Equality in Hearing Aid Access: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Importance Most people with hearing impairment do not acquire hearing aids, but it is unknown what sociodemographic factors influence access. Objective To systematically review, synthesize and meta-analyze sociodemographic characteristics of people who do and do not acquire hearing aids after hearing loss diagnosis. Data sources We pre-registered the study (PROSPERO: CRD42023428580), and searched MEDLINE, EMBASE, and Web of Science from inception to 27 October 2025. Search terms included hearing aids and cohort study terms. Study selection We included observational studies with participants aged ≥18 years who had hearing loss confirmed through pure-tone audiometry, with any sociodemographic characteristics of those who do and do not access hearing aids. Data extraction and synthesis Using the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines, three authors independently extracted data, assessing study quality using the Newcastle-Ottawa Scale. We calculated ratios of people with hearing loss from each sociodemographic characteristic who acquired hearing aids. We used random effects meta-analyses. Main outcomes and measures Hearing aid acquisition. Results 36 studies including 300,946 people met criteria. Men were more likely to acquire hearing aids than women (n, 287,964; RR, 1.09[1.02-1.17]; I 2 , 98.7%), White people more than other ethnic/racial groups (n, 274,860; RR, 1.26[1.07-1.55]; I 2 , 99.9%), >12 years vs. ≤12 years of education (n, 5,970; RR, 1.17[1.04, 1.32]; I 2 , 79.4%), and pension recipients more than non-recipients (n, 1,678; RR, 1.50 [1.08,2.09], I 2 , 87.4%). There were no differences in hearing aid acquisition by very low household income (≥US$45,000/year vs. 2 , 0%), employment status (currently employed vs. not employed (RR, 0.58[0.34-1.00]; I 2 , 84.2%), relationship status (currently married/partnered vs. not; RR, 0.96[0.87-1.16]; I 2 , 70.0%), living alone vs. with others (RR, 0.82[0.57-1.18]; I 2 , 97.7%) or rural vs. urban areas (RR, 1.20[0.86-1.68]; I 2 , 94.4%). Conclusion and Relevance Underserved groups (women, minorities, those with less education and not receiving pensions) are less likely to get hearing aids, even after hearing testing. Meta-analyses had high heterogeneity, so findings are not generalizable. Some underserved people can access hearing aids, and it is important to further investigate the barriers and enablers. No studies controlled for hearing severity, so findings are limited by confounding by indication. Key Points Question What groups of people with diagnosed hearing loss acquire hearing aids? Finding: Among 300,946 individuals with hearing loss (36 studies), men, those with >12 years of education, pension recipients, and White individuals were more likely to acquire hearing aids. We found no differences based on household income, employment status, relationship status, rural vs. urban areas, and living arrangement. Meaning After accessing hearing services, hearing aid acquisition is related to sex, ethnicity or race, education, and pension status, but no other sociodemographic indicators. Further research is needed about enablers and barriers to obtaining hearing aids for women and ethnic minorities.
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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 | 0.025 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.039 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".