Spatio-temporal characteristics of Tuberculosis in Ghana [version 3; peer review: 1 approved, 1 approved with reservations]

• Published in: F1000 research Vol. 11; p. 200
• Main Authors: Iddrisu, Abdul-Karim, A. Amikiya, Emmanuel, Kwame Bukari, Francis
• Format: Journal Article
• Language: English
• Published: London, UK F1000 Research Limited 11.03.2024
• Subjects: baseline predictors and TB hot-spots; Bayesian spatial and space-time models; Tuberculosis relative risk
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.109053.3

Background Tuberculosis (TB) continues to be a prominent contributor to global mortality, standing as the second most fatal infectious disease and holding the seventh position among the top ten causes of death in Ghana. There is insufficient literature regarding the utilization of Bayesian hierarchical models, specifically within the framework of Integrated Nested Laplace Approximation (INLA), for examining the spatial and spatio-temporal dynamics of tuberculosis risk in Ghana. This study addresses this gap by determining TB hotspots regions in Ghana using the Bayesian modeling framework within the INLA. Methods TB data were sourced from the Ghana Health Service and National Tuberculosis Programme for the 10 administrative regions of Ghana, from 2008 to 2017. The relative risk of TB for each region and year was estimated utilizing Bayesian spatial and spatio-temporal modeling frameworks. Baseline predictors of TB risk were also considered. Maps for TB risks were created to visualized regions with TB hotspots. Model fitting and parameter estimation were conducted using R version 4.3.2. Results Among the baseline predictors, factors such as TB cure rate, TB success rate, knowledge about TB, HIV prevalence, percentage of literacy, and high income were found to be most significant in influencing the TB risk across the ten regions in Ghana. We noted an increased risk of TB infection in the Northern zone and the Eastern and Greater Accra regions in the Southern zone. Spatio-temporal distribution of TB infection risk was predominantly concentrated in the Southern zone. Clustering of TB risk was observed among neighboring regions. Conclusion To achieve a significant reduction in TB cases, it is essential to allocate resources to TB hotspots regions and also implement measures to control significant predictors of TB infection risk.