Examination of the Synthetic Control Method for Evaluating Health Policies with Multiple Treated Units

• DOI: 10.1002/hec.3258
• Publication Date: 2015-10-07T09:29:14Z
• Authors: Noémi Kreif; Richard Grieve; Dominik Hangartner; Alex James Turner; Silviya Nikolova; Matt Sutton
• Keywords: Models, Statistical; Health Policy; 610; policy evaluation; 3. Good health; 03 medical and health sciences; synthetic control method; pay-for-performance; 0302 clinical medicine; difference-in-differences; Humans; Hospital Mortality; Reimbursement, Incentive; Research Articles

AbstractThis paper examines the synthetic control method in contrast to commonly used difference‐in‐differences (DiD) estimation, in the context of a re‐evaluation of a pay‐for‐performance (P4P) initiative, the Advancing Quality scheme. The synthetic control method aims to estimate treatment effects by constructing a weighted combination of control units, which represents what the treated group would have experienced in the absence of receiving the treatment. While DiD estimation assumes that the effects of unobserved confounders are constant over time, the synthetic control method allows for these effects to change over time, by re‐weighting the control group so that it has similar pre‐intervention characteristics to the treated group.We extend the synthetic control approach to a setting of evaluation of a health policy where there are multiple treated units. We re‐analyse a recent study evaluating the effects of a hospital P4P scheme on risk‐adjusted hospital mortality. In contrast to the original DiD analysis, the synthetic control method reports that, for the incentivised conditions, the P4P scheme did not significantly reduce mortality and that there is a statistically significant increase in mortality for non‐incentivised conditions. This result was robust to alternative specifications of the synthetic control method. © 2015 The Authors. Health Economics published by John Wiley & Sons Ltd.