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

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 preschoolage
children in California as a comparison group.