Spatiotemporal epidemiology and associated risk factors of tuberculosis incidence and mortality in Indonesia 2017–2022: a nationwide space-time hierarchical analysis
1
Issued Date
2026-12-01
Resource Type
eISSN
14787954
Scopus ID
2-s2.0-105031610285
Pubmed ID
41620746
Journal Title
Population Health Metrics
Volume
24
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Population Health Metrics Vol.24 No.1 (2026)
Suggested Citation
Farkhan A., Pakasi T.T., Sulistyo S., Salsabila A., James Maude R., Rotejanaprasert C. Spatiotemporal epidemiology and associated risk factors of tuberculosis incidence and mortality in Indonesia 2017–2022: a nationwide space-time hierarchical analysis. Population Health Metrics Vol.24 No.1 (2026). doi:10.1186/s12963-026-00458-5 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115600
Title
Spatiotemporal epidemiology and associated risk factors of tuberculosis incidence and mortality in Indonesia 2017–2022: a nationwide space-time hierarchical analysis
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Indonesia is the second-highest contributor to global tuberculosis (TB) cases, accounting for 10% of the total. While previous studies have explored TB patterns in specific regions, a comprehensive nationwide analysis at a fine spatial scale is lacking. This study investigated spatiotemporal patterns of TB incidence and mortality, identified geographical hotspots, and examined their association with risk factors to inform public health policy. Methods: This retrospective study analyzed notified TB cases and deaths during treatment from Indonesia’s National Tuberculosis Surveillance System across 514 districts between 2017 and 2022. Spatiotemporal Bayesian hierarchical modeling was employed to identify high-risk areas and assess associations with potential risk factors. The best-fitting model was determined by evaluating various spatial and temporal random effect structures and likelihood assumptions. Results: TB incidence fluctuated with a trough during the COVID-19 pandemic and an overall increase, while mortality increased over time. Incidence hotspots clustered in urbanized areas, while mortality hotspots were scattered across the country. The best-fitting model to estimate risk factors for both outcomes was Poisson likelihood. This indicated that TB incidence was spatiotemporally positively linked to better healthcare access (RR: 1.016; 95% CI: 1.007–1.025) and higher municipal human development index (MHDI, RR: 1.062; 95% CI: 1.049–1.075). Mortality was associated with low treatment coverage (RR: 0.610; 95% CI: 0.552–0.674) and success rates (RR: 0.595; 95% CI: 0.491–0.721). Conclusions: Fluctuating TB incidence, hotspots concentrated in urbanized areas with better healthcare access and higher MHDI as well as increasing mortality linked to poor treatment outcomes underscore the need for targeted public health interventions to expand access to care, improve treatment adherence, and address the socioeconomic disparities driving TB mortality.