Associations between hearing loss and clinical outcomes: population-based cohort study
Bibliographic record
Abstract
Background: Hearing loss (HL) is a leading cause of disability worldwide, but its clinical consequences and population burden have been incompletely studied. Methods: We did a retrospective population-based cohort study of 4,724,646 adults residing in Alberta between April 1, 2004 and March 31, 2019, of whom 152,766 (3.2%) had HL identified using administrative health data. We used administrative data to identify comorbidity and clinical outcomes, including death, myocardial infarction, stroke/transient ischemic attack, depression, dementia, placement in long-term care (LTC), hospitalization, emergency visits, pressure ulcers, adverse drug events and falls. We used Weibull survival models (binary outcomes) and negative binomial models (rate outcomes) to compare the likelihood of outcomes in those with vs without HL. We calculated population-attributable fractions to estimate the number of binary outcomes associated with HL. Findings: The age-sex-standardized prevalence of all 31 comorbidities at baseline was higher among participants with HL than those without. Over median follow-up of 14.4 y and after adjustment for potential confounders at baseline, participants with HL had higher rates of days in hospital (rate ratio 1.65, 95% CI 1.39, 1.97), falls (RR 1.72, 95% CI 1.59, 1.86), adverse drug events (RR 1.40, 95% CI 1.35, 1.45), and emergency visits (RR 1.21, 95% CI 1.14, 1.28) compared to those without, and higher adjusted hazards of death, myocardial infarction, stroke/transient ischemic attack, depression, heart failure, dementia, pressure ulcers and LTC placement. The estimated number of people with HL who required new LTC placement annually in Canada was 15,631, of which 1023 were attributable to HL. Corresponding estimates for new dementia among people with HL were 14,959 and 4350, and for stroke/TIA the estimates were 11,582 and 2242. Interpretation: HL is common, is often accompanied by substantial comorbidity, and is associated with significant increases in risk for a broad range of adverse clinical outcomes, some of which are potentially preventable. This high population health burden suggests that increased and coordinated investment is needed to improve the care of people with HL. Funding: Canadian Institutes of Health Research; David Freeze chair in health services research.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".