Black Older Americans Have Lower Prevalence of Hearing Loss Than Their White Peers: Findings From Two Large Nationally Representative Surveys
Bibliographic record
Abstract
Purpose: The purpose of this study was to investigate Black–White differences associated with hearing loss among older adults living in the United States. Method: Secondary data analysis was conducted using the 2017 American Community Survey (ACS) with a replication analysis of 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+ years) in 2017 was 467,789 non–Hispanic Whites (NHWs) and 45,105 non–Hispanic Blacks (NHBs). In the 2016 ACS, there were 459,692 NHW and 45,990 NHB respondents. Measures of hearing loss, age, race/ethnicity, education level, and household income were based on self-report. Data were weighted to adjust for nonresponse and differential selection probabilities. Results: The prevalence of hearing loss was markedly higher among older NHWs (15.4% in both surveys) in comparison with NHBs (9.0% in 2017 and 9.4% in 2016, both ethnic differences p < .001). In the 2017 ACS, the age- and sex-adjusted odds of hearing loss were 69% higher for NHWs compared with NHBs, which increased to 91% higher odds when household income and education level were also taken into account ( OR = 1.91; 95% confidence interval [CI; 1.85, 1.97]). Findings from the 2016 ACS were very similar (e.g., 65+ fully adjusted OR = 1.81; 95% CI [1.76, 1.87]). Conclusions: NHWs have a much higher prevalence and almost double the odds of hearing loss compared with NHBs. Unfortunately, the ACS survey does not allow us to explore potential causal mechanisms behind this association.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".