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Annu Rev Public Health. 2017 Mar 20;38:351-370. doi: 10.1146/annurev-publhealth-031816-044208.

Evaluating the Health Impact of Large-Scale Public Policy Changes: Classical and Novel Approaches.

Author information

1
Centers for Health Policy, Primary Care and Outcomes Research; Center on Poverty and Inequality; and Institute for Economic Policy Research, Stanford University, Stanford, California 94305; email: basus@stanford.edu.
2
Department of Medicine, Stanford University, Stanford, California 94305; email: ameghani@stanford.edu.
3
Center for Primary Care, Harvard Medical School, Boston, Massachusetts 02115.
4
Department of Epidemiology and Department of Social and Behavioral Health Sciences, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario M5T 3M7, Canada; email: aa.siddiqi@utoronto.ca.
5
Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina 27599.

Abstract

Large-scale public policy changes are often recommended to improve public health. Despite varying widely-from tobacco taxes to poverty-relief programs-such policies present a common dilemma to public health researchers: how to evaluate their health effects when randomized controlled trials are not possible. Here, we review the state of knowledge and experience of public health researchers who rigorously evaluate the health consequences of large-scale public policy changes. We organize our discussion by detailing approaches to address three common challenges of conducting policy evaluations: distinguishing a policy effect from time trends in health outcomes or preexisting differences between policy-affected and -unaffected communities (using difference-in-differences approaches); constructing a comparison population when a policy affects a population for whom a well-matched comparator is not immediately available (using propensity score or synthetic control approaches); and addressing unobserved confounders by utilizing quasi-random variations in policy exposure (using regression discontinuity, instrumental variables, or near-far matching approaches).

KEYWORDS:

difference-in-differences; instrumental variables; near-far matching; propensity score; regression discontinuity; synthetic controls

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