Spatial and Spatio-Temporal Epidemiology最新文献

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.

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.

Objective

To analyze the spatial patterns and factors associated with tuberculosis incidence in the municipalities of Paraná, Brazil.

Materials and methods

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.

Results

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).

Conclusions

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.

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.

Epidemic models serve as a useful analytical tool to study how a disease behaves in a given population. Individual-level models (ILMs) can incorporate individual-level covariate information including spatial information, accounting for heterogeneity within the population. However, the high-level data required to parameterize an ILM may often be available only for a sub-population of a larger population (e.g., a given county, province, or country). As a result, parameter estimates may be affected by edge effects caused by infection originating from outside the observed population. Here, we look at how such edge effects can bias parameter estimates for within the context of spatial ILMs, and suggest a method to improve model fitting in the presence of edge effects when some global measure of epidemic severity is available from the unobserved part of the population. We apply our models to simulated data, as well as data from the UK 2001 foot-and-mouth disease epidemic.

Spatial patterns are common in infectious disease epidemiology. Disease mapping is essential to infectious disease surveillance. Under a group testing protocol, biomaterial from multiple individuals is physically combined into a pooled specimen, which is then tested for infection. If the pool tests negative, all contributing individuals are generally assumed to be uninfected. If the pool tests positive, the individuals are usually retested to determine who is infected. When the prevalence of infection is low, group testing provides significant cost savings over traditional individual testing by reducing the number of tests required. However, the lack of statistical methods capable of producing maps from group testing data has limited the use of group testing in disease mapping. We develop a Bayesian methodology that can simultaneously map disease prevalence using group testing data and identify risk factors for infection. We illustrate its real-world utility using two datasets from vector-borne disease surveillance.

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.

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.

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.

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.