home edit2 whole TSD November 2002 PDF format - OEHHA

The variability, or heterogeneity, of results among studies may exist due to numerous factors, including
differences in study design, exposures experienced by study subjects, methods and accuracy of
exposure ascertainment, length of follow-up, and control of confounders (such as smoking).
As described in OEHHA (1998), 30 studies, contributing a total of 39 effect estimates, were utilized in
the meta-analysis. The pooled relative risks for lung cancer from all 39 risk estimates combined varied
with the statistical model used, 1.04 (95% C.I. = 1.02-1.06) under the fixed-effects model and 1.33
(95% C.I. = 1.21-1.46) with the random-effects model. However, significant evidence of heterogeneity
was found (DerSimonian and Laird Q-statistic = 214.59, 38 d.f., p < 0.001). Heterogeneity in this
context refers to large between-study variability. The presence of heterogeneity undermines the validity
of the pooled estimates, and suggests the need for additional analysis to identify the sources of
heterogeneity. As discussed in detail in Appendix C of OEHHA (1998), this involved deriving pooled
estimates for a variety of subsets of the reports.
Through subset analysis, several factors were identified which strongly influenced both the magnitude
and the degree of heterogeneity of the pooled risk estimates: (1) whether or not a study adjusted for
smoking, (2) study design (3) the exposure assessment, as developed from occupational categories, (4)
the presence of selection bias, as manifested by an observed “healthy worker effect”, and other study
characteristics (See Appendix C of OEHHA (1998)). By stratifying the meta-analysis on whether the
risk estimates accounted for smoking, the effect of failure to control for this exposure on the pooled
estimate became readily apparent. Not only did the positive association between diesel-exhaust
exposure and lung cancer persist, but the pooled risk estimate increased to 1.43 (95% C.I. = 1.31-
1.57, random-effects model) with little evidence of heterogeneity among the 12 studies controlling for
smoking.
The case-control studies (15 included in the meta-analysis) gave a summary estimate of 1.44 (95% C.I.
= 1.33-1.56), again with little evidence of heterogeneity, while the estimate based on the results of the
cohort studies remained heterogeneous. The lower pooled RR estimate and substantial heterogeneity
obtained from the cohort subanalysis was probably due at least in part to failure to adjust for smoking,
as only one of sixteen cohort studies controlled for this confounder, while most case-control studies did
(11 of 14 studies, accounting for 17 of the 20 case-control risk estimates).
The “healthy worker effect” (HWE - here based on significantly lower than expected all-cause
mortality) is a manifestation of selection bias related to hiring and retention of workers who are typically
healthier than the general population, resulting in spuriously lower risk estimates for a variety of illnesses,
including those potentially related to occupational exposures. Subsetting the cohort studies into those
with and those without an obvious healthy worker effect markedly reduced the degree of heterogeneity
in the group without the HWE (Q-statistic = 11.190, 9 d.f., p = 0.27), and produced an increase in the
magnitude of the pooled relative risk (RR = 1.52, 95% C.I. = 1.36-1.71-1.78, random-effects model).
In contrast, those studies whose results were characterized by the presence of a HWE continued to
show substantial heterogeneity, and the pooled risk estimates declined. Thus, selection bias is likely to
have played a role in the heterogeneity observed among the cohort studies. Selection bias results from
choosing a study sample that is not representative of the entire population that could have been studied,
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