Analysis of health risk factors for older adults living alone in China and establishment and evaluation of a nomogram prediction model

Front Public Health. 2024 Mar 19:12:1309561. doi: 10.3389/fpubh.2024.1309561. eCollection 2024.

Abstract

Objective: To understand the health status of older adults living alone in China and analyze the influencing factors, so as to provide reference for improving the health status of older adults living alone.

Methods: Based on CGSS data from China General Social Survey (2017), the influencing factors of health status of older adults living alone were analyzed by unconditional Logistic regression, and the R software was used to develop a nomogram for predicting the risk of self-assessed unhealthy adverse outcomes.

Results: Gender, annual income, mandarin listening level and participation in medical insurance were the influencing factors of self-rated health of older adults living alone. Age and annual income are the influencing factors of physiological health. Annual income and Internet use were influential factors for mental health. C-Statistic of nomogram prediction model was 0.645. The calibration curve showed that goodness of fit test (χ2 = 58.09, p < 0.001), and the overall prediction ability of the model was good.

Conclusion: The health status of older adults living alone in the home-based older adults care is worrying, and it is affected by various factors. We should pay more attention to older adults living alone, improve the ability of listening and distinguishing mandarin and the use of health information platforms for older adults living alone, and further implement medical insurance policies and health services. Announcing the solution to promote healthy home-based care for older adults living alone.

Keywords: Chinese general social survey; health; internet use; mandarin proficiency; nomogram; older adults living alone.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Health Status
  • Home Environment*
  • Income
  • Nomograms*
  • Risk Factors

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This paper is supported by National Natural Science Foundation of China (Grant Nos. 72164033, 72364031), the Natural Science Foundation of Ningxia (Grant Nos. 2022AAC02036, 2023AAC03224).