Advanced Development and Utilization of Assembled Aging Trajectory Files from Multiple Datasets

项目总结 这个项目的目标是创建一个独特的、全面的老化轨迹数据研究库。 Taset、相关资源和分析方法，可用于回答 老龄化及相关科学。具体地说，通过协调和合并多个数据集，该项目将产生- 获取所需的数据基础架构，以了解护理环境、老年综合征、身体状况等随时间的变化 CAL功能和多个级别的共享风险因素(患者、提供者、社区、医疗保健系统和 社会)和跨多个领域(生物、行为、社会文化和物理/建筑环境) 包括慢性病和急性病病史，如新冠肺炎，暴露在空气污染中，邻里- 胡德社会经济和卫生保健系统因素(目标1)。将为用户制定分析策略- 确定的队列及其倾向得分匹配的对照组，例如，患有慢性阻塞性肺病的老年人 病情包括阿尔茨海默病和相关痴呆(ADRD)、糖尿病、心力衰竭、终末期再痴呆 鼻咽癌、转移性癌症和艾滋病毒。最先进的分析方法被用来识别农业的模式。 老年人经历的ING轨迹(护理环境、老年综合征、身体功能) 生命的最后几年及其与共同风险因素和远期结局的关联(目标2)。来自Assem的- 出血轨迹文件在AIM 1中，通过对齐起始索引时间(例如年龄截止点)来导出队列 和确诊时的时间(例如，ADRD、中风、慢性肾脏疾病)。既是基于模型的方法，也是主要的 然后，使用中国学习算法来发现轨迹的多水平和潜在的交互预测器- RIES(例如，独立活着受益人的功能迅速下降)和具体结果(例如，呼吸系统 新冠肺炎确诊的医疗保险受益人中的呼吸机使用情况)(目标3)。独特的资源是 然后共享以传播资源，包括数据集、文档、源代码和方法(AIM 4)。在这个项目的最后，研究基础设施，以调查风险分担之间的关系 因素和老化轨迹将准备好使用和复制，使调查人员具有前所未有的能力 解决老龄化科学面临的新挑战。这将使研究人员了解潜在的过程和 与护理环境中的可逆性伤残期相关的系统，以及可能 用于支持功能恢复和减少老年综合征，包括认知能力下降，用于 目的是减少繁重的护理过渡，并保持功能独立性。这个项目 还将创造必要的资源和方法，以评估创新和干预措施的影响- 在患者、提供者、社区、医疗保健系统和社会/政策层面实施，以改善护理 老年人的质量和结果。

PROJECT SUMMARY This goal of this project is to create a unique and comprehensive research repository of aging trajectory da- tasets, related resources, and analytic methods that can be used to answer new and important questions in aging and related sciences. Specifically, by harmonizing and merging multiple data sets this project will gener- ate the data infrastructure needed to understand change over time in care settings, geriatric syndromes, physi- cal functioning, and shared risk factors at multiple levels (patient, provider, community, healthcare system, and society) and across multiple domains (biological, behavioral, sociocultural, and physical/built environments) including chronic conditions and history of acute illness such as COVID-19, exposure to air pollution, neighbor- hood socioeconomic, and health care system factors (Aim 1). Analytic strategies will be developed for user- defined cohorts and their propensity score-matched controls, e.g., older adults who were living with chronic conditions including Alzheimer's disease and related dementias (ADRD), diabetes, heart failure, end-stage re- nal disease, metastatic cancer, and HIV. State-of-the-art analytic methods are used to identify patterns of ag- ing trajectories (care setting, geriatric syndromes, physical functioning) experienced by older adults during the final years of life and their association with shared risk factors and distal outcomes (Aim 2). From the assem- bled trajectory file in Aim 1, cohorts are derived by aligning an originating index time such as age cutoff point and time at diagnosis (e.g., ADRD, stroke, chronic kidney disease). Both a model-based approach and ma- chine learning algorithms are then used to discover multilevel and potentially interactive predictors of trajecto- ries (e.g., rapid functional decline in independent living beneficiaries) and specific outcomes (e.g., respiratory ventilator usage among Medicare beneficiaries diagnosed with COVID-19) (Aim 3). The unique resources are then shared to disseminate resources including datasets, documentation, source code, and methodology (Aim 4). At the end of this project, the research infrastructure to investigate the relationship between shared risk factors and aging trajectories will be ready to use and replicate, giving investigators unprecedented ability to solve new challenges in aging science. This will allow researchers to understand the underlying processes and systems associated with reversible periods of disability across care settings, and interventions that may be used to support recovery of function and reduction of geriatric syndromes including cognitive decline, for the purpose of reducing burdensome care transitions, and maintenance of functional independence. This project will also create the resources and methods needed to evaluate the impact of innovations and interventions im- plemented at the patient, provider, community, healthcare system, and society/policy levels to improve care quality and outcomes for older adults.