Department of Social Epidemiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
Hakubi Center, Kyoto University, Kyoto, Japan.
BMJ. 2024 Sep 23;386:e079377. doi: 10.1136/bmj-2024-079377.
To investigate whether health insurance generated improvements in cardiovascular risk factors (blood pressure and hemoglobin A (HbA) levels) for identifiable subpopulations, and using machine learning to identify characteristics of people predicted to benefit highly.
Secondary analysis of randomized controlled trial.
Medicaid insurance coverage in 2008 for adults on low incomes (defined as lower than the federal-defined poverty line) in Oregon who were uninsured.
12 134 participants from the Oregon Health Insurance Experiment with in-person data for health outcomes for both treatment and control groups.
Health insurance (Medicaid) coverage.
The conditional local average treatment effects of Medicaid coverage on systolic blood pressure and HbA 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 HbA among individuals with high predicted benefits.
In the in-person interview survey, mean systolic blood pressure was 119 (standard deviation 17) mm Hg and mean HbA 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 HbA. 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. HbA 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%)).
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.
调查医疗保险是否能改善可识别亚人群的心血管风险因素(血压和血红蛋白 A(HbA)水平),并使用机器学习来识别极有可能受益的人群的特征。
随机对照试验的二次分析。
俄勒冈州 2008 年为收入较低(定义为低于联邦贫困线)的未参保成年人提供的医疗补助保险。
来自俄勒冈健康保险实验的 12134 名参与者,他们有治疗组和对照组的健康结果的个人数据。
医疗保险(医疗补助)覆盖。
使用机器学习因果森林算法(工具变量)对医疗补助覆盖对收缩压和 HbA 的条件局部平均治疗效果。根据算法,比较具有医疗补助覆盖积极预测获益特征的个体与其他人的特征。计算了在高预测获益个体中医疗补助覆盖对血压和 HbA 的影响。
在面对面访谈调查中,平均收缩压为 119(标准差 17)mmHg,平均 HbA 浓度为 5.3%(标准差 0.6%)。我们的因果森林模型显示,医疗补助覆盖对收缩压和 HbA 的影响存在异质性。例如,基线医疗费用较低的个体,从获得医疗补助覆盖中获益的预测更高。对于预测获益较高的人,医疗补助覆盖显著降低收缩压(-4.96mmHg(95%置信区间-7.80 至-2.48))。对于预测获益较高的人,HbA 也因医疗补助覆盖而显著降低,但幅度在临床意义上无差异(-0.12%(-0.25%至-0.01%))。
尽管医疗补助覆盖平均没有改善心血管风险因素,但在该人群中观察到了显著的异质性。例如,预测获益较高的个体更有可能没有或仅有较低的前期医疗费用。我们的发现表明,医疗补助覆盖对一些人,特别是对血压,改善了心血管风险因素,尽管这些益处可能被那些没有受益的人稀释。