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).
Evaluating the Health Impact of Large-Scale Public Policy Changes: Classical and Novel Approaches
S. Basu,Ankita Meghani,A. Siddiqi
Published 2017 in Annual Review of Public Health
ABSTRACT
PUBLICATION RECORD
- Publication year
2017
- Venue
Annual Review of Public Health
- Publication date
2017-03-20
- Fields of study
Medicine, Business, Political Science
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
CITATION MAP
EXTRACTION MAP
CLAIMS
CONCEPTS
- difference-in-differences approaches
A policy evaluation method that compares outcome changes over time between policy-affected and policy-unaffected groups.
Aliases: difference-in-differences, DID
- instrumental variables
An identification strategy that uses an external variable related to exposure but not directly to the outcome except through exposure.
Aliases: IV
- near-far matching approaches
A matching approach that pairs units close on confounders but far on exposure-related variables to strengthen causal identification.
Aliases: near-far matching
- propensity score approaches
A matching or weighting strategy that uses estimated treatment probabilities to balance observed characteristics between groups.
Aliases: propensity score
- quasi-random variation
Variation in policy exposure that is as-if random and can support causal inference without random assignment.
Aliases: as-if random variation
- regression discontinuity
A quasi-experimental design that uses a cutoff-based assignment rule to estimate an effect near the threshold.
Aliases: RD
- synthetic control approaches
A comparative method that builds a weighted combination of control units to approximate an affected population.
Aliases: synthetic control
REFERENCES
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