Occupational noise-induced hearing loss in high-income countries: A multi-country analysis of compensation records
Bibliographic record
Abstract
Background Occupational Noise-Induced Hearing Loss (ONIHL) presents a significant occupational health challenge worldwide, with profound economic and societal implications. This study assesses socio-demographics of ONIHL cases, calculates incidence rates and trends (2013–2022), compares ONIHL burden with other occupational diseases, and quantifies annual compensation costs across Australia, Canada, Germany, Hong Kong and New Zealand. Materials and Methods A retrospective analysis of ONIHL compensation data sourced from governmental compensation records in five high-income countries was conducted. Incidence rates were computed based on accepted compensation cases per employed workforce and analyzed over a decade-long study period. Results A total of 131,433 newly compensated ONIHL cases were identified across five countries. Males accounted for 96.3 % of cases, with varying age distributions observed among countries. The construction and manufacturing sectors consistently emerged as primary contributors to ONIHL. ONIHL ranked among the top three occupational conditions in all study countries, with incidence rates ranging from 5 to 148 per 100,000 employed workers. Compensation costs for rehabilitation exceeded USD 800 million during the study period, with Germany incurring the highest expenses. Conclusion Our findings emphasize the need for intensified ONIHL prevention efforts, particularly within the manufacturing and construction industries across high-income countries. The study highlights the disproportionate representation of older males among the new ONIHL cases, whether due to their concentration in noisy settings or inherent susceptibility to noise-induced hearing changes. The financial costs are substantial, with nearly USD 2 billion spent on ONIHL compensation over the study period, including pension costs for ONIHL cases in Germany. This study illuminates the significant burden of ONIHL as a leading occupational disease, offering insights for areas to target intensified prevention and interventions efforts for accelerated reduction in new ONIHL cases.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 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".