Bayesian spatial modelling of tuberculosis-HIV co-infection in Ethiopia

• Published in: PloS one 2023-03, Vol.18 (3), p.e0283334-e0283334
• Main Authors: Gemechu, Leta Lencha, Debusho, Legesse Kassa
• Format: Article
• Language: English
• Subjects: Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Care and treatment; Clustering; Coinfection - epidemiology; Collaboration; Comorbidity; Computer and Information Sciences; Coronaviruses; Diagnosis; Disease hot spots; Earth Sciences; Epidemiology; Ethiopia - epidemiology; HIV; HIV Infections - complications; HIV Infections - epidemiology; HIV patients; Human immunodeficiency virus; Humans; Infections; Latent Tuberculosis; Medical diagnosis; Medicine and Health Sciences; Pandemics; People and Places; Prevention; Public health; Risk factors; Risk management; Spatial Analysis; Statistical analysis; Tuberculosis; Tuberculosis - complications; Tuberculosis - epidemiology
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0283334

An in-depth analysis of the epidemiological patterns of TB/HIV co-infection is essential since it helps to target high-risk areas with effective control measures. The main objective of this study was to assess the spatial clustering of TB/HIV co-infection prevalence in Ethiopia for the year 2018 using district-level aggregated TB and HIV data obtained from the Ethiopian Federal Ministry of Health. The global Moran's index, Getis-Ord [Formula: see text] local statistic, and Bayesian spatial modeling techniques were applied to analyse the data. The result of the study shows that TB among people living with HIV (PLHIV) and HIV among TB patients prevalence were geographically heterogeneous. The highest prevalence of TB among PLHIV in 2018 was reported in the Gambella region (1.44%). The overall prevalence of TB among PLHIV in Ethiopia in the same year was 0.38% while the prevalence of HIV among TB patients was 6.88%. Both district-level prevalences of HIV among TB patients and TB among PLHIV were positively spatially autocorrelated, but the latter was not statistically significant. The local indicators of spatial analysis using the Getis-Ord statistic also identified hot-spots districts for both types of TB/HIV co-infection data. The results of Bayesian spatial logistic regression with spatially structured and unstructured random effects using the Besag, York, and Mollié prior showed that not all the heterogeneities in the prevalence of HIV among TB patients and TB among PLHIV were explained by the spatially structured random effects. This study expanded knowledge about the spatial clustering of TB among PLHIV and HIV among TB patients in Ethiopia at the district level in 2018. The findings provide information to health policymakers in the country to plan geographically targeted and integrated interventions to jointly control TB and HIV.