Challenges and Solutions in Studying the Impact of Interventions in the Analysis of Longitudinal Health Service Data

Abstract

Changes to health services require monitoring and analysis to determine if the desired improvements are achieved. Longitudinal studies, such as interrupted time series, involve the collection and analysis of data to explore the outcome of interest over time. Interrupted time-series design provides an estimate of the associated effect of an intervention using longitudinal data. Health services research increasingly uses these studies to inform policy and practice development. In many cases, the data which allow this investigation have been collected over many days, weeks, months, or even years. Analysis and interpretation need to allow for any underlying trends and the possible presence of seasonality. This analysis aims to identify changes in longitudinal health service data and the times at which they occur. We outline two case studies, one without seasonality (Case Study 1) and one with (Case Study 2). We present an overview of the challenges of longitudinal studies and identify solutions for future researchers.

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