Types of multidimensional vulnerability and well-being among the retired in the U.S

Aging Ment Health. 2021 Jul;25(7):1361-1372. doi: 10.1080/13607863.2020.1768212. Epub 2020 Jun 4.

Abstract

Background: An extensive study investigated the risk factors for low well-being in post-retirement. Most previous studies have taken a unidimensional perspective, focusing on single factors such as financial status, physical health, and mental health.

Objective: Drawing on the vulnerability framework, we first identify and describe the empirical subgroups of vulnerability among retirees in the United States across four major domains of later life: material, physical, social, and mental vulnerability. Then, we investigate the association between vulnerability profiles and well-being.

Method: The sample included 3,158 retirees aged 65+ who participated in the Health and Retirement Study (HRS). Latent class analysis was utilized to identify the heterogeneous subgroups of vulnerability, and then a series of OLS regression analyses was conducted to examine the relationship between patterns of vulnerability and well-being.

Results: Five vulnerability patterns were identified: material vulnerable (12%), health & social vulnerable (14%), material, health & social vulnerable (6%), least vulnerable (34%), and social vulnerable (35%). The health & social vulnerable group had the strongest negative influence on well-being among all subgroups. As the largest subgroup, the social vulnerable group's negative influence on well-being stood out, with a stronger effect than that of material privation experienced by those in the material vulnerable group.

Conclusion: By empirically identifying subgroups of differential vulnerability patterns among retirees, this study showed that post-retirement vulnerability reflects complex interactions among multiple disadvantages. Findings of this study enhance understanding of the disparities in well-being within the retired population, pointing to the possibility of targeted policy and program development.

Keywords: Latent Class Analysis (LCA); Retirement; vulnerability; well-being.

MeSH terms

  • Humans
  • Mental Health*
  • Retirement*
  • Risk Factors
  • United States / epidemiology