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.