title: Use of interrupted time series analysis in evaluating health care quality improvements.
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.
authors.affiliation: Group Health Research Institute and the Department of Health Services Research, University of Washington, Seattle, Wash. Electronic address: Penfold.r@ghc.org.
authors.forename: Robert B
authors.initials: RB
authors.lastname: Penfold
authors.forename: Fang
authors.initials: F
authors.lastname: Zhang
mesh_terms: D014150:Antipsychotic Agents / Q000627:therapeutic use; D001289:Attention Deficit Disorder with Hyperactivity / Q000175:diagnosis / Q000188:drug therapy*; D002675:Child, Preschool; D003695:Delivery of Health Care / Q000592:standards* / Q000639:trends; D005260:Female; D006302:Health Services Research; D006801:Humans; D065186:Interrupted Time Series Analysis; D008297:Male; D010372:Pediatrics / Q000592:standards* / Q000639:trends; D011785:Quality Assurance, Health Care; D058996:Quality Improvement; D014481:United States
chemical_list: D014150:Antipsychotic Agents
keywords: interrupted time series; quality improvement; quasi-experimental; research design