The heterogeneity of the homebound: A latent class analysis of a national sample of homebound older adults

• Published in: Journal of the American Geriatrics Society (JAGS) 2023-07, Vol.71 (7), p.2163-2171
• Main Authors: Mather, Harriet, Kleijwegt, Hannah, Bollens‐Lund, Evan, Liu, Bian, Garrido, Melissa M., Kelley, Amy S., Leff, Bruce, Ritchie, Christine S., Ornstein, Katherine A.
• Format: Article
• Language: English
• Subjects: Aged; Aging; Comorbidity; complexity; Dementia; Dementia disorders; Elder care; Home health care; homebound; Homebound Persons; home‐based medical care; Humans; Latent Class Analysis; Logistic Models; Mortality; Older people; Self Report; Sociodemographics; United States - epidemiology
• ISSN: 0002-8614; 1532-5415
• DOI: 10.1111/jgs.18295

Background Homebound status is a final common pathway for people with a variety of diseases and conditions. There are 7 million homebound older adults in the United States. Despite concerns regarding their high healthcare costs and utilization and limited access to care, the unique subsets within the homebound population are understudied. Better understanding of distinct homebound groups may enable more targeted and tailored approaches to care delivery. Therefore, in a nationally representative sample of homebound older adults we used latent class analysis (LCA) to examine distinct homebound subgroups based on clinical and sociodemographic characteristics. Materials and Methods Using data from the National Health and Aging Trends Study (NHATS) 2011–2019, we identified 901 newly homebound persons (defined as never/rarely leaving home or leaving home only with assistance and/or difficulty). Sociodemographic, caregiving context, health and function, and geographic covariates were derived from NHATS via self‐report. LCA was used to identify the existence of distinct subgroups within the homebound population. Indices of model fit were compared for models testing 1–5 latent classes. Association between latent class membership and 1 year mortality was examined using a logistic regression. Results We identified four classes of homebound individuals differentiated by their health, function, sociodemographic characteristics, and caregiving context: (i) Resource constrained (n = 264); (ii) Multimorbid/high symptom burden (n = 216); (iii) Dementia/functionally impaired (n = 307); (iv) Older/assisted living (n = 114). One year mortality was highest among the older/assisted living subgroup (32.4%) and lowest among the resource constrained (8.2%). Conclusions This study identifies subgroups of homebound older adults characterized by distinct sociodemographic and clinical characteristics. These findings will support policymakers, payers, and providers in targeting and tailoring care to the needs of this growing population.