Cognitive function trajectories and influencing factors in Chinese older adults with self-reported hearing impairment: findings from CHARLS 2013–2020
Bibliographic record
Abstract
OBJECTIVES: Hearing impairment is linked to an increased risk of cognitive decline, yet the progression of cognitive function in affected individuals remains unclear. This study examined cognitive function trajectories in older adults with self-reported hearing impairment and identified key influencing factors. METHODS: Data from China Health and Retirement Longitudinal Study (CHARLS, 2013-2020) were analyzed. Sociodemographic and health-related factors, including age, education, sensory impairments, chronic conditions, hearing aid use, depression, vision, sleep patterns, social interactions, smoking, drinking, retirement status, and fuel use, were assessed. Cognitive function was evaluated using episodic memory, orientation, attention, and executive function. Group-Based Trajectory Modeling (GBTM) identified cognitive trajectories, while logistic regression examined influencing factors. RESULTS: Three cognitive trajectories were identified: low-functioning decline (L, 23.5%), middle-functioning decline (M, 38.5%), and high-functioning stabilization (H, 38.0%). The likelihood of M increased with younger age (60-69 years: OR = 8.56; 70-79 years: OR = 4.54), absence of physical (OR = 2.03) or visual impairment (OR = 1.65), and short naps (≤ 30 min, OR = 1.59). H was associated with younger age (60-69 years: OR = 60.36; 70-79 years: OR = 12.11), absence of physical (OR = 2.20) or visual disability (OR = 1.84), mild depression (OR = 3.91), and shorter naps (OR = 2.05). Poor hearing (OR = 0.46), illiteracy (OR = 0.03), and non-retirement (OR = 0.23) reduced the likelihood of being in the M or H groups (all p < 0.05). CONCLUSIONS: Cognitive trajectories in hearing-impaired older Chinese adults fall into three categories: low-functioning decline, middle-functioning decline, and high-functioning stabilization. Age, education, sensory impairments, depression, nap duration, and retirement status influence these trajectories. Given the limited generalizability of these preliminary findings, further research is needed to clarify the potential confounding and mediating relationships among these factors before they can inform early intervention and policy initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".