The Burden of Occupational Noise-Induced Hearing Loss From 1990 to 2019: An Analysis of Global Burden of Disease Data
Bibliographic record
Abstract
OBJECTIVES: The relationship between long-term exposure to occupational noise and hearing loss has been extensively documented. We aimed to assess spatial and temporal changes in the burden of occupational noise-induced hearing loss (ONIHL) in 204 countries and territories with varying socio-demographic indexes (SDI) from 1990 to 2019. DESIGN: Temporal and spatial trends in age-standardized disability-adjusted life year rates (ASDR) for ONIHL were estimated by sex, age, SDI level, country, and geographic region from 1990 to 2019. We used the Joinpoint model to calculate annual average percentage changes to assess such trends and projected trends in ASDR for ONIHL globally and across different income regions from 2020 to 2044 using an age-period-cohort model. We fitted the relationship between ASDR and SDI, ASDR and healthcare access and quality index, respectively. RESULTS: Overall, the global burden of ONIHL has decreased since 1990, especially in middle and lower SDI regions. In 2019, the global ASDR for ONIHL was 84.23 (95% confidence interval: 57.46 to 120.52) per 100,000 population. From 1990 to 2019, the global ASDR for ONIHL decreased by 1.72% (annual average percentage change = -0.05, 95% confidence interval: -0.07 to -0.03). Our projections showed a decreasing trend in the global ONIHL burden until 2044. ASDR and SDI ( R = -0.8, p < 0.05), ASDR and healthcare access and quality index ( R = -0.75, p < 0.05) showed significant negative correlations. CONCLUSIONS: The global ONIHL burden has decreased over the past three decades, especially in regions with middle and lower SDI levels. However, the global ONIHL burden still remained severe in 2019, notably among males, the middle-aged and elderly, and regions with lower SDI levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| 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".