Lung cancer risk after exposure to polycyclic aromatic hydrocarbons: a review and meta-analysis

Environmental Health Perspectives, June 15, 2004 by Ben Armstrong, Emma Hutchinson, John Unwin, Tony Fletcher

Publication Bias

There was little evidence that the URR was related to its standard error or to number of cases (p > 0.20), factors that might relate to publication. It is evident in Table 2 that although the very high URRs derive from the smaller studies with lower exposures, some of the extremely low estimated URRs do also. Further, neither Egger's test nor Begg's test (p > 0.20) gave evidence for publication bias (Sutton 2000). Applying a trim-and-fill analysis (designed to correct for publication bias, if any) made negligible difference to the mean.

Dust

Because our information on dust exposure was for each cohort or sometimes for broad job group within studies (Table 1), we could not use conventional methods for controlling for confounding (stratification or inclusion of dust in multiple regression analyses). We adopted an ad hoc approach to use the data we had in order to shed what light we could on this issue:

We compared relative risks estimated at 100 [micro]g/[m.sup.3] years BaP in cohorts in which we had identified substantial dust exposure with those in which there was less. If generic dust were an important cause of lung cancer in these cohorts, one would expect greater apparent risks per unit PAH (BaP) where it was accompanied by dust. Results are shown at the bottom of Table 3. There was no significant association between estimated relative risk per unit PAH (BaP) exposure and dust exposure in the industry. This gives some reassurance that dust is not the predominant cause of the association seen in this cohort between PAH and lung cancer.

Sensitivity Analysis

By investigating dependence of URRs on study characteristics (Table 3), we have already implicitly examined sensitivity of results to these characteristics (study design, smoking adjustment, exposure information, etc) and found little such sensitivity. Here we report investigations of sensitivity of our results to three statistical modeling assumptions.

First, we repeated analyses using the linear model (RR = 1 bx). We found very similar rankings of URRs (Spearman's correlation = 0.99). Fitted relative risks at the maximum exposure found in each plant were also similar. However, there was some variation in URRs of individual cohorts; those with lower exposures typically had lower URRs with the linear model, and those with higher exposures higher URRs. For example, the URR for Swaen's 1997 study of asphalt workers was 15.23 with the exponential model but 3.13 with the linear model; the relative risk predicted at the actual mean exposure in this cohort of 10 [micro]g/[m.sup.3] years, however, was 1.31 for both models. Because methods are not available to rigorously allow for the highly non-regular sampling error in the linear estimates in meta-analyses, we view means and the assessment of heterogeneity of URRs estimated under this model cautiously. Nevertheless, it is reassuring that the mean estimated relative risk at 100 [micro]g/[m.sup.3] years BaP was similar (1.19 compared with 1.20, both highly significant). The patterns of variation of risk across industries were broadly similar, although with some important differences (e.g., means for coke, gas, aluminum, and other were 1.22, 2.25, 1.04, and 4.41, respectively, in linear model vs 1.17, 1.15,1.16, and 10.9, respectively, in log-linear model).

Second, we repeated analyses using alternative criteria for choice of contrast: