Spillover Effects of Medical Innovations: A Universal and Scalable Approach to Measure the Societal Value of Avoided Informal Care Time.

BACKGROUND AND

objectiveInformal caregiving is essential to comprehensive patient care, yet sustained caregiving can lower caregivers' quality of life and productivity, with implications for labor supply and economic growth. Most studies quantify caregiver burden without linking it to patients' utility, obscuring how health gains translate into caregiving demand. We extend a published framework for valuing the social impact of medical interventions by incorporating informal caregiving as a scalable dimension of societal value, enabling estimation of caregiver productivity effects where direct caregiver data are unavailable.

methodsWe established a generic relationship between patients' quality-adjusted life-years (QALYs) and caregiving time by leveraging published correlations between EQ-5D and daily care time. Incremental QALYs were converted into age- and ICD10 chapter-specific hourly reductions in caregiving time. To address the "QALY-trap," we established a QALY-composition approximation. Using nationally representative time-use data, we estimated how freed time is reallocated to paid work, unpaid work, and leisure, and we quantified the socioeconomic value of avoided informal care time as opportunity costs, including direct effects on paid and unpaid work and broader impacts.

resultsWe demonstrated use in a case study of relapsing-remitting multiple sclerosis treated with ofatumumab across five countries. Ofatumumab reduced informal caregiving between 17.34 and 18.38 h per treated patient-year. In Germany, caregiver productivity gains were $468 per patient-year and represented about 7.2% of the patient productivity component of total social impact. Findings were directionally similar across settings.

conclusionsThe method incorporates the caregiver perspective into economic evaluations by linking patient utility gains to changes in caregiving demand and their societal consequences. The framework provides a transparent and transferable approximation for estimating caregiver productivity spillovers when direct caregiver data are unavailable.