Prevalence of hearing loss in children living in low‐ and middle‐income countries over the last 10 years: A systematic review
Bibliographic record
Abstract
AIM: To summarize the literature on the prevalence of pediatric hearing loss in low- and middle-income countries (LMICs). METHOD: A systematic review initially identified 2833 studies, of which 122 met the criteria for inclusion. Eighty-six of those studies included diagnoses and were included in a meta-analysis. RESULTS: The meta-analysis indicated a 1% (95% confidence interval = 0.8-2.0) prevalence of childhood hearing loss across LMICs. There was significant heterogeneity between studies and evidence of publication bias. The prevalence of mild and moderate cases of hearing loss was higher than more severe cases and there were fewer cases of mixed hearing loss compared to conductive or sensorineural hearing loss. No differences were identified between the prevalence of unilateral versus bilateral hearing loss or hearing loss according to sex. The quality of the studies, age of participants, and location of data collection may have influenced the results. High variability in the reporting of etiology made the causes of hearing loss unclear. INTERPRETATION: The literature indicates that 1% of children in LMICs have hearing losses. However, most studies missed children with acquired hearing loss, which may lead to under-reporting of global prevalence. This systematic review is an initial step toward developing and implementing population-appropriate treatment and prevention programs for childhood hearing loss in LMICs. WHAT THIS PAPER ADDS: The prevalence of childhood hearing loss in low- and middle-income countries is 1%. Reporting of hearing loss etiology was highly variable.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".