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Racial/Ethnic Disparities in Health Care and Challenges in Insurance Plan Choices among Older People with Alzheimer’s Disease and Related Dementia: A Mixed Methods Study of Medicare Options

Racial/Ethnic Disparities in Health Care and Challenges in Insurance Plan Choices among Older People with Alzheimer’s Disease and Related Dementia: A Mixed Methods Study of Medicare Options
患有阿尔茨海默病和相关痴呆症的老年人在医疗保健方面的种族/民族差异以及保险计划选择的挑战:医疗保险选项的混合方法研究
批准号:
10723148
负责人:
Elham Mahmoudi
金额:
$72.68万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-08-01 至 2028-04-30

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项目成果

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中文摘要
翻译
项目总结/摘要 Medicare是居住在美国的老年人(65岁以上)的主要健康保险。 选择两种不同的计划类型:传统的医疗保险(TM)和医疗保险优势(MA)。马 在过去的十年中,注册人数翻了一番,目前覆盖了所有医疗保险受益人的45%。这 增长的部分原因是黑人和西班牙裔MA入学者比例的增加。而 有证据表明,一些MA患者群体获得了更好的护理质量, 种族/民族健康差距,不知道这是否是真正的社会经济弱势黑人 和西班牙裔医疗保险受益人,他们患阿尔茨海默病的风险要大得多, 相关性痴呆(ADRD)因为他们有更复杂的医疗保健需求,但得到的协调较少 护理和更少的预防性服务,他们在潜在的可预防的风险显着更高 住院、30天再入院和其他不良健康事件的发生率高于白色患者。 此外,千年评估计划的质量也有相当大的差异。许多MA计划在黑人-和西班牙裔- 人口稠密的县的质量评级较低,这可能导致种族/族裔健康差异。此外,委员会认为, 受益人的健康计划决策是复杂的,可能会加剧种族/民族健康差距 ADRD的受益者。最后,种族/民族卫生保健差异研究主要是观察性的 和描述性的,从而限制了它在告知政策方面的有用性。这项混合方法研究的具体目标是 是使用创新的计量经济学因果方法(1)检查和解释种族/民族差异16 MA与TM的护理连续性、预防性服务接受情况和护理质量的衡量标准;(2) 在16项护理连续性措施中检查MA计划内部和之间的种族/民族差异, 接受预防服务和护理质量;(3)探讨健康计划决策 ADRD受益人和/或其照顾者面临的挑战。我们的多学科团队, 在ADRD研究、种族/民族医疗差异分析、索赔数据和 计量经济学,定性和混合方法,卫生政策是唯一有资格进行这一创新 research.这项研究将为医疗保险政策提供信息,以促进更公平的医疗保健, 社会经济弱势黑人和西班牙裔医疗保险受益人健康计划决策 关于ADRD与我们的国家政策咨询委员会的利益攸关方,包括一个受益人与民主与发展 和/或护理人员,我们有能力传播研究结果并促进质量和公平的健康 照顾患有ADRD的种族/少数民族医疗保险受益人。
英文摘要
Project Summary/Abstract Medicare is the primary health insurance for older adults (65+) living in the U.S. Eligible beneficiaries must choose between two different plan types: traditional Medicare (TM) and Medicare Advantage (MA). MA enrollment has doubled in the last decade, currently covering about 45% of all Medicare beneficiaries. This growth has been driven, in part, by an increasing proportion of Black and Hispanic MA enrollees. While evidence suggests that some MA patient populations receive better-quality care and experience fewer racial/ethnic health disparities, it is unknown whether this is true for socioeconomically disadvantaged Black and Hispanic Medicare beneficiaries, who are at substantially greater risk of incident Alzheimer’s disease and related dementia (ADRD). Because they have more complex healthcare needs but receive less coordinated care and fewer preventive services, they are at significantly higher risk of potentially preventable hospitalization, 30-day hospital readmission, and other adverse health events than their White counterparts. Further, there is considerable variation in the quality of MA plans. Many MA plans in Black- and Hispanic- populated counties have low quality ratings, which may contribute to racial/ethnic health disparities. Moreover, beneficiaries’ health plan decision-making is complex, potentially exacerbating racial/ethnic health disparities among ADRD beneficiaries. Finally, racial/ethnic health care disparity research has been mainly observational and descriptive, thus limiting its usefulness in informing policy. The specific aims of this mixed methods study are to use innovative econometric causal methods to (1) examine and explain racial/ethnic disparities in 16 measures of care continuity, receipt of preventive services, and quality of care in MA vs. TM; (2) examine racial/ethnic disparities within and between MA plans in 16 measures of care continuity, receipt of preventive services, and quality of care; and (3) explore health plan decision-making challenges among beneficiaries with ADRD and/or their caregivers. Our multidisciplinary team, with decades of experience in ADRD research, racial/ethnic healthcare disparity analysis, claims data and econometrics, qualitative and mixed methods, and health policy is uniquely qualified to conduct this innovative research. This study will inform Medicare policies to promote more equitable health care and facilitate easier health plan decision-making for socioeconomically disadvantaged Black and Hispanic Medicare beneficiaries with ADRD. With our national Policy Advisory Committee of stakeholders, including a beneficiary with ADRD and/or a caregiver, we are well-positioned to disseminate findings and promote quality and equitable health care for racial/ethnic minority Medicare beneficiaries with ADRD.
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Using Machine Learning to Improve Readmission Prediction in Alzheimer's Disease and Related Dementia
Using Machine Learning to Improve Readmission Prediction in Alzheimer's Disease and Related Dementia
Using Machine Learning to Improve Readmission Prediction in Alzheimer's Disease and Related Dementia
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