Identifying Risk Factors For Late-Life Dementia Based On Job Characteristics During The Working Life

根据工作期间的工作特征识别晚年痴呆症的风险因素

• 批准号: 10213393
• 负责人: Peter Hudomiet
• 金额: $ 52.87万
• 依托单位: RAND CORPORATION
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10213393
• 关键词: Adult; Affect; Age; Alzheimer' s Disease; Alzheimer' s disease related dementia; American; Brain; Brain Diseases; Characteristics; Cognition; Cognitive; Collection; Complement; Complex; Critical Thinking; Data; Databases; Dementia; Descriptor; Deterioration; Dictionary; Dimensions; Disease; Disease Progression; Education; Educational Background; Elderly; Face; Future; Gender; Genetic Risk; Health and Retirement Study; Impaired cognition; Individual; Information Networks; Intervention; Life; Link; Measurement; Measures; Medical; Methodological Studies; Methodology; Methods; Modeling; Occupational; Occupations; Outcome; Participant; Physical activity; Population; Probability; Process; Race; Records; Reproducibility; Research; Retirement; Risk; Risk Factors; Science Policy; Social Sciences; Statistical Methods; Techniques; Testing; Time; Validation; Work; base; brain health; career; cognitive ability; cognitive function; cognitive reserve; daily functioning; dementia risk; effective therapy; genetic risk factor; genome wide association study; human old age (65+); interest; longitudinal dataset; mathematical theory; modifiable risk; multidimensional data; novel; physical conditioning; skills; social; social science research; theories

Title: Identifying risk factors for late-life dementia based on job characteristics during the working life Project participants: Péter Hudomiet (RAND, PI), Irineo Cabreros (RAND), Michael D. Hurd (RAND), and Susann Rohwedder (RAND) Revised Abstract 01/05/2021 To date, no treatment is available for Alzheimer’s disease and most types of Alzheimer’s disease-related dementias (AD/ADRD), even though this devastating condition affects many older adults. However, there are several known modifiable risk factors for AD/ADRD, which offer hope of finding interventions that may delay its onset. Investigating how job characteristics are related to the risk of AD/ADRD is a promising way of identifying such modifiable risk factors, because individuals spend a substantial portion of their lives working. Differences in job characteristics and work activities, for example, may explain (at least partially) why highly educated individuals face a substantially lower risk of developing dementia than lower educated individuals do. We propose to investigate the relationship between a large number of job characteristics and AD/ADRD using novel estimation methods, and a systematic and reproducible approach. We will use hundreds of occupational job characteristics from the Occupational Information Network (O*NET) database, which can be linked to the nationally representative Health and Retirement Study (HRS). The O*NET is promising for dementia research because it includes many measures describing cognitive activities, such as whether a job requires memorization, critical thinking, mathematical reasoning, or social orientation. The O*NET has hundreds of measures that are often strongly correlated. We will develop statistical methods to optimally process the items into easy-to-use low dimensional measures; and study their explanatory power for dementia. This study has four specific aims. First, we will develop and implement a methodology which we call Occupation-Wide Association Study (OWAS), to derive occupational risk factors of AD/ADRD. OWAS is inspired by the statistical approach employed in Genome-Wide Association Studies, which is used to identify genetic risk factors of various medical conditions. As such, the OWAS methodology will leverage established statistical techniques to handle the complex O*NET data with highly correlated items, and to correct for the number of false positives in multiple hypotheses testing. To complement the data-driven OWAS methodology, our second aim is to construct more detailed job characteristic measures guided by prior medical and social science research, such as quantitative skills, executive cognitive functions, job control, and social orientation. Third, we will examine the explanatory power of the developed scores for various cognitive outcomes in the HRS, such as age-adjusted probabilities of dementia and longitudinal change in cognition. Fourth, we will test how much of the correlation between basic demographic covariates and dementia can be explained by the developed job measures. We are particularly interested in testing how much of the explanatory power of education for AD/ADRD shrinks after controlling for the new job measures. We will also explore effects of other variables such as gender, race, and the physical health of individuals.

标题：根据工作期间的工作特征确定晚年痴呆症的危险因素 项目参与者：PéterHudomiet（Rand，PI），Irineo Cabreros（Rand），Michael D. Hurd（Rand）和Susann Rohwedder（Rand） 修订摘要01/05/2021 迄今为止，即使这种毁灭性的疾病会影响许多老年人，即使阿尔茨海默氏病和大多数类型的与阿尔茨海默氏病相关的痴呆症（AD/ADRD）的治疗尚无治疗。但是，AD/ADRD有几种已知的可修改风险因素，这些风险因素提供了寻找可能延迟其发作的干预措施的希望。调查工作特征与AD/ADRD风险有关的方式是确定此类可修改风险因素的一种承诺方式，因为个人花费很大一部分生命。例如，工作特征和工作活动的差异可以解释（至少部分地）为什么受过良好教育的人面临的痴呆症的风险要比受过较低的受过教育的人所面临的大幅低。 我们将使用来自职业信息网络（O*NET）数据库的数百个职业工作特征，可以与国家代表性的健康和退休研究（HRS）链接。 O*网络对痴呆症研究有希望，因为它包括许多描述认知活动的措施，例如工作是否需要记忆，批判性思维，数学推理或社会取向。 O*网具有数百种通常密切相关的测量值。我们将开发统计方法，以将项目最佳地处理为易于使用的低维度测量值；并研究其对痴呆症的利用能力。 这项研究具有四个具体目标。首先，我们将开发和实施一种方法，我们称之为范围内的协会研究（OWAS），以导致AD/ADRD的职业风险因素。 OWAS的灵感来自全基因组关联研究中的统计方法，该方法用于鉴定各种医疗状况的遗传危险因素。因此，OWAS方法将利用已建立的统计技术来处理具有高度相关项目的复杂O*净数据，并在多个假设测试中纠正误报数量。 为了完成数据驱动的OWAS方法，我们的第二个目的是构建以先前的医学和社会科学研究为指导的更详细的工作特征措施，例如定量技能，执行认知功能，工作控制和社会取向。 第三，我们将研究HRS中各种认知结果的开发得分的爆炸能力，例如痴呆症的年龄调整可能性和认知纵向变化。第四，我们将测试可以通过开发的工作测量来解释基本人口协变量与痴呆之间的相关性。我们特别有兴趣测试在控制新的工作措施后，在收缩了多少爆炸性的AD/ADRD缩水。我们还将探索其他变量的影响，例如性别，种族和个人的身体健康。