Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: a review

Abstract

Objectives: Interrupted time series (ITS) designs are frequently used in public health to examine whether an intervention or exposure has influenced health outcomes. Few reviews have been undertaken to examine the design characteristics, statistical methods, and completeness of reporting of published ITS studies.

Study design and setting: We used stratified random sampling to identify 200 ITS studies that evaluated public health interventions or exposures from PubMed (2013-2017). Study characteristics, details of statistical models and estimation methods used, effect metrics, and parameter estimates were extracted. From the 200 studies, 230 time series were examined.

Results: Common statistical methods used were linear regression (31%, 72/230) and autoregressive integrated moving average (19%, 43/230). In 17% (40/230) of the series, we could not determine the statistical method used. Autocorrelation was acknowledged in 63% (145/230) of the series. An estimate of the autocorrelation coefficient was given for only 1% of the series (3/230). Measures of precision were reported for 63% of effect measures (541/852).

Conclusion: Many aspects of the design, methods, analysis, and reporting of ITS studies can be improved, particularly description of the statistical methods and approaches to adjust for and estimate autocorrelation. More guidance on the conduct and reporting of ITS studies is needed to improve this study design.

Keywords: Interrupted time series; Public health; Quasi-experimental; Reporting quality; Review; Segmented regression; Statistical methods.