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Creation and verification of a predictive nomogram model for the incidence of social isolation among China’s older population

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Title: Creation and verification of a predictive nomogram model for the incidence of social isolation among China’s older population
Authors: Mei You, Yuan Ding, Zixuan Wei, Nannan Han, Annuo Liu
Source: Front Public Health
Frontiers in Public Health, Vol 13 (2025)
Publisher Information: Frontiers Media SA, 2025.
Publication Year: 2025
Subject Terms: predictive accuracy, social isolation, nomogram model, risk factors, older adult population, Public Health, Public aspects of medicine, RA1-1270
Description: ObjectivesTo explore the risk factors associated with social isolation among the older adult in China, develop a nomogram model to forecast the risk, and evaluate its predictive accuracy.MethodsAn investigation was conducted into the demographic, socioeconomic, health, and health behavior aspects of the older adult population. Using logistic regression and backward stepwise analysis, a nomogram model was constructed to predict the risk of social isolation by screening independent risk factors.ResultsSocial isolation was prevalent in 42.1% of Chinese older adults. Nomogram prediction models were created for the five screened variables, which included type of residence, health self-assessment, disability, depression, and sedentary hours. The nomogram model had an AUC of 0.734 (95%CI: 0.701–0.767) in the training cohort, and an AUC of 0.653 (95%CI: 0.580–0.725) in the validation cohort. The Hosmer-Lemeshow goodness-of-fit test revealed that there was a good fit (p > 0.05). DCA results showed that clinical intervention had a high net benefit in the older adult when the threshold probability was 20–85% for the training cohort and 30–65% for the control cohort.ConclusionSocial isolation is a common issue for the older adult population in China. The prediction model using a nomogram for the older adult can efficiently detect and screen high-risk individuals for social isolation, forecasting its occurrence. The proposed nomogram may serve as a preliminary screening tool for social isolation risk but requires further optimization to improve accuracy. Future research should incorporate additional predictors or advanced modeling techniques to enhance clinical utility.
Document Type: Article
Other literature type
ISSN: 2296-2565
DOI: 10.3389/fpubh.2025.1571509
Access URL: https://doaj.org/article/8df17b199aca464da88f227dca258a80
Rights: CC BY
Accession Number: edsair.doi.dedup.....de025a3ad9533bf8a0ef0ea1042ef329
Database: OpenAIRE
Description
Abstract:ObjectivesTo explore the risk factors associated with social isolation among the older adult in China, develop a nomogram model to forecast the risk, and evaluate its predictive accuracy.MethodsAn investigation was conducted into the demographic, socioeconomic, health, and health behavior aspects of the older adult population. Using logistic regression and backward stepwise analysis, a nomogram model was constructed to predict the risk of social isolation by screening independent risk factors.ResultsSocial isolation was prevalent in 42.1% of Chinese older adults. Nomogram prediction models were created for the five screened variables, which included type of residence, health self-assessment, disability, depression, and sedentary hours. The nomogram model had an AUC of 0.734 (95%CI: 0.701–0.767) in the training cohort, and an AUC of 0.653 (95%CI: 0.580–0.725) in the validation cohort. The Hosmer-Lemeshow goodness-of-fit test revealed that there was a good fit (p > 0.05). DCA results showed that clinical intervention had a high net benefit in the older adult when the threshold probability was 20–85% for the training cohort and 30–65% for the control cohort.ConclusionSocial isolation is a common issue for the older adult population in China. The prediction model using a nomogram for the older adult can efficiently detect and screen high-risk individuals for social isolation, forecasting its occurrence. The proposed nomogram may serve as a preliminary screening tool for social isolation risk but requires further optimization to improve accuracy. Future research should incorporate additional predictors or advanced modeling techniques to enhance clinical utility.
ISSN:22962565
DOI:10.3389/fpubh.2025.1571509