Black-White Differences in Hearing Problems Among Older Americans: Findings From Two Large Representative Surveys
Bibliographic record
Abstract
Abstract The purpose of this study is to investigate Black-White differences in hearing problems among older adults living in the United States. Secondary data analyses were conducted using the 2017 American Community Survey (ACS) with a replication analysis in the 2016 ACS. The ACS is an annual nationally representative survey of Americans living in community settings and institutions. The sample size of older Americans (age 65+) in 2017 was 467,789 Non-Hispanic Whites (NHW) and 45,105 Non-Hispanic Blacks (NHB). In the 2016 ACS, there were 459,692 NHW and 45,990 NHB respondents aged 65+. Measures of hearing problems, age, race/ethnicity, education level and household income were based on self-report. Data were weighted to adjust for non-response and differential selection probabilities. The prevalence of hearing problems was markedly higher among older NHW (15.4% in both waves) in comparison to NHB (9.0% in 2017; 9.4% in 2016; both p<.001). In the 2017 ACS, the age-sex adjusted odds of hearing loss were 69% higher for NHW compared to NHB, which increased to 91% higher odds when household income and education level were taken into account (OR=1.91; 95% CI=1.85, 1.97). Further analyses by 10 year age cohorts indicated comparable findings (fully adjusted ORs range from 1.89 to 1.98). Findings from the 2016 ACS were very similar (e.g., 65+ fully adjusted OR=1.81). NHW have a much higher prevalence and almost double the odds of hearing loss compared NHB. Future research should investigate if melanin plays an otoprotective role through enhancing the antioxidant capability of cochlea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".