Health-system-adapted data envelopment analysis for decision-making in universal health coverage.

Objective: To develop and test a method that allows an objective assessment of the value of any health policy in multiple domains.

Methods: We developed a method to assist decision-makers with constrained resources and insufficient knowledge about a society's preferences to choose between policies with unequal, and at times opposing, effects on multiple outcomes. Our method extends standard data envelopment analysis to address the realities of health policy, such as multiple and adverse outcomes and a lack of information about the population's preferences over those outcomes. We made four modifications to the standard analysis: (i) treating the policy itself as the object of analysis, (ii) allowing the method to produce a rank-ordering of policies; (iii) allowing any outcome to serve as both an output and input; and (iv) allowing variable return to scale. We tested the method against three previously published analyses of health policies in low-income settings.

Results: When applied to previous analyses, our new method performed better than traditional cost-effectiveness analysis and standard data envelopment analysis. The adapted analysis could identify the most efficient policy interventions from among any set of evaluated policies and was able to provide a rank ordering of all interventions.

Conclusion: Health-system-adapted data envelopment analysis allows any quantifiable attribute or determinant of health to be included in a calculation. It is easy to perform and, in the absence of evidence about a society's preferences among multiple policy outcomes, can provide a comprehensive method for health-policy decision-making in the era of sustainable development.