This part of the mid-term review of the Health and Retirement Study (HRS) provides an overall assessment of the utility of HRS data for research targeting the nature and influence of social connectedness. As one of the major dimensions of the social aspects of psychosocial influences, social connectedness is among the most complicated in terms of definition, conceptualization, and measurement. However, the century-long body of theory and findings couple with a recent resurgence of research on the critical impact of these ties for health, illness, and health care to call for an examination of the richness in and limitations of current HRS data.
This assessment is comprised of three broad steps: 1) an overview of the nature of social connectedness, and of the dimensions and methodological approaches that can and have been used in studying health, health care, and aging; 2) the range, strengths and limitations of the HRS data on each approach; and 3) suggestions for potential directions to increase the utility of data collected and further research contributions from the HRS. While no tabular listing of items relevant to social connectedness is presented, the sets of items that tap this notion are referenced throughout.
Overall, the HRS represents one of, if not the most impressive data sets regarding the ability to examine the influence of social connectedness on health, illness and health care. Given different theoretical and methodological traditions of social connectedness (e.g., the local or ego-centered perspective; social support perspective; social capital perspective; Pescosolido 2006a), the HRS either currently offers a way to tap into various views of social connectedness or holds the potential to do so. Specifically, the HRS includes four kinds of social connectedness data: socio-demographic proxies that represent a tie (e.g., marital status) with detailed data on the nature of the bond; social support batteries which offer respondent perceptions of the overall positive and negative aspects of sets of relationships; eco-centric tie data, which provide a list of names or roles that can provide support (i.e. latent ties); and networks of event response in which respondents list individuals who were called upon (e.g., activated ties) under certain conditions. Given the individually-based and national scope of the HRS, the collection of full or complete network data is not feasible at present.
Four strategies could improve the collection and use of social connectedness data in the HRS. First, data collection sections that are explicit or implicit ego-centric name generators or activated ties lists could be expanded and refined to provide more complete data. Under the “looping” structure of the HRS, both the ego-centric and event response batteries can serve as a foundation for expanded network batteries. Second, given the increasing role of social media in contemporary American lives, the HRS section on the use of technology should be reviewed and expanded to tap into virtual ties. Third, locator data designed to improve follow-up of the HRS samples can form the basis of a network roster and for analyses of the dynamics of ties and its influence on health and health care. Fourth, while it is not possible to “go back” and recapture data about social connectedness, a sub study which targets the named “social convoy” over a person’s life (defined only as time in the HRS) would provide invaluable data that could not be collected from any other existing study. That is, while subject to a variety of criticism (e.g., telescoping effects), the ability to collect data on extent of turnover and the reasons for shifts in social connectedness would allow an analysis of the impact of social network dynamics in later life, potentially reveal key turning points in social network support, and offer targeted points of interventions for fostering the social connectedness that has, to date, been shown (in the HRS and other studies) to be so essential to health and well-being.
Social Isolation and Medicare Spending: Among Older Adults, Objective Isolation Increases Expenditures While Loneliness Does Not
