Evaluating the impact of health policies: using a difference-in-differences approach

Excerpt

Constrained healthcare resources worldwide have made evaluating the impact of population health interventions increasingly important to maximize health and equity, while minimizing costs. However, the effects of population-level exposures such as health policies can seldom be evaluated through randomized controlled trials (RCTs). The following article will examine how the difference-in-differences method can be used to estimate the causal effect of such interventions. While this method was formalized and is extensively used in the field of economics (Meyer 1995), its first application is believed to have originated in the field of public health in 1855 (Snow 1855). The difference-in-differences method emulates a randomized design by measuring changes in outcomes over time between exposed and control groups. But unlike an RCT where the researcher randomly assigns exposure status; in a difference-in-differences design, researchers use “natural experiments” to assign exposure status, thus known as a quasi-experimental model (Dimick and Ryan 2014; Ryan et al. 2015). Repeated outcome data are necessary to conduct a difference-in-differences analysis. The data can be in the form of longitudinal data (also known as panel data); sources may include payer/claims data, patient’s electronic medical records or data from established cohort studies. Alternately, repeated cross-sectional data such as national surveys for example Demographic and Health Surveys (DHS) can be used. …