Trading off health and financial protection benefits with multiobjective optimization

Countries which are introducing a system of Universal health coverage have to make a number of key tradeoffs, of which one is the tradeoff between the level of coverage and the degree to which patients are exposed to potentially catastrophic financial risk. In this study, we first present a way in which decision makers might be supported to focus on in a particular part of the tradeoff curve and ultimately choose an efficient solution. We then introduce some multiobjective optimization models for generating the tradeoff curves given data about potential treatment numbers, costs, and benefits. Using a dataset from Malawi, we demonstrate the approach and suggest a core index metric to make specific observations on the individual treatments. Moreover, as there has been some debate about the best way to measure financial exposure, we also investigate the extent to sensitivity of our results to the precise technical choice of financial exposure metric. Specifically, we consider two metrics, which are the total number of cases protected from catastrophic expenditure and a convex penalty function that penalizes out‐of‐pocket expenditures in an increasingly growing way, respectively.