OBJECTIVES: Cost-effectiveness analysis (CEA) is critical for identifying high-value interventions for diseases with the highest burden. This study examines whether available CEA evidence is commensurate with the burden associated with 21 major disease categories.

METHODS: Cost per quality-adjusted life year and cost per disability-adjusted-life-year (DALY) studies published from 2010-2019 were identified using the Tufts Medical Center CEA and Global Health CEA Registries. Disease burden, measured in population DALYs, was retrieved from the 2019 Global Burden of Disease estimates. The relationship between literature volume and disease burden was analyzed using ordinary least squares linear regression, and stratified by geographic region and country income level. Additionally, using a list from the Disease Control Priorities Network on essential interventions for a universal health coverage package, we analyzed which essential interventions were supported by cost-effectiveness evidence.

RESULTS: Disease areas that fell below the regression line but associated with high burden were deemed “under-studied” compared to expected study volume. Under-studied disease areas varied by region and income level, and revealed large research disparities between high- and upper-middle-income countries (HUMICs) and low- and lower-middle-income countries (LLMICs). Available CEA evidence for under-studied diseases was 26 times higher in HUMICs (e.g., mental/behavioral disorders, sense organ diseases, congenital anomalies) than LLMICs (e.g., cardiovascular/circulatory diseases, neonatal disorders). Of 60 essential interventions, only 33 had any supporting CEA evidence and only 21 were set in a low- or middle-income country. Available CEA evidence concluded 20 of those 21 interventions are cost-effective, with base-case findings not exceeding 3 times the GDP-per-capita.

CCONCLUSIONS: Our analysis highlights disease areas and regions that require significant policy attention. Research gaps for highly prevalent, lethal, or disabling diseases, as well as essential interventions may be stifling potential efficiency gains. Research disparities between HUMICs and LLMICs suggest there are substantial funding opportunities for improving allocative efficiency in developing health systems.