Interrupted time series regression for the evaluation of public health interventions: a tutorial

By J. L. Bernal, S. Cummins, Antonio Gasparrini. International Journal of Epidemiology, 2016

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Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ef4ec703f1baad1f89622b5b25e432584feeedaa

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Citations in the corpus

Selecting and Improving Quasi-Experimental Designs in Effectiveness and Implementation Research (2018, background): “An interrupted time series (ITS) design involves collection of outcome data at multiple time points before and after an intervention is introduced at a given point in time at one or more sites (6, 13).”
The use of controls in interrupted time series studies of public health interventions. (2018, methodology): “approach whereby comparisons are instead made across time within a single population.(2) This”
Experimental and Quasi-Experimental Designs in Implementation Research (2019, background): “Relative to an RCT, ITS designs can also allow for a more comprehensive assessment of the longitudinal effects of an intervention (positive or negative), as effects can be traced over all included time points (Bernal et al., 2017; Penfold and Zhang, 2013).”
You Can't Stay Here (2017, methodology): “Therefore, we use another causal inference strategy—Interrupted Time Series [2]—to measure the causal effects of the ban.”