Hearing loss as a risk factor for dementia: a systematic review and meta-analysis from a global perspective
Bibliographic record
Abstract
Objectives Hearing loss is a risk factor for dementia with estimated hazard ratios (HRs) of 1.28–2.39. However, whether intercontinental variability exists in this relationship remains unexplored.Method MEDLINE, PsychInfo, Academic Search Ultimate, Web of Science, and EMBASE were searched, from inception to 2024, for cohort studies of dementia-free individuals with baseline hearing assessments ≥2-year follow-up, and incident dementia outcomes. Random-effect and multilevel models with subgroup difference tests were conducted.Results Forty-nine studies analysed cohorts from North America (n = 20), Europe (n = 20), Asia (n = 7), and Oceania (n = 2). Binary hearing loss was associated with increased dementia risk (HR = 1.32 [95% CI: 1.23–1.41]) with HRs being largest for Oceania and smallest for Asia (p <0.001). In a sensitivity analysis excluding Oceania, HRs did not differ significantly by continent. Imprecise estimates create uncertainty around whether mild (HR = 1.35 [95% CI: 0.86–2.11]), moderate (HR = 1.39 [95% CI: 0.57–3.35]) or severe (HR = 1.66 [95% CI: 0.59–4.64]) hearing loss are associated with increased dementia risk, with little evidence that HRs by severity differ by continent (p = 0.059).Conclusion Findings indicate that the association between hearing loss and dementia is consistent globally, though HRs may vary slightly by continent.Registration This review was pre-registered on PROSPERO (CRD42024545209) and the OSF (https://osf.io/kew29/).
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".