Temporal trends (2008–2017) in serious hearing loss: Findings from a nationally representative sample of older Americans
Bibliographic record
Abstract
Hearing loss is a source of great public health concern. Previous research showed a decline in the prevalence of hearing loss among older adults. However, it is unclear whether this trend persists in recent years. The purpose of this study was to investigate the temporal trend in prevalence of hearing loss among older Americans (age 65+) between 2008 and 2017. To this end, we conducted a secondary analysis of 10 cross-sectional surveys: the 2008–2017 annual American community surveys (ACS). The ACS were conducted by the US Census through mail, internet, phone, and in-person meetings. ACS includes Americans aged 65+ living in the community and in group quarters such as nursing homes (n = 5,359,651). Annual response rates were above 89%. Whether the participant has hearing problems is assessed based on response to the ACS question “Is this person deaf or does he/she have serious difficulty hearing?”. During the decade 2008–2017, the prevalence of serious hearing loss in the older American population (65+) decreased from 16.3 to 14.8% (p < 0.001), indicating an 11% decline after adjustments for sex and race. When age was taken into account, this was attenuated to a 4% decline. Substantial sex differences were observed: Among females aged 65+, the age-race adjusted odds of serious hearing loss declined 10% over the decade, while males experienced a 2% increase. Older females had a substantial decrease in the prevalence of self-reported serious hearing loss between 2008 and 2017. Future research is needed to understand the cause of this trend.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".