Abstract
The objectives of this study were to determine the heterogeneity of data sources used to construct a job-exposure matrix (JEM) for occupational noise, and to calculate pooled exposure estimates for different job titles using different sources. The JEM was populated with measurements from government databases, private industry, and the published literature. Data were organized by job title using the US standard occupational classification (SOC). Using data from the literature as prior information, adjusted mean exposure was calculated for both the government and industry data following a simple Bayesian approach. A meta-analysis was conducted to measure data heterogeneity across sources and to calculate a pooled exposure estimate for each SOC and SOC group. In total, 715,867 measurements across 259 SOCs were analyzed. Using the data from literature as a prior, 15 of 28 applicable SOCs in the government and industry data had adjusted mean exposures above the OSHA action level (85 dBA). The meta-analysis showed that 63% of SOCs, and 78% of SOC groups, had moderate to high heterogeneity. Fifty-one percent of SOCs and 43% of SOC groups had pooled estimated exposures >85 dBA. The pooled estimates suggested that workers in 131 of 259 SOCs (51%) were exposed beyond the threshold of 85 dBA.
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Acknowledgements
This work was supported by the National Institute of Occupational Safety and Health Grant #R21OH0 10482: Development of a United States/Canadian Job-Exposure Matrix (JEM) for Noise. This paper represents original research by the authors. This research has not been previously published and is not currently under consideration at any other journal.
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Appendices
Appendix 1
Appendix 1 Search terms used during the literature review
PubMed (June 2014)
Search 1
noise exposure N=8779
noise exposure (United States OR Canada) N=1859
Search 2
occupational exposure[mesh] OR hearing loss, noise-induced[mesh]) (united states OR canada) N=14,241
noise[mesh] "Occupational Exposure/statistics and numerical data"[Mesh] N=113
with Humans and English lang. filters N=87
with (United States OR Canada) N=29
Search 3
(("service industry"[All Fields] OR ("industry"[MeSH Terms] OR "industry"[All Fields])) OR ("workplace"[MeSH Terms] OR "workplace"[All Fields])) AND ((("noise"[MeSH Terms] OR "noise"[All Fields]) OR ("sound"[MeSH Terms] OR "sound"[All Fields])) OR ((("noise"[MeSH Terms] OR "noise"[All Fields]) AND exposure[All Fields]) OR ("occupational exposure"[MeSH Terms] OR ("occupational"[All Fields] AND "exposure"[All Fields]) OR "occupational exposure"[All Fields])))) AND (("united states"[MeSH Terms] OR ("canada"[MeSH Terms])) AND ("humans"[MeSH Terms] AND English[lang]) N=3469
Search 4
"noise"[MeSH Terms] OR "sound"[MeSH Terms] AND ("occupational exposure"[MeSH Terms] OR "occupational exposure"[All Fields]) N=1097
Search 5
(("noise"[MeSH Terms] OR "noise"[All Fields]) OR ("sound"[MeSH Terms] OR "sound"[All Fields]) AND ("occupational exposure"[MeSH Terms] OR "occupational exposure"[All Fields])) N=1640
Search 6
((((((("Noise/statistics and numerical data"[Mesh])) OR ("Occupational Diseases/statistics and numerical data"[Mesh])) OR ("Hearing Loss, Noise-Induced/statistics and numerical data"[Mesh])) OR ("Environmental Exposure/statistics and numerical data"[Mesh]))) AND ((("service industry") OR industry[MeSH Terms]) OR workplace[MeSH Terms])) AND ((United States OR Canada)) N=1829
Search 7
(((((((((((((("Noise/statistics and numerical data"[Mesh])) OR ("Occupational Diseases/statistics and numerical data"[Mesh])) OR ("Hearing Loss, Noise-Induced/statistics and numerical data"[Mesh])) OR ("Environmental Exposure/statistics and numerical data"[Mesh]))) NOT asbestos)))))) AND (((((((((((((((forestry[Title/Abstract]) OR oil extraction[Title/Abstract]) OR gas extraction[Title/Abstract]) OR transportation[Title/Abstract]) OR trucking[Title/Abstract]) OR warehouses[Title/Abstract]) OR warehousing[Title/Abstract]))))) OR (((((fishers[Title/Abstract]) OR seafarers[Title/Abstract])) OR fisheries[Title/Abstract]) OR fishing[Title/Abstract])) OR (((("service industry") OR industry[MeSH Terms]) OR workplace[MeSH Terms])) OR ((((((((((((((("food industry"[Title/Abstract]) OR coffee[Title/Abstract]) OR barista[Title/Abstract]) OR cafe[Title/Abstract]) OR restaurants[Title/Abstract]) OR construction[Title/Abstract]) OR building[Title/Abstract]) OR factory[Title/Abstract]) OR mining[Title/Abstract]) OR coal[Title/Abstract]) OR coking[Title/Abstract]) OR metallurgy[Title/Abstract]) OR farm[Title/Abstract]) OR farming[Title/Abstract]) OR agriculture[Title/Abstract]))))) AND (united states OR canada) N=2464
Search 8
(((((noise[Mesh] OR occupational diseases[Mesh] OR hearing loss, noise-induced[Mesh] OR environmental exposure[Mesh]) NOT asbestos))) AND (((((((((((((((forestry[Title/Abstract]) OR oil extraction[Title/Abstract]) OR gas extraction[Title/Abstract]) OR transportation[Title/Abstract]) OR trucking[Title/Abstract]) OR warehouses[Title/Abstract]) OR warehousing[Title/Abstract]))))) OR (((((fishers[Title/Abstract]) OR seafarers[Title/Abstract])) OR fisheries[Title/Abstract]) OR fishing[Title/Abstract])) OR (((("service industry") OR industry[MeSH Terms]) OR workplace[MeSH Terms])) OR ((((((((((((((("food industry"[Title/Abstract]) OR coffee[Title/Abstract]) OR barista[Title/Abstract]) OR cafe[Title/Abstract]) OR restaurants[Title/Abstract]) OR construction[Title/Abstract]) OR building[Title/Abstract]) OR factory[Title/Abstract]) OR mining[Title/Abstract]) OR coal[Title/Abstract]) OR coking[Title/Abstract]) OR metallurgy[Title/Abstract]) OR farm[Title/Abstract]) OR farming[Title/Abstract]) OR agriculture[Title/Abstract]))))) AND (united states OR canada) N=9112
Embase (June 2014)
Search 9
'occupational accident'/exp OR 'occupational accident' AND ('noise'/exp OR noise) AND (united AND states OR 'canada'/exp OR canada) N=71
Scopus (June 2014)
Search 10
(noise AND occupational exposure AND statistics AND (united states OR canada)) N=37
Appendix 2
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Appendix 3
Appendix 3 Formula derivations in the Bayesian method section
We assumed the observations from government and from industry follow a normal distribution respectively: and . We specified informative normal priors as follows:
Based on these two priors, we derived the posterior distribution of μG and that of μL:
Then the estimated exposure level by SOC was the posterior mean of the posterior distribution:
We constructed a 95% credible interval for and , based on their posterior distributions:
Based on the formula of posterior distribution, is a weighted average of , the mean from government data and , the mean from literature data; is a weighted average of , the mean from industry data and , the mean from literature data. For , the weight of government data is based on its number of observations and the standard deviation. The higher the weight of government data, the more the government data contributes to . Therefore, this weight represents the contribution of government data:
Similarly, we applied the above formula to calculate ContributionI, the contribution of industry data to estimate .
Appendix 4
Appendix 5
Appendix 5 Summary of sample mean differences across data sources
Of the 27 SOCs present in three sources of data, the government and industry data each provided the largest sample mean exposure for 10 (37%) of the SOCs. Among the 203 SOCs that are common to both government data and industry data, 131 (65%) SOC sample means in government data are greater than their sample means in industry data. We illustrate the nature of these differences across sources within these 203 SOCs in the table below. Sample mean from the government data was more than 2 dBA or exactly 2 dBA greater than the mean from the industry data in 45% of the SOCs but there are also 21% SOCs in which the sample mean from the industry data was more than 2 dBA or exactly 2 dBA greater than the sample mean from the government data.
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Cheng, W., Roberts, B., Mukherjee, B. et al. Meta-analysis of job-exposure matrix data from multiple sources. J Expo Sci Environ Epidemiol 28, 259–274 (2018). https://doi.org/10.1038/jes.2017.19
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DOI: https://doi.org/10.1038/jes.2017.19
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