Factors associated with institutionalization among the oldest old: Results based on the nationally representative study 'Old Age in Germany (D80+)'

Abstract

To examine the factors associated with institutionalization among individuals aged 80 years and over in Germany (total sample and stratified by sex). Methods/Design: We used data from the nationally representative "Old Age in Germany (D80+)" (analytic sample: n = 9572 individuals), including individuals aged 80 years and over in Germany. Institutionalization (private living vs. institutionalization) served as an outcome measure. For the written interview, data collection took place from November 2020 to April 2021. Multiple logistic regressions of the overall sample (also stratified by sex) were applied. Results In the analytic sample, 10.2% (95% CI: 9.2%?11.3%) of the participants were institutionalized. The odds of being institutionalized were positively associated with being female (OR: 2.02, 95% CI: 1.08 to 3.80), being 90 years and over (compared to 80-84 years, OR: 1.67, 95% CI: 1.17 to 2.40), not being married (e.g., being single compared to being married: OR: 14.06, 95% CI: 6.73 to 29.37), higher education (e.g., high education compared to low education: OR: 1.88, 95% CI: 1.25 to 2.84), more favorable self-rated health (OR: 1.32, 95% CI: 1.07 to 1.62) and greater functional impairment (OR: 15.34, 95% CI: 11.91 to 19.74). Sex-stratified regressions were also conducted, mostly yielding similar results. Conclusion: Our study highlighted the role of several sociodemographic factors (particularly marital status, e.g., being single) and functional impairment for the risk of institutionalization among the oldest old in Germany. This study confirms findings in studies in younger samples that functional decline is the main factor associated with institutionalization. As functional decline may be modifiable, efforts to maintain functional abilities may be important. This knowledge is important for relevant groups (such as clinicians and policy-makers) because it may guide early intervention and prevention efforts, can help allocate healthcare resources effectively and shape policies to support independent living. Further insights using longitudinal data is recommended.