We provide new insights into the finding that Medicaid increased emergency department (ED) use from the Oregon experiment. Using nonparametric causal machine learning methods, we find economically meaningful treatment effect heterogeneity in the impact of Medicaid coverage on ED use. The effect distribution is widely dispersed, with significant positive effects concentrated among high-use individuals. A small group - about 14% of participants - in the right tail with significant increases in ED use drives the overall effect. The remainder of the individualized treatment effects is either indistinguishable from zero or negative. The average treatment effect is not representative of the individualized treatment effect for most people. We identify four priority groups with large and statistically significant increases in ED use - men, prior SNAP participants, adults less than 50 years old, and those with pre-lottery ED use classified as primary care treatable. Our results point to an essential role of intensive margin effects - Medicaid increases utilization among those already accustomed to ED use and who use the emergency department for all types of care. We leverage the heterogeneous effects to estimate optimal assignment rules to prioritize insurance applications in similar expansions.
翻译:我们从俄勒冈试验中发现,医疗援助计划增加了应急部门(ED)的使用。使用非参数性因果机学方法,我们发现在医疗援助覆盖对ED使用的影响方面,具有经济上有意义的治疗效果异质性。效果分布很分散,在使用率高的个人中集中产生了显著的积极影响。右尾端的一小群——大约14%的参与者——促进了总体效果。个人化治疗效果的其余部分要么与零分化,要么与负分不开。平均治疗效果不能代表大多数人的个人化治疗效果。我们确定四个优先组,在ED使用方面有较大和统计上显著的增加。我们确定四个优先组,即男性、以前SNAP参与者、年龄不到50岁的成年人,以及使用ED前使用被归类为初级护理治疗的人群。我们的结果表明,密集边缘效应具有基本作用。医疗援助提高了那些已经习惯使用ED的人群和那些使用应急部门进行所有类型护理的人的利用率。我们利用了多种效应来估计最佳分配规则,以便在类似的扩展中优先进行保险应用。