Examining the association of lung cancer and highly correlated fibre size-specific asbestos exposures with a hierarchical Bayesian model.

Published

Journal Article

BACKGROUND: Asbestos is a known carcinogen. However, little is known about the differential effects of size-specific asbestos fibres. Previous research has examined the relationship with lung cancer of each fibre group in the absence of others. Attempts to model all fibre groups within a single regression model have failed due to high correlations across fibre size groups. METHODS: We compare results from frequentist models for individual fibre size groups, and a hierarchical Bayesian model that included all fibre groups to estimate the relationship of size-specific asbestos fibre groups to lung cancer mortality. The hierarchical model assumes partial exchangeability of the effects of size-specific asbestos fibre groups to lung cancer, and is capable of handling the strong correlation of the exposure data. RESULTS: When fibre groups are modelled independently with a frequentist model, there appears to be an increase in the dose-response with increasing fibre size. However, when subject to a hierarchical structure, this trend vanishes, and the effects of distinct fibre groups appear largely similar. CONCLUSIONS: This is the first occasion where distinct asbestos fibre groups have been assessed in a single regression model; however, even the use of a hierarchical modelling structure does not appear to overcome all the statistical fluctuations arising from the high correlations across fibre groups. We believe these results should be compared with other occupational cohorts with similar fibre group information. Finally, results for the smallest fibre group may be suggestive of a carcinogenic potential for nanofibres.

Full Text

Cited Authors

  • Hamra, GB; Loomis, D; Dement, J

Published Date

  • May 2014

Published In

Volume / Issue

  • 71 / 5

Start / End Page

  • 353 - 357

PubMed ID

  • 24569623

Pubmed Central ID

  • 24569623

Electronic International Standard Serial Number (EISSN)

  • 1470-7926

Digital Object Identifier (DOI)

  • 10.1136/oemed-2013-101965

Language

  • eng

Conference Location

  • England