Dear Editor

We read with interest the article by Sorahan et al., “Cancer risks in a historical UK cohort of benzene exposed workers” [1]. We note that the authors showed an increased SMR and SRR for lung cancer among this group. They comment that “there was evidence of increased mortality for lung and lip cancers and for ANLL, and increased morbidity for lung and pleural cancers. There is no reason to suspect that benzene is responsible for the increased lung and pleural cancer risks in this study.” The authors then go on to point out that it is likely that some members of the cohort were exposed to other lung carcinogens. However, we feel that if this was a likely explanation for the observed excess, then the SMR for lung cancer would probably have been heterogeneously distributed among the industries studied, the risk being elevated only in those industries with known exposures to lung carcinogens. This does not seem to have been the case. Further , confounding by cigarette smoking seems unlikely because the SMR for some other tobacco-related causes are not significantly elevated (e.g. non-maliganant diseases of the respiratory system).

There have been two papers based on a Chinese cohort of benzene exposed workers [2,3], which are relevant to this issue. The first of these, showed an RR for lung cancer of 2.31 among exposed non-smokers, and while 95% confidence intervals were not reported the authors did state this was statistically significant [2]. The second reported a relative risk for lung cancer of 1.4 (95% CI 1.0-2.0) among benzene exposed males [3]. Therefore, we think it is important to recognize that while the excess described in this paper may be due to exposure to other carcinogens, it is also possible that we are seeing accumulating evidence of an association between benzene exposure and lung cancer.

References

1 Sorahan T, Kinlen LJ, Doll R. Cancer risks in a historical UK cohort of benzene exposed workers. Occupational and Environmental Medicine 2005; 62:231-236.

2 Yin SN, Li GL, Tain FD, et al. A retrospective cohort study of leukemia and other cancers in benzene workers. Environmental Health Perspectives 1989; 82:207-13.

3 Yin SN, Haze RB, Linet MS, et al. A cohort study of cancer among benzene exposed workers in China: Overall results. American Journal of Industrial Medicine 1996; 29:227-35.

Dear Editor,

We have read the study on respiratory disease and cardiovascular morbidity by Koskela and coworkers with great interest.[1] They found no obvious effect of direct dust exposure on ischaemic heart disease (IHD) among granite workers and workers in metal industry such as foundry workers and iron foundry workers in Finland. Furthermore, there was a weak association between dust exposure and chronic bronchitis and pneumoconiosis, respectively, among granite workers. However, there was an association between chronic bronchitis and IHD among granite workers and iron foundry workers.

We want to make one comment regarding a possible bias towards unity- effect by the inclusion of non-exposed workers.

The study by Koskela et al. comprises six cohorts. Three of these cohorts consist of 1000 iron foundry workers, 1000 metal product workers and 1000 electrical workers, respectively. The subjects selected into the cohorts were 400 current and 400 former male workers with the longest duration of employment and a further 200 with the shortest duration of employment.

A possible association between air pollutants and IHD may have a similar mechanism as the association between smoking and IHD. Smoking more than 25 cigarettes per day is estimated to double the risk of IHD compared with non-smokers. This risk decreases after quitting smoking and becomes closer to the risk of non-smokers.[2] Thus, former dust exposed workers followed for 16 years may ultimately have a risk similar to non-exposed workers. Consequently, the findings of the study by Koskela and coworkers may underestimate the relation between dust exposure and the occurrence of IHD. We certainly agree with our Finnish colleagues that efforts to prevent IHD should both include the prevention of respiratory diseases and the control of dust exposure.

We do not have any competing interest regarding this letter.

References:

1. Koskela R-S, Mutanen P, Sorsa J-A, Klockars M. Respiratory disease and cardiovascular morbidity. Occup Environ Med 2005; 62: 650-655.

2. Doll R, Peto R, Wheatley K, Gray R, Sutherland I. Mortality in relation to smoking: 40 years’ observations on male British doctors. BMJ 1994; 309: 901-911.

Bengt Sjögren, MD, PhD
Work Environment Toxicology
Institute of Environmental Medicine
Karolinska Institutet
P.O. Box 210
SE-171 77 Stockholm
Sweden
Tel: 46 8 524 822 29
Fax: 46 8 31 41 24
E-mail: Bengt.Sjogren@ki.se

Carl-Göran Ohlson, MD, PhD
Department of Occupational and Environmental Medicine
Örebro University Hospital
SE-701 85 Örebro
Sweden
Tel.: +46 19 6022468
Fax.: +46 19 120404
E-mail: carl-goran.ohlson@orebroll.se

Dear Editor,

Sir, the recent paper by Lin et al.[1] in the November issue of the journal was a thought provoking piece of work. In their paper the authors try to prove the theoretically derived hypothesis that biomarkers of exposure have smaller variance ratios and would typically provide less biased surrogates of exposure compared to air measurements. Although I entirely agree with the theoretical part of this scientific discussion, I do not think the data presented in their paper actually support the notion.

I would like to raise three issues to support my case. First, in order to proof the hypothesis one would have to compare concurrently collected environmental and biological data gathered from the same individuals during the same time period. The main analysis presented in the paper by Lin et al.1 does not fulfil this criterion, because as the authors pointed out datasets were added with biomarker data only. These added datasets might not have been representative for the other situations considered and as the authors point out might be from exposure situations, which favours biomonitoring a priori (exposure with a long residential half-life). When we restrict ourselves to the data presented in Figure 5 we see no statistically significant difference in variance ratios between the two measures of exposure except for pesticides, but these exposure situations remarkably show variance ratios that favour air measurements. There is also a puzzling issue when one compares table 3, figure 4 and figure 5. From figure 4 one learns that 64 lambda values could be estimated for biomarker measurement datasets and 43 for air measurement datasets. One would expect that when the authors fall back to exposure settings where both measurements were performed, they would present results for 43 datasets. However, in figure 5 results are presented for 54 datasets. It becomes even more puzzling when one considers table 3 where 33 air measurement datasets and 46 biomarker measurement datasets are mentioned. In addition, comparing figure 4c with 5c it is remarkable that the number of datasets with biomarkers with intermediate residence time increases from 17 to 18. Unless the residence time of the biomarkers was recoded this would have been impossible.

The second issue that might have biased the presented results is the unequal number of both monitored workers and occasions. From table 1 and Appendices A and B it is clear that in most situations more air samples were collected than biomarker samples. As was shown earlier[2], when the number of observations increases (more individuals measured on more occasions covering a larger area of possible conditions) the estimated between- and within-worker variance components have a tendency to increase as well. So if one does not restrict the datasets to the same individuals measured over the same period, comparing variance ratios of air and biomarker data becomes problematic. From the Appendices it looks like that in most cases the data collection covered the same period, but not necessarily the same individuals (see for instance reference 24 in Appendix B, where 249 airborne styrene measurements were performed on 48 individuals during a period of 2-3 months, while 146 blood styrene measurements were taken from 29 individuals in a period of 3-4 months).

The third issue I would like to raise is the comparison of environmental versus occupational exposures. Based on the presented fold ranges and lambda values the authors claim “biomonitoring may be more advantageous in environmental settings than in occupational settings”. Here the authors appear to forget that fold ranges and lambda’s are by definition of a relative nature. A fold range of 100 at nanogram level might be biologically totally irrelevant, while a fold range of 2 around a level where biological effects can be expected, might be relevant to study in an epidemiological setting. When we studied a group of pig farmers in The Netherlands3 their between-farmer fold range in average air exposure to endotoxins was very low (bwR95=4.1) and the variance ratio such that we could expect heavily biased exposure response relations (λ= 4.7). Nevertheless even within a group so uniformly exposed meaningful exposure- response relations could be discerned by applying an innovative exposure assessment method where measurement results were modelled and exposure predicted based on diary information.

Finally, I would like to comment and I am convinced that the authors will fully agree, that one should not choose between air sampling and biomonitoring, but one should perform both whenever possible. Not only because more data is needed to prove the case for biomonitoring (for which currently data is insufficient), but also air and dermal measurements are a necessity in order to link (internal) exposure to possibilities for hazard control. Prevention of occupational and environmental exposures before they reach harmful concentrations will continue to be our first priority in the field of occupational and environmental health.

References:

1. Lin YS, Kupper LL, Rappaport SM. Air samples versus biomarkers for epidemiology. Occupational and Environmental Medicine 2005; 62:750-60.

2. Kromhout H, Symanski E, Rappaport SM. A comprehensive evaluation of within- and between-worker components of occupational exposure to chemical agents. the Annals of Occupational Hygiene 1993; 37:253-270.

3. Preller L, Kromhout H, Heederik D, Tielen MJM. Modeling long-term average exposure in occupational exposure-response analysis. Scandinavian Journal of Work Environment and Health 1995; 21:504-12.

The Editor,

We appreciate Dr. Kromhout’s comments regarding our article “Air samples versus biomarkers for epidemiology”[1] and are pleased that he supports our recommendation that both air samples and biomarkers be collected whenever possible. Kromhout raises three points in his letter. First, he suggests that our conclusion that biomarkers tend to be better surrogates for exposures than air samples might have been biased by our inclusion of some data from studies that contained only biomarker measurements. Second, he questions our comparisons of air measurements and biomarkers in situations where sample sizes differ between the two types of data. And finally, Kromhout argues that because variance ratios (values of lambda) are relative measures, they should not be the sole criteria for choosing between air and biological measurements in an epidemiology study. We will respond to each of these points in turn.

We agree that it is best to compare environmental and biomarker data that were collected concurrently from the same subjects. Indeed, as we indicated, we made every effort to do so and were successful in 36 of the 47 biomarker datasets (77%) listed in table 1. We have no reason to believe, as Kromhout suggests, that the remaining 11 biomarker datasets were less representative of biomarker measurements than those with concurrent air measurements. But, even if we restrict our analyses to datasets with concurrent air and biological measurements, 62% of these datasets had lambda ratios less than one (median=0.46, p.755, par. 2), indicating that a typical biomarker should be a less biasing surrogate for exposure in an epidemiology study than a typical air measurement. This was the main conclusion of our analyses. Although sample sizes were small for stratified comparisons, we also observed that lambda ratios for metals were significantly smaller than one (Figure 5B), while those for pesticide exposures were significantly greater than one. Although Kromhout concluded from the latter result that air measurements would be favoured for pesticides, we cautioned that a lambda ratio greater than one could also result from multiple routes of pesticide exposure including inhalation, dermal contact, and ingestion; if this were the case, then biomarkers would be preferred for pesticide exposures as well. Regarding the issue of sample sizes, Kromhout suggests that the variance components estimated in our study might have been influenced by the relative numbers of air and biomarker measurements in our database. His argument is based upon previous work, which indicated that the within- subject variance components, estimated from air measurements of workplace exposures, tended to be greater when sample sizes were greater than 10 measurements than when they were less than 10 measurements’.[2] In fact, the median number of repeated observations from a given subject in our database was three for both biomarkers (range: 2-30) and air measurements (range: 2-37). So, different sample sizes were unlikely to have influenced our results. We also note that the variance components estimated in our study were obtained after adjustment for fixed time effects, whereas those from Kromhout’s study were not.[2] Because sample sizes tend to be positively associated with the duration of a study, larger numbers of measurements tend to suggest longer periods of observation which can introduce trends and seasonal effects into the data. Indeed, in our study we identified significant fixed effects of time in about 1⁄2 of the sets of biomarker measurements and about 1/3 of the sets of air measurements. We reported in our paper that ignoring such fixed time effects tended to increase the estimated within-person variance component and to decrease the estimated between-person variance component, consistent with the earlier work of Symanski et al.[3] Concerning the relative nature of the variance ratio (lambda), we agree that it should not be the sole criterion used to choose between air and biological measurements. As we indicated on p.758 of our paper, “…the optimal measure of exposure for an epidemiology study depends not only on variance ratios of the air and biomarker measurements (smaller is better), but also on projected sample sizes (larger is better), based on practical considerations and costs, and knowledge of the dominant route of exposure (if multiple routes, biomarkers are preferred).”

Finally, Kromhout questioned the numbers of datasets we used for various analyses and noted a discrepancy regarding the numbers of datasets referred to in Figures 4C and 5C. His confusion may have resulted from the fact that some studies reported multiple biomarkers for a given air exposure (e.g., reference 24, appendices B and D) and, occasionally, two types of air exposure for a given biomarker (e.g., ref 26, appendices B and D). Thus, 43 datasets generated 64 estimates of lambda for biomarkers (Figure 4) and generated 54 lambda ratios (Figure 5). While rechecking our files, we found that one set of blood lead measurements had been coded as an intermediate-term biomarker for the estimate of lambda (Figure 4C) and as a long-term biomarker for the estimate of the lambda ratio (Figure 5C). We also discovered that an asterisk (indicating statistical significance) had been inadvertently deleted from Figure 5B under the bar ‘Metal’. We apologize for any confusion this may have caused.

Sincerely,

S.M. Rappaport, Ph.D.
Yu-Sheng Lin, Ph.D.
Lawrence L. Kupper, Ph.D.

School of Public Health
University of North Carolina
Chapel Hill, NC 27599-7431
U.S.A.

References

1. Lin YS, Kupper LL, Rappaport SM. Air samples versus biomarkers for epidemiology. Occup Environ Med. 2005 Nov;62(11):750-60.

2. Kromhout H, Symanski E, Rappaport SM. A comprehensive evaluation of within- and between-worker components of occupational exposure to chemical agents. Ann Occup Hyg. 1993;37(3):253-70.

3. Symanski E, Kupper LL, Kromhout H, Rappaport SM. An investigation of systematic changes in occupational exposure. Am Ind Hyg Assoc J. 1996;57(8):724-35.