Bayesian hierarchical spatiotemporal modelling of tuberculosis—Human immunodeficiency virus co-infection in Ethiopia

Understanding the epidemiological patterns of tuberculosis-human immunodeficiency virus (TB-HIV) co-infection over space and time is crucial because it assists in identifying areas with high risks that need special control strategies. This article aimed to determine districts in Ethiopia that are most vulnerable to TB-HIV co-infection by examining the spatiotemporal patterns of the co-infection across four years, from 2015 to 2018. The study’s data came from Ethiopia’s Federal Ministry of Health. The data was analysed by applying the Bayesian hierarchical spatiotemporal modelling. We considered four models with different space–time interaction structures via the Integrated Nested Laplace Approximation (INLA) in the R-INLA package. In addition, we have applied the Deviance Information Criterion to select the most suitable model. The mean raw annual TB-HIV relative risk (RR) continuously decreased from 2015 to 2018, and the raw RRs of co-infection varied over districts and years. The spatiotemporal model, which allows for space–time interaction with independent spatial random effect and dependent temporal random effect, was the preferred model for describing the variations in TB-HIV co-infection across different districts over time. The prior variance for the spatial structured random effect had a smaller precision mode than the spatial unstructured random effect. This difference reveals that the former accounted for more spatial autocorrelation than the latter, indicating an information-borrowing effect amongst districts. Furthermore, the findings exhibit that the relative risk of TB-HIV co-infection had significant spatiotemporal variation and clustering. Through this research, further information was obtained regarding the temporal evolution of the geographical spread of TB and HIV co-infection at the district level in the country. It also made it possible to determine districts that should receive priority for control actions because of their high risk of co-infection.