Bayesian spatio-temporal modelling of tuberculosis in Vietnam: Insights from a local-area analysis.

Abstract

Spatial analysis and disease mapping have the potential to enhance understanding of tuberculosis (TB) dynamics, whose spatial dynamics may be complicated by the mix of short and long-range transmission and long latency periods. TB notifications in Nam Dinh Province for individuals aged 15 and older from 2013 to 2022 were analyzed with a variety of spatio-temporal methods. The study commenced with an analysis of spatial autocorrelation to identify clustering patterns, followed by the evaluation of several candidate Bayesian spatio-temporal models. These models varied from simple assessments of spatial heterogeneity to more complex configurations incorporating covariates and interactions. The findings highlighted a peak in the TB notification rate in 2017, with 98 cases per 100,000 population, followed by a sharp decline in 2021. Significant spatial autocorrelation at the commune level was detected over most of the 10-year period. The Bayesian model that best balanced goodness-of-fit and complexity indicated that TB trends were associated with poverty: each percentage point increase in the proportion of poor households was associated with a 1.3% increase in TB notifications, emphasizing a significant socioeconomic factor in TB transmission dynamics. The integration of local socioeconomic data with spatio-temporal analysis could further enhance our understanding of TB epidemiology.