Use of interrupted time series analysis in evaluating health care quality improvements

Acad Pediatr. Nov-Dec 2013;13(6 Suppl):S38-44. doi: 10.1016/j.acap.2013.08.002.

Abstract

Interrupted time series (ITS) analysis is arguably the strongest quasi-experimental research design. ITS is particularly useful when a randomized trial is infeasible or unethical. The approach usually involves constructing a time series of population-level rates for a particular quality improvement focus (eg, rates of attention-deficit/hyperactivity disorder [ADHD] medication initiation) and testing statistically for a change in the outcome rate in the time periods before and time periods after implementation of a policy/program designed to change the outcome. In parallel, investigators often analyze rates of negative outcomes that might be (unintentionally) affected by the policy/program. We discuss why ITS is a useful tool for quality improvement. Strengths of ITS include the ability to control for secular trends in the data (unlike a 2-period before-and-after t test), ability to evaluate outcomes using population-level data, clear graphical presentation of results, ease of conducting stratified analyses, and ability to evaluate both intended and unintended consequences of interventions. Limitations of ITS include the need for a minimum of 8 time periods before and 8 after an intervention to evaluate changes statistically, difficulty in analyzing the independent impact of separate components of a program that are implemented close together in time, and existence of a suitable control population. Investigators must also be careful not to make individual-level inferences when population-level rates are used to evaluate interventions (though ITS can be used with individual-level data). A brief description of ITS is provided, including a fully implemented (but hypothetical) study of the impact of a program to reduce ADHD medication initiation in children younger than 5 years old and insured by Medicaid in Washington State. An example of the database needed to conduct an ITS is provided, as well as SAS code to implement a difference-in-differences model using preschool-age children in California as a comparison group.

Keywords: interrupted time series; quality improvement; quasi-experimental; research design.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.
  • Review

MeSH terms

  • Antipsychotic Agents / therapeutic use
  • Attention Deficit Disorder with Hyperactivity / diagnosis
  • Attention Deficit Disorder with Hyperactivity / drug therapy*
  • Child, Preschool
  • Delivery of Health Care / standards*
  • Delivery of Health Care / trends
  • Female
  • Health Services Research
  • Humans
  • Interrupted Time Series Analysis*
  • Male
  • Pediatrics / standards*
  • Pediatrics / trends
  • Quality Assurance, Health Care
  • Quality Improvement*
  • United States

Substances

  • Antipsychotic Agents