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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

项目摘要

项目成果

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中文摘要
翻译
项目摘要/摘要 联邦医疗保险是居住在美国的老年人(65岁以上)的主要医疗保险。符合条件的受益人必须 在两种不同的计划类型中选择:传统Medicare(TM)和Medicare Advantage(MA)。体量 在过去十年中,参保人数翻了一番,目前覆盖了约45%的联邦医疗保险受益人。这 增长在一定程度上是由黑人和西班牙裔硕士注册人数比例的增加推动的。而当 有证据表明,一些MA患者群体得到了更高质量的护理,经历的更少 种族/民族健康差异,尚不清楚在社会经济上处于不利地位的黑人是否如此 和西班牙裔医疗保险受益人,他们发生阿尔茨海默病的风险大大增加, 相关痴呆症(ADRD)。因为他们有更复杂的医疗需求,但协调程度较低 护理和更少的预防性服务,他们面临着显著更高的潜在可预防风险 住院、30天再次住院和其他不良健康事件比他们的白人同行更多。 此外,MA计划的质量也有相当大的差异。许多并购计划都是黑人和西班牙裔的 人口稠密的县的质量评级较低,这可能会导致种族/民族健康差距。此外, 受益人的健康计划决策复杂,有可能加剧种族/民族健康差距 在ADRD的受益者中。最后,种族/民族医疗保健差异的研究主要是观察性的。 和描述性,从而限制了它在提供政策信息方面的有用性。这项混合方法研究的具体目的 是使用创新的计量经济因果方法来(1)检查和解释16个国家的种族/民族差异 MA与TM的护理连续性、预防性服务的接受和护理质量的措施;(2) 在16项护理连续性措施中审查MA计划内部和之间的种族/族裔差异, 接受预防服务和护理质量;以及(3)探讨保健计划的决策 ADRD受益人和/或其照顾者面临的挑战。我们的多学科团队,拥有 在ADRD研究、种族/民族医疗保健差异分析、索赔数据和 计量经济学、定性和混合方法以及卫生政策是唯一有资格进行这一创新的方法 研究。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和更容易 社会经济弱势黑人和西班牙裔医疗保险受益人的健康计划决策 和ADRD在一起。与我们的利益攸关方国家政策咨询委员会,包括ADRD的一名受益者 和/或护理者,我们有能力传播研究结果并促进高质量和公平的健康 照顾种族/少数民族医疗保险受益人与ADRD。
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
Using Machine Learning to Improve Readmission Prediction in Alzheimer's Disease and Related Dementia
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