Document jyZzgkD4GDBEyz3qw6ZE4qGEk

Original article Scand J Work Environ Health 2006;32(1):22-31 Update of a meta-analysis on lung cancer and welding by Denis Ambmise, MD, Pascal Wild, PhD,1 Jean-Jacques Moulin, MD1 Ambroise D, Wild P, Moulin J-J. Update of a meta-analysis on lung cancer and welding. Scand J Work Environ Health 2006;32(1):22-31. Objectives This study is an update, over the period 1954-2004, of a previous meta-analysis completed in 1994. It was aimed at assessing lung cancer risk among welders, while addressing heterogeneity, publication bias, and confounding issues. Methods Combined relative risks (CRR) and their variances were calculated using fixed and random effects models. Heterogeneity was tested using the Q statistic. The publication bias was estimated using funnel plots, and Egger's regression and partially controlled by excluding studies with positive reporting bias. Results The literature provided 60 studies eligible for the meta-analysis. No heterogeneity was observed. The fixed effect CRR for all of the welders and all of the studies was 1.26 (95% CI 1.20-1.32) after partial control of publication bias. No difference was observed according to welding activities. Smoking did not appear to be a marked confounder in the relationship between lung cancer and welding, but the effect of asbestos could not be assessed. Conclusions The meta-analysis showed a 26% excess of lung cancer for welders without any difference according to welding activities. Key terms epidemiologic study; heterogeneity; publication bias; stainless steel. Following the publication of the IARC Monograph on the carcinogenic effects of exposure to chromium, nick el, and welding (1), we published a meta-analysis of ep idemiologic studies on lung cancer among welders (2). The study covered the period 1954-1994, was aimed at assessing the relationship between lung cancer and welding activities, in particular welding stainless steel, which generates exposure to chromium and nickel com pounds, and welding in shipyards, which is likely to be associated with asbestos exposure (1). This meta-analysis, which showed a 30% to 40% increase in the relative risk of lung cancer among weld ers as compared with rates in the general population, found no excess lung cancer risk among shipyard and stainless steel welders when compared with mild steel welders (2). Another outcome of the meta-analysis was the difficulty to assess the relative importance of smok ing and concomitant exposure to asbestos as possible confounding factors (2). Our present report is an update of the previous meta analysis throughout the period 1954-2004. It takes a closer look at the issues of study heterogeneity and publication biases in light of recent methodological developments (3-6). Another aim was to investigate further the comparison of shipyard, stainless steel, and mild steel welders and improve the assessment of con founding due to smoking. Study population and methods Selection of studies A search was conducted of the literature using the Medline, Toxline, and NIOSHTIC2 databases and the keywords "welding & cancer", "welding & mortality", "welding & respiratory tract neoplasm", "welder & can cer", "lung & cancer & occupational & epidemiology", "respiratory tract neoplasm & occupational & case con trol study". The abstracts of the several hundred papers thus se lected were screened for their potential interest. The se lected papers were read extensively and complemented by the references quoted therein. Finally a total of 80 potentially interesting studies were thus included. 1 Departement Epidemiologie en Entreprises - INRS (Department of Industry-based Epidemilogy--French National Institute for Research and Safety), Vandoeuvre Les Nancy, France. Correspondence to : Dr P Wild, Departement Epidemiologie en Entreprises - INRS, BP 27, 54501 Vandoeuvre Les Nancy, France. [E-mail: pascal.wild@inrs.fr] 22 Scand J Work Environ Health 2006, vol 32, no 1 Meta-analysis of lung cancer and welding To test heterogeneity, we used the standard Q sta tistic (3-4). The potential sources of heterogeneity that were addressed were study design and welding catego ries (as in our previous meta-analysis), study countries, publication years, types of relative risks, mortality or morbidity endpoints, and analytical approaches to con trol for smoking. To assess the effect of publication bias, we con structed funnel plots which are a scatterplot of the LnRR; versus their precision w; (6). Publication bias was re vealed by asymmetry of the funnel plot (6). To meas ure this funnel plot asymmetry, we used a linear regres sion approach (the so-called Egger regression) by re gressing the standardized coefficients (LnRR/se(LnRR) against 1/se(LnRR) (6). In the absence of publication bias, the regression line runs through the origin. The intercept of the regression line provides a measure of the funnel plot asymmetry, the larger its deviation from zero the more pronounced the asymmetry (6). All of the analyses were repeated with restriction on the set of studies without positive reporting bias. Results Description of the studies The complete listing of the included studies (22, 25-26, 30-37, 40-88) is given in table 1. These studies exam ined populations in 14 different countries, most of the studies having been conducted in the United States (26 studies) and Europe (26 studies), of which 12 were from Nordic countries. The numbers of studies according to years of publication were 21 studies between 1954 1985, 17 studies between 1986-1993, and 22 studies af ter 1993. Altogether 24 (40 %) new studies had not been included in the former meta-analysis. Table 1. Relative risks and 95% confidence intervals (95% CI) according to study design and welding category. [All = all welding categories, SY = shipyard welding, MS = mild steel welding, SS = stainless steel welding, B = only positive associations were reported, Y = not included in our previous meta-analysis (2)] Study Country Welding category Observed number a Relative risk b 95% CI Bias New studyc Morbidity and mortality statistics Enterline et al, 1963 (40) Menck et al, 1976 (41) Decoufle et al, 1978 (42) Population census,1978 (43) Gottlieb et al, 1980 (44) Petersen et al, 1980 (45) Milne et al, 1983 (46) Gallagher et al, 1983 (47) Sjogren et al, 1986 (22) Firth et al, 1993 (48) Milham et al, 1997 (49) Burnett et al, 1997 (50) Andersen et al, 1999 (51) United States United States United States United Kingdom United States United States United States Canada Sweden New Zealand United States United States Nordic countries Population based case-control studies Breslow et al, 1954 (52) Blot et al, 1978 (53) Blot et al, 1980 (54) Gerin et al, 1984 (55) United States United States United States Canada Buiatti et al, 1985 (56) Kjuus et al, 1986 (57 Schoenberg et al, 1987 (58) Lerchen et al, 1987 (59) Benhamou et al, 1988 (60) Ronco et al, 1988 (61) Zahm et al, 1989 (62) Hull et al, 1989 (63) Morabia et al, 1992 (64) Finkelstein et al, 1995 (65) de Stefani et al, 1996 (66) Italy Norway Norway United States United States United States United States France Italy United States United States United States United States United States Canada Uruguay All All All All All All All All All All All All All All SY SY MS All SS All All SS All SY SY All All All All SS MS SY All All All 34 SMR 0.92 0.64-1.29 48 SMR 1.37 1.01-1.82 B 11 MOR 0.85 0.50-1.50 246 SMR 1.51 1.33-1.71 B 8 MOR 3.50 0.73-16.70 B 37 PMR 0.99 0.61-1.40 5 MOR 1.31 0.43-3.59 74 PMR 1.45 1.15-1.83 B 193 SIR 1.30 1.12-1.50 200 MOR 1.40 1.21-1.61 B Y 267 PMR 1.31 1.16-1.50 Y 1097 PMR 1.24 1.18-1.30 B Y 650 SIR 1.23 1.14-1.33 Y 14 IOR 7.18 1.62-31.74 B 11 IOR 0.70 0.32-1.41 11 MOR 0.9 0.4-2.3 2 IOR 1.2 0.1-9.4 B 12 IOR 2.4 1.0-5.4 B 10 IOR 3.3 1.2-9.2 B 7 IOR 2.8 0.9-8.5 B 28 IOR 1.9 0.9-3.7 B 16 IOR 3.3 1.2-9.3 B 38 IOR 1.19 0.75-1.88 33 IOR 2.5 1.1-5.5 6 IOR 2.2 0.5-9.1 B 19 IOR 3.2 1.4-7.4 B 18 IOR 1.42 0.79-2.88 B 6 MOR 2.93 0.87-9.82 29 IOR 1.2 0.7-2.1 34 IOR 0.9 0.5-1.8 56 IOR 1.6 0.8-3.1 37 IOR 1.7 0.9-3.1 18 IOR 1.5 0.8-2.7 B 18 MOR 1.07 0.57-1.91 Y 18 IOR 1.4 0.7-2.8 Y (continued) 24 Scand J Work Environ Health 2006, vol 32, no 1 Ambroise et al Table 1. Continued. Study Country Welding category Observed number a Relative risk b 95% CI Bias New studyc Van Loon et al, 1997 (67) Jockel et al, 1998 (25) Pezzoto et al, 1999 (68) Gustavson et al, 2000 (69) Matos et al, 2000 (70) Industrial cohort studies Dunn et al 1968 (71) Ott et al, 1976 (72) Redmond et al, 1981 (73) Beaumont et al, 1981 (36) Polednak et al, 1981 (74) McMillan et al, 1983 (75) Fletcher et al, 1984 (76) Newhouse et al, 1985 (77) Rinsky et al, 1988 (78) Tola et al, 1988 (32) Merlo et al, 1989 (79) Simonato et al, 1991 (30-31) Moulin et al, 1993 (33) Danielsen et al, 1993 (80) Park et al, 19994 (81) Lauritsen et al, 1996 (26) Danielsen et al, 1996 (82) Austin et al, 1997 (83) Stern et al, 1997 (84) Milatou-Smith et al, 1997 (34) Danielsen et al, 1998 (85) de Silva et al, 1999 (86) Becker et al, 1999 (35) Danielsen et al, 2000 (87) Puntoni et al, 2001 (88) Steenland et al, 2002 (37) Netherlands Germany Argentina Sweden Argentina United States United States United States United States United States United States United States United Kingdom United Kingdom United Kingdom United States Finland Finland Italy Italy England Italy Scotland Italy Denmark Denmark Denmark France France France France Norway United States Norway Norway Norway Norway Norway United States United States Sweden Sweden Norway United States Germany Norway Italy United States All All All All All All All All SY SS All MS SY All SY SY SY MS SY SS SS All SS SY MS All SS SY SS All MS SY All MS All SS SS All All All MS SS SY All SS SY SY MS 63 IOR 0.86 0.46-1.58 Y 233 IOR 1.25 0.94-1.65 Y 11 IOR 1.1 0.4-3.1 Y 99 IOR 1.44 1.08-1.92 Y 5 IOR 1.6 0.4-6.2 Y 49 SMR 1.05 0.78-1.39 2 SMR 1.00 0.11-3.61 Y 14 SMR 1.51 0.82-2.53 50 MRR 1.28 0.89-1.84 7 SMR 1.24 0.5-2.55 17 SMR 1.50 0.87-2.39 10 SMR 1.75 0.84-3.22 5 PMR 1.04 0.34-2.43 8 SMR 1.46 0.62-2.88 26 SMR 1.13 0.80-1.57 41 MOR 1.13 0.76-1.68 27 SIR 1.15 0.76-1.67 14 SIR 1.35 0.74-2.26 16 SMR 1.67 0.95-2.71 1 SMR 0.76 0.02-4.21 7 SMR 1.01 0.40-2.08 3 SMR 1.11 0.23-3.26 7 SMR 1.40 0.56-2.88 2 SMR 1.46 0.18-5.28 7 SMR 1.50 0.60-3.09 30 SMR 1.57 1.06-2.23 23 SMR 1.62 1.03-2.44 3 SMR 0.91 0.19-2.67 3 SMR 0.92 0.19-2.69 19 SMR 1.24 0.75-1.94 9 SMR 1.59 0.73-3.02 9 SIR 2.50 1.14-4.75 7 MOR 2.73 1.20-6.30 Y 26 MOR 1.3 0.8-2.1 Y 46 MOR 1.5 1.0-2.4 Y 20 MOR 1.5 0.8-2.6 Y 6 SIR 1.03 0.38-2.26 Y 50 SIR 1.33 0.99-1.76 Y 7 MOR 0.76 0.28-2.10 Y 92 PMR 1.23 0.99-1.51 Y 2 SMR 0.41 0.05-1.48 Y 6 SMR 1.64 0.60-3.58 Y 10 SIR 1.55 0.74-2.84 Y 4 SMR 1.10 0.30-2.81 Y 28 MRR 1.30 0.80-2.12 Y 9 SIR 1.27 0.58-2.42 Y 33 SMR 1.61 1.11-2.26 Y 108 MRR 1.22 0.93-1.59 Y a Observed numbers of lung cancer cases (case-control studies), lung cancer deaths (mortality studies), lung cancer diagnoses (cancer incidence stud ies). b Relative risks and 95% CI abstracted from the IARC report (30). There were 13 population surveys, 20 case-control studies, and 27 industry-based cohorts, of which 4 in cluded a nested case-control study. Some studies that provided results for more than one welding category may have contributed to several welding categories. This possibility led to 83 relative risks extracted from the published papers. The distribution of the relative risks was 28 SMR values, 6 PMR values, 9 SIR val ues, 24 IOR values, 13 MOR values, and 3 MRR val ues. Most of the relative risks (44 of 83 = 53%) provid ed information on nonspecific welding categories, smaller numbers (17, 9, and 13 relative risks) concerned shipyard, mild steel, and stainless steel welding, respec tively. For five studies (26, 30, 33, 55, 74), relative risks concerning the category nonspecific welders were ex cluded from the CRR calculation whenever all of the subgroups of the same populations (ie, shipyard, mild steel, or stainless steel welders) were already included in the calculation. Scand J Work Environ Health 2006, vol 32, no 1 25 Meta-analysis of lung cancer and welding A positive reporting publication bias (ie, studies cod ed B) was identified for 6 (46%) of 13 relative risks in the population surveys and for 11(41%) of the 27 rela tive risks in population-based case-control studies, whereas no such bias was detected in the cohort studies (0 of 43 studies) (table 1). With regard to smoking, the high proportion of adjusted relative risks provided by the case-control studies [19 (70%) of 27] contrasted with the low proportions observed in the population sur veys [2 (15%) of 13) and the cohort studies [6 (14%) of 43]. When the observed numbers, relative risks, and 95% confidence intervals were considered according to study designs and welding category, it appeared that the great est numbers of observed cases were obtained in the pop ulation surveys [6 (46%) of 13 with 200 or more lung cancer cases among the welders]. This result contrasted with the study size of the case-control and cohort stud ies, in which most of the observed numbers of cases were below 100. Another contrast is that the population surveys provided results on the nonspecific welding cat egory only, whereas several case-control or cohort stud ies could assess lung cancer risks according to shipyard, mild steel, or stainless steel welding separately. Meta-analysis Figure 1 and table 2 show the effect of the selection bias. In the total database a significant funnel plot asymme try, as computed from the Egger regression, was detect ed. When studies with a positive reporting bias were excluded, the overall CRR decreased from 1.28 (95% CI 1.24-1.32) to 1.26 (95% CI 1.21-1.32). In addi tion, the studies with a positive reporting bias were 10000 - 1000 - o o Studies with positive reporting bias Studies without reporting bias O O 100 - o * \ 10 - YfV-o . * ^ # o 1 1 o o c <1 o o ------------------------------------ 1-------------------------- 9-J ------------------------------ 1------------------1-------------1--------- t--------t------t----- t-----------------0.5 1 2 3 4 5 6 7 8 RR Figure 1. Funnel plot of the precision versus relative risk (RR) on a logarithmic scale [Y axis: precision = (variance)-1; X axis: RR (CRR = 1.26)]. Table 2. Meta-analysis considering publication bias. a (CRR = combined relative risks, 95% CI = 95% confidence interval) Type of study Number of relative risks Fixed effects CRR 95% CI Random effects CRR 95% CI Q statistic P-value Egger regression Intercept 95% CI Biased a 16 1.30 1.25-1.36 1.50 1.32-1.70 32.3 0.006 1.40 0.83-1.972 Unbiased b 61 1.26 1.20-1.32 1.26 1.20-1.32 41.3 0.97 0.06 -0.25-0.43 All studies 77 1.28 1.24-1.32 1.28 1.24-1.32 75.0 0.51 0.30 0.02-0.62 a Papers indicating that only positive associations are reported. b Papers without any indication of selective reporting (ie, "unbiased results"). 26 Scand J Work Environ Health 2006, vol 32, no 1 Ambroise et al heterogeneous, and, when they were excluded, the asymmetry in the funnel plot, as assessed by the Egger regression, virtually disappeared. All further description is therefore restricted to the studies without this posi tive reporting bias. Table 3 presents the results according to study de sign and endpoints, types of relative risks, and welding categories. The meta-analysis using fixed and random effect models led to similar and significantly elevated CRR values for the case-control and cohort studies, 1.27 (95% CI 1.11-1.46) and 1.29 (95% CI 1.19 - 1.40), re spectively (table 3). The CRR was slightly lower for the population surveys (1.24, 95% CI 1.17-1.31). The mor tality and morbidity studies gave similar estimations for the CRR. Similarly, the CRR values did not differ ac cording to the type of relative risks, except for the MOR values, which provided a lower CRR value (1.21, 95% CI 0.99-1.48). The CRR values for the welding sub groups were similar (1.32 for shipyard and mild steel welders and 1.31 for stainless steel welders). The CRR was smaller (1.24) for the nonspecific welding catego ry. No heterogeneity was detected according to study design, types of relative risk, and welding category. Countries and years of publication were also tested as to whether they could be sources of heterogeneity. The results were lower when the studies published in North America were considered versus those published in northern Europe or in other countries [CRR 1.22 (95% CI 1.13-1.32) using random and fixed effects for North America, CRR 1.27 (95% CI 1.20-1.35) for north European studies and CRR 1.30 (95% CI 1.14-1.49) for all other countries]. Three periods were defined for the years of publication, each of them including approxi mately one-third of the relative risks. The CRR values using fixed or random effects did not differ [CRR 1.09 (95% CI 0.96-1.24) for 1954-1985, 1.34 (95% CI 1.21 1.48) for 1986-1993, and 1.27 (95% CI 1.20 - 1.34) for 1994-2004]. No trend according to years of publication was thus detected, but the CRR of the first period was significantly lower than the overall CRR. Among the 13 studies that had no reporting bias and provided tobacco adjusted relative risks, 11 reported rel ative risks higher than unity (22, 25, 26, 35, 58, 61-62, 66, 68-70). When crude and adjusted relative risks were available, it appeared that no or only slight confound ing due to smoking was detected (25-26, 61, 69-70). The results were similar for the cohort studies that used Axelson's method (34, 37,79-80, 82, 87). The CRR val ues were higher for the studies with adjustment for to bacco smoking (1.29, 95% CI 1.17-1.43) than for those without adjustment (1.24, 95% CI 1.17-1.30), the find ing indicating that the lung cancer excesses could not be only due to smoking. Discussion The aim of our present meta-analysis was to improve our previous one (2) by including a greater number of studies and improving the methods used to address between-study heterogeneity and publication bias. We thus included 24 studies providing 28 relative risks that had not been included earlier. This rather large number is Table 3. Meta-analysis considering potential sources of heterogeneity (only for studies without any indication on result reporting (ie, "unbiased results").(CRR = combined relative risks, 95% CI = 95% confidence interval, IOR = incidence odds ratio, SIR = standardized incidence ratio, MOR = mortality odds ratio, SMR = stamdardized mortality ratio) Number of relative risks Fixed effects CRR 95% CI Random effects CRR 95% CI Q statistic P-value Population surveys Case-control studies Cohort studies Exposure Unspecified Shipyards Mild steel Stainless steel Morbidity studies Mortality studies a IOR SIR MOR SMR Other 7 16 38 26 16 8 11 22 35 13 9 10 22 7 1.24 1.17-1.31 1.27 1.11-1.46 1.29 1.19-1.40 1.24 1.18-1.31 1.32 1.16-1.51 1.32 1.10-1.59 1.31 1.06-1.61 1.26 1.19-1.34 1.25 1.14-1.37 1.28 1.11-1.47 1.26 1.18-1.34 1.21 0.99-1.48 1.26 1.11-1.43 1.26 1.15-1.38 1.23 1.15-1.32 1.27 1.11-1.46 1.29 1.19-1.40 1.24 1.18-1.31 1.32 1.16-1.51 1.32 1.10-1.59 1.31 1.06-1.61 1.26 1.19-1.34 1.25 1.14-1.37 1.28 1.11-1.47 1.26 1.18-1.34 1.21 0.98-1.49 1.26 1.11-1.43 1.26 1.15-1.38 6.9 13.0 20.6 18.4 14.3 3.51 3.83 15.4 24.1 10.3 5.0 9.5 14.4 1.9 0.33 0.60 0.99 0.82 0.50 0.83 0.95 0.80 0.90 0.59 0.75 0.39 0.85 0.93 a Except studies providing proportional mortality ratio. Scand J Work Environ Health 2006, vol 32, no 1 27 Meta-analysis of lung cancer and welding due to the fact that, after the IARC multicentric study published at the beginning of the 1990s (30-31), efforts have been made in industrialized countries to set up sev eral epidemiologic studies specifically aimed at assess ing lung cancer risks of stainless steel versus mild steel welders and at improving methods for taking smoking into account. Our present meta-analysis failed to detect substan tial heterogeneity since the CRR values were similar according to the different potential sources of hetero geneity. A possible exception was the low CRR for the studies published before 1984. This low figure was pri marily due to three population surveys in the United States (40, 42, 45), whose relative risk estimates were lower than one and based on MOR and PMR values. The results of a meta-analysis may be biased if the probability of publishing a study is influenced by the results (3, 6, 89). In particular, finding increased risks may increase the chances of publication and thus result in an overestimation of CRR values. A publication bias was detected in this meta-analysis using a funnel plot and Egger regression. Our strategy to control for this publication bias consisted in excluding studies identi fied as having a positive reporting bias. Despite the loss of power due to study exclusion, and even if this meth od probably underestimates publication bias, we con sidered that this approach led to a better effect estimate. This strategy seemed effective, as no publication bias, as assessed by Egger's regression, was detected after the exclusion of B-coded studies. According to our present meta-analysis, the estimate of lung cancer risk among welders, as compared with nonwelders, was 1.26. Another main outcome was that the CRR values were similar for mild steel and stain less steel welders. These results do not support the hy pothesis that stainless steel welders are at higher lung cancer risk than mild steel welders, despite their proba ble exposure to chromium and nickel compounds in welding fumes. This finding agrees with the results of our previous meta-analysis (2), as well as with the con clusions of the large multicentric European study (30 31) and with the IARC monograph on lung cancer among welders (1). A few studies, mostly industrial cohorts, investigat ed the existence of a dose-response relationship, but most of them used different dose surrogates. It was therefore impossible to compute dose-related CRR val ues. The multicentric European study included a large cohort of 11 092 welders from nine European countries (30-31). The cohort was followed for mortality and can cer incidence. An ad hoc job-exposure matrix was de veloped to assess exposure to chromium (metal, trivalent, hexavalent) and nickel (90). This study failed to detect any dose-response relationship between lung can cer occurrence and cumulative exposure to hexavalent 28 Scand J Work Environ Health 2006, vol 32, no 1 chromium or nickel (31). In 14 other studies, employ ment duration as a welder was used as a quantitative exposure estimate, but the time periods used differed between the studies. In six papers, the results for the welders with >20 years of exposure were available (18, 24, 26, 33, 37, 66). Four of them showed an SMR above one, but none of them reached significance. Van Loon et al (67) built a "cumulative probability of exposure" that combines the probability of exposure and exposure duration in three categories. They found a statistically significant dose-response relationship (trend P=0.03), but this trend disappeared after adjustment for smoking and other occupational exposures. Gustavsson et al (69), applying a job-exposure matrix, calculated a cumulative dose but failed to demonstrate any relationship, as did Jockel et al (25) using an index based on an estimation of the lifetime number of welding hours. In fact, only three studies reported a significant dose-response rela tionship. Two of them (36, 80) were conducted among shipyard welders and suggested a possible confounding by asbestos exposure. On the whole, despite some positive results, the data are too limited to reach a conclusion about a dose-re sponse relationship between welding and lung cancer risk. Another outcome of our present meta-analysis is that shipyard welders have lung cancer CRR values that are similar to those of other welders (table 3). This obser vation confirms the previous result of the IARC study (31). However, this result is surprising, as several au thors considered asbestos exposure to be more likely to have occurred in shipyards than in factories (2, 91, 92). This conclusion raises the question of the extent to which asbestos exposure among welders could explain the lung cancer excesses. The use of asbestos protec tion devices (blanket, aprons, gloves, etc) was common among welders, so that welding is considered an occu pation for which asbestos exposure is likely to have oc curred (31, 91, 92). This assumption is confirmed by the fact that significant excesses of mesothelioma have been reported for several cohorts of welders (21, 22, 31, 35, 51, 75, 82, 91, 92). However, some case-control stud ies have reported that asbestos-adjusted odds ratios re mained elevated (10, 23, 25, 67). In our present meta-analysis, it was impossible to evaluate the part played by exposure to asbestos with respect to lung cancer excess, as the information avail able from the reviewed epidemiologic studies was too poor to be used in the calculation of the CRR values. Smoking is a major risk factor to be considered in epidemiologic studies focused on lung cancer risk. Some population surveys in the United States have suggested that welders may smoke more than the general male population (93-95), so that smoking could act as a confounder. Unfortunately, the information on smoking is Ambroise et al very often lacking in the selected studies. The only di rect evidence against a strong role of smoking comes from the case-control studies, which did not find sub stantial changes in the relative risks after adjustment. The adjusted CRR values seemed to confirm this ob servation. A residual confounding can however not be ruled out. In conclusion, our meta-analysis showed a 26% ex cess of lung cancer for welders and little heterogeneity between the studies once the positive reporting bias was accounted for. No difference between mild steel and stainless steel welders could be shown. This risk can not be explained by confounding by smoking. 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