The Affordable Care Act has led to significant gains in insurance coverage and reduced the cost of preventive care for millions of Americans. There is considerable interest in understanding how these changes will impact the use of preventive care services and health outcomes. Obtaining unbiased estimates of the impact of insurance on these outcomes is challenging because of inherent differences between insured and uninsured individuals. This article reviews common experimental and quasi-experimental approaches researchers have used in the past to address this problem, including RCTs, differences-in-differences analyses, and regression discontinuity. In each case, the key assumptions underlying the models are discussed alongside some of the main research findings related to prevention and health. The review concludes with a discussion of how experimental and quasi-experimental methods can be used to study the impact of the Affordable Care Act on preventive care and health outcomes.
Copyright © 2016 American Journal of Preventive Medicine. All rights reserved.