Associations Between Hearing Loss and Depressive Symptom Trajectories in Middle-Aged and Older People in China: Retrospective Analysis
Bibliographic record
Abstract
BACKGROUND: Hearing loss and depression are important health issues among the middle-aged and older population. OBJECTIVE: This study aimed to investigate the associations between hearing loss and depressive symptom trajectories in the Chinese middle-aged and older adult population. METHODS: The survey data of 2011, 2013, 2015, and 2018 waves collected in the China Health and Retirement Longitudinal Study were used for analysis. The latent growth mixture modeling approach was used to explore the trajectories of depressive symptoms. Hearing loss was identified through self-reporting, and depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression scale. The associations between hearing loss and depressive symptom trajectories were examined using logistic regression models. RESULTS: A total of 4768 participants without depressive symptoms at baseline were included for analysis. Among them, 4 depressive symptom trajectories, including "stable low symptoms" (n=3656, 76.68%), "slowly progressing symptoms" (n=503, 10.55%), "relieved symptoms after progression" (n=467, 9.79%), and "rapidly progressing symptoms" (n=142, 2.98%) were identified. Hearing loss was found to be significantly associated with the trajectory of "rapidly progressing symptoms." CONCLUSIONS: The trajectories of depressive symptoms in middle-aged and older people have 4 types with distinct patterns. Hearing loss is associated with the progression of depressive symptoms, and its impact is more significant for males, affecting not only symptom severity but also progression speed. These findings indicate that the mental health status of middle-aged and older people with hearing loss requires careful consideration, and timely interventions should be provided.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".