Evaluating the impact of complex interventions on patient-centered outcomes is a critical concern in public health, as natural experiments are generally not scientifically controlled. Randomized controlled trials, the “gold standard” for evidence-generation of health interventions, are often infeasible and impractical regarding health care reform [1]. As such, data from natural experiments in public health do not typically arise from randomized controlled trials [2]. According to the 2018 Annual Review of Public Health, interrupted time series (ITS) designs are aptly situated for studying the impacts of large-scale public health policies [3]. ITS designs borrow from traditional case-crossover designs and serve as quasi-experimental methodology able to assess the impact of an intervention retrospectively and account for temporal dependency [4].
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