Interrupted Time Series for Public Health

Interrupted time series analysis, usually implemented as segmented regression, is a strong quasi-experimental design for evaluating the effect of a public-health intervention introduced at a known point in time. By tracking a population-level outcome — prescribing rates, infections, injuries, hospital admissions — over many equally spaced periods before and after the intervention, it asks whether the outcome's level jumped and whether its underlying trend changed when the intervention took effect, relative to the pre-intervention trajectory projected forward as the counterfactual. The segmented-regression formulation was popularized for intervention research by Wagner, Soumerai and colleagues, and Lopez Bernal, Cummins and Gasparrini's 2017 International Journal of Epidemiology tutorial is the standard modern guide for public-health applications, covering autocorrelation, seasonality, and the use of comparison series.