Global and Site-Specific Osteoarthritis Burden, 1990–2021, and Rehabilitation Capacity Modeling for Indonesia: A Spatiotemporal Analysis - Dina Wulandari (Bioscientia Medicina : Journal of Biomedicine and Translational Research, 2026)
Background: Osteoarthritis is a major and growing cause of disability, but prevalence does not directly measure clinically assessed rehabilitation need, demand, utilization, or unmet need. Objective: We quantified global, regional, national, and joint-site burden and linked rehabilitation-amenable prevalence to transparent capacity scenarios for Indonesia. Methods: We integrated annual WHO/IHME rehabilitation-amenable osteoarthritis estimates for 195 countries (1990–2021), published GBD 2019 site-specific prevalence, published GBD 2021 incidence and years lived with disability, and WHO rehabilitation specifications. EAPC was calculated from annual posterior-mean age-standardized rates using log-linear regression; published joinpoint AAPC retained its original name. Deterministic scenarios converted Indonesia’s 2021 prevalence into direct-contact hours and multidisciplinary FTE-year equivalents. Results: In 2021, 371.6 million people globally and 9.08 million in Indonesia had osteoarthritis-related functioning problems for which rehabilitation could be beneficial. From 1990 to 2021, prevalence increased by 137.7% globally and 199.8% in Indonesia. Prevalence EAPC was 0.34% annually globally and 0.55% in Indonesia. Knee osteoarthritis had the highest site-specific rate, whereas hand incidence and YLDs had the fastest published AAPCs in several middle-SDI settings. Indonesia’s foundational, scale-up, and comprehensive scenarios produced 5,673, 29,223, and 93,914 direct-contact FTE-year equivalents. Conclusion: Rehabilitation-amenable osteoarthritis burden increased faster in Indonesia than globally and varied by joint site and development level. Scenario outputs are model-based decision quantities, not national staffing requirements or workforce gaps, and require stakeholder validation with local severity, capacity, delivery, referral, adherence, and workforce data.