# Spatiotemporal epidemiology, geographic hotspots, and risk factor associations of drug-resistant tuberculosis incidence in Indonesia: a Bayesian hierarchical modelling approach

## TL;DR

This study maps drug-resistant tuberculosis hotspots in Indonesia and finds that urban areas and socioeconomic factors are key drivers.

## Contribution

A novel Bayesian hierarchical modeling approach to identify DR-TB hotspots and assess risk factors at the district level in Indonesia.

## Key findings

- DR-TB hotspots are concentrated in urbanized regions like Jabodetabek and Greater Surabaya.
- Higher poverty rates and improved sanitation are positively associated with DR-TB incidence.
- Successful first-line TB treatment is linked to reduced DR-TB incidence.

## Abstract

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.

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.

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).

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.

The online version contains supplementary material available at 10.1186/s40249-026-01418-9.