Unveiling Tuberculosis Dynamics in Indonesia for Effective Control and Prevention: A Panel Regression and Clustering Approach

Novi Reandy Sasmita (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia)
Mutiara Syifa (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia)
Mhd Khairul (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia)
Latifa Rahayu (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia)
Zurnila Marli Kesuma (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia)
Selvi Mardalena (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia)
Rumaisa Kruba (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia)
Virasakdi Chongsuvivatwong (Department of Epidemiology, Faculty of Medicine, Prince of Songkla University, Hat Yai, Thailand)
M. Ischaq Nabil Asshiddiqi (School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong)

Journal of Public Health and Pharmacy
Vol. 6 No. 2 (2026)

Introduction: Tuberculosis (TB) remains a critical global health challenge, with Indonesia ranking second in global TB burden. This study examines factors influencing TB incidence across Indonesian provinces and applies clustering to guide targeted interventions aligned with TB eradication goals by 2030. Specifically, these findings inform Indonesia’s End TB 2030 roadmap by identifying provincial heterogeneity that necessitates differentiated resource allocation and strengthened health governance frameworks. Methods: This ecological time-series study design analyzed data from 34 Indonesian provinces (2020–2022), including TB cases, healthcare services, HIV cases, smoking prevalence, food management places, and public facilities. Descriptive statistics summarized variable distribution, while panel data regression identified key factors using multicollinearity checks, model selection, and assumption testing. Fuzzy Possibilistic C-Means (FPCM) clustering grouped provinces based on similarity characteristics. Results: TB cases rose from 10,351 in 2020 to 21,303 in 2022. This study underscores the multifaceted factors influencing TB incidence in Indonesia. Significant factors included healthcare services (?1 = -8.37), HIV cases (?2 = 13.76), smoking prevalence (?3 = 905.32), food management places (?4 = 1.62), and public facilities (?5 = 1.11). This study proves that TB is not only influenced by health factors but also by non-health factors. Fuzzy clustering using the FPCM identified three clusters based on their possibilistic membership degrees: Cluster 3, with high HIV prevalence and public facilities, requiring urgent action; Cluster 2, needing improved healthcare and smoking reduction; and Cluster 1, with moderate challenges. Conclusions: Health and environmental factors significantly influence TB incidence. Addressing cluster-specific needs, such as enhancing healthcare, reducing HIV and smoking prevalence, and improving public health standards, is essential for TB control. Future studies should expand variables and periods to deepen insights.