Ecological regression analysis of environmental benzene exposure and childhood leukaemia: sensitivity to data inaccuracies, geographical scale and ecological bias
Ecological regression analysis of environmental benzene exposure and childhood leukaemia: sensitivity to data inaccuracies, geographical scale and ecological bias
Benzene is classified as a group 1 human carcinogen by the International Agency for Research on Cancer, and it is now accepted that occupational exposure is associated with an increased risk of various leukaemias. However, occupational exposure accounts for less than 1% of all benzene exposures, the major sources being cigarette smoking and vehicle exhaust emissions.
Whether such low level exposures to environmental benzene are also associated with the risk of leukaemia is currently not known. In this study, we investigate the relationship between benzene emissions arising from outdoor sources (predominantly road traffic and petrol stations) and the incidence of childhood leukaemia in Greater London. An ecological design was used because of the rarity of the disease, the difficulty of obtaining individual level measurements of benzene exposure and the availability of data. However, some methodological difficulties were encountered, including problems of case registration errors, the choice of geographical areas for analysis, exposure measurement errors and ecological bias. We use a Bayesian hierarchical modelling framework to address these issues, and we investigate the sensitivity of our inference to various modelling assumptions.
bayesian hierarchical models, benzene, childhood leukaemia, ecological regression, environmental epidemiology, markov random fields
155-174
Best, Nicky
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Cockings, Samantha
53df26c2-454e-4e90-b45a-48eb8585e800
Bennett, James
3597877b-a326-4fc3-af50-07e59109e993
Wakefield, Jon
199000fa-19e2-4785-a6ad-77dbd9bc623b
Elliott, Paul
5e19ee8c-f18a-417e-ad53-bc807fdfd191
2001
Best, Nicky
d7d79436-e852-41b3-8007-c4a2d7871b9c
Cockings, Samantha
53df26c2-454e-4e90-b45a-48eb8585e800
Bennett, James
3597877b-a326-4fc3-af50-07e59109e993
Wakefield, Jon
199000fa-19e2-4785-a6ad-77dbd9bc623b
Elliott, Paul
5e19ee8c-f18a-417e-ad53-bc807fdfd191
Best, Nicky, Cockings, Samantha, Bennett, James, Wakefield, Jon and Elliott, Paul
(2001)
Ecological regression analysis of environmental benzene exposure and childhood leukaemia: sensitivity to data inaccuracies, geographical scale and ecological bias.
Journal of the Royal Statistical Society: Series A (Statistics in Society), 164 (1), .
(doi:10.1111/1467-985X.00194).
Abstract
Benzene is classified as a group 1 human carcinogen by the International Agency for Research on Cancer, and it is now accepted that occupational exposure is associated with an increased risk of various leukaemias. However, occupational exposure accounts for less than 1% of all benzene exposures, the major sources being cigarette smoking and vehicle exhaust emissions.
Whether such low level exposures to environmental benzene are also associated with the risk of leukaemia is currently not known. In this study, we investigate the relationship between benzene emissions arising from outdoor sources (predominantly road traffic and petrol stations) and the incidence of childhood leukaemia in Greater London. An ecological design was used because of the rarity of the disease, the difficulty of obtaining individual level measurements of benzene exposure and the availability of data. However, some methodological difficulties were encountered, including problems of case registration errors, the choice of geographical areas for analysis, exposure measurement errors and ecological bias. We use a Bayesian hierarchical modelling framework to address these issues, and we investigate the sensitivity of our inference to various modelling assumptions.
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Published date: 2001
Keywords:
bayesian hierarchical models, benzene, childhood leukaemia, ecological regression, environmental epidemiology, markov random fields
Organisations:
PHEW – P (Population Health), Remote Sensing & Spatial Analysis
Identifiers
Local EPrints ID: 15861
URI: http://eprints.soton.ac.uk/id/eprint/15861
ISSN: 0964-1998
PURE UUID: 7897b602-94a0-4738-800f-8bade006aae4
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Date deposited: 08 Jun 2005
Last modified: 16 Mar 2024 03:21
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Contributors
Author:
Nicky Best
Author:
James Bennett
Author:
Jon Wakefield
Author:
Paul Elliott
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