Evaluation of Statistical Methods for Staggered Adoption Interventions using Medical Claims Data

Oregon Convention Center 

Population-level interventions often face practical constraints that require a non-randomized and staggered implementation. In this work, we provide a motivating example of a population-based payment intervention program that was implemented in a staggered fashion by a health provider. Leveraging patient demographic information, clinical registry data, and medical claims data, we illustrate how one could evaluate the program's impact on quality-of-care indicators such as cancer screening, diabetes control, and hypertension control. To our advantage, significant progress has been made recently in the development of statistical methods to estimate the effects of non-randomized staggered interventions. We showcase how these state-of-the-art methods can be used to estimate the effects of the program and under which settings they provide unbiased effect estimates. Moreover, we discuss how these methods approach additional challenges encountered in our real-world example, including heterogeneity across clinics, time-varying confounding, treatment switching, and spatial correlation. Finally, this work includes a comparative assessment of the considered methods based on simulation studies.

staggered adoption

medical claims

causal inference 

Health Policy Statistics Section