Association Between Social Determinants of Health and Hearing Loss and Hearing Intervention in Older US Adults
Bibliographic record
Abstract
Objective Estimate the prevalence of hearing loss and hearing assistance device use among older adults in the United States, and assess for associations with select social determinants of health (SDOH). Study Design Cross-sectional US population-based study using National Health and Nutrition Examination Survey (NHANES) 2017–March 2020 (pre-pandemic) data. Setting Non-institutionalized civilian adult US population. Methods US adults aged ≥70 years who completed NHANES audiometry exams were included. Sample weights were applied to provide nationally representative prevalence estimates of hearing loss and hearing assistance device use. Logistic regression analyses assessed associations between SDOH and both hearing loss and hearing assistance device use. Results The overall prevalence of hearing loss was 73.7%. Among those with nonprofound hearing loss, the prevalence of hearing assistance device use was 31.3%. Older individuals (odds ratio [OR], 6.3 [3.668–10.694] comparing ages 80+ versus 70–74 yr) and with lower education (OR, 3.8 [1.455–9.766] comparing Conclusion The prevalence of hearing loss among older adults in the United States remains roughly stable compared with previous population-based estimates, whereas the prevalence of hearing assistance device use is slightly increased. Population-level disparities exist both in the prevalence of hearing loss and hearing assistance device use across SDOH.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".