Spatial and Spatio-Temporal Epidemiology最新文献

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Spatial modeling and risk assessment of chagas disease vector distribution in Espírito Santo, Brazil: A comprehensive approach for targeted control

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-02-01 DOI: 10.1016/j.sste.2025.100710

Stefanie Barbosa Potkul Soares , Gustavo Rocha Leite , Guilherme Sanches Corrêa-do-Nascimento , Karina Bertazo del Carro , Blima Fux

Chagas disease, a persistent and life-threatening infection caused by the protozoan Trypanosoma cruzi, remains a significant public health concern in Latin America. Despite the Brazilian State of Espírito Santo (ES) not being classified as a high-risk area, the presence of epidemiologically significant triatomines like Panstrongylus megistus suggests a latent risk of T. cruzi transmission. This study, employing spatial modeling, assesses the distribution of key triatomine species in ES and predicts areas at risk for Chagas disease transmission. Our models, constructed with Maxent, KUENM, and QGIS, identified high suitability for most species in ES's southeast and south regions, with P. diasi showing high suitability in the central-west. Notably, 13 autochthonous cases of vector-borne Chagas disease were reported between 2001 and 2023. The risk assessment highlighted significant risk areas corresponding to the locations of these cases, indicating that most regions in ES are at higher risk of P. megistus presence. These findings provide crucial insights for enhancing regional epidemiological surveillance and inform targeted vector control strategies, effectively addressing latent risks.

{"title":"Spatial modeling and risk assessment of chagas disease vector distribution in Espírito Santo, Brazil: A comprehensive approach for targeted control","authors":"Stefanie Barbosa Potkul Soares , Gustavo Rocha Leite , Guilherme Sanches Corrêa-do-Nascimento , Karina Bertazo del Carro , Blima Fux","doi":"10.1016/j.sste.2025.100710","DOIUrl":"10.1016/j.sste.2025.100710","url":null,"abstract":"<div><div>Chagas disease, a persistent and life-threatening infection caused by the protozoan <em>Trypanosoma cruzi</em>, remains a significant public health concern in Latin America. Despite the Brazilian State of Espírito Santo (ES) not being classified as a high-risk area, the presence of epidemiologically significant triatomines like <em>Panstrongylus megistus</em> suggests a latent risk of <em>T. cruzi</em> transmission. This study, employing spatial modeling, assesses the distribution of key triatomine species in ES and predicts areas at risk for Chagas disease transmission. Our models, constructed with Maxent, KUENM, and QGIS, identified high suitability for most species in ES's southeast and south regions, with <em>P. diasi</em> showing high suitability in the central-west. Notably, 13 autochthonous cases of vector-borne Chagas disease were reported between 2001 and 2023. The risk assessment highlighted significant risk areas corresponding to the locations of these cases, indicating that most regions in ES are at higher risk of <em>P. megistus</em> presence. These findings provide crucial insights for enhancing regional epidemiological surveillance and inform targeted vector control strategies, effectively addressing latent risks.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"52 ","pages":"Article 100710"},"PeriodicalIF":2.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136169","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 fast approach for analyzing spatio-temporal patterns in ischemic heart disease mortality across US counties (1999–2021)

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-02-01 DOI: 10.1016/j.sste.2024.100700

A. Urdangarin , T. Goicoa , P. Congdon , M.D. Ugarte

Ischemic heart disease (IHD) remains the primary cause of mortality in the US. This study focuses on using spatio-temporal disease mapping models to explore the temporal trends of IHD at the county level from 1999 to 2021. To manage the computational burden arising from the high-dimensional data, we employ scalable Bayesian models using a “divide and conquer” strategy. This approach allows for fast model fitting and serves as an efficient procedure for screening spatio-temporal patterns. Additionally, we analyze trends in four regional subdivisions, West, Midwest, South and Northeast, and in urban and rural areas. The dataset on IHD contains missing data, and we propose a procedure to impute the omitted information. The results show a slowdown in the decrease of IHD mortality in the US after 2014 with a slight increase noted after 2019. However, differences exists among the counties, the four big geographical regions, and rural and urban areas.

引用次数: 0

On the lagged non-linear association between air pollution and COVID-19 cases in Belgium

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-02-01 DOI: 10.1016/j.sste.2024.100709

Sara Rutten , Marina Espinasse , Elisa Duarte , Thomas Neyens , Christel Faes

Exposure to air pollution has been proposed as a determinant of COVID-19 dynamics. While the connection between air pollution and COVID-19 has been established for several countries worldwide, few such analyses exist in Belgium. Therefore, we examine this potential association in Belgium, using COVID-19 cases of all 581 municipalities between September 2020 and January 2022. We employ a Bayesian spatio-temporal negative binomial model, allowing for potential non-linear and lagged effects of pollution. Comparing different single-pollutant models, we find that the model providing the best fit to the data contains black carbon. At the median pollution level, a cumulative risk of

1.66 (1.57, 1.74)

over 8 weeks is found for this pollutant, compared to the 5% pollution quantile. In addition, the study reveals a remarkable similarity in COVID-19 incidence between adjacent municipalities in Belgium.

Our findings suggest paying careful attention to highly air polluted areas when preparing for future pandemics of respiratory diseases.

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引用次数: 0

Geospatial distribution of the adoption of dipeptidyl-peptidase-4 inhibitors for type 2 diabetes among Medicare beneficiaries

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-02-01 DOI: 10.1016/j.sste.2025.100711

Jack Cordes , Robert J. Glynn , Alexander M. Walker , Sebastian S. Schneeweiss

Objective

To characterize the geospatial distribution of the adoption of dipeptidyl-peptidase-4 inhibitor (DPP-4i) antidiabetics versus second generation sulfonylureas (SU).

Methods

Using Medicare claims data 2012–2017, two cohorts were built with new-users of either sitagliptin or saxagliptin each versus active comparator SU. For each ZIP Code tabulation area (ZCTA), the proportion DPP-4i prescribing was used in a local indicator of spatial association hotspot analysis. Multilevel logistic models were used to quantify the variation in medication use at the individual, ZCTA, state, and region levels.

Results

DPP-4i utilization proportion was low (sitagliptin median = 0.22; interquartile range 0.15 to 0.33; saxagliptin median = 0.025; 0.00 to 0.069). Clustering was observed for sitagliptin (Moran's I = 0.32) and saxagliptin (Moran's I = 0.20). States and ZCTAs accounted for 8.1 % and 13.3 % of variation in sitagliptin and saxagliptin prescribing, respectively.

Conclusions

Variation across ZCTAs suggests neighborhood factors may be important determinants of prescribing.

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引用次数: 0

Estimating subnational under-five mortality rates using a spatio-temporal Age-Period-Cohort model

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-02-01 DOI: 10.1016/j.sste.2024.100708

Connor Gascoigne , Theresa Smith , John Paige , Jon Wakefield

Subnational estimates of under-five mortality rates (U5MRs) are a vital statistic for the United Nations to reduce mortality inequalities between high-income and Low-and-Middle Income Countries (LMICs). Current methods of modelling U5MR in LMICs smooth across trends in age and year of death, but not birth-cohort, to reduce uncertainty in estimates caused by data-sparsity. Using survey data from Kenya, we innovatively apply an Age-Period-Cohort model which accounts for spatial trends and the complex survey design of the data to estimate subnational U5MRs in Kenya. After validating our results against current methods, the inclusion of cohort can provide new insights into U5MRs. We ensure our method is flexible and can be applied to other LMICs.

引用次数: 0

Association between urban green space and transmission of COVID-19 in Oslo, Norway: A Bayesian SIR modeling approach 挪威奥斯陆城市绿地与COVID-19传播的关系：贝叶斯SIR建模方法

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2024-11-26 DOI: 10.1016/j.sste.2024.100699

Halvor Kjellesvig , Suleman Atique , Lars Böcker , Geir Aamodt

Background:

Access to green spaces can provide opportunities for physical activities and social interactions in urban areas during times with strict social distancing. In particular COVID-19 transmission is reduced in ventilated areas. During several waves of the pandemic, this study explores the association between access to urban green spaces and COVID-19 transmission at the district level in Norway’s capital, Oslo.

Methods:

We used daily numbers of confirmed laboratory PCR tests on district levels reported from the second to the fifth wave of the COVID-19 pandemic, from October 15, 2020 to April 15, 2022 in Oslo. We included the population’s access to urban green spaces using two objective measurements: percentage of green area (%Ga) and vegetation cover (NDVI) using 300 and 1000 m buffers. The socio-demographic variables percentage of low-income population, average life expectancy and population density were also included. A Bayesian Susceptible–Infected–Removed (SIR) model was used to take advantage of the daily updated data on COVID-19 incidence and account for spatial and temporal dependencies in the statistical analysis.

Results:

We found that low income as well as population density were significantly associated with incidence of COVID-19, but for the second and third waves only. For the second wave, a one percent increase in the proportion with low income at district level increased the risk of COVID-19 by 7 % (95 % CI: 3 % - 11 %) We did not find associations between access to green space and incidence rate for any of the buffer sizes. The second and third waves were more governed by socio-demographic factors than the fourth and fifth wave.

Conclusions:

Incidence rate of COVID-19 was not associated with access to green space, but to the socio-demographic variables; income, population density, and life expectancy. Access to green space is equally distributed among districts in Oslo which may explain our findings.

背景：在严格保持社交距离的时期，城市地区的绿地可以为体育活动和社会互动提供机会。特别是在通风区域，COVID-19的传播减少了。在几波大流行期间，本研究探讨了挪威首都奥斯陆地区城市绿地使用与COVID-19传播之间的关系。方法：利用2020年10月15日至2022年4月15日在奥斯陆报告的第二波至第五波COVID-19大流行期间每天的实验室PCR确认检测数。我们使用两种客观的测量方法：使用300米和1000米缓冲区的绿地面积百分比（%Ga）和植被覆盖（NDVI）来纳入人口对城市绿地的访问。社会人口变量包括低收入人口百分比、平均预期寿命和人口密度。采用贝叶斯易感-感染-去除（SIR）模型，利用每日更新的COVID-19发病率数据，并在统计分析中考虑时空依赖性。结果：我们发现低收入和人口密度与COVID-19发病率显著相关，但仅适用于第二和第三波。在第二波浪潮中，地区一级低收入人口比例每增加1%，COVID-19的风险就会增加7% （95% CI: 3% - 11%）。我们没有发现任何缓冲面积的绿地使用与发病率之间存在关联。第二次和第三次浪潮比第四次和第五次浪潮更受社会人口因素的支配。结论：2019冠状病毒病发病率与绿地可及性无关，而与社会人口学变量相关；收入，人口密度和预期寿命。在奥斯陆，绿地在各区之间分布均匀，这可以解释我们的研究结果。

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引用次数: 0

Employment industry and opioid overdose risk: A pre- and post-COVID-19 comparison in Kentucky and Massachusetts 2018–2021 就业行业和阿片类药物过量风险：2018-2021年肯塔基州和马萨诸塞州covid -19前后的比较

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2024-11-23 DOI: 10.1016/j.sste.2024.100701

Sumeeta Srinivasan , Shikhar Shrestha , Daniel R. Harris , Olivia Lewis , Peter Rock , Anita Silwal , Jennifer Pustz , Sehun Oh , Gia Barboza-Salerno , Thomas J. Stopka

The COVID-19 pandemic has exacerbated the risk of opioid-related harm, and previous studies suggest a connection between opioid overdose risk and industry of employment. We used descriptive and spatial-statistical tests with opioid overdose data from the vital records offices of Kentucky and Massachusetts to examine opioid overdose rates by employment industry before and after COVID-19 emergency declarations. Both states had consistently high rates of opioid-related overdose mortality for individuals employed in the construction and arts, recreation, food services, and accommodation service industries. Additionally in both states, census tracts with a high percentage of renters and non-Hispanic Black residents were more likely to be located in fatal opioid-related overdose hotspots following the initial surge of COVID-19 cases. In Kentucky, census tracts with higher percentages of employment in the transportation and other services were more likely to be located in an overdose hotspot before and after the COVID-19 emergency declaration, while in Massachusetts the same was true for census tracts with high employment in manufacturing, agriculture, forest, and fisheries, and hunting.

2019冠状病毒病大流行加剧了阿片类药物相关伤害的风险，之前的研究表明，阿片类药物过量风险与就业行业之间存在联系。我们对肯塔基州和马萨诸塞州生命记录办公室的阿片类药物过量数据进行了描述性和空间统计检验，以检查COVID-19紧急声明前后就业行业的阿片类药物过量率。这两个州在建筑和艺术、娱乐、食品服务和住宿服务行业就业的个人中，与阿片类药物相关的过量死亡率一直很高。此外，在这两个州，在COVID-19病例最初激增之后，租房者和非西班牙裔黑人居民比例较高的人口普查区更有可能位于致命的阿片类药物过量热点地区。在肯塔基州，交通和其他服务业就业比例较高的人口普查区更有可能位于COVID-19紧急声明前后的过量热点地区，而在马萨诸塞州，制造业、农业、森林、渔业和狩猎等就业比例较高的人口普查区也是如此。

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引用次数: 0

Investigating interaction effects of social risk factors and exposure to air pollution on pediatric lymphoma cancer in Georgia, United States 调查社会风险因素和暴露于空气污染对美国佐治亚州小儿淋巴瘤癌症的交互影响

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2024-11-01 DOI: 10.1016/j.sste.2024.100698

Theresa Unseld , Katja Ickstadt , Kevin Ward , Jeffrey M. Switchenko , Howard H. Chang , Anke Hüls

Childhood cancer constitutes a major cause of death in children. In a recent study of the Georgia Cancer Registry, joint exposures to environmental and social/behavioral stressors were associated with spatial clustering of lymphomas and reticuloendothelial neoplasms among the 159 counties in Georgia, USA. The present study aims to further investigate these associations on a more granular level. Bayesian Poisson and zero-inflated Poisson regression models with spatial and non-spatial variance structures were utilized to investigate whether county-specific cancer patterns may be explained by single or combinations of social stressors and ambient air pollution while adjusting for confounding and accounting for overfitting using differences in expected log predictive densities. While we did not find associations between lymphoma rates and social variables, air pollution, or their interactions, our proposed analysis workflow can serve as a blueprint for future studies investigating dependencies in regression models that feature combinations of unobserved and observed dependency structures.

儿童癌症是儿童死亡的主要原因。在最近对佐治亚癌症登记处进行的一项研究中，在美国佐治亚州的 159 个县中，环境和社会/行为压力因素的共同暴露与淋巴瘤和网状内皮肿瘤的空间聚集有关。本研究旨在从更细的层面进一步研究这些关联。我们利用具有空间和非空间方差结构的贝叶斯泊松回归模型和零膨胀泊松回归模型来研究县域特定癌症模式是否可由社会压力因素和环境空气污染的单一或组合来解释，同时利用预期对数预测密度的差异来调整混杂因素并考虑过度拟合。虽然我们没有发现淋巴瘤发病率与社会变量、空气污染或它们之间的相互作用有关联，但我们提出的分析工作流程可作为未来研究的蓝图，用于调查回归模型中的依赖关系，这些回归模型具有未观察到的和观察到的依赖结构组合。

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引用次数: 0

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

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

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

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.

每一种大流行病都有其特定的时空背景。然而，空间模式几乎总是只在单个大流行的背景下被考虑。到目前为止，对不同大流行之间空间相似性或差异性的考虑还很有限。本研究采用贝叶斯疾病绘图空间模型来估算 1890 年、1918 年和 2020 年大流行的超额死亡率。采用稳健线性回归评估生态决定因素与超额死亡率之间的关联。在每一次大流行中，都观察到瑞士各地超额死亡率的空间变化，但不同大流行的空间模式有所不同。不同的决定因素导致了超额死亡率，而这些因素在 COVID-19 和之前的大流行中各不相同。COVID-19 造成的空间死亡率过高很可能是由于文化和公共教育部的差异造成的，而在以往的大流行中，流动性、原有肺结核或偏远山区生活很可能是造成空间死亡率过高的原因。

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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 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

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

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 (Canis latrans), 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.

鼠疫是一种通常由跳蚤传播并在哺乳动物体内存留的细菌性疾病，鼠疫流行地区的区域气候特征有助于为鼠疫监测提供信息。我们利用加利福尼亚州公共卫生部病媒传染病科（CDPH-VBDS，1983-2015 年）筛查出的 7954 只郊狼（Canis latrans）（鼠疫的哨兵物种）来识别和绘制加利福尼亚州适合鼠疫的当地气候，从而为正在进行的采样和监测计划提供经验信息。利用空间点过程，我们比较了血清阳性和血清阴性郊狼在 PRISM 气候组 30 年平均气候变量（主要是温度和湿度）的前两个主成分所定义的 "空间 "内的分布情况。这种方法既能确定与同期鼠疫阳性啮齿动物和其他食肉动物样本的 CDPH-VBDS 图谱一致的区域，也能确定与其他地方血清阳性样本相似但历史采样很少或没有采样的其他鼠疫适宜区，从而为有限监测资源的优先排序提供新的数据信息。

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引用次数: 0

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