A Bayesian procedure for estimating the causal effects of nursing home bed-hold policy

Nursing home bed-hold policies provide continuity of care for Medicaid beneficiaries by paying nursing homes to reserve beds so residents can return to their facility of occupancy following an acute hospitalization. In 2001, Michigan implemented bed-hold policies in nursing homes. We investigated the impact of these policies on mortality and hospitalizations using 1999-2004 quarterly data from nursing homes in Michigan and nursing homes in 11 states that did not implement such policies. Synthetic Control has been used to estimate the effects of policies by accounting for changes over time unrelated to the intervention. Synthetic Control is intended for scalar continuous outcome at each period, and assumes a single treated unit and multiple control units. We propose a Bayesian procedure to overcome these limitations. It imputes the outcomes of nursing homes in Michigan if they were not exposed to the policy by matching to non-exposed nursing homes that are associated with the exposed ones in the pre-policy period. Because sampling from a Bayesian model is computationally challenging, we describe an approximation procedure that can be implemented using existing software. Our approach can be applied to other studies that examine the impact of policies.