Objectives To investigate whether health insurance generated improvements in cardiovascular risk factors (blood pressure and hemoglobin A1c (HbA1c) levels) for identifiable subpopulations, and using machine learning to identify characteristics of people predicted to benefit highly. Design Secondary analysis of randomized controlled trial. Setting Medicaid insurance coverage in 2008 for adults on low incomes (defined as lower than the federal-defined poverty line) in Oregon who were uninsured. Participants 12 134 participants from the Oregon Health Insurance Experiment with in-person data for health outcomes for both treatment and control groups. Interventions Health insurance (Medicaid) coverage. Main outcomes measures The conditional local average treatment effects of Medicaid coverage on systolic blood pressure and HbA1c using a machine learning causal forest algorithm (with instrumental variables). Characteristics of individuals with positive predicted benefits of Medicaid coverage based on the algorithm were compared with the characteristics of others. The effect of Medicaid coverage was calculated on blood pressure and HbA1c among individuals with high predicted benefits. Results In the in-person interview survey, mean systolic blood pressure was 119 (standard deviation 17) mm Hg and mean HbA1c concentrations was 5.3% (standard deviation 0.6%). Our causal forest model showed heterogeneity in the effect of Medicaid coverage on systolic blood pressure and HbA1c. Individuals with lower baseline healthcare charges, for example, had higher predicted benefits from gaining Medicaid coverage. Medicaid coverage significantly lowered systolic blood pressure (−4.96 mm Hg (95% confidence interval −7.80 to −2.48)) for people predicted to benefit highly. HbA1c was also significantly reduced by Medicaid coverage for people with high predicted benefits, but the size was not clinically meaningful (−0.12% (−0.25% to −0.01%)). Conclusions Although Medicaid coverage did not improve cardiovascular risk factors on average, substantial heterogeneity was noted in the effects within that population. Individuals with high predicted benefits were more likely to have no or low prior healthcare charges, for example. Our findings suggest that Medicaid coverage leads to improved cardiovascular risk factors for some, particularly for blood pressure, although those benefits may be diluted by individuals who did not experience benefits. All data used in this study are available online from the National Bureau of Economic Research’s Public Use Data Archive and can be accessed at . Statistical code available from the corresponding author on reasonable request.

中文翻译：

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医疗补助覆盖率对心血管危险因素的异质性影响：随机对照试验的二次分析


目的 调查健康保险是否改善了可识别亚群的心血管危险因素 （血压和血红蛋白 A1c （HbA1c） 水平），并使用机器学习来识别预计受益率高的人群的特征。设计 随机对照试验的二次分析。2008 年为俄勒冈州没有保险的低收入（定义为低于联邦定义的贫困线）的成年人设定 Medicaid 保险范围。参与者 12 134 名参与者来自俄勒冈州健康保险实验，使用治疗组和对照组健康结果的面对面数据。干预措施 健康保险 （Medicaid） 覆盖范围。主要结局指标 使用机器学习因果森林算法（带有工具变量），医疗补助覆盖率对收缩压和 HbA1c 的条件局部平均治疗效果。将基于算法的 Medicaid 覆盖率具有积极预测益处的个体特征与其他个体的特征进行了比较。计算 Medicaid 覆盖率对预测收益高的个体的血压和 HbA1c 的影响。结果 在面对面访谈调查中，平均收缩压为 119 （标准差 17） mm Hg，平均 HbA1c 浓度为 5.3% （标准差 0.6%）。我们的因果森林模型显示 Medicaid 覆盖率对收缩压和 HbA1c 的影响存在异质性。例如，基线医疗保健费用较低的个人从获得 Medicaid 保险中获得的预期收益更高。对于预计会获益较高的人群，医疗补助覆盖率显著降低了收缩压（-4.96 毫米汞柱（95% 置信区间 -7.80 至 -2.48））。 对于具有高预期益处的人群，Medicaid 覆盖率也显著降低了 HbA1c，但大小没有临床意义（-0.12%（-0.25% 至 -0.01%））。结论 尽管 Medicaid 覆盖率平均没有改善心血管危险因素，但在该人群内的影响存在很大异质性。例如，具有高预期收益的个体更有可能之前没有或较低的医疗保健费用。我们的研究结果表明，医疗补助覆盖率会改善一些人的心血管风险因素，尤其是血压，尽管这些好处可能会被没有体验到好处的人稀释。本研究中使用的所有数据均可从美国国家经济研究局的公共使用数据档案中在线获取，并可在 上访问。应合理要求，可从通讯作者处获得统计代码。