Volume 25, Issue 1-2 - Special Issue: Enhancing Data Resources to Support Research on Aging; Guest Editors: Terrie Moffitt and John W. R. Phillips

In the absence of effective pharmacological treatment to halt or reverse the course of Alzheimer’s disease and related dementias (ADRDs), population-level research on the modifiable determinants of dementia risk and outcomes for those living with ADRD is critical. The Harmonized Cognitive Assessment Protocol (HCAP), fielded in 2016 as part of the U.S. Health and Retirement Study (HRS) and multiple international counterparts, has the potential to play an important role in such efforts. The stated goals of the HCAP are to improve our ability to understand the determinants, prevalence, costs, and consequences of cognitive impairment and dementia in the U.S. and to support cross-national comparisons. The first wave of the HCAP demonstrated the feasibility and value of the more detailed cognitive assessments in the HCAP compared to the brief cognitive assessments in the core HRS interviews. To achieve its full potential, we provide eight recommendations for improving future iterations of the HCAP. Our highest priority recommendation is to increase the representation of historically marginalized racial/ethnic groups disproportionately affected by ADRDs. Additional recommendations relate to the timing of the HCAP assessments; clinical and biomarker validation data, including to improve cross-national comparisons; dropping lower performing items; enhanced documentation; and the addition of measures related to caregiver impact. We believe that the capacity of the HCAP to achieve its stated goals will be greatly enhanced by considering these changes and additions.

The Health and Retirement Study is an amazing resource for those studying aging in the United States, and a fantastic model for other countries who have created similar longitudinal studies. The raw amount of information, from data on income, wealth, and use of health services to employment, retirement, and family connections on to the collection of clinical biomarkers can be both empowering and overwhelming to a researcher. Luckily through the process of engagement with the research community and constant improvement, these reams of data are not only consistently growing in a thoughtful and focused direction, they are also explained and summarized to increase the ease of use for all. One of the very useful areas of the HRS is the Contextual Data File (CDF), which is the focus of this review. The CDF provides access to easy-to-use helpful community-level data in a secure environment that has allowed researchers to answer questions that would have otherwise been difficult or impossible to tackle. The current CDF includes data in six categories (University of Michigan Institute for Social Research. 2017. HRS Data Book: The Health and Retirement Study: Aging in the 21st Century, Challenges and Opportunities for Americans . Ann Arbor: University of Michigan. Also available at https://hrs.isr.umich.edu/about/data-book, 17): 1. Socio-economic Status and Demographic Structure 2. Psychosocial Stressors 3. Health Care 4. Physical Hazards 5. Amenities 6. Land Use and the Built Environment. Each of these areas have allowed researchers to answer interesting questions such as what is the impact of air pollution on cognition in older adults (Ailshire, J., and K. M. Walsemann. 2021. “Education Differences in the Adverse Impact of PM 2.5 on Incident Cognitive Impairment Among U.S. Older Adults.” Journal of Alzheimer’s Disease 79 (2): 615–25), the impact of neighborhood characteristics on obesity in older adults (Grafova, I. B., V. A. Freedman, R. Kumar, and J. Rogowski. 2008. “Neighborhoods and Obesity in Later Life.” American Journal of Public Health 98: 2065–71), or even what do we gain from introducing contextual data to a survey analysis (Wilkinson, L. R., K. F. Ferraro, and B. R. Kemp. 2017. “Contextualization of Survey Data: What Do We Gain and Does it Matter?” Research in Human Development 14 (3): 234–52)? My review focuses on the potential to expand contextual data in a few of these areas. From new data sets developed and released by the U.S. Census Bureau, to improved measurements of climate and environmental risk, there are numerous new data sources that would be a boon to the research community if they were joined together with the HRS. The following section begins by breaking down the opportunity provided by community or place-based data before moving on to specific recommendations for new data that could be included in the HRS contextual data file.

Selection bias is an ongoing concern in large-scale panel surveys where the cumulative effects of unit nonresponse increase at each subsequent wave of data collection. A second source of selection bias in panel studies is the inability to link respondents to supplementary administrative records, either because respondents do not consent to link or the matching algorithm fails to locate their administrative records. Both sources of selection bias can affect the validity of conclusions drawn from these data sources. In this article, I discuss recently proposed methods of reducing both sources of selection bias in panel studies, with a special emphasis on reducing selection bias in the US Health and Retirement Study.

A recent report from the National Academies of Sciences, Engineering, and Medicine (NASEM) highlights rising rates of working-age mortality in the United States, portending troubling population health trends for this group as they age. The Health and Retirement Study (HRS) is an invaluable resource for researchers studying health and aging dynamics among Americans ages 50 and above and has strong potential to be used by researchers to provide insights about the drivers of rising U.S. mortality rates. This paper assesses the strengths and limitations of HRS data for identifying drivers of rising mortality rates in the U.S. and provides recommendations to enhance the utility of the HRS in this regard. Among our many recommendations, we encourage the HRS to prioritize the following: link cause of death information to respondents; reduce the age of eligibility for inclusion in the sample; increase the rural sample size; enhance the existing HRS Contextual Data Resource by incorporating longitudinal measures of structural determinants of health; develop additional data linkages to capture residential settings and characteristics across the life course; and add measures that capture drug use, gun ownership, and social media use.

The aim of this paper is to evaluate the utility of the Health and Retirement Study (HRS) for studying the impact of working conditions on individuals’ health, well-being and labor supply decisions at older ages. I provide a brief overview of the information on working conditions that is currently available in the HRS and discuss implications for studies on the effects of working conditions on the individual life course. I conclude with a discussion of how recent and projected trends in the U.S. workforce are reflected in the current HRS survey content.