The effects of loneliness and social isolation on cognitive impairment-free life expectancy in older adults

Abstract

Objectives

This article aimed to examine the effects of social connection comprising loneliness and social isolation on cognitive impairment-free life expectancy (CIFLE).

Methods

Data on 28,563 older adults (aged 65+) were drawn from the Chinese Longitudinal Healthy Longevity Survey with a median follow-up of 4.00 years. Multistate Markov models were used to estimate the independent and joint effects of social connection with CIFLE. Cognitive impairment was measured by the modified Mini-Mental State Examination.

Results

For men and women, respectively, reduced CIFLEs at age 65 associated with loneliness were 0.95 (95% CI: 0.41–1.48) and 1.35 (95%: CI 0.77–1.90) years, and those associated with social isolation were 2.23 (95% CI: 1.67–2.78) and 2.49 (95% CI: 1.67–3.30) years. Compared with those with neither loneliness nor social isolation (‘neither’ group), older adults at age 65 with both loneliness and social isolation (‘both group’) lost CIFLEs of 2.68 (95% CI: 1.89–3.48) and 3.51 (95% CI, 2.55–4.47) years for men and women, respectively. Similar patterns were observed in the oldest-old adults (age 85 or over). A growth trend transpired in the difference of the proportion of the remaining CIFLE between ‘neither’ group and ‘both’ group with age.

Conclusion

Loneliness and social isolation are associated with decreased CIFLE in older Chinese adults. Policy makers and the public must be informed that early identification and management of loneliness and social isolation, especially when coexisting, are crucial.

Keywords:

• Loneliness
• social isolation
• health expectancy
• cognitive functioning
• transition models

Acknowledgements

We thank the PKU Center for Healthy Aging and Development for providing the Chinese Longitudinal Healthy Longevity Survey (CLHLS), which was the data source of this work. The data and study documents are publicly available at the website: https://opendata.pku.edu.cn/dataverse/CHADS. This work was not preregistered.

Disclosure statement

All authors declare that they have no conflicts of interest.

Additional information

Funding

The research was supported by the National Science Foundation of China (grant numbers 72074086 and 71673093); and the Fundamental Research Funds for the Central Universities, HUST, China (grant number 2022WKZDJC009). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.