Spatiotemporal epidemiology, geographic hotspots, and risk factor associations of drug-resistant tuberculosis incidence in Indonesia: a Bayesian hierarchical modelling approach
Issued Date
2026-12-01
Resource Type
ISSN
20955162
eISSN
20499957
Scopus ID
2-s2.0-105029915118
Journal Title
Infectious Diseases of Poverty
Volume
15
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Infectious Diseases of Poverty Vol.15 No.1 (2026)
Suggested Citation
Farkhan A., Lawpoolsri S., Soonthornworasiri N., Pakasi T.T., Sulistyo S., Salsabila A., Maude R.J., Surendra H., Rotejanaprasert C. Spatiotemporal epidemiology, geographic hotspots, and risk factor associations of drug-resistant tuberculosis incidence in Indonesia: a Bayesian hierarchical modelling approach. Infectious Diseases of Poverty Vol.15 No.1 (2026). doi:10.1186/s40249-026-01418-9 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115190
Title
Spatiotemporal epidemiology, geographic hotspots, and risk factor associations of drug-resistant tuberculosis incidence in Indonesia: a Bayesian hierarchical modelling approach
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Indonesia ranks among the countries with the highest burden of drug-resistant tuberculosis (DR-TB), contributing approximately 7.4% of global cases, many of which are likely underdiagnosed. To support targeted public health surveillance and control efforts, this study aimed to characterize the spatiotemporal distribution of DR-TB incidence in Indonesia, identify geographic hotspots, and examine associations with health system and socioeconomic factors. Methods: We conducted a nationwide retrospective analysis using annual DR-TB notification data from 2017 to 2022 across all 514 districts, obtained from the national tuberculosis information system. Multivariable Bayesian spatiotemporal regression models were fitted under alternative likelihood assumptions and space-time random effect structures. Model selection criteria were used to identify the best-fitting models for hotspot detection and estimation of risk factor associations. Results: DR-TB predominantly affected individuals aged 25–54 years, aligning with the working-age population. Hotspots were concentrated in urbanized regions, including the Jabodetabek megacity, Greater Surabaya, and districts in South Sumatra. The best-fitting model identified a protective association between first-line treatment success rates and DR-TB incidence [incidence rate ratio (IRR): 0.508; 95% credible interval (CrI): 0.368–0.702]. In contrast, DR-TB incidence was positively associated with the proportion of the population living below the poverty line (IRR: 1.028; 95% CrI: 1.013–1.044), households with improved sanitation access (IRR: 1.006; 95% CrI: 1.002–1.010), and increased municipal human development index (IRR: 1.068; 95% CrI: 1.049–1.094). Conclusions: DR-TB hotspots were primarily concentrated in urban areas, highlighting the need for targeted interventions. Improving first-line tuberculosis treatment success rates and addressing socioeconomic drivers, such as poverty, are critical for controlling DR-TB. Public health policies should prioritize workplace-based support for improving treatment adherence, provide safeguards for TB patients affected by poverty, and underscore the importance of a multisectoral TB surveillance and control program.