Association of cumulative average sensory impairments with cognitive function and depressive symptoms: Two prospective cohort studies
Bibliographic record
Abstract
Globally, over 2.2 billion people have a vision impairment and over 1.5 billion live with hearing impairment, which are significant public health concerns given the meaningful impacts on individual and society. We aimed to investigate whether long-term average visual, hearing, and dual sensory impairment was independently associated with cognitive impairment, incident dementia, and incident depressive symptoms. We used data from the Health and Retirement Study (HRS) and the China Health and Retirement Longitudinal Study (CHARLS), two nationally representative and prospective cohorts of community middle aged and older adults. Average sensory impairment was calculated using the area under the curve divided by follow-up time from wave 3 (1996) to wave 15 (2020) in HRS and wave 1 (2011) to wave 4 (2018) in CHARLS. Cox regression models adjusted for multiple covariates were used to estimate adjusted hazard ratios (HRs) and 95 % confidence intervals (95 % CIs). For each one standard deviation (SD) increment in average visual impairment, the risk of developing cognitive impairment, incident dementia, and incident depressive symptoms increased by 12 %, 34 %, and 39 % in CHARLS and 11 %, 14 % and 10 % in HRS. Similar results were found for each SD increment in average hearing impairment and dual sensory impairment. Nonlinear dose-response relationships were identified between visual impairment and dementia, as well as dual sensory impairment and dementia in both cohorts. The diagnosis of cognitive impairment, dementia, and depression were based on subjective assessment. Multi-level approaches aimed at improving access to sensory care are needed to improve middle-aged and older adults' visual and auditory functions. • Stronger associations with dementia/depression were seen in participants with dual sensory impairments, showing additive effects. • Nonlinear dose-response links were found between impairment levels and dementia, showing faster cognitive decline with severity. • Visual and dual sensory impairments linked more strongly to dementia in lower SES participants and women. • Results emphasize the need for policies on sensory screening and care to reduce dementia and depression risks. • Tailored strategies can prevent sensory impairment, promoting healthy aging and reducing dementia burden in LMICs and HICs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".