Modelling of occupational respirable crystalline silica exposure for quantitative exposure assessment in community-based case-control studies

2011 ◽  
Vol 13 (11) ◽  
pp. 3262 ◽  
Author(s):  
Susan Peters ◽  
Roel Vermeulen ◽  
Lützen Portengen ◽  
Ann Olsson ◽  
Benjamin Kendzia ◽  
...  
2011 ◽  
Vol 68 (Suppl_1) ◽  
pp. A47-A48
Author(s):  
S. Peters ◽  
R. Vermeulen ◽  
L. Portengen ◽  
A. Olsson ◽  
B. Kendzia ◽  
...  

Author(s):  
Mark Elwood

This chapter presents study designs which can test and show causation. Cohort and intervention studies compare people exposed to an agent or intervention with those unexposed or less exposed. Case-control studies compare people affected by a disease or outcome with a control group of unaffected people or representing a total population. Surveys select a sample of people, not chosen by exposure or outcome. Cohort studies may be prospective or retrospective; case-control studies are retrospective; surveys are cross-sectional in time, but retrospective or prospective aspects can be added. In part two, strengths, weaknesses and applications of these designs are shown. Intervention trials, ideally randomised, are the prime method of assessing healthcare interventions; special types include crossover trials and community-based trials. Non-randomised trials are noted. The strengths and weaknesses of cohort studies, case-control studies, and surveys are shown.


2019 ◽  
Vol 76 (Suppl 1) ◽  
pp. A21.1-A21
Author(s):  
Susan Peters ◽  
Jerome Lavoue ◽  
Marissa Baker ◽  
Hans Kromhout

Exposure assessment quality is a fundamental consideration in the design and evaluation of observational studies. High quality exposure assessment is particularly relevant for outcomes with long latency, such as cancer, where detailed information on past exposures are often missing and must therefore be estimated.For the IARC Monograph on welding, the exposure group provided an overview of assessment methods used in the key epidemiological studies. Strengths and weaknesses of each study were assessed, along with their potential effects on interpretation of risk estimates.For the association between lung cancer and welding fume exposure, 9 cohort and 10 case-control studies were reviewed. For ocular melanoma and ultraviolet radiation (UVR) from welding, 7 case-control studies were reviewed. Quality criteria were: full occupational histories, and standardized, blinded and quantitative exposure assessment. Additional criteria for lung cancer: specifically assessing welding fumes and using information on welding tasks. For ocular melanoma: assessing artificial and solar radiation separately, taking into account eye burns, eye protection and welding type.Exposure assessment of welding fumes by applying a ‘welding-exposure matrix’ (n=2) or welding-specific questionnaires (n=3) were considered highest quality, followed by case-by-case expert assessment (n=5) or general job-exposure matrices (JEMs, n=4). Job title alone was considered less informative (n=5). For exposure to UVR, JEMs were most informative (n=2), followed by self-reported eye burns and self-reported exposure from specific welding types (n=2), although caution is advised regarding recall bias. Assessing welding fume exposure or ever exposure to welding arcs as proxy for UVR was considered less informative. For both exposures, ever versus never welder, or assessments based on data collected from proxies, were considered least informative.The overall evaluation was that there is sufficient evidence in humans for the carcinogenicity of welding fumes and ultraviolet radiation from welding.


2017 ◽  
Vol 75 (2) ◽  
pp. 155-159 ◽  
Author(s):  
Igor Burstyn ◽  
Paul Gustafson ◽  
Javier Pintos ◽  
Jérôme Lavoué ◽  
Jack Siemiatycki

ObjectivesEstimates of association between exposures and diseases are often distorted by error in exposure classification. When the validity of exposure assessment is known, this can be used to adjust these estimates. When exposure is assessed by experts, even if validity is not known, we sometimes have information about interrater reliability. We present a Bayesian method for translating the knowledge of interrater reliability, which is often available, into knowledge about validity, which is often needed but not directly available, and applying this to correct odds ratios (OR).MethodsThe method allows for inclusion of observed potential confounders in the analysis, as is common in regression-based control for confounding. Our method uses a novel type of prior on sensitivity and specificity. The approach is illustrated with data from a case-control study of lung cancer risk and occupational exposure to diesel engine emissions, in which exposure assessment was made by detailed job history interviews with study subjects followed by expert judgement.ResultsUsing interrater agreement measured by kappas (κ), we estimate sensitivity and specificity of exposure assessment and derive misclassification-corrected confounder-adjusted OR. Misclassification-corrected and confounder-adjusted OR obtained with the most defensible prior had a posterior distribution centre of 1.6 with 95% credible interval (Crl) 1.1 to 2.6. This was on average greater in magnitude than frequentist point estimate of 1.3 (95% Crl 1.0 to 1.7).ConclusionsThe method yields insights into the degree of exposure misclassification and appears to reduce attenuation bias due to misclassification of exposure while the estimated uncertainty increased.


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