The Health and Retirement Study: Contextual Data Augmentation

Christopher Dick

doi:10.1515/fhep-2021-0068

Article

The Health and Retirement Study: Contextual Data Augmentation

Christopher Dick

Published/Copyright: March 7, 2022

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From the journal Forum for Health Economics and Policy Volume 25 Issue 1-2

Abstract

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.

Keywords: contextual data; climate; evictions

Corresponding author: Christopher Dick, Demographic Analytics Advisors and Georgetown University, Washington, DC, USA, E-mail: chris@demoadvisors.com

Funding source: National Institute on Aging

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Received: 2021-12-23

Accepted: 2022-02-10

Published Online: 2022-03-07

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Articles in the same Issue

https://doi.org/10.1515/fhep-2021-0068

Keywords for this article

contextual data; climate; evictions