Bridging the gap: aligning economic research with disease burden.

ABSTRACT

INTRODUCTION: Cost-effectiveness analysis (CEA) is critical for identifying high-value interventions that address significant unmet need. This study examines whether CEA study volume is proportionate to the burden associated with 21 major disease categories. METHODS: We searched the Tufts Medical Center CEA and Global Health CEA Registries for studies published between 2010 and 2019 that measured cost per quality-adjusted life-year or cost per disability-adjusted life-year (DALY). Stratified by geographical region and country income level, the relationship between literature volume and disease burden (as measured by 2019 Global Burden of Disease estimates of population DALYs) was analysed using ordinary least squares linear regression. Additionally, the number of CEAs per intervention deemed 'essential' for universal health coverage by the Disease Control Priorities Network was assessed to evaluate how many interventions are supported by cost-effectiveness evidence. RESULTS: The results located below the regression line but with relatively high burden suggested disease areas that were 'understudied' compared with expected study volume. Understudied disease areas varied by region. Higher-income and upper-middle-income country (HUMIC) CEA volume for non-communicable diseases (eg, mental/behavioural disorders) was 100-fold higher than that in low-income and lower-middle-income countries (LLMICs). LLMIC study volume remained concentrated in HIV/AIDS as well as other communicable and neglected tropical diseases. Across 60 essential interventions, only 33 had any supporting CEA evidence, and only 21 had a decision context involving a low-income or middle-income country. With the exception of one intervention, available CEA evidence revealed the 21 interventions to be cost-effective, with base-case findings less than three times the GDP per capita. CONCLUSION: Our analysis highlights disease areas 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. Large research disparities between HUMICs and LLMICs suggest funding opportunities for improving allocative efficiency in LLMIC health systems.