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Record W4308966888 · doi:10.2196/39314

Dual Sensory Impairment as a Predictor of Loneliness and Isolation in Older Adults: National Cohort Study

2022· article· en· W4308966888 on OpenAlexvenueno aff
Qiong Wang, Shimin Zhang, Yi Wang, Dan Zhao, Chengchao Zhou

Bibliographic record

VenueJMIR Public Health and Surveillance · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
FundersShandong UniversityNational Natural Science Foundation of ChinaChina Medical BoardNational Science Foundation
KeywordsLonelinessSocial isolationGeneralized estimating equationGerontologyLongitudinal studyCohortCohort studyStructural equation modelingOddsPsychologyOdds ratioPublic healthSocial supportMedicineGeeDemographyLogistic regressionPsychiatrySocial psychology

Abstract

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BACKGROUND: Loneliness and social isolation are global public health challenges. Sensory impairments (SIs) are highly prevalent among older adults but are often ignored as a part of normal aging. Identifying the role of SIs in loneliness and social isolation could provide insight into strategies for improving public health among older adults. OBJECTIVE: This study aims to analyze the effects of SIs on loneliness and social isolation among older adults in rural and urban China. METHODS: This cohort study of 3069 older adults (aged 60+) used data from 4 waves (2011, 2013, 2015, and 2018) of the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of adults aged 45 years or older. SIs include hearing impairment (HI), vision impairment (VI), and dual sensory impairment (DSI). DSI is defined as the co-occurrence of VI and HI. Participants with complete data on hearing, vision, social isolation, and loneliness were included in the analysis. Generalized estimating equation models adjusted for covariates were used to examine the relationships of DSI with loneliness and social isolation among older adults. RESULTS: Older adults in rural areas have higher prevalence of DSI, loneliness, and social isolation than their urban counterparts. In rural areas, participants with VI only (odds ratio [OR] 1.34, 95% CI 1.12-1.62; P=.002), HI only (OR 1.32, 95% CI 1.02-1.71; P=.03), and DSI (OR 1.84, 95% CI 1.56-2.18; P<.001) were more likely to experience loneliness compared with participants without SIs. DSI showed a statistically significant association with loneliness compared with VI only (OR 1.37, 95% CI 1.22-1.54; P<.001) and HI only (OR 1.39, 95% CI 1.13-1.72; P=.002). In urban areas, participants with VI only (OR 2.44, 95% CI 1.57-3.80; P<.001), HI only (OR 2.47, 95% CI 1.41-4.32; P=.002), and DSI (OR 1.88, 95% CI 1.24-2.85; P=.003) were more likely to experience loneliness compared with participants without SIs. DSI was not associated with the increased likelihood of loneliness compared with HI only or VI only. SIs were not associated with social isolation among older adults in urban and rural areas. Until 2018, 86.97% (2669/3069) reported VI, but only 27.11% (832/3069) and 9.45% (290/3069) were treated with glasses and cataract surgery, respectively; besides, 75 individuals received both glasses and cataract surgery treatment. The prevalence of HI was 74.39% (2283/3069) in 2018, but only 0.72% (22/3069) were treated with a hearing aid. CONCLUSIONS: SIs are associated with an increased risk of loneliness rather than social isolation. A compounded risk of DSI on loneliness exists in rural areas rather than in urban areas. These findings expand our knowledge about the effects of SIs on loneliness and social isolation in non-Western populations. Interventions targeting HI only and DSI might be particularly effective for mitigating loneliness of older adults in urban and rural areas, respectively. Considering the high prevalence and low treatment rate of SIs, measures should be taken to make treatment more accessible.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.342
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations65
Published2022
Admission routes1
Has abstractyes

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