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

标题：根据工作期间的工作特征确定老年痴呆症的风险因素 项目参与者：Péter Hudomiet（兰德，PI）、Irineo Cabreros（兰德）、Michael D.赫德（兰德）和苏珊·罗韦德尔（兰德） 修订摘要01/05/2021 迄今为止，阿尔茨海默病和大多数类型的阿尔茨海默病相关痴呆症（AD/ADRD）没有治疗方法，尽管这种毁灭性的疾病影响着许多老年人。然而，AD/ADRD有几个已知的可改变的风险因素，这为找到可能延迟其发作的干预措施提供了希望。调查工作特征如何与AD/ADRD风险相关是识别此类可改变的风险因素的一种有希望的方法，因为个人一生中很大一部分时间都在工作。例如，工作特征和工作活动的差异可以解释（至少部分地）为什么受过高等教育的人比受教育程度较低的人患痴呆症的风险要低得多。 我们建议调查大量的工作特征和AD/ADRD之间的关系，使用新的估计方法，和一个系统的和可重复的方法。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，该数据库可以链接到具有全国代表性的健康和退休研究（HRS）。O*NET对痴呆症研究很有希望，因为它包括许多描述认知活动的指标，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*NET有数百个通常密切相关的度量。我们将开发统计方法，以最佳方式将项目处理为易于使用的低维措施;并研究其对痴呆症的解释力。 这项研究有四个具体目标。首先，我们将开发和实施一种方法，我们称之为职业范围协会研究（OWAS），以获得AD/ADRD的职业危险因素。OWAS的灵感来自全基因组关联研究中采用的统计方法，该方法用于识别各种医疗条件的遗传风险因素。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的复杂O*NET数据，并校正多个假设测试中的误报数量。 为了补充数据驱动的OWAS方法，我们的第二个目标是在先前的医学和社会科学研究的指导下构建更详细的工作特征测量，如定量技能，执行认知功能，工作控制和社会取向。 第三，我们将检查HRS中各种认知结果的开发分数的解释力，例如痴呆的年龄调整概率和认知的纵向变化。第四，我们将测试基本人口统计学协变量和痴呆症之间的相关性有多大程度上可以通过开发的工作措施来解释。我们特别感兴趣的是，在控制了新的就业措施后，测试教育对AD/ADRD的解释力有多大。我们还将探讨其他变量的影响，如性别，种族和个人的身体健康。

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.