ArticleBiostatistics (Oxford, England)2018
A Bayesian procedure for estimating the causal effects of nursing home bed-hold policy.
Article in Biostatistics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- State Medicaid Bed-Hold Policies and Hospitalizations and ED Visits Among Long-Stay Nursing Home Residents.JAMA health forum · 2026Article
- Estimation of Lockdowns' Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data.International journal of environmental research and public health · 2022Article
- Short-term impact of nursing home SARS-CoV-2 vaccinations on new infections, hospitalizations, and deaths.Journal of the American Geriatrics Society · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
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.
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