OBJECTIVES: Productivity loss is an imperative societal cost to consider but is also one that is under researched and quantified. The Institute for Clinical and Economic Review will be using a proxy productivity algorithm (Jiao and Basu, 2023) to estimate and incorporate productivity loss into future value assessments when direct data are lacking. The productivity algorithm estimates changes in productivity based on national trends in quality of life, but it has not yet been validated. We compare market productivity loss estimates generated by the algorithm with empiric estimates from real-world evidence.

METHODS: We used a US employment survey of 3870 persons with multiple sclerosis and estimated productivity loss across severity, as measured by Expanded Disability Status Scale (EDSS) score. We leveraged the reported ages in the survey dataset and produced proxy estimates using the productivity algorithm. Estimates were compared by percent and absolute difference.

RESULTS: Survey results showed market productivity loss associated with EDSS scores of 0 to 9, ranging from $1754 to $50,480, respectively. The productivity algorithm generated a range of $0 to $66,999, respectively. Overall, the percent difference in these findings ranged from −91.57% to 100%. The average difference across all EDSS scores was 5.03%, and differences were wider at the ends of the severity spectrum vs the middle.

CONCLUSIONS: The productivity algorithm allows for inclusion of productivity loss costs when major data gaps exist, thereby allowing for more routine use of societal perspective in cost-effectiveness analyses. It produced, on average, only marginally different estimates that were generated from a disease-specific survey. However, additional research is needed to understand the impact of observed differences across severity levels when estimating the lifetime burden of productivity losses in value assessment.