An association between lead and meningioma and its modification by the ALAD
rs1800435 polymorphism was observed based on expert assessment of exposure but not when using a JEM exposure matrix. Based on kappa statistics, there was fair to moderate agreement between the exposure metrics derived from the JEM and expert exposure assessment methods 24
. While the JEM displayed reasonable specificity compared to the expert assessment, its sensitivity was only modest. As expected, the kappa statistics, sensitivity and specificity values did not vary appreciably when subjects were stratified by genotype. Although neither the expert nor the JEM approach is perfect, we believe that in this case, the expert approach is likely to be more accurate because, unlike the JEM, expert assessment has the ability to account for within-job variability by using detailed questionnaire-based work history information (e.g. specific tasks, control measures etc.) specific to the study at hand.
The exposure prevalence for lead, based on expert assessment, was approximately 40% among controls () which may seem high. We believe the prevalence is realistic because of the calendar time of the study, and because we considered all exposures (including low exposures). The mean blood lead level in the US population circa 1970 was 12.8 ug/dL 25
. Though we estimated occupational exposure prevalence in our study, this figure does indicate fairly ubiquitous exposure to lead.
The risk estimates and corresponding 95% CI and p-values observed for the expert-assessed lead data differ slightly from those previously reported16
. This is because the previous analyses considered only those jobs with an exposure intensity of greater than or equal to 10 μg/m3
to be exposed to lead. To facilitate comparison between the expert and JEM assessment methods, we did not impose this restriction in the current analysis.
Expert- and JEM-based exposure assessments have been compared in previous case-control studies 9–14
. In our study, we observed slightly higher levels of agreement between expert- and JEM-based exposure assessments than observed in other studies examining various exposures 9–11
. For example, in a case-control study of glioma, Benke et al (2001) calculated a kappa of 0.33 for ever exposure to lead 9
while we calculated a kappa of 0.5 for ever exposure to lead among meningioma cases and controls. Even though we observed a higher kappa value, 0.5 only represents a moderate level of agreement 24
. As with our study, previous evaluations of various exposures in case-control studies observed poor sensitivity, yet high specificity, for JEMs compared to expert assessments 12–14
. Rybicki et al (1997), for example, observed a sensitivity of 0 and a specificity of 0.93 when comparing lead exposure estimates derived from a JEM versus expert assessment 12
Although in this paper we consider expert assessment as the more accurate method, it is also imperfect. The quality of the assessment depends on the experience of the expert 5
, and there may be differences in exposure assignment as the study progresses, although the latter issue can be somewhat mitigated with detailed and standardized rules 1,26
. While the ability to account for within-job variability is a strength of expert assessment because of the potential gain in accuracy, this gain may be offset by limitations in the ability of participants to recall detailed work information. The use of self-reported job histories also raises issues of response bias (i.e. cases indicate greater exposures to lead because of their disease status), but this is not likely to be a problem in our study given that our questionnaire was designed and administered in such a way as to to assess the potential for exposure to a wide variety of agents without prior knowledge of what exposures would be of most interest. Thus, any resulting misclassification of exposure would likely be non-differential, and the risk estimates would most typically be biased towards the null.
Use of a biomarker for cumulative lead exposure such as bone lead measurements rather than questionnaires would have been ideal. However, evaluation of the association between lead exposure and brain tumors was not the primary objective of this study when it was initiated, and, as such, biomarker data for lead exposure were not collected. In a previous comparison of exposure assessment methods including biomarker data, Tielemans et al (1999) found that assessment of individuals as exposed versus unexposed to chromium by job specific questionnaires compared better to urinary measurements than when using a JEM to assess exposure 15
. Although urinary chromium concentrations were clearly increased in subjects classified as exposed by the job specific questionnaire, the exposed group from the job specific questionnaire was restricted to those individuals that were determined to be highly exposed, and kappa statistics indicated only poor to moderate agreement. While in the absence of actual measurement data, expert assessment is considered the best approach to date for assessing past exposures in population-based case-control studies 5
, resources should be directed towards developing better methods that address the limitations of expert assessments.
Expert assessment has been reported to provide greater statistical power than other methods (including JEM-based exposure assessment) for detecting associations between exposure and disease 27
. In the analysis of gene-environment effect modification, statistical power becomes an even greater issue as studies typically require large sample sizes to detect effect modification 28
. It has been demonstrated that even small errors in the assessment of environmental factors can result in biased interaction parameters and substantially increased sample size requirements for the detection of effect modification 6,7
. In our analyses, misclassification of exposure by the JEM as compared to the expert assessment resulted in smaller odds ratios and less likelihood of detecting an effect. These results indicate that investigators would benefit from using the most accurate method of exposure assessment available, since the attenuating effects of exposure misclassification would result in increased sample size requirements to detect effect modification 29
that would offset any savings from using a less costly method of exposure assessment 1
As genome-wide association studies identify genetic polymorphisms associated with disease, there is increasing interest and need for evaluating interaction with environmental factors. Although we recognize the need for replication of the effect modification results given the small sample size of variant carriers exposed to lead, preliminary findings suggest that high quality exposure data are likely to improve the ability to detect genetic effect modification.