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

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.

Rates of blood cultures positive for vancomycin-resistant Enterococcus in Ontario: a quasi-experimental study

“…We used an interrupted time series Poisson regression stratified by VRE control strategy (i.e., ceased or continued screening) to determine whether the slope of VRE-positive blood culture incidence rates was different after versus before the change in screening practice (slope change). For both cohorts, we examined the slope change by fitting an interaction between the intervention and time in the Poisson model (level and slope change model 34 ). As with the slope, the slope change was reported as an IRR.…”

Impacts of energy-efficiency investments on internal conditions in low-income households

“…Lopez Bernal et al, 2016). However, as no obvious trend over time was observed within the baseline and follow-up periods, these terms were excluded from the regression models.…”

“…Autocorrelation reflects the internal correlation within a time series, showing the degree to which the different measurements are interdependent (cf. Lopez Bernal et al, 2016). An autoregressive model was constructed by adding a weight specifying that the error covariance decreases as the time distance between measurements increases in order to control for the observed dependency (Jones & Subramanian, 2011).…”

“…This approach allows the impacts of the energy-efficiency interventions to be estimated with a maximum level of statistical power, while adjusting for external conditions. Interrupted time-series analysis is a valuable study design for evaluating the effectiveness of public health interventions (Lopez Bernal, Cummins, & Gasparrini, 2016). It involves the analysis of repeated observations over time, usually at equal time intervals before and after an intervention (the 'interruption') in order to detect whether the intervention has produced an effect different from underlying exogenous, secular trends.…”

Virtual agent-mediated appraisal training: a single case series among Dutch firefighters

“…This quasi-experimental design aims to determine whether there is a change in the outcome level or slope from baseline to post-intervention, i.e. whether this is an effect of the training on top of any possible natural development or attrition (Bernal, Cummins, & Gasparrini, 2016; Penfold & Zhang, 2013; Shadish, Kyse, & Rindskopf, 2013; Wagner, Soumerai, Zhang, & Ross‐Degnan, 2002). The study consisted of three phases: pre-training phase of eight days, a two-week intervention (training) phase, and an eight-day post-training phase.…”