EARLY EFFECTS OF 2014 US MEDICAID EXPANSIONS ON MORTALITY: DESIGN-BASED INFERENCE FOR IMPACTS ON SMALL SUBGROUPS DESPITE SMALL-CELL SUPPRESSION

• 成果类型: Article
• 署名作者: Mann, Charlotte Z.; Hansen, Ben B.; Gaydosh, Lauren
• 署名单位: University of Michigan System; University of Michigan; University of Texas System; University of Texas Austin
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS1910
• 发表日期: 2024
• 页码: 2887-2908
• 关键词: randomization inference; adjustment; variables; BIAS

摘要

Since 2014, states in the U.S. can choose whether to adopt Medicaid expansion as part of the Affordable Care Act (ACA), relaxing eligibility requirements. This heterogeneity in policy adoption between states raises the question-would there be a difference in health outcomes for states that have not expanded insurance access if they did expand Medicaid eligibility? In this study we estimate the effect of ACA Medicaid expansion on county-level allcause mortality in the U.S. in 2014 overall and for subgroups relevant to the racial politics surrounding the ACA. We bring a causal approach to this challenge which emphasizes observational study design, including prespecifying all analyses, matching counties on pretreatment covariates, and employing design-based inference. A challenge facing analyses like this one is gaining access to mortality outcomes, as statistical agencies in the U.S. and elsewhere suppress cell counts of 10 or fewer in public use data. We develop a rank-sum test statistic accommodating outcomes that are coarsened in this way and that lends itself to design-based inference with county-aggregated data. As applied to impact analysis of the ACA's Medicaid expansion, the proposed method's inferences from coarsened, publicly available data are substantively the same as those that would be drawn from the complete, restricted-access data.