intersectoral actions for prevention, early diagnosis, and prompt treatment, is urgently required for the effective control of CT in Brazilian municipalities.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"51 ","pages":"Article 100693"},"PeriodicalIF":2.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322811","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

Identifying points of interest (POIs) as sentinels for infectious disease surveillance: A COVID-19 study 确定兴趣点 (POI) 作为传染病监测的哨兵：COVID-19 研究

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Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-09-20 DOI: 10.1016/j.sste.2024.100691

Fangye Du , Liang Mao

{"title":"Identifying points of interest (POIs) as sentinels for infectious disease surveillance: A COVID-19 study","authors":"Fangye Du , Liang Mao","doi":"10.1016/j.sste.2024.100691","DOIUrl":"10.1016/j.sste.2024.100691","url":null,"abstract":"<div><div>Traditional surveillance relies on medical facilities, such as clinics and laboratories, as sentinels to monitor disease activities. Few studies have investigated the feasibility of using Point of Interests (POIs) as sentinels for disease surveillance. POIs, such as restaurants, retail stores, and churches, are places where people often interact with one another and thus play a critical role in transmission of infectious diseases like influenza and COVID-19. To fill this gap, we proposed a method to estimate people's potential crowdedness at POIs and explored its utility as an early indicator to signal local disease outbreaks. In a case study in Florida, USA, we utilized weekly foot traffic data at 0.3 million POIs to calculate their weekly crowdedness, and tested local correlations between the crowdedness of each POI and its surrounding COVID-19 incidences with different time lags. We identified 261 POIs as potential sentinels that could signal the risk one to three weeks ahead of disease outbreaks. Most of these sentinel POIs provided food/drink services, ambulatory healthcare and religious/civic services. They were characterized by a relatively large group of customers and a stable patronization over time. This research provides new insights into improving current disease surveillance systems by incorporating more diverse and widely distributed POIs.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"51 ","pages":"Article 100691"},"PeriodicalIF":2.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312688","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

Unveiling spatio-temporal mysteries: A quest to decode India's Dengue and Malaria trend (2003-2022) 揭开时空之谜：解读印度登革热和疟疾趋势（2003-2022 年）

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-09-11 DOI: 10.1016/j.sste.2024.100690

Bhaskar Mandal, Sharmistha Mondal

{"title":"Unveiling spatio-temporal mysteries: A quest to decode India's Dengue and Malaria trend (2003-2022)","authors":"Bhaskar Mandal, Sharmistha Mondal","doi":"10.1016/j.sste.2024.100690","DOIUrl":"10.1016/j.sste.2024.100690","url":null,"abstract":"<div><p>Dengue and malaria are two mosquito-borne diseases that are dangerous globally, especially in tropical and subtropical regions. In India, these two diseases pose severe health issues as they account for 74.37 % of the total vector-borne disease burden in the country. The present study examined the spatio-temporal patterns of prevalence of dengue and malaria across all states in India. Data related to epidemiological statistics were obtained from the Central Bureau of Health Intelligence (CBHI) and the National Vector Borne Disease Control Program (NVBDCP) for 2003–2017 and 2018–2022, respectively. In this study, we have utilized the Mann-Kendall test, Modified Mann-Kendall test, Sens's slope, Innovative trend analysis, and Percent Bias for trend analysis. Furthermore, a hotspot analysis was conducted to compare and examine the evolving patterns of these diseases over space and time. The Mann-Kendall test showed a significant increase in dengue cases throughout India, with Sen's slope showing the fastest growth in Punjab. West Bengal exhibited the most significant ITA slope increase. The PBIAS slope showed a gradual rise from the southern to the northern and north-eastern states. Mann-Kendall results indicated a statistically significant decline in malaria cases, dropping mostly in Odisha, followed by the northern, southern, and north-eastern states. Only Mizoram displayed an insignificant upward trend in malaria cases. Hotspot analysis revealed that dengue fever hotspots expanded in India's central, western, and northern regions, affecting 66.72 % of the country, whereas significant coldspots remain unchanged. Malaria hotspots covered 47.46 % of north-eastern, eastern coastal, and northern areas, while coldspots almost remained unchanged. This study provides valuable insights for health authorities to prioritize and identify the regions that need immediate intervention regarding these two mosquito-borne diseases.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"51 ","pages":"Article 100690"},"PeriodicalIF":2.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241568","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

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-09-04 DOI: 10.1016/j.sste.2024.100681

Helena Baptista , Jorge M. Mendes , Ying C. MacNab

{"title":"Similarity- and neighbourhood-based dynamic models for infection data: Uncovering the complexities of the COVID-19 infection risks","authors":"Helena Baptista , Jorge M. Mendes , Ying C. MacNab","doi":"10.1016/j.sste.2024.100681","DOIUrl":"10.1016/j.sste.2024.100681","url":null,"abstract":"<div><p>Understanding spatial and temporal risk dependencies and correlation is crucial when studying infectious diseases which spread out in consecutive waves. By analysing weekly COVID-19 case data collected from the disease’s first reported case on March 3, 2020, to April 22, 2021, in 278 municipalities in Mainland Portugal, we demonstrate that the complexity of infection risks varies based on the outbreak’s severity, suggesting that a single model definition is insufficient to explain the multifaceted underlying phenomena. This study employs a dynamic, conditionally specified Gaussian Markov random field model with a novel approach to characterise COVID-19 infection risk dependencies through the similarity of areal-level covariates within a Bayesian hierarchical model framework that accounts for each identifiable wave. The results indicate that the neighbourhood-based conditional autoregressive model, which is static and based on an adjacency-based neighbourhood matrix, do not necessarily captures the disease’s complex spatial–temporal nature. Furthermore, the best-fitting dynamic model may not necessarily be the best predicting model in certain situations, which can lead to inadequate resource allocation in epidemic situations. Accurate forecasting can help inform decisions regarding difficult-to-measure impacts, potentially saving lives. Implementing the proposed novel approach would have produced information that would have been overwhelmingly critical to the respective authorities in protecting those in more unfavourable economic or other conditions.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"51 ","pages":"Article 100681"},"PeriodicalIF":2.1,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000480/pdfft?md5=6ca8df5688d6b9bf17bdb73f6cdc70ac&pid=1-s2.0-S1877584524000480-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148926","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

An ecological study mapping socioeconomic inequalities in tuberculosis incidence in a southern state of Brazil 绘制巴西南部一个州结核病发病率社会经济不平等图谱的生态研究

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-08-22 DOI: 10.1016/j.sste.2024.100689

Lucas Vinícius de Lima , Gabriel Pavinati , Isadora Gabriella Silva Palmieri , Pedro Henrique Paiva Bernardo , Vitória Maytana Alves dos Santos , Melissa Ferrari Gomes , Juliana Taques Pessoa da Silveira , Francisco Beraldi de Magalhães , Nelly Lopes de Moraes Gil , Gabriela Tavares Magnabosco

{"title":"An ecological study mapping socioeconomic inequalities in tuberculosis incidence in a southern state of Brazil","authors":"Lucas Vinícius de Lima , Gabriel Pavinati , Isadora Gabriella Silva Palmieri , Pedro Henrique Paiva Bernardo , Vitória Maytana Alves dos Santos , Melissa Ferrari Gomes , Juliana Taques Pessoa da Silveira , Francisco Beraldi de Magalhães , Nelly Lopes de Moraes Gil , Gabriela Tavares Magnabosco","doi":"10.1016/j.sste.2024.100689","DOIUrl":"10.1016/j.sste.2024.100689","url":null,"abstract":"<div><h3>Objective</h3><p>To analyze the spatial patterns and factors associated with tuberculosis incidence in the municipalities of Paraná, Brazil.</p></div><div><h3>Materials and methods</h3><p>Ecological study examining new tuberculosis cases from 2018 to 2022 in Paraná’s 399 municipalities. Incidence coefficients, relative risk, and local indicator of spatial autocorrelation were estimated. Negative binomial models were applied to identify associated factors.</p></div><div><h3>Results</h3><p>High-risk areas were observed in the coastal/port, north, and northeast regions. The following factors positively influenced tuberculosis incidence: municipal development index (incidence rate ratio [IRR]: 1.07; 95 % confidence interval [95 % CI]: 1.01–1.14), hospitalizations due to inadequate environmental sanitation (IRR: 1.07; 95 % CI: 1.01–1.14), and Gini index (IRR: 1.09; 95 % CI: 1.02–1.16).</p></div><div><h3>Conclusions</h3><p>Paradoxically, in municipalities with elevated development indices yet marked by socioeconomic disparities—including deficiencies in sanitation—substantial tuberculosis clusters persist. This suggests that income inequality might play a role in perpetuating the incidence even in regions that are otherwise considered developed.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"51 ","pages":"Article 100689"},"PeriodicalIF":2.1,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142087561","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