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Original Article
Questionnaire-based algorithm for assessing occupational noise exposure of construction workers
  1. Kate Lewkowski1,
  2. Kahlia McCausland1,
  3. Jane S Heyworth2,
  4. Ian W Li2,
  5. Warwick Williams3,
  6. Lin Fritschi1
  1. 1School of Public Health, Curtin University, Perth, Western Australia, Australia
  2. 2School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia
  3. 3National Acoustic Laboratories, Sydney, New South Wales, Australia
  1. Correspondence to Ms Kate Lewkowski, School of Public Health, Curtin University, Perth, WA 6845, Australia; katherine.lewkowski{at}curtin.edu.au

Abstract

Objectives Occupational noise exposure is a major cause of hearing loss worldwide. In order to inform preventative strategies, we need to further understand at a population level which workers are most at risk.

Methods We have developed a new questionnaire-based algorithm that evaluates an individual worker’s noise exposure. The questionnaire and supporting algorithms are embedded into the existing software platform, OccIDEAS. Based on the tasks performed by a worker during their most recent working shift and using a library of task-based noise exposure levels, OccIDEAS estimates whether a worker has exceeded the full-shift workplace noise exposure limit (LAeq,8h≥85 dBA). We evaluated the validity of the system in a sample of 100 construction workers. Each worker wore a dosimeter for a full working shift and was then interviewed using the OccIDEAS software.

Results The area under the receiver operating characteristic curve was 0.81 (95% CI 0.72 to 0.90) indicating that the ability of OccIDEAS to identify construction workers with an LAeq,8h≥85 dBA was excellent.

Conclusion This validated noise questionnaire may be useful in epidemiological studies and for workplace health and safety applications.

  • exposure assessment
  • noise
  • construction
  • validation
  • questionnaire

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Footnotes

  • Contributors LF, KL, WW, JSH and IWL were involved in the study design, data analysis and interpretation. KL designed the noise algorithms under direct supervision of LF with advice from WW. KL and KM assembled the library of task-based noise exposure levels and conducted the data collection. KL drafted this manuscript with advice from all other authors.

  • Funding This work is funded by the National Health and Medical Research Council (NHMRC) (grant1059562). LF is supported by a fellowship from the NHMRC.

  • Competing interests None declared.

  • Patient consent Obtained.

  • Ethics approval Curtin University Human Research Ethics Committee.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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