Abstract
ObjectiveTo develop and test a method that allows an objective assessment of the value of any health policy in multiple domains.MethodsWe 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.ResultsWhen 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.ConclusionHealth-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.
Highlights
In 2015, the United Nations adopted 17 sustainable development goals, reflecting a commitment to end poverty in all forms by 2030
Among the targets of the third goal is the establishment of universal health coverage (UHC),[1] ensuring “all people and communities can use the promotive, preventive, curative, rehabilitative and palliative health services they need, of sufficient quality to be effective, while ensuring that the use of these services does not expose the user to financial hardship”
We developed and tested a method for decision-making in health policy when the population’s preferences among potential outcomes are unknown
Summary
In 2015, the United Nations adopted 17 sustainable development goals, reflecting a commitment to end poverty in all forms by 2030. Among the targets of the third goal is the establishment of universal health coverage (UHC),[1] ensuring “all people and communities can use the promotive, preventive, curative, rehabilitative and palliative health services they need, of sufficient quality to be effective, while ensuring that the use of these services does not expose the user to financial hardship”.2. Achieving this requires countries to expand the number of health conditions covered, improve the quality of services, increase the number of people covered and provide protection against financial risk.[3]. Analytical models such as extended cost‒effectiveness analyses can make the health, financial and equity effects of policies explicit.[4,5,6,7,8] The newest recommendations of the Second Panel on Cost–Effectiveness in Health and Medicine advocate including an impact inventory of the non-health outcomes of medical interventions, such as economic productivity.[9,10] other than reporting multiple outcomes, no method exists for decision-making that balances these many, and sometimes conflicting, domains
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