Effect of ACA Medicaid Expansion on Diabetes: Diagnosis, Treatment, Patient Compliance, and Health Outcomes

 描述(由申请人提供)：糖尿病及其并发症是医疗费用、费用增长以及患者发病率和死亡率的最大贡献者之一。糖尿病往往得不到及时的诊断。当它被诊断出来时，患者往往不遵守复杂的治疗方案。糖尿病的健康负担不成比例地落在穷人和少数民族身上。我们项目的一个中心目标是研究医疗补助计划的ACA扩展将如何影响新加入的人中的糖尿病诊断、治疗和结果。我们将使用一些州扩大医疗补助计划和其他州选择不这样做而产生的自然实验；结合四个扩大州和五个非扩大州900万人口的详细、纵向电子健康记录(EHR)来做到这一点。多个州对医疗补助资格的冲击和大规模获得电子健康记录的结合是独一无二的。之前关于成人医疗补助扩展的研究规模小、持续时间短，缺乏详细的健康记录，或者这些限制的组合。现有的最好的研究是俄勒冈州健康保险实验，在两年的时间里，将6000名新的医疗补助接受者与6000名对照组在一个州进行了比较。俄勒冈州的研究发现，糖尿病诊断和糖尿病药物的使用大幅增加，但血糖水平没有显著变化。然而，医疗补助对结果的任何影响都将缓慢显现。相比之下，我们估计我们将有100-150,000名新的医疗补助接受者，5年的治疗前数据来建立基线健康并将接受治疗的个人与对照组相匹配，6年的研究期间的治疗后数据(之后可能进行跟踪)，以及几个接受治疗的州，具有不同的医疗补助计划，并有可能扩展到更多的州。这将使我们能够研究近期和中期结果，并评估治疗效果是否因个人特征或医疗补助计划的性质而异。第二个项目目标是创新因果推理方法。我们将结合差异法(DID)、匹配法和多重归因法。我们将使用匹配和多种归罪方法来匹配处于非扩展状态的控制人，以匹配处于扩展状态的新的医疗补助参与者。我们将使用分布滞后回归来描绘随着时间的推移任何治疗效果的出现。就我们所知，DID、匹配和多重归因法并没有结合在一项研究中。这一组合设计将有助于研究其他医疗保险的扩展。

 DESCRIPTION (provided by applicant): Diabetes and its complications are among the largest contributors to healthcare costs, cost growth, and patient morbidity and mortality. Diabetes is often not timely diagnosed. When it is diagnosed, patients often fail to comply with a complex treatment regimen. The health burden of diabetes falls disproportionately on the poor and on ethnic minorities. A central goal of our project is to examine how the ACA expansion of Medicaid will affect diabetes diagnosis, treatment, and outcomes among the newly enrolled. We will do so using the natural experiment that results from some states expanding Medicaid and others choosing not to do so; combined with detailed, longitudinal electronic health records (EHR) for a large population of 9 million persons in four expansion and five non-expansion states. The combination of a multi-state shock to Medicaid eligibility and large-scale access to EHR is unique. Prior studies of adult Medicaid expansions have been small-scale, short-duration, lacked detailed health records, or a combination of these limitations. The best available study, the Oregon Health Insurance Experiment, compared 6,000 new Medicaid recipients to 6,000 controls in a single state, for a two-year period. The Oregon research found large increases in diabetes diagnosis and use of diabetes medication, but no significant change in blood sugar levels. Yet any effect of Medicaid on outcomes will emerge slowly. In contrast, we estimate that we will have 100-150,000 new Medicaid recipients, 5 years of pre-treatment data to establish baseline health and match treated individuals to controls, 6 years of post-treatment data in the study period (with potential follow-up after that), and several treated state, with different Medicaid programs, and potential to expand to additional states. This will let us study both near- and medium term outcomes, and assess whether treatment effects vary with personal characteristics or the nature of the Medicaid program. A second project goal is to innovate in causal inference methods. We will combine difference-in-differences (DiD), matching, and multiple imputation methods. We will use matching and multiple imputation methods to match control persons in non-expansion states, to match to new Medicaid enrollees in expansion states. We will use distributed lag regressions to map the emergence of any treatment effect over time. DiD, matching, and multiple imputation methods have not to our knowledge been combined in a single study. This combined design will be useful in studying other health insurance expansions.