Use of Interrupted Time Series in Service Delivery and Public Health Research

Only a badly brought up person would compare mean pre- with mean post-intervention data to estimate an intervention effect in a time series. Such a person would ignore a natural trend (e.g. was the outcome already improving), seasonal effects and auto-correlation. This paper describes two acceptable statistical approaches that mitigate these issues, one of which is favoured on the grounds that is it much simpler to use.[1] This would be useful to those engaged in quality control activities in data-rich environments.

— Richard Lilford, CLAHRC WM Director

References:

  1. Lagarde M. How to do (or not to do)… Assessing the impact of policy change with routine longitudinal data. Health Policy Plan. 2012. 27:76-83.
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