A Quantitative General Population Job Exposure Matrix for Occupational Noise Exposure

21.04.2020

The objective of this study was to create a quantitative JEM for occupational noise exposure assessment of the general working population.

Zara Ann Stokholm, Mogens Erlandsen, Vivi Schlünssen, Ioannis Basinas, Jens Peter Bonde, Susan Peters, Jens Brandt, Jesper Medom Vestergaard, Henrik Albert Kolstad

Occupational noise exposure is a known risk factor for hearing loss and also adverse cardiovascular effects have been suggested. A job-exposure matrix (JEM) would enable studies of noise and health on a large scale. The objective of this study was to create a quantitative JEM for occupational noise exposure assessment of the general working population. Between 2001–2003 and 2009–2010, we recruited workers from companies within the 10 industries with the highest reporting of noise-induced hearing loss according to the Danish Working Environment Authority and in addition workers of financial services and children day care to optimize the range in exposure levels. We obtained 1343 personal occupational noise dosimeter measurements among 1140 workers representing 100 different jobs according to the Danish version of the International Standard Classification of Occupations 1988 (DISCO 88). Four experts used 35 of these jobs as benchmarks and rated noise levels for the remaining 337 jobs within DISCO 88. To estimate noise levels for all 372 jobs, we included expert ratings together with sex, age, occupational class, and calendar year as fixed effects, while job and worker were included as random effects in a linear mixed regression model. The fixed effects explained 40% of the total variance: 72% of the between-jobs variance, −6% of the between-workers variance and 4% of the within-worker variance. Modelled noise levels showed a monotonic increase with increasing expert score and a 20 dB difference between the highest and lowest exposed jobs. Based on the JEM estimates, metal wheel-grinders were among the highest and finance and sales professionals among the lowest exposed. This JEM of occupational noise exposure can be used to prioritize preventive efforts of occupational noise exposure and to provide quantitative estimates of contemporary exposure levels in epidemiological studies of health effects potentially associated with noise exposure.

https://doi.org/10.1093/annweh/wxaa034

 

 

About the Author
Dr Ioannis Basinas Senior Scientist

Ioannis is an exposure scientist and chartered statistician with a background in epidemiology, working as a Senior Scientist in IOM's Research Division. His research activities focus primarily on the assessment of human exposure to dangerous substances including bio-aerosols, inorganic dusts, pesticides, nanomaterials and other chemical agents both in support of regulatory and epidemiological research.  Ioannis has substantial experience on the assessment of exposure to organic dusts and the related health risks particularly among agricultural working populations and a strong interest on the use of statistical modelling as a method for improving quantitative exposure and risk assessment for population studies. Recent activities, among others, include research aiming to characterise levels of exposure to pesticide in workplaces, the improvement of methodologies available for pesticide exposure in occupational epidemiological research, evaluation exercises of the exposure tools used within REACH, the exposure assessment for a study of cognitive impairment in former English professional footballers, and the evaluation of trends in exposure to dangerous substances at an EU level. Ioannis won the Thomas Bedford Memorial Prize in 2018.

Contact details:

Qualifications:

  • PhD in Medicine (Specialization: Occupational epidemiology and hygiene)
  • MSc in Toxicology and Environmental Epidemiology
  • BSc in Environmental sciences

Committee and Society Memberships:

  • Royal Statistical Society: Chartered Statistician (CStat)  

Research Interests:

  • Quantitative exposure assessment
  • Statistical modeling of exposure
  • Assessment of health risks
  • Exposure control and prevention
  • Intervention studies