Alzheimer's Disease and Related Dementia Care within the Medicare Program

摘要： 数以百万计的美国人患有阿尔茨海默氏症和相关痴呆症，还有数百万人患有痴呆症 预计在接下来的几十年里会发展成痴呆症；痴呆症是一种改变生活的疾病，具有高度的 相关发病率。这些患者中的大多数都是医疗保险的受益人。随着新的治疗方法的出现， 由于这种治疗的潜在成本，未来的改善和风险都是有希望的。 再加上越来越多的患者，特别是患者、家庭和 医疗保险计划(预计到2050年，总支出将达到1万亿美元/年)。尽管有承诺 和风险，在传统的按服务收费的医疗保险中，关于当前痴呆症护理的信息有限 计划(又名TM)，是Medicare的两个组成部分中较大的一个(另一个组成部分是Medicare Advantage (Ma)，由私人计划管理)。在全国范围内审查医疗保健的一个主要障碍 医疗保险计划是索赔数据中痴呆症诊断的不确定有效性。为了解决这些问题，我们 首先将使用一种新颖的数据集，该数据集具有来自痴呆症登记处的纵向数据的个体级别链接， 电子健康记录和联邦医疗保险索赔(2006-17)预测哪些患者与痴呆症相关 诊断代码有真实的疾病。然后，我们将把这种方法应用于所有TM的国家数据集 受益人确定痴呆症受益人(2006-21)，评估护理模式，并检查其影响 关于受益人接受符合指南的痴呆症护理的医疗保险政策变化。我们将利用一个 在自然实验中，政策变化改变了TM和MA患者在每个县的分布， 即，由于强制MA基准变化而导致MA渗透率的变化。先前的研究发现，MA 对于一些慢性疾病，例如糖尿病和MA，与TM相比，具有更好的过程质量 渗透性有利地影响指南的遵守和TM对此类情况的护理。我们将调查 TM中MA渗透对痴呆症护理的影响我们有三个目标：目标1)验证基于索赔的 确诊为痴呆症患者的痴呆症定义；目的2)检查MA的影响 对TM痴呆患者进行指南一致性诊断评估的穿透性；以及Aim 3)检查 MA渗透对TM痴呆患者指南一致性治疗的影响。总而言之，我们 将应用现代数据科学方法来识别医疗保险计划中的患者 痴呆，然后检查医疗保险计划的变化如何影响每个县的痴呆症护理 美国。这些医疗保险政策的变化既有助于产生证据，也可能有助于 未来改善痴呆症护理的干预措施，例如，通过调整医疗保险质量激励措施。 此外，这些数据可以帮助患者、家属、临床医生和政策制定者了解我们如何 改善对这一快速增长的患者群体的护理。

Abstract: Millions of Americans have Alzheimer's Disease and Related Dementias (“dementia”), with many millions more expected to develop it over the next few decades; dementia is a life-altering condition with high levels of associated morbidity. Most of these patients are Medicare beneficiaries. With new treatments on the horizon, there is both a promise for future improvements and risk because of the potential cost of such treatments combined with growing numbers of patients, especially risk of fiscal strain for patients, families, and the Medicare program (total spending is estimated to reach $1 trillion dollars/year by 2050). Despite the promise and risk, there is limited information about current dementia care within the traditional fee-for-service Medicare program (aka TM), the larger of Medicare's two components (the other component is Medicare Advantage (MA), which is administered by private plans). One major barrier to examining care nationally within the Medicare program is the uncertain validity of dementia diagnoses in claims data. To address these issues, we first will use a novel dataset with individual-level linkages of longitudinal data from a dementia registry, electronic health record, and Medicare claims (2006-17) to predict which patients with dementia-related diagnosis codes have true disease. We then will apply this approach to a national dataset of all TM beneficiaries to identify beneficiaries with dementia (2006-21), assess care patterns, and examine the impact of Medicare policy changes on beneficiaries' receipt of guideline-concordant dementia care. We will exploit a natural experiment in which policy changes shift the distribution of patients in TM vs. MA within each county, i.e., changes in MA penetration because of mandated MA benchmark changes. Prior work has found that MA has better process quality compared to TM for some chronic conditions, e.g., diabetes, and that MA penetration favorably impacts guideline adherence and care in TM for such conditions. We will investigate the effect of MA penetration on dementia care within TM. We have three aims: Aim 1) Validation of a claims-based dementia definition among those who have a dementia diagnosis; Aim 2) Examination of the impact of MA penetration on guideline-concordant diagnostic evaluation for TM dementia patients; and Aim 3) Examination of the impact of MA penetration on guideline-concordant treatments for TM dementia patients. In summary, we will apply modern data science approaches to identify the patients in the Medicare program who have dementia, then examine how changes in the Medicare program affects dementia care within each county in the United States. These Medicare policy changes both help generate evidence and could lend themselves to future interventions to improve dementia care, e.g., through adjustments in Medicare quality incentives. Moreover, these data could help inform patients, families, clinicians, and policy makers about how we can improve care for this rapidly expanding population of patients.