Associations Between Hearing Loss and Health-Related Costs: A Retrospective Population-Based Cohort Study
Bibliographic record
Abstract
PURPOSE: Hearing loss (HL) is a leading cause of disability worldwide, but its health-related costs have been incompletely studied. Our objective was to examine the association between HL and direct health care costs and identify subgroups in which costs associated with HL are especially high. METHOD: This was a retrospective population-based cohort study of adults treated in a universal health care system between April 2008 and March 2019. HL was identified using administrative health data. We estimate health care costs in 2023 Canadian dollars, including costs for hospitalization, provider claims, ambulatory care visits, prescription medications, and long-term care (LTC). RESULTS: = 55 years [interquartile range: 43-68] vs. 35 years [24-50]) and had more comorbidities (1 [0-2] vs. 0 [0-1]) at baseline than participants without, whereas the likelihood of female sex, rural residence, and material deprivation were similar between groups with and without HL. Over median follow-up of 11.0 years, total age-sex adjusted annual health costs and each of its component costs were significantly higher in participants with HL compared to those without (annual total costs: $6,871, 95% confidence interval [CI] [$6,778, $6,962] vs. $4,716, 95% CI [$4,729, $4,763]). After full adjustment (a maximum of 29 comorbidities), annual costs remained significantly higher in participants with HL overall and for certain subcomponents (provider claims, ambulatory visits, and medications), whereas adjusted costs of hospitalization and LTC were lower among people with HL. The magnitude of the incremental costs among participants with HL was most pronounced for younger participants, men, or those with less comorbidity. Total projected annual direct health costs for Alberta residents with HL were $1.01 billion in 2023, of which $125 million (95% CI [$116, $135 million]) was attributable to HL specifically. CONCLUSIONS: Compared to those without HL, health costs were markedly higher among participants with HL, partially due to a higher burden of comorbidity. The relatively high population attributable costs of HL suggest that better prevention, recognition, and management of this condition could yield substantial economic benefits. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.27353439.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".