Noise and Occupational Medicine

Phillips, Andrew L; Cooney, Ryan; Harris, Zachary D.; Myrtil, Daphne; Hodgson, Michael

doi:10.1097/jom.0000000000001728

Cited by 4 publications

(12 citation statements)

References 17 publications

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“…Best practices are determined largely by organizations that operate most efficiently and productively by managing and utilizing information effectively, including leveraging technology to minimize identified risks 11,29 . For companies and public entities, the compelling “business case” to widely adopt a best practices approach to NIHL prevention and risk management means they would need to exceed minimum regulatory compliance and recordkeeping requirements.…”

Section: The Solutionmentioning

confidence: 99%

“…Because noise exposures are typically complex, variable, and dynamic, they are often not accurately characterized by a single, time-weighted average (TWA) noise dosimetry measurement. 11 The relationship between magnitude and duration of noise exposure, and the risk for development of NIHL is dose-and time-dependent, but determinants of individual susceptibility and disease progression remain poorly characterized. 4 OSHA's PEL for noise of 90 dBA TWA represents an exposure that is known to increase the risk of NIHL.…”

Section: The Regulationsmentioning

confidence: 99%

“…Occupational NIHL is significantly underreported or undiagnosed in relation to specific work employment. [9][10][11][12][13][14] Both primary prevention of noise exposure and secondary prevention of NIHL through early detection and intervention before irreversible injury occurs "are critical to preserve worker quality of life." 1 As with many other occupational diseases, the genetic, biomolecular, and pathophysiological mechanism(s) of NIHL do not need to be completely or perfectly understood to effectively prevent it.…”

mentioning

confidence: 99%

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Audiometric data analysis for prevention of noise‐induced hearing loss: A new approach

Craner

2022

American J Industrial Med

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Compliance with noise regulations in the past three decades has significantly reduced workplace noise exposures, particularly in the loudest industries and occupations. However, the overall effectiveness of hearing conservation programs in preventing occupational noise‐induced hearing loss remains uncertain and unquantified, while the incidence and cost of occupational hearing loss remain inexplicably high. This review/commentary critically explores this paradox by examining why the billions of annual audiograms conducted worldwide have not been aggregately utilized or applied to predict early NIHL in groups of workers or to measure the efficacy of exposure controls. Principal contributory reasons include regulation of noise as a safety standard rather than as a health standard, the inherent complexity of audiometric data, and the lack of a standardized method of interpretation for audiograms. The unsuccessful history of efforts to develop and adopt methods and tools to analyze aggregate audiometric data is described. Consequently, the Standard Threshold Shift—a regulatorily defined, lagging indicator of individual, irreversible hearing loss that is not an effective preventive metric—remains the de facto standard of care. A population‐based Best Practices approach is proposed to leverage the raw audiometric data already available and turn it into actionable data for effective secondary prevention to strategically manage and reduce occupational hearing loss risk. This approach entails statistical methods and information management tools necessary to transform audiometry from a compliance‐driven, individual screening test with limited preventive capability into a medical surveillance process directly linked to aggregate corrective and prevention actions.

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Section: The Solutionmentioning

confidence: 99%

Section: The Regulationsmentioning

confidence: 99%

mentioning

confidence: 99%

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Audiometric data analysis for prevention of noise‐induced hearing loss: A new approach

Craner

2022

American J Industrial Med

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“…It has been evaluated that almost one-third of all cases can be ascribed to noise exposure and over 12% of the citizens worldwide is in danger of hearing loss from noise [ 3 ]. Even in America, NIHL ranks second among the most common occupational diseases [ 4 ]. NIHL is well acknowledged to be associated with occupational environment and genetic inheritance.…”

Section: Introductionmentioning

confidence: 99%

Genetic association study between TAB2 polymorphisms and noise-induced-hearing-loss in a Han Chinese population

et al. 2021

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Noise-induced-hearing-loss(NIHL) is a common occupational disease caused by various environmental and biological factors. To investigate the association between TAB2 and the susceptibility of NIHL of people exposed to occupational environments, a genetic association study was performed on selected companies with 588 cases and 537 healthy control subjects. Five selected single nucleotide polymorphisms (SNPs) in TAB2,incoluding rs2744434, rs521845, rs652921, rs7896, rs9485372, were genotyped after a collection of DNA samples. Evident differences in participants between the case group and the control group reveals the result that people with the TAB2 has a high probability of getting NIHL. The results show that rs521845 is deeply associated with the risk of NIHL and is available for the diagnosis in the future.

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“…Steelmaking is one of the high-risk occupations in the area of hearing problems (11,12). The NIHL can be avoided by means of a control hierarchy prioritizing the use of engineering controls over administrative controls and personal protective equipment (13).…”

Section: Introductionmentioning

confidence: 99%

Changes of Occupational Noise-Induced Hearing Loss due to Working in the Steel Industry and Associated Effective Factors: Application of Bayesian Multivariate Multilevel Modeling using Skew Distribution : Rate of NIHL in Steel Workers

Nikpour

Fesharaki

2020

ircmj

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Background:There have been a limited number of studies on the relationships between time and occupational and demographical variables with the mean changes of low-frequency hearing (LFH) and high-frequency hearing (HFH). Objectives: This study investigated the rate of occupational noise-induced hearing loss (NIHL) due to working in the steel industry and associated effective factors. Materials and Methods: This historical cohort study was conducted within 2000 to 2010. Two LFH and HFH definitions of NIHL were used in this study. The average changes of LFH and HFL were considered the response variables. In addition, time and occupational (i.e., shift work and work experience) and demographical (i.e., age and educational level) variables were regarded as the independent variables. For data analysis, Bayesian multivariate multilevel modeling using skew distribution and OpenBUGS (version 3.2.2) and R (version 2.13.2) software were used in this study. Results: The present study was performed on 1,959 male workers with a mean age of 36.64±3.92 years. Among these subjects, 913 (46.6%), 134 (6.8%), and 912 (46.6%) participants were day workers, weekly-rotating shift workers, and routinely-rotating shift workers, respectively. The obtained results showed that age, work experience, educational level, and shift work had significant relationships with the changes of LFH and HFH. Conclusion:Overall, the findings of this 10-year historical cohort study demonstrated a relationship between time and demographical and occupational variables with the changes of LFH and HFL. Therefore, it is recommended to design preventive measures to reduce the deleterious effects of such variables on LFH and HFL.

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Noise and Occupational Medicine

Cited by 4 publications

References 17 publications

Audiometric data analysis for prevention of noise‐induced hearing loss: A new approach

Audiometric data analysis for prevention of noise‐induced hearing loss: A new approach

Genetic association study between TAB2 polymorphisms and noise-induced-hearing-loss in a Han Chinese population

Changes of Occupational Noise-Induced Hearing Loss due to Working in the Steel Industry and Associated Effective Factors: Application of Bayesian Multivariate Multilevel Modeling using Skew Distribution : Rate of NIHL in Steel Workers

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