Spatial and Spatio-Temporal Epidemiology最新文献

{"title":"Spatiotemporal Bayesian modeling of the risk of congenital syphilis in São Paulo, SP, Brazil","authors":"Renato Ferreira da Cruz ,&nbsp;Joelma Alexandra Ruberti ,&nbsp;Thiago Santos Mota ,&nbsp;Liciana Vaz de Arruda Silveira ,&nbsp;Francisco Chiaravalloti-Neto","doi":"10.1016/j.sste.2024.100651","DOIUrl":"10.1016/j.sste.2024.100651","url":null,"abstract":"<div><p>The aim of this study is to analyze the spatiotemporal risk of congenital syphilis (CS) in high-prevalence areas in the city of São Paulo, SP, Brazil, and to evaluate its relationship with socioeconomic, demographic, and environmental variables. An ecological study was conducted based on secondary CS data with spatiotemporal components collected from 310 areas between 2010 and 2016. The data were modeled in a Bayesian context using the integrated nested Laplace approximation (INLA) method. Risk maps showed an increasing CS trend over time and highlighted the areas that presented the highest and lowest risk in each year. The model showed that the factors positively associated with a higher risk of CS were the Gini index and the proportion of women aged 18–24 years without education or with incomplete primary education, while the factors negatively associated were the proportion of women of childbearing age and the mean per capita income.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100651"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140756648","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":"Assessing and attenuating the impact of selection bias on spatial cluster detection studies","authors":"Joseph Boyle ,&nbsp;Mary H. Ward ,&nbsp;James R. Cerhan ,&nbsp;Nathaniel Rothman ,&nbsp;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":"2024-06-01","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}

{"title":"Anaemia prevalence and socio-demographic factors among women of reproductive age (WRA): A geospatial analysis of empowered action group (EAG) states in India","authors":"Manabindra Barman","doi":"10.1016/j.sste.2024.100644","DOIUrl":"10.1016/j.sste.2024.100644","url":null,"abstract":"<div><p>Anaemia remains a major nutritional-related health concern for women under reproductive age (WRA) in developing nations like India as well as the Indian EAG states. According to NFHS round-5, EAG states constitute 57% of WRA having any form of anaemia, higher than many other states of India and other developed and developing nations. This study aimed to assess the frequency of anaemia among the WRA in India's eight EAG states. Also, it attempts to analyse the causes associated with anaemia by the women's background characteristics with spatial correlation with its co-variates across 291 districts of the EAG states. One of the most current Demographic and Health Survey's (DHS) cross-sectional data is the NFHS-5th (2019–21) round taken, conducted by the IIPS under the administration of MoHFW, India. This study only included 315,069 women under reproductive age (WRA). The variables related to anaemia among women's (WRA) background socio-demographic characteristics were assessed using bivariate statistics and multinominal logistic regression analysis to comprehend the spatial correlation between women and their determinant factors. Among the EAG states, the overall prevalence of anaemia was 57%, varying from 42.6% in Uttarakhand to 65.3% in Jharkhand. Multinominal logistic regression analyses reveal that the chances of anaemia are remarkably more prevalent in younger women (15–19 years of age), women living in rural areas, no educated and primary level educated women, women belonging to the middle to poorest wealth quintile, women no longer living together, women of the Christian religion, women who are not exposed to reading newspapers, underweight BMI women, and scheduled tribe women. Mainly, the prevalence is observed in the North-eastern and southeastern states of Bihar, Jharkhand, Odisha, Chhattisgarh, some parts of Madhya Pradesh, Uttar Pradesh, and Rajasthan, which is shown by the hotspot map. According to the findings of this study, numerous factors like family, socioeconomic, educational, awareness, and individual characteristics such as caste and domicile all lead to a risk of anaemia. The WRA suffers from anaemia as a result of their socioeconomic background and awareness, which leads to a lack of nourishment, and they seek nutrient deficiencies. To overcome this anaemia, multiple discipline policies and initiatives need to be taken targeting women's wellness and nutritional status by increasing women's education and socioeconomic status.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100644"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139883098","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":"Modelling the spatial risk pattern of dementia in Denmark using residential location data: A registry-based national cohort","authors":"Prince M. Amegbor ,&nbsp;Clive E. Sabel ,&nbsp;Laust H. Mortensen ,&nbsp;Amar J. Mehta","doi":"10.1016/j.sste.2024.100643","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100643","url":null,"abstract":"<div><p>Dementia is a major global public health concern that is increasingly leading to morbidity and mortality among older adults. While studies have focused on the risk factors and care provision, there is currently limited knowledge about the spatial risk pattern of the disease. In this study, we employ Bayesian spatial modelling with a stochastic partial differential equation (SPDE) approach to model the spatial risk using complete residential history data from the Danish population and health registers. The study cohort consisted of 1.6 million people aged 65 years and above from 2005 to 2018. The results of the spatial risk map indicate high-risk areas in Copenhagen, southern Jutland and Funen. Individual socioeconomic factors and population density reduce the intensity of high-risk patterns across Denmark. The findings of this study call for the critical examination of the contribution of place of residence in the susceptibility of the global ageing population to dementia.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100643"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139737907","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":"Mapping high probability area for the Bacillus anthracis occurrence in wildlife protected area, South Omo, Ethiopia","authors":"Fekede Regassa Joka","doi":"10.1016/j.sste.2024.100657","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100657","url":null,"abstract":"<div><p>Anthrax is a zoonotic disease caused by a spore-forming gram-positive bacterium, Bacillus anthracis. Increased anthropogenic factors inside wildlife-protected areas may worsen the spillover of the disease at the interface. Consequently, environmental suitability prediction for B. anthracis spore survival to locate a high-risk area is urgent. Here, we identified a potentially suitable habitat and a high-risk area for appropriate control measures. Our result revealed that a relatively largest segment of Omo National Park, about 23.7% (1,218 square kilometers) of the total area; 36.6% (711 square kilometers) of Mago National Park, and 29.4% (489 square kilometers) of Tama wildlife Reserve predicted as a high-risk area for the anthrax occurrence in the current situation. Therefore, the findings of this study provide the priority area to focus on and allocate resources for effective surveillance, prevention, and control of anthrax before it causes devastating effects on wildlife.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100657"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140906263","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":"The complex link between socioeconomic deprivation and COVID-19. Evidence from small areas of Catalonia","authors":"Enrique López-Bazo","doi":"10.1016/j.sste.2024.100648","DOIUrl":"10.1016/j.sste.2024.100648","url":null,"abstract":"<div><p>This ecological study assesses the association between the incidence rate of COVID-19 confirmed cases and socioeconomic deprivation in the Catalan small areas for the first six waves of the pandemic. The association is estimated using Poisson regressions and, in contrast to previous studies, considering that the relationship is not linear but rather depends on the degree of deprivation. The results show that the association between deprivation and incidence varied between waves, not only in intensity but also in its sign. Although it was insignificant in the first, third and fourth waves, the association was positive and significant in the second, becoming significantly negative in the fifth and sixth waves. Interestingly, the evidence suggests that the link between both magnitudes was not homogeneous throughout the distribution of deprivation, the pattern also varying between waves. The results are discussed in view of the role of non-pharmacological interventions and vaccination, as well as potential biases (for example that associated with differences between population groups in the propensity to be tested in each wave).</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100648"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000157/pdfft?md5=009e9437b68e204ea4e4ef2c19b9d541&pid=1-s2.0-S1877584524000157-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140197141","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}

{"title":"A two–stage bayesian model for assessing the geography of racialized economic segregation and premature mortality across US counties","authors":"Yang Xu ,&nbsp;Leslie A McClure ,&nbsp;Harrison Quick ,&nbsp;Jaquelyn L Jahn ,&nbsp;Issa Zakeri ,&nbsp;Irene Headen ,&nbsp;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":"2024-06-01","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}

{"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,&nbsp;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":"2024-06-01","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}

{"title":"Predicting COVID-19 hospitalizations: The importance of healthcare hotlines, test positivity rates and vaccination coverage","authors":"Vera van Zoest ,&nbsp;Karl Lindberg ,&nbsp;Georgios Varotsis ,&nbsp;Frank Badu Osei ,&nbsp;Tove Fall","doi":"10.1016/j.sste.2024.100636","DOIUrl":"10.1016/j.sste.2024.100636","url":null,"abstract":"<div><p>In this study, we developed a negative binomial regression model for one-week ahead spatio-temporal predictions of the number of COVID-19 hospitalizations in Uppsala County, Sweden. Our model utilized weekly aggregated data on testing, vaccination, and calls to the national healthcare hotline. Variable importance analysis revealed that calls to the national healthcare hotline were the most important contributor to prediction performance when predicting COVID-19 hospitalizations. Our results support the importance of early testing, systematic registration of test results, and the value of healthcare hotline data in predicting hospitalizations. The proposed models may be applied to studies modeling hospitalizations of other viral respiratory infections in space and time assuming count data are overdispersed. Our suggested variable importance analysis enables the calculation of the effects on the predictive performance of each covariate. This can inform decisions about which types of data should be prioritized, thereby facilitating the allocation of healthcare resources.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"48 ","pages":"Article 100636"},"PeriodicalIF":3.4,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000030/pdfft?md5=e41ddfc5e71a08c21d18542145e8cd5c&pid=1-s2.0-S1877584524000030-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139496983","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}

{"title":"Psychosis prevalence in London neighbourhoods; A case study in spatial confounding","authors":"Peter Congdon","doi":"10.1016/j.sste.2023.100631","DOIUrl":"10.1016/j.sste.2023.100631","url":null,"abstract":"<div><p>Analysis of impacts of neighbourhood risk factors on mental health outcomes frequently adopts a disease mapping approach, with unknown neighbourhood influences summarised by random effects. However, such effects may show confounding with observed predictors, especially when such predictors have a clear spatial pattern. Here, the standard disease mapping model is compared to methods which account and adjust for spatial confounding in an analysis of psychosis prevalence in London neighbourhoods. Established area risk factors such as area deprivation, non-white ethnicity, greenspace access and social fragmentation are considered as influences on psychosis. The results show evidence of spatial confounding in the standard disease mapping model. Impacts expected on substantive grounds and available evidence are either nullified or reversed in direction. It is argued that the potential for spatial confounding to affect inferences about geographic disease patterns and risk factors should be routinely considered in ecological studies of health based on disease mapping.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"48 ","pages":"Article 100631"},"PeriodicalIF":3.4,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584523000680/pdfft?md5=dda0e980c567d1c109bc5b42f55f59b4&pid=1-s2.0-S1877584523000680-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138683407","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}