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Record W4407757093 · doi:10.3389/fpubh.2025.1455340

Noise exposure, hearing loss and cognitive impairment: a cross-sectional study based on an occupational health surveillance cohort in China

2025· article· en· W4407757093 on OpenAlexaboutno aff
Lei Huang, Linjuan Li, Juan Wang, Shushan Zhang, Huiyi Wu, Yajia Lan, LI Jun-ying, Yang Zhang

Bibliographic record

VenueFrontiers in Public Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
FundersDepartment of Science and Technology of Sichuan Province
KeywordsCross-sectional studyHearing lossMedicineCohortEnvironmental healthCognitive impairmentCohort studyChinaOccupational safety and healthCognitionAudiologyPsychiatryPathologyGeography

Abstract

fetched live from OpenAlex

Background High-intensity noise is associated with noise-induced hearing loss (NIHL). There is also evidence that noise exposure is related to cognitive impairment. This research aimed to analyze the associations and potential pathways of cumulative noise exposure (CNE), hearing loss and cognitive impairment. Methods A total of 560 research subjects were included in this research from May 2021 to April 2022 in western China. The demographic features, occupational features, and CNE were investigated and examined. Hearing loss was evaluated according to the National standard GB/T 7583–1987 of China. The Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used to assess cognitive function. Structural equation modeling (SEM) was used to analyze the potential pathways and specific effect sizes of CNE, hearing loss and cognitive impairment. Results The mean age of the research subjects was 34.3 (SD, 9.89). Men accounted for 96.4% (540/560) and women accounted for 3.6% (20/560). A total of 62.3% (349/560) held a college degree or above. The regression analysis showed that high dose CNE was related to MMSE ( β = −1.069 (−1.539, −0.600)) and MoCA ( β = −1.040 (−1.726, −0.355)) scores. The monaural threshold weighted value of the right ear (MTWV R ) greater than 40 dB was associated with both MMSE ( β = −1.183 (−2.033, −0.333)) and MoCA ( β = −1.420 (−2.647, −0.193)) scores. The monaural threshold weighted value of the left ear (MTWV L ) greater than 40 dB was also associated with MMSE ( β = −1.540 (−2.389, −0.690)) and MoCA ( β = −1.685 (−2.915, −0.456)) scores. The SEM result (Model C) showed that the standard effect of CNE- hearing loss path, CNE-MMSE path, and hearing loss-MMSE path were 0.142 ( p < 0.001), −0.151 ( p < 0.001), and −0.030 ( p = 0.376). The Model D showed that the standard effect of CNE- hearing loss path, CNE- MoCA path, and hearing loss- MoCA path were 0.143 ( p < 0.001), −0.048 ( p = 0.267), and − 0.050 ( p = 0.047). The CNE had only a direct effect on the MMSE score. Conversely, the CNE had only an indirect effect on the MoCA score, while hearing loss was borderline associated with MoCA. The total effects of CNE on MMSE and MoCA scores were −0.151 and −0.007, respectively. Conclusion Job-related noise exposure is not only associated with NIHL but also with early cognitive impairment in occupational groups. However, there is not enough evidence indicating that NLHL mediates the associations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.413
Teacher spread0.375 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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