Measuring and correcting for the moral dilemma of cost-effectiveness and disadvantaged populations

Cost-utility analysis (CUA) is an essential tool for assessing the value of public health programs. However, programs serving disadvantaged populations are often less cost-effective than the same interventions serving well-resourced groups. Using CUA to select funding priorities risks perpetuating health inequities, racism, and discrimination. To treat like cases alike, interventions that are cost-effective in one population should be available to people who differ foremost in their access to quality healthcare. One solution is to increase the value of gains in the disadvantaged population. However, the appropriate degree of correction remains unresolved. I propose using CUA results broken down by equity-relevant subgroups to identify multipliers needed to make investments across groups appear equally cost-effective. For example, I present my unadjusted findings that offering genetic testing to black women is less cost-effective compared to white women because of racial differences in the quality of care. Multiplying gains for black women by 1.5 would make the investment numerically equally valuable for both groups. Further review of empirical data is needed to confirm this relationship across interventions, settings, and equity-relevant groups. By correcting for differences in healthcare quality or access, my proposed approach reduces the risk of unfair discrimination against disadvantaged populations. This minimalist correction does not attempt to equalize health outcomes or prioritize the worse off, which may justify further weighting. While the selection of fairness goals is a normative question, empirical evidence is integral to determining the magnitude of differential benefits and can provide tools for meeting these goals.