摘要Background:Tuberculosis (TB) and diabetes mellitus (DM) co-morbidity is a growing public health challenge, particularly in Indonesia, where TB incidence remains high and DM prevalence is increasing. DM co-morbidity is known to increase the risk of TB incidence and have negative effects on TB treatment outcomes. This study aims to analyze the geographical co-distribution of TB and DM and their sociodemographic determinants in Indonesia, to help inform public health response and targeting of screening programs.Methods:Using data from the 2023 Indonesian Health Survey (SKI), a nationally representative, population-based survey, we applied a Bayesian geostatistical model to estimate disease prevalence and assess associations with key sociodemographic factors.Results:The predicted TB prevalence varied from 0.1% to 3.0%, highest in eastern Indonesia, particularly Papua, while DM prevalence ranged from 0.6% to 6.2%, concentrated in Java and Sumatra. Approximately 62 districts showed more than a 50% posterior probability that both TB and DM prevalences simultaneously exceed their respective national thresholds. The proportion of the poor population is significantly associated with higher TB prevalence (0.106; 95% CrI: 0.039, 0.174), while population density has a strong positive correlation with DM prevalence (0.198; 95% CrI: 0.156, 0.241). Proportion of the poor population (- 0.053; 95% CrI: - 0.096, - 0.009) and hospital services (- 0.071; 95% CrI: - 0.116, - 0.027) show a negative association with DM prevalence.Conclusion:Spatial analysis revealed significant regional variations, with high TB-DM co-distribution observed in rapidly urbanizing and high-poverty districts, including parts of West Java, East Java, Sumatra, and Kalimantan in Indonesia. These findings emphasize the need for strengthened TB-DM integration in healthcare services, especially in areas that have a high prevalence of both diseases. Strengthening integrated disease management strategies in local areas can help mitigate the burden of both TB and DM in Indonesia, particularly given likely low case detection and health care access in lower income regions.