Down Syndrome: Toward Optimal Trajectories and Health Equity using Medicaid Analytic eXtract (DS -TO-THE-MAX)

唐氏综合症：使用 Medicaid Analytic eXtract (DS -TO-THE-MAX) 实现最佳轨迹和健康公平

基本信息

批准号：
10274393
负责人：
Eric S Rubenstein
金额：
$ 174.35万
依托单位：
BOSTON UNIVERSITY MEDICAL CAMPUS
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-30 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10274393
关键词：
Achievement Adult Advocate African American Age Age of Onset Algorithms Alzheimer&apos s Disease American Amyloid beta-Protein Precursor Autopsy Big Data Cause of Death Cessation of life Child Chromosome 21 Chronic Clinical Clinical Research Clinical Trials Color Communities Complement Congenital Heart Defects Data Data Set Data Sources Dementia Development Diagnosis Disabled Persons Down Syndrome Early identification Elderly Enrollment Epidemiology Etiology Genetic Goals Health Hispanics Impairment Incidence Individual Inpatients Insurance Carriers Intellectual functioning disability Intervention Investigation Lead Life Expectancy Linear Models Link Long-Term Care Longevity Longitudinal cohort Low income Machine Learning Medicaid Medical Medicare Methods Modeling Morbidity - disease rate National Institute on Aging Neurofibrillary Tangles Obstructive Sleep Apnea Outcome Outpatients Pain Participant Pattern Persons Policies Population Population Heterogeneity Population Research Practice Guidelines Premature Mortality Prevalence Procedures Proxy Public Health Quality of life Race Research Research Personnel Retrospective cohort Risk Sampling Senile Plaques Sensitivity and Specificity Services Sleep Statistical Methods System Trisomy United States Centers for Medicare and Medicaid Services United States National Institutes of Health Wisconsin Work classification trees cohort disability effective intervention experience health equity health knowledge indexing innovation insight interest large datasets mortality novel population based prevent programs public health insurance racism random forest regression trees resilience sex social determinants social health determinants success treatment guidelines trend ventilation

项目摘要

PROJECT ABSTRACT Down syndrome (DS), a trisomy of chromosome 21 and the most common genetic cause of intellectual disability, was once a condition in which children would rarely see adulthood. In 1950, the estimated mean life expectancy for a person with DS was 26 years and median age at death was 4 years. With improvements in recognition and treatment of co-occurring conditions of DS, such as congenital heart defects, the estimated median life expectancy in 2010 was 53 years (median age at death was 58). There is now a large and diverse population with DS across all ages who are in dire need of solutions and treatments for medical issues that were inconceivable 60 years ago. Two conditions of high interest that often presage other morbidity, greatly harm quality of life, and lead to premature mortality are obstructive sleep apnea (OSA) and dementia. OSA is an episodic sleep-state collapse of the upper airway which results in reduction or lack of ventilation during sleep and is prevalent in more than half of people with DS. The triplication of chromosome 21 in DS is associated with an overproduction of the amyloid precursor protein and is pivotal to the accelerated development of dementia and Alzheimer's disease. Even when not the proximal cause of death, in post-mortem autopsy nearly all older adults with DS have beta amyloid plaques and neurofibrillary tangles that signify Alzheimer's disease. More needs to be known about causes, course, and the impact of social determinants on these conditions and resulting premature mortality, especially as clinical DS samples are often under powered and often lack participants of color. To meet the directives of the National Institutes of Health's Investigation of Co-occurring conditions across the lifespan in Down syndrome project and the National Institute on Aging's priorities we propose to create Down Syndrome: Towards Optimal Trajectories and Health Equity using Medicaid Analytic eXtract (DS-TO-THE-MAX). DS-TO-THE-MAX will identify a retrospective longitudinal cohort of >100,000 adults with DS and >4,000,000 adults without DS from 10 years of Medicaid Analytic eXtract data plus additional Medicare data for dual enrollees. Our innovation lies in a DS sample orders of magnitude larger than past work, a social determinants of health framework, and novel machine learning and quantitative bias analysis methods. We will complete two aims: 1) Assess epidemiology and social determinants of obstructive sleep apnea, dementia, and mortality 2) Use machine learning to identify risk algorithms for obstructive sleep apnea, dementia, and mortality. The results of this project will strengthen the epidemiologic and public health basis for DS research and population level intervention. In building the DS-TO THE MAX cohort and assessing risk, resilience, social determinants we will have a vital big data set to complement clinical research and work toward the population with DS living long, healthy, and self-determined lives.

项目摘要唐氏综合症 (DS)，一种 21 号染色体三体性疾病，是智力障碍最常见的遗传原因残疾曾经是儿童很少能看到成年的一种状况。 1950年，估计 DS 患者的平均预期寿命为 26 岁，死亡中位年龄为 4 岁。和改善 DS 并发疾病（例如先天性心脏病）的识别和治疗缺陷，2010 年预计中位预期寿命为 53 岁（死亡中位年龄为 58 岁）。现在有大量不同年龄段的 DS 患者，他们迫切需要解决方案以及 60 年前无法想象的医疗问题的治疗方法。高利息的两个条件往往预示着其他疾病，严重损害生活质量，并导致过早死亡阻塞性睡眠呼吸暂停（OSA）和痴呆症。 OSA 是一种间歇性睡眠状态下上颌塌陷导致睡眠期间通气减少或缺乏的气道，在超过一半的人中普遍存在患有 DS 的人。 DS 中 21 号染色体的三倍体与淀粉样前体蛋白，对于痴呆和阿尔茨海默病的加速发展至关重要疾病。即使不是最近的死亡原因，在尸检中几乎所有老年人患有 DS 的人有 β 淀粉样斑块和神经原纤维缠结，这表明阿尔茨海默病。更多的需要了解这些情况的原因、过程以及社会决定因素的影响以及由此导致的过早死亡，特别是因为临床 DS 样本通常动力不足且经常缺乏有色人种的参与者。满足美国国立卫生研究院 (National Institutes of Health) 调查的指示唐氏综合症项目和国家研究所在整个生命周期中同时发生的情况我们建议创建唐氏综合症的老龄化优先事项：迈向最佳轨迹和健康使用 Medicaid Analytic eXtract (DS-TO-THE-MAX) 进行公平分析。 DS-TO-THE-MAX 将识别回顾性纵向队列，包括超过 100,000 名患有 DS 的成年人和超过 4,000,000 名没有 DS 的成年人，从 10 年开始多年的医疗补助分析提取数据以及双重参保者的额外医疗保险数据。我们的创新在于DS样本比过去的工作大几个数量级，是健康的社会决定因素框架，以及新颖的机器学习和定量偏差分析方法。我们将完成两个目标：1) 评估阻塞性睡眠呼吸暂停、痴呆症和痴呆症的流行病学和社会决定因素 2) 使用机器学习来识别阻塞性睡眠呼吸暂停、痴呆症和死亡。该项目的结果将加强 DS 的流行病学和公共卫生基础研究和人口层面的干预。在建立 DS-TO THE MAX 队列和评估风险时，复原力、社会决定因素，我们将拥有重要的大数据集来补充临床研究和致力于让 DS 患者过上长寿、健康和自主的生活。