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i ) .tun. irrn/. Hyg.. Viil. -M. N K. fp. K.'-WI. 5(10ft Cr<"*n Ccpjrifhi <0 2U0O PWi>hcJ > Elsevier Socncc Ltd on hctolf nl Bmit Occupational Hsjicov Society PH: S0003-4878(00)0004S -4 A" ,ieh" ''era:`l '"wX*W-4S780/S20.00 The Quantitative Risks of Mesothelioma and Lung Cancer in Relation to Asbestos Exposure JOHN T. HODGSON* and ANDREW DARNTON Epidemiology and Medical Statistics Unit, Health and Safety Executive, Magdalen House, Stanley Precinct. Bootle L20 3QZ, UK Mortality reports on asbestos exposed cohorts which gave information on exposure levels from which (as a minimum) a cohort average cumulative exposure could be estimated were reviewed. At exposure levels seen in occupational cohorts it is concluded that the exposure specific risk of mesothelioma from the three principal commercial asbestos types is broadly in the ratio 1:100:500 for chrysotlle, amosite and crocidoliie respectively. For lung cancer the. conclusions are less clear cut Cohorts exposed only to crocidolite or amosite record similar exposure specific risk levels (around 5% excess lung cancer per f/ml.yr): but chryso lite exposed cohorts show a less consistent picture, with a clear discrepancy between the mortality experience of a cohort of chrysotile textile workers in Carolina and the Quebec miners cohort Taking account of the excess risk recorded by cohorts with mixed fibre exposures (generally<1 %). the Carolina experience looks uptypically high. It is suggested that a best estimate lung cancer risk for chrysotile alone would be 0.1%, with a highest reasonable estimate of 0.5%. The risk differential between chrysotile and the two amphiboie fibres for lung cancer is thus between 1:10 and 1:50. Examination of the Inter-study dose response relationship for the amphiboie fibres suggests a non-linear relationship for all three cancer endpoints (pleural and peritoneal mesotheli omas, and lung cancer). The peritoneal mesothelioma risk is proportional to the square of cumulative exposure, lung cancer risk lies between a linear and square relationship and pleural mesothelioma seems to rise less than linearly with cumulative dose. Although these non-linear relationships provide a best fit to the data, statistical and other uncertainties mean that a linear relationship remains arguable for pleural and lung tumours (but not for perito neal tumours). Based on these considerations, and a discussion of the associated uncertainties, a series of quantified risk summary statements for different levels of cumulative exposure are presented. Crown Copyright 2000 Published by Elsevier Science Ltd on behalf of British Occupational Hygiene Society. All rights reserved Keywords: asbestos: amphiboie hypothesis; exposure-response; Jung cancer; mesothelioma: quantified risk assess ment INTRODUCTION There has been much debate on the relative hazard ousness of the three main asbestos types: crocidolite. amosite and chrysotile (commonly known as blue, brown and white asbestos respectively), but no sys tematic attempt to quantify the differences. Existing published quantitative risk assessments have mostly not distinguished between the fibre types, and none Received 17 September 1999; in final form 5 June 2000. "Author to whom correspondence should be addressed. Tel.: +44-151-9514566; fax: +44-151-95114703; e-mail: john .hodgson@hse.gsi.gov.uk has produced quantified estimates of the risk from amphiboles (a collective mineralogical term covering crocidolite and amosite). A review commissioned by the HSE in the 1980s from Professors Richard Doll and Julian Peto (1985) gave estimates for chrysotile alone; more recently a review by the Health Effects institute (1991) produced estimates for an unspecified mixture of fibre types. An 1NSERM review (1996) also ignored differences in fibre type, and drew heav ily on the HEI review. The studies included in this review were selected by reviewing the material referenced in the Doll and Peto. HEI and INSERM reports and identifying all cohort mortality reports for which quantified data on 565 HWBUI0009364 566 i. T. Hodgson and A. Oamton exposure was available either as an average for ihe cohort as a whole, or for individual subgroups. Seven teen such cohorts were identified (Albin eial.. 1990a: de Klerk et al.. 1994; Dement el ui, 1994: Emerline et al., 1987: Finkelstein. 1984; Hughes et al.. 1987: Liddell el al.. 1997; McDonald et'ai. 1983b. 19X4; Neubetger and Kundi. 1990; Newhouse and Sullivan, 1989; Peto et al.. 1985: Piolatto ei al.. 1990; Seidinan el al.. 1986; Seidinan and Selikoff. 1990; SluisCremer et al.. 1992; Talcotc et at.. 1989). Three of the selected cohorts have been split into sub-cohorts which have been separately treated in this review: the South African crocidoftte and amosice mining cohorts have been treated separately; the New Orleans asbes tos cement cohort has been split into the two separate plants covered, since the mix of fibres used in the two plants was different: and the Carolina textile cohort has been split by sex, since the results for men and women were rather different. The cohorts have been referred to by their geographical location except for cohorts 3 (Enierltne et al., 1987) and 17 (Newhouse and Sullivan, 1989) which are identified by a com pany name, and cohort 15 (Albin era/., 1990a) where the name of the principal author on the cohort has been used. Information extracted Information was extracted from the identified reports on the following: The number ofdeaths in the cohort from all causes and from lung cancer, and the corresponding SMRs; Dose specific lung cancer SMRs (or rates), where, available; The number of mesothelioma deaths in the cohort (for pleural and peritoneal mesothelioma separately); 17ie rates of mesothelioma by categories of time since first exposure; The process/type of work being carried out; Cohort recruitment period and duration of follow up; Average age at first exposure, when available; The type(s) of asbestos fibre used in the process; The average fibre levels for the entire cohort and the average employment duration for workers in the cohort, or simply the average cumulative exposure for the entire cohort; Information about the smoking habits of the work ers in the cohort where available; The sex of the workers. Some general issues on the summary of outcome and exposure measures are discussed below. A more detailed discussion on some of these points is given in Appendix A. and the extracted data is shown in full in Tables 12 and 13. Excess tang cancer measure Excess overall lung cancer mortality has been expressed as a percentage excess of expected lung cancer mortality per unit of cumulative exposure. Rl = lOO(0L-L)/<i .X) Where 0L and E, are the numbers of observed and expected lung cancers, respectively and X is cohort mean exposure. This estimate of the lung cancer risk is described as the 'cohort average' estimate. 95% confidence limits for the cohort average estimate RL have been calculated assuming a Poisson distribution for Me.tr/ihelioma measure Mesothelioma mortality was expressed as a per cent of expected mortality from all causes (adjusted to an age of first exposure of 30) per unit of cumulat ive exposure. *m = mOMK.EMjC) Where 0M is the number of mesothelioma deaths. Eajj the total expected deaths from all causes adjusted to an age of first exposure of 30. and X the mean cumulative exposure. (See Appendix A for a dis cussion of this measure, and the calculation of E*uj). When the expected all causes mortality was not avail able, the denominator was taken to be the total observed deaths less the total of asbestos-related deaths (mesothelioma, asbestosis and any excess lung cancer deaths). A 95% confidence interval for/?M was calculated assuming a Poisson distribution for 0M- Treatment of 'best evidence ' cause of death data In some studies causes of death have been assigned in two ways, one based purely on data given on the death certificates (DC), the other using other data (e.g. autopsy reports) to establish a 'best evidence' (BE) cause of death. For lung cancer this review has gener ally used the DC data, since this preserves compar ability with the reference rates, and with the majority of other studies. For mesothelioma however, the BE data has been used, since reference rates are inappro priate, and most studies use some sort of best evi dence judgement to identify mesotheliomas. It might be thought that where reference rates are derived from DC data (as in the SMR analyses in this report) the observed deaths on a DC basis should always be used. The argument is not as clear cut as it seems. The coding of death certificates is subject to a range of errors, and the net error in the count of deaths coded to lung cancer on national death certifi cates will be determined by the balance of these errors across the whole population. One of these errors is the tendency of pleural mesothelioma deaths to be coded to lung cancer. In the population as a whole, ) HWBUI0009365 Quantitative risks of mesothelioma and lung cancer 3G7 this error is very small, but in an asbestos exposed cohort it may have a substantial effect. Leaving the miscoded mesotheliomas in the lung cancer count will overstate the true lung cancer SMR. Excluding them will in theory understate it, but only to the small extent that this error affects the population as a whole. The best available approximation to a true estimate of the risk is therefore to exclude the miscoded'mesotheliomas, and this has been done for this review. Derivation of cohort mean exposure estimates Mean exposure for cohorts was calculated in differ ent ways, depending on the available information. When data was given for separate exposure groups, the cohort mean was calculated by weighting the indi vidual group means by the expected deaths from lung cancer in the group. On the assumption that excess risk is proportional to cumulative exposure, this weighting preserves the same proportionality when the results from subgroups with different exposures are aggregated, it is therefore the optimal statistical measure of aggregate exposure. Where mean exposure values for individual dose categories were not given, the midpoints were used. The top exposure category was usually given as an open interval (e.g, exposures>lOO f/ml.yr): in these cases a value was chosen based on a view of the high est likely exposure and the distribution of individuals across all exposure categories. It was assumed that where the highest category contains a relatively small proportion of the population, the category mean will be a smaljfir multiple of the lower band than other wise. For cohorts where results for exposure specific sub groups were not given, the cohort mean was either given directly (cohorts 4, 13 and 15); derived from information given on the distribution of individual doses (cohorts I and 7), or on the exposure of internal controls (cohort 17), or by multiplying a mean exposure level by mean exposure duration (cohorts 8 and 14). Exposure estimates given in particle counts were converted to Counts of 'regulated fibres' (fibres with an aspect ratio greater than 3:1, and !ength>=5 microns), using conversion factors calculated by the report authors where possible. The most commonly used conversion was 1 mppcf (million particles per cubic foot)=3 fAnl (fibres per millilitre), and this was the value adopted for the Johns Manville cohort, where a conversion was not given. For the Massachu setts cohort, where the fibre involved was crocidoliie (rather than chrysotile as in the other cohorts with particle counts), an independent expert hygienist was asked for an assessment (see Appendix B). The exposure estimates for Wittenoom have been questioned by Rogers (1990) who has suggested-- having re-examined some of the original samples using modem light and electron microscopy--that the levels may have hcen underestimated by up to a factor of 10. Details of this reassessed data were to be pub lished. but these have not so far appeared in print. It is therefore difficult to know whether to make an adjustment to the published estimates, and if so by how much. Similar comments may of course apply to other cohorts and introducing a correction might then distort rather than correct the overall picture, dc Klerk and colleagues, developing estimates of environmen tal risk at Wittenoom (1992) use a factor of 4 without detailed discussion. The effect of using this adjusted exposure level is examined as a variant of the main analyses. Exposure-specific risk estimates It is generally assumed that the most reliable guide to dose-specific risk is provided by exposure analyses using estimates of individual exposure. This is clearly the case when these individual exposure values can be accurately determined. However this assumption is very much not the case in the studies in this review. Not only are there the inevitable problems of extrapo lating earlier exposures on the basis of more recent measurements', there are also problems of converting the most usual historic measurements (in terms of particle counts) to the more relevant measure of fibre counts. Direct fibre counting only became generally used in the 1970s. In these circumstances it is at least arguable that global assessments of average exposure, set against overall mortality outcomes, should be preferred. Exposure-response regressions with inaccurate indi vidual exposure assignments will produce a slope estimate biased downwards. Use of an overall assess ment will also minimise the error introduced by con version from particle counts to fibres, since these average conversion factors will represent a more accurate conversion for the totality of exposure than for a particular individual. However, the arguments are not all one way. Over all mortality outcomes can only be assessed against some outside reference--usually the regional or national population--and this may not represent a true baseline level for the exposed population in ques tion. Assessment of an internal exposure response gives some check on this issue. A complete absence of exposure response must cast some doubt on any overall excess being counted as a measure of risk (the Albin and Connecticut cohorts are examples of this). Cohort-level risk measures were chosen for this review both because these allow a wider range of data to be assessed than if attention is restricted to internal exposure response analyses and since (as argued above) cohort-level exposure estimates are likely to be more accurate than individual exposures. Smoking The evidence on the joint effect of smoking and asbestos exposure on lung cancer has been reviewed HWBUI0009366 568 J. T. Hodgson and A. Damtnn recently (Vainio and Bofetta, 1994) who conclude that the overall evidence indicates an interaction in the multiplicative region. This implies that the rela tive risk of lung cancer due to asbestos exposure will be the same for smokers and non-smokers alike. Thus SMRs for lung cancer based on a reference popu lation with the same smoking habits as the cohort members should only reflect the effect on mortality due to asbestos exposure. An earlier review by Berry et at. (1985) estimated that the effect of asbestos exposure was about 1.8 times greater in non-smokers than in smokers (though with confidence limits which did not exclude a simple multiplicative interaction). If this is the case the observed effect of asbestos on lung cancer rates will be greater in populations with lower smoking prevalence. However, given the rela tive lung, cancer risks typical of smoking (about 15fold) and asbestos exposure (about 2-fold) together with the generally high prevalence of smoking in the observed populations, the scope for bias--if there is indeed a differential effect of the scale suggested-- is limited. In either case, a problem arises when the smoking habits of the cohort members differ from those of the reference population, which is the case for some of the cohorts reviewed. For this reason, any information about smoking given in the studies was summarised. The amount of information given was very variable, and could be categorised as follows: 1. No information given, (Ferodo, US Insulators. Pat erson. South Africa, Johns Manvilte. Albin). 2. The percentage of the cohort that smoked, usually based on a cross sectional survey conducted in a particular year, (Connecticut. Baiangero, Quebec, Pennsylvania, Rochdale, Wittenoom). 3. Comparison of the prevalence of smoking in the cohort and the reference population. (New Orle ans, Massachusetts, Carolina). 4. Estimation of the effect of any differences in prevalence--for example calculation of smoker adjusted lung cancer SMRs, (Vocklabruck) 5. Data on prevalence of smoking within exposure categories--but with no external comparison (Ontario).. Most studies fell within the first two of the above categories. In these cases only subjective judgements could be made by the authors about the smoking hab its of the cohort members. Also, cross sectional stud ies were often based on a small proportion of the cohort and may not be very representative. For most studies which addressed the issue the authors con cluded that there was no major difference in smoking prevalence or that the slight differences in prevalence were not likely to change the expected number of lung cancer deaths in a substantial way. Of the studies where comparative smoking data were given, the Vocklabruck cohort showed the largest difference in cohort smoking habits and those of the general popu lation. and this was the only study where an explicit adjustment for smoking was made. Unadjusted data was used for all other studies. Fibre type amt industry process For the purpose of summarising the information given in the studies, each cohort was given a fibre type classification of 1. 2 or 3 letters according to the type of fibre used, with the letters y, a and o rep resenting chrysotile. amosite and crocidolite exposures respectively. For example: yao' 'yo' 'a' means all three commercial asbestos types were used in the cohort means chrysotile and crocidolite were used means only amosite was used The order of the letters indicates the relative impor tance of the fibres used. Very small quantities of fibre were ignored in some cohorts (Carolina, New Orleans plant 1, Connecticut), the reasoning for this in each case is set out in Appendix A (Table 14). In a similar way. for display in tabular and graphical data sum maries, industry process was coded as follows. M Mines C Cement T Textiles 1 Insulation Products F Friction Products L Lagging and work with insulation O Other Meta-anahlic issues The aim of a meta-analysis is to identify where evi dence from different studies is discrepant: ideally, to explain the reasons for the discrepancies; and where data from different studies are coherent to combine them into a common summary which will be more precise and soundly based than the estimate from any single study. For this review the coherence of esti mates of i?L and Rm from different studies has been assessed in a Poisson regression framework, fitting a common value of the parameter of interest across a group of studies and testing the residual deviance between the observed and predicted numbers of events (mesothelioma or lung cancer deaths) in the studies in the group. Confidence limits around the group estimates were calculated by profile likelihood methods. Confidence limits are not shown for the means of groups which show very significant hetero geneity, since such limits have no ready interpret ation. Indeed, in this situation it is not clear that the mean' itself has any natural meaning. Faced with clearly discrepant data, purely statistical criteria can not be used to decide on a `correct' summary or compromise estimate. The statistical analyses in this report only take account of the statistical variability of the mortality v HWBUI0009367 fiiiSi:;= Quantitative risks of mesothelioma and lung cancer 5 69 outcomes. The statistical variability in expected mor tality levels and cohort average exposures are ignored. This means that calculated confidence inter vals will be narrower and statistical distinctions sharper than they would be if these variabilities were known and allowed for. This needs to be bome in mind in the interpretation of these analyses. RESULTS Overview Figure 1 shows a graphical comparison of the mesothelioma and lung cancer risk coefficients. In order to plot zero values (which convert to minus infinity on the log scale), convenient nominal positive values smaller than any real non-zero value in the (relevant) data have been used. These are in the range 0.001-0.002 for and between 0.0001 and 0.0003 for The three panels of Fig. 1 display the same data, with each cohort represented by its cohort code, fibre type and process. Cohorts which did not show a statistically significant excess of lung cancer (RL) are shown in brackets. Both risk measures cover about three orders of magnitude. For the bulk of the data risk estimates for lung cancer and mesothelioma are strongly correlated with Rl, very .roughly equal to 100 RM. This hetero geneity seems more readily explicable in terms of fibre type than process. For example there are mining and asbestos cement cohorts at both extremes of the risk scale, while all the amphibole cohorts are at the high risl;end of the scale. But there are not really enough examples within each category statistically to draw definitive conclusions of this type. Total mesothelioma The summarised data for total (pleural and peritoneal) mesothelioma mortality are shown in Table 1 and Fig. 2. The estimates of for crocidolite cohorts are closely grouped around an average value of Oil. Similarly, the two amosite cohorts show results statistically consistent with their average of 0.10. The results from mixed fibre cohorts cover a wide range from a value close to that seen for the crocidolite cohorts (K,,=0.59 for Ontario) to values nearly three orders of magnitude lower, close to those seen in the chrysotile mining cohorts. The test for het erogeneity is very clearly significant (P<0.001). The ranking of mixed cohorts by mesothelioma risk does not appear to correspond either to process or fibre mix. If the exposure estimate for Wittenoom is increased by a factor of 4, the summary value of RM falls to 0.15, and the consistency of the three crocidolite values is completely lost (P<0.00l). Three of the six chrysotile cohorts had no observed mesothelioma deaths. The rates in the two chrysotile mining cohorts are similar at around 0.0015, while the (a) 0.1 0.01 [17) [5a) 0.001 cohort code -jtkM 15 12 (13a) $11 5c 2m (10) L_/_ 16 2f (b) f 0.01 [y") [ya) 0.001 . zero (c) 1 0.1 0.01 (F) (C) 0.001 fibre type o* yao (a) VC V (yi y yao yy process m" m tflTf c ! CT (M) 1 rzero zero F 0.01 0.1 1 Lung cancer risk (Rt) T 10 Fig. I. Comparison of exposure-specific risks of mesothelioma and lung cancer (% per f/ml.yr), with cohorts labelled by cohort code, fibre type and process. (Note: the two coincident cohorts in the top right of the chart are Ontario (4. yo, C) and SA crocidolite mines (!3o. o, M). Symbols in brackets indicate a non-significant lung cancer excess). HWBUI0009368 o ) 570 I. T. Hodgson and A. Damion - CL "T i. ~j iI a ; 3 "i ;3: X S *J ^ 11-S II 9~ *! U 5 2 I 5 fl li a acc < 3-- *K *--3* 1 -= 11 II 8,1 ~ .rs e; u < _-g.5- I"Pg- 2 *J 3 if 'i : c S' c :ddd V -- -v --- * .--a _ -- s -- Mr#*. res *r-t 5S, ~S 2 = ^5^2^2 3 3 535 5 5 5^3? 3 =. 3 = 3 3 => = 3 g z- g 3 2 2 3 S S3~. r'2'g2rKc;25 =-gg333 2 s 2 d j ^ 3 3 2 3 5 3 2 2S: S S 3 = 3 --* *5 -- o=3 Ss3 do oV 5 --s d 'S 2 vc vs S' -- .S "r *r> irt - 3^G-S"--0-^-TM* <'1G"cSc('1 G^r* Gff*1!' -S-r Gr-i 3SGS iSrGn^^S=x: '-'--------- d d c c 5 d 5 " cdd 2 d dc d dd 2 c Ow *mn un odd mO soS'S'f'i-T'rtj- r~ -~ r v\ 5 m ri n n - - 5 ; -5S--- ~ = 3 = =.=.=. = = = ^* =*3---------- 535CCI 5^5 ` c S >r- <* i? "r it--n. rsv, rS*" J2O ;o^ P-SSsS. o --' -- -- :Z '3pi s> n- Si: cmm e; -=! 5 p = e.5'5f-- 5^!' ^ *3 so t ^ r) ri t d vs 2 S' pd d -- M -v S' c> vr. t N J, PI ?. VS l2 N 2 5-xS-M"fC O 3 S c*i --SO 2o | H5 >SS; C*E232:2f'- - <*> = = o oo an ?, g. ?> I, 3. , >. 5, ^ >, >. oss -- S ooo-j|5t-ts-oo-- s-SSt-uu- *=g 111 &s: I al a a > sIII- " i ilfiS!lf8oOjO. HO<>3ii.c:u,zZ. e si * C I* I o S|3t 30 ggg 3 .5 S|.SO Stl, a pi e*!ip Ti 3 "v 52 uoauzuti^ i E i |l 3i C-i -- Reduced` by a fuctor,,of 0.67 to excludei expected deaths less than 10 yr Irotn lirst exjjoxuru (see Appendix C). Expected all cause mortality in plant partitioned in proportion lo share of expected lung cancer. 1 HWBUI0009369 Quantitative risks of mesothelioma and lung cancer 571 Cohort group Fig. 2, Exposure-specific mesothelioma mortality (R,,) by cohort and fibre type groupings, showing 951t confidence intervals. Group means labelled in capitals. Confidence intervals not shown for groups with very significant heterogeneity. two cases seen in among men in the Carolina cohort produce an estimate, with wide confidence limits, of 0.013--^jout an order of magnitude higher than for the mines cohorts. The very wide confidence limits for the three cohorts where no cases were observed arc statistically consistent with either end of this range. Indeed there is no significant heterogeneity between RM estimates in the chrysotile group, although the total shows some tendency to heterogen eity (P=0.11). If the mines cohorts are excluded, the central combined estimate of /?M increases to 0.0033, but with wide confidence limits (0.0006-0.01) and with a similar level of heterogeneity (P=0.14). With the Carolina men excluded, the remaining data are coherent (P for heterogeneity=0.69), and the mean estimate of is 0.001 (95% Cl 0.0007 to 0.0013) No summary estimate of RM has been calculated for the mixed fibre cohorts, since these are so clearly stat istically heterogeneous. This heterogeneity is plausi bly explicable by variations in the mix of fibres encountered. The estimates from the pure fibre cohorts suggest a difference in potency approaching two orders of magnitude between chrysotile and amosite, and a further five-fold difference between amosite and crocidolite. If these gross differences are even approximately correct, quite small variations in the fibre mix in the cohorts exposed to several fibre types could have important effects on the mesotheli oma risk in the cohort. This would have the conse quence that the generally measured fibre levels would be an unreliable estimate of the true risk status. This will be particularly true where the history of usage of different fibre types has varied over time. Lung cancer The summary data for lung cancer is shown in Table 2 and Fig. 3. The pure fibre groupings are less coherent for f?L than for RM. although the general pic ture is similar, with higher values for the amphibole cohorts, lower values for most of the chrysotile cohorts and intermediate values for the mixed exposure groups. The Carolina cohort is the one clear exception to this pattern. The mean estimate for the three crocidolite cohorts is 4.2% per f/ml.yr (95% Cl 2.8-S.8). The two amosite cohorts give somewhat dif ferent results, and despite the wide confidence limits on the South African data they are not statistically consistent (P=0.022). Their joint mean is 5.2% per f/ml.yr (95% Cl 4.0-6.5). The five amphibole cohorts taken together are also not a statistically consistent group (P=0.027), with a joint mean of 4.8% per f/mi.yr (95% Cl 3.9-5.8). The heterogeneity is mainly due to the SA amosite cohort, and if this is set aside the remaining four amphibole cohorts are just statisti cally consistent (P=O.072) with a joint mean of 5.1 % per f/ml.yr (95% Cl 4.1-6.2). If the exposure estimate for Wittenoom is increased by a factor of 4, the sum mary value of Rl falls to 2 for the combined amphi bole cohorts and to 1.1 for the three crocidolite HWBUI0009370 m- o *) l) 572 J. T. Hodgson and A. Daemon i5 = c ^ ri joiMs ho ooo : 33 =5 y-- 2_ ov i3 ^= C M so fj Z; W 'O ^ -**- ~- r-; r-, _J ,, *r* TM * in in in x t*^ ^ S'* C so" C ~ J 3 rr q d v o ^ "f *n ^ coddsijTiTdSoSriiff i oPS'f-Sn --. -q :> o o c r! r-i -- cd?33d 1,-0 r i*-M3rirn! 5 v -- /i ri -j *? irt 'n - ia vO v>, ^ C , iSSS: *j > *j 7* f5%; 33 5J O ^t <-i ' ' fl r~tO<3 nc<Js^T.-,,S* S, fff-w''*- no *x ri o 2 2 fI r( /*! t & r~ Q v no C!3?2 eo oo <7n ttrt ^ ~ 0| *t -r ^ I*, f". 2 V> 2 <2 ^ -0 '8 8- i/t ^ vCPNrtrtf..MC"5v:1'*! ddS^d ?7 7 -y f--1 0*1 "T -" _j ^, ^7 ^^25 ^^ - - vnV: c:"* -jt --I --''*! --' -- f*i c c NO ^ ^ d?" "V'l ^ <-> ^ ^ 5 3S T ,, m r-J *r oo . r% rn ri m <o --. -nnrtE- e\ in rNC'jfn^5^^*`'r` ?i "1 - I OC OSS ~S ouo^joh-uh HHUW- S S i 38 11 so gs >1 <N 33 *OO a 122 I** g 6> |>8238 Mil* g" * _ > !i*8^= : ,S O 5 S ___S T3 5zal 73 > :i2o,<2 o jSS=,, lozoao<i" 3 & fCMIfS tn O 3 P** - >>so o ot oi n -- no -- HWBUI0009371 todastLr :u.. % Quantitative risks of mesothelioma and lung cancer 573 Cohort/ group Fig. 3. Exposure-specific excess lung cancer mortality (ftJ by cohort and fibre type groupings, showing 95^ confidence intervals. Group means labelled in capitals. Confidence intervals not shown for groups with very significant heterogeneity. cohorts, but both groupings now show very signifi cant heterogeneity (PcO.OOl). Among the,mixed cohorts, two stand out with parti cularly high'*values (Ontario and Albin). Both are asbestos cement cohorts, and both also had high lev els of mesothelioma mortality. The values for RL for these two cohorts are both more than six times the level of the next highest observation. The heterogeneity among the mixed fibre cohorts is driven principally by three of them: Ontario, US/Canada Insulators and the Johns Manviile retirees. Other reviewers (Doll and Peto, 1985; Hughes and Weill, 1986), have remarked on the unusually high risk estimate implied by the Ontario cohort and have suggested that the exposure estimates for this group may have been underestimated. Another potential contribution to the high risk of lung cancer in this cohort is exposure to silica: 8 out of 26 workers with post mortem examinations showed signs of silicosis (Finkelstein and Vingilis. 1984). There are clearly considerable uncertainties in the estimation of average exposures for the US/Canada Insulators cohort, since this is averaged over a very large cohort with no doubt very variable exposure experiences and over a long time period. The size of this group means that the value adopted for it will determine statistically the average risk in this group. The study of retirees from the Johns Manviile asbes tos products company is unusual in basing ils esti mates exclusively on follow-up of retired individuals from the age of 65. There is no obvious theoretical reason why this should produce a seriously biased estimate of risk, though asbestos related mortality at ages below 65 will be missed. This cohort has been followed up almost to extinction, and if the impact of asbestos exposure on mortality eventually declines after the cessation of exposure, then cohorts with near complete lifetime follow up will tend to show rather lower excess mortalities than those where survivors form a substantial proportion of the cohort. In addition, the Johns Manviile cohort was one where the authors had not suggested a conversion factor from panicles to fibres, and this review has used the most commonly used value of 3 f/ml=l mppef. If this' conversion implies higher exposure than in fact took place (the recent review by Lash et al. (1997), used a value of 1.4 borrowed from the New Orleans cohort), then the risk coefficient implied here would be too low. If these three cohorts are excluded from the group the remaining eight are just statistically consistent (/MX056), and their joint mean is 0.32 (95% Cl 0.16-0.50). The six chrysotile cohorts fall into two groups: the two Carolina cohorts give values around 6% per f/ml.yr; the other four, including the two mines cohorts and dominated by the large Quebec cohort, are consistent with a joint Rt estimate of 0.06% per f/ml.yr (95% Cl 0.043-0.079). The Connecticut and New Orleans (chrysotile only) cohorts give central estimates of RL substantially above this value, (0.80 HWBUI0009372 574 i. T. HixlysiMi und A. Oarnion and 1.3 respectively) bur both confidence intervals are very wide. Even if the mines cohorts are excluded there is still very dear statistical inconsistency between the Carolina results and those from Con necticut and New Orleans I/MX00I3). The Carolina results are also out of line with the two other (mixed fibre) textile cohorts--Rochdale and Pennsylvania-- whose 957c confidence intervals for /?L have no over lap with those for Carolina. RISK ASSESSMENT AT MODERATE AND HIGHER CUMULATIVE EXPOSURES Mesothelioma The quantified risk for mesothelioma at the kinds of cumulative exposure levels recorded in the reviewed cohorts--say. from 10 f/ml.yr upwards-- presents a reasonably coherent picture, with values of RM, in round figures, of 0.5. 0.1 and 0.001 (at most 0.003) for crocidolite. amosice and chrysotile respect ively (see Fig. 2). Lung cancer It is more difficult to come to a clear view of the quantified risks of lung cancer, because of the incon sistency of the results especially for the chrysotile cohorts (see Fig. 3). The amphibole estimates are reasonably consistent. In round figures the estimates fall in the range 2-10% per f/ml.yr. The mean for the crocidolite group is rather lower (4.2) than that for the amosite group (5.2), though their confidence lim its overlap substantially. The mean risk for all amphi bole cohorts is 4.8% per f/ml.yr (95%CI 3.9-5:8), but with some evidence of heterogeneity (P=0.027). If the SA amosite cohort data are set aside, the remaining data are reasonably consistent (P=0.072), and the mean estimate becomes 5.1 (95%CJ 4,1-6.2). In round figures, a value of 5% per f/mlyr would rep resent a reasonable risk estimate for both amphibole fibre types. The pure chrysotile cohorts produce estimates of RL spanning two orders of magnitude, from a value of 6.7 for the Carolina women to 0.03 for Balengero mine. How should this very wide range of RL esti mates be interpreted? As far as evidence from `pure1 exposure goes there are only two strongly informative cohorts: Quebec and Carolina. The differences between these two has been studied and discussed extensively but, finally, inconclusively. The hypoth esis that mineral oil used to suppress dust in the Caro lina plant may have contributed to the lung cancer excess has been addressed by an internal case-control analysis of this factor reported by Dement et at. (1994) and Dement (1991)). The most recent report (Dement et of., 1994), shows that the odds ratios for different cumulative asbestos exposure categories are essentially unchanged by the addition of a variable representing subjects' typical level of exposure to mineral oil (slight, moderate, high). The coefficients for these categories in the joint model were not reported, but were as follows, expressed as odds ratios relative to 'slight' exposure: Mineral oil exposure Odds ratio Moderate 1.12 High 1.47 (Dement, personal communication) 95% Confidence interval 0.57-2.21 0.8-2.75 Although these ORs are not statistically significant (and do not form a statistically significant trend), there is some suggestion that mineral oil may have a role in enhancing the asbestos effect, particularly since all the effect of exposure Juration is absorbed in the asbestos measure (workers were assigned to oil exposure categories according to the assessed oil exposure level at which they had spent the longest proportion of their employment in the plant). Early results from this case control study showed a cross tabulation of cases and controls by asbestos exposure and mineral oil category (Dement, 1991), without for mal modelling. Crude odds ratios on this data suggest that the asbestos response is progressively steeper with increasing mineral oil category. If mineral oil does have an enhancing effect, the anomalous increase in estimated exposure specific lung cancer risk for men in the Rochdale cohort first exposed after 1950 could be explained, since dust suppression using mineral oil was introduced from that date (Peto et al.. 1985). The regression slope estimate of /tL for the men first exposed after 1950 is i .3 (95%C10.37-2.6). three times the value for men first exposed between 1930 and 1950. The plausible suggestion that the longer fibre used in textile processes are responsible seems to be con tradicted by the comparative analyses of lung fibre burdens in Quebec and Carolina cohorts reported by Sebastien et al. (1989). They found that the pro portionate distribution of fibres by length was very similar in Quebec and Carolina lungs. Nevertheless, the notion that the longer fibres used in textile pro cesses do represent a higher risk, is consistent with experimental evidence that longer fibres are more car cinogenic (Meldrum, 1996; Stanton et at., 1981; Miller et al., 1999). Green et at. (1997) have shown that the mean length and aspect ratio of chrysotile fibres in the lungs of Carolina workers are greater than in a local population control series; and than in the lungs of workers from the Albin cohort (Albin er at., 1990a,b). Both studies on the lung content of Carolina work ers have found amphibole (crocidolite or. amosite) fibres in an appreciable proportion of them, though at much lower levels than for chrysotile and its associa ted (remolite. Sebastien et at. (1989 report that amphi bole fibres at concentrations >0.1 f/|ig (fibres >5 microns long) were only found in the lungs of work- "hi J HWBUI0009373 o ( Quantitative risks of mesothelioma and lung cancer 575 ers hired before 1940. which conflicts with the period of known use of crocidolite yam (in very small quan tities--see Appendix A) in the plant after 1950. This raises the possibility that some amphibole formed part of the exposure mix in this cohort in an early period. Green et al. (1997) show that the levels of amphibole are higher in Carolina workers than in local controls (2-fold difference in geometric mean. 1M5.031) but much less strikingly than 'for chtysotile (5-fold, PC0.0001) or tremolite (14-fold, P<0.0001). They also report that amphibole at levels >1.0 f/|ig (all fibre lengths) were found in only one of the ten lung cancer eases for whom this datum was available. This last observation limits the extent to which amphibole exposure--perhaps unrecognised--might play a role in this cohort. Whatever mechanism is in play does not appear to apply--to the same extent, at least-- to the other two textile cohorts reviewed. As already pointed out, the Pennsylvania and Rochdale cohorts (with mixed fibre exposures) both give substantially lower estimates of RL. If it is accepted that some such feature of the pro cessing in the Carolina cohort has genuinely produced a much higher risk than seen in other chrysolite cohorts the question can be asked how typical these features are of the bulk of applications? Looked at in the wider context of cohorts with mixed fibre exposure, the RL Value for Carolina looks untypically high. Setting aside the possibility that amphibole presents a higher risk of lung cancer, the observations of Rt_ from mixed fibre cohorts can be taken as informative^of the RL level for chrysotile. This sug gests that in typical applications (including other tex tile processes) RL for chrysotile is generally lower than the value derived from the Carolina cohort. The median RL for the 16 cohorts with some chrysotile exposure is 0.5. compared to 4.5 for Carolina men and 6.7 for Carolina women. All but two of the mixed fibre cohorts give an RL estimate less than 1. and of the two exceptions one (Albin) has a confidence limit including zero, and the other (Ontario) shows features suggestive of significant exposure to crocidolite (see below. Fig. 4 and related text). To the extent that amphibole fibres make a dispro portionate contribution to the lung cancer risk in the mixed exposure cohorts--and the evidence presented here suggests that they do--the typical risk of lung cancer from chrysotile exposure would be even lower. In most circumstances a value of 0.5% per f/ml.yr should probably be regarded as an upper limit to the lung cancer risk from pure (commercial) chrysotile. The mean RL estimate for mixed fibre cohorts exclud ing the three with particular interpretational difficult ies is 0.32% per f/ml.yr with an upper 95% confi dence limit of 0.50. It 'should be noted that a value of 0.5% per f/ml.yr is not as far out of line with the Carolina observations as it might seem. The 'cohort average' risk estimate from this cohort (6.7 for women, 4.7 for men/ prob- Fie. 4. Comparison of excess mortality from pleural and perito nea) mesothelioma, showing fibre type. ably overestimates the risk, which from internal analysis is 1 for women and 3 for men (Dement ei at.. 1994, p. 439). The exposure response regressions on this cohort give an intercept close to zero excess risk at zero dose, and there is thus no reason to sus pect serious error in the reference rates (with conse quential doubts about interpreting the slope). There is also the possibility of inaccuracies in the conversion of particle counts to fibre counts. One early report on this cohort (McDonald er al., 1983a) suggested that the average conversion factor should be about 6 f/ml to I mppef. Jf this were true, the risk per f/ml.yr would be halved. A 'best estimate' of the lung cancer risk would be lower than 0.5% per f/ml.yr. Noting that the mean risk of the mixed fibre cohorts (excluding the three mentioned above) is 0.32% per f/ml.yr, and that the amphibole risk is over 10 times higher, it is possible that virtually all the observed risk could be explained by rather less than 10% of amphibole in the mixed exposures. However there is no direct evidence on which an estimate of the risk of `pure' chrysotile could be based. Apart from the Balangero cohort, all the chrysotile evidence considered here effectively relates to Canadian chtysotile. since this was the dominant source of fibre for the other chrysotile cohorts. The risk of 'commercial' chrysotile as esti mated from the mining cohorts is 0.06% per f/ml.yr. Given that the processing of chrysotile may produce some additional risk, the best estimate should be set higher than the mines level, say at 0.1% per f/ml.yr. The overall risk, of a mixture of 96% chrysotile with a risk of 0.1, and 4% amphibole with a risk of 5.1 would be 0.3% per f/ml.yr. EXTRAPOLATION TO LOW EXPOSURES All these cohort observations reflect the effect of exposure to high levels of asbestos. The main interest HWBUI0009374 57f, J. T. Hodyson and A. Darmon in quantitative risk assessment in current cnmliiions is to apply this evidence to the estimation of the risks associated with exposure levels 100-1 ODD times lower. The standard assumption is that, other things being equal, the risk will be proportional to dose: but this is more a cautious default assumption than any thing more soundly based. To quote from the HEI review: "The assumption of dose-linearity for lowdose assessment purposes is thus a widely accepted and scientifically reasonable compromise rather than art established scientific principle of carcinogenesis". However, if the true relationship between exposure and response was not linear, the impact on low dose extrapolations could be dramatic. There is some indi cation in the present data suggesting a non-linear exposure response, particularly for peritoneal meso thelioma. and the next'sections examine this question. Relationship ofpleural and peritoneal mesothelioma Figure 4 plots the percentage excess mortality from peritoneal mesothelioma against that from pleural mesothelioma. Cohorts with no mesothelioma cases of either kind are excluded. Cohorts with no perito neal mesotheliomas are plotted on the peritoneal scale on or close to the 0.01 ordinate. The positioning of the cohort points strongly suggests a pattern of two alignments, one defined by the pure crocidolite cohorts, the other by the two pure amosite cohorts. Four mixed exposure cohorts lie very close to the amosite line: the US/Canada Insulators, New Orleans plant 1, the Johns Manville retirees and the A(bin cohorts. All but the last of these clearly had amosite as the main amphibole fibre. The point representing the Ontario cohort lies very close to the crocidolite line, suggesting perhaps that the anomalous results from this cohort may be explained by underestimated exposure to crocidolite. The position of the (male) Carolina cohort seems somewhat anomalous. The single peritoneal meso thelioma in this group is the only one in a cohort without material amphibole exposure, and the equal ity between pleural and peritoneal numbers (one of each) is only otherwise seen in cohorts with much higher levels of mesothelioma (and substantial amphibole exposure). The possibility of unrecognised amphibole exposure again suggests itself, but too much should not be read into this single peritoneal case, it is clear that the three fibre types produce dif ferent mesothelioma responses overall. The question of differential responses by mesothelioma she can really only be addressed for the amphibole fibres. This relationship does not depend on quantified exposure data, and if it is real it should be reproduced in other cohorts with predominant . amphibole exposure. The most informative cohorts will be those with crocidolite or amosite exposure, but not both. A Medline search identified eight such cohorts. The relevant data are summarised in Table 3, and a plot of the percent excess mortalities from these cohorts (and the pure fibre quantified cohorts) is shown in Fig. 5. There is still an apparent separation between cro cidolite and amosite cohorts, though the segregation is now less clear cut (as might be expected given the small numbers often involved). There is. of course considerable statistical uncertainty in both of these variables, and a simple regression (in which uncer tainty about \c' values is ignored) would be mislead ing. Table 4 summarises the results of regressions in which the fit is optimised in both variables simul taneously (fit being measured by deviance, assuming Poisson variation for the numbers of mesotheliomas at each site;. Fitting a single line through all the data produces a line with a slope (on the log-log scale) of 1.2. but the overall fit is unsatisfactory (PcO.OOD, Allowing the two fibres to have separate fits makes a very sig nificant improvement to the fit (PCO.OOl), and both fits have steeper slopes (2.3 for crocidolite and 3.1 for amosite -- not shown in table). These slopes are not very precisely determined, and constraining them to be equal does not materially degrade the fit (P=0.75). The central estimate for this common slope is 2.4. $ This model provides a very close statistical fit to all but two of the cohorts. The two exceptions are the gas mask cohorts in Canada (McDonald and McDon ald. 1978) and in Leyland (Acheson et at. 1982). which contribute 6.1 and 4.3 respectively to the total deviance. Possible reasons for these cohorts to be untypical can be identified. The Leyland cohort was not ascertained from employment records, but from occupational details recorded on the wartime popu lation register compiled in September 1939. If the numbers directly involved with gas mask assembly have been over estimated the percentage excess mor talities will be proportionately under estimated. If, for example, only 2/3rds of the identified women were in fact exposed, the expected mortality denominator would fall to around 120, and the residual falls from 6.1 to 4.3--still an outlier, but materially less extreme (P=0.038 instead of 0.014). The overall excess mor tality from mesothelioma retorted in the Leyland cohort is much lower than in the Nottingham cohort engaged on the same process: 2.7% at Leyland and 16.5% at Nottingham, again suggesting the possibility of underestimation (eg by dilution of the exposed population), perhaps substantial. The assessment of mesothelioma in the Canadian gas mask cohort was particularly exhaustive, involv ing review of pathological data for all cancer cases. Three of the six peritonea! cases were only identified after this review, (f the number of peritoneal meso theliomas is reduced by three, the residual for this cohort falls from 4.5 (P=0.034) to 2.0 (P=0.16). However these are post-hoc rationalisations, and it is not clear whether it is better to remove these cohorts from the model or not. Despite the large \ ) HWBUI0009375 Quantitative risks of mesothelioma and lung cancer 57? Tabic 3. Additional data on pleural and peritoneal mesothelioma from cohorts with predominant exposure to crocidolite or amosite (but not both), and without reported quantified cumulative exposures Cohort . No. Reference Process Fibre Sex Expected all cause morality Pleural No. % Excess mortality Peritoneal No. % Excess mortality 18 Jones er al. (1996) 0f 19 Acheson er at. (1982) Gas masks 0 f (Leyland group) 20 McDonald and OY mf McDonald (1978) 21 Hilt ei al. (1981) O 0 ro 22 Levin er al. (1998) 1 3m 23 Parolari er al. (1987) 1 a mf 24 Finkelstein (1989) I am 25 Acheson er al. (1984) I ay m 400* 185 41* 5" 133.6 115.1 1.89 298.8 53 3 3 1 4 2 4 13 i.6 7.3 20 3. 1.7 1.3 14 2 6 I 2 i 2 1 3.5 t.l 14.6 20 1.5 0.87 106 0.33 "Estimated as observed deaths less asbestos related deaths. ''Estimated assuming 25% mortality from age 31 to 68. tissue. If true, it is presumably related to the dynamics controlling the distribution of asbestos fibres around the body. Note that this relationship does not depend on the cumulative exposure, and is therefore not sub ject to the uncertainties attached to exposure esti mation. Whatever its physical/biological explanation, these observations imply that at least one of these out comes has a non-linear relationship with exposure. Fig. 5. Joint distribution of excess mortality from pleural and peritoneal mesothelioma, showing fibre type. (Note: Label size (area) roughly proportion to total mesothelioma numbers in each cohort). residuals for these two cohorts, the overall residual deviance for the inclusive data (model 2) indicates a satisfactory fit (P=0.22). If the two outliers are removed, the separate fibre model fits the data almost exactly, and the slopes for the two fibres are very similar (model 3) and higher (around 3.2) than the 2.4 for the fit including them. In either case the single line model is rejected in favour of separate fits to the two fibre types, with similar slopes. The peritoneal rate is proportional to at least the square--perhaps as much as the cube--of the pleural rate. . The form of the relationship is unusual and some what surprising, since both outcomes reflect the effect of the same carcinogenic insult to the same type of Pleural mesothelioma and cumulative exposure To examine this question more closely. Fig. 6 shows a plot of excess mortality from pleural meso thelioma against cumulative exposure with cohorts represented by their fibre type code. Figure 7 shows a similar plot for peritoneal mesothelioma. The points for the pure amphibole cohorts show a clear pattern of alignment, with the slopes for pleural mesotheli oma less than 1 and those for peritoneal mesotheli oma greater than 1. Table 5 summarises the results of Poisson regression fits to the relationship between percentage excess mortality from pleural cancer and cumulative exposure, and the observed data points and selected regression lines are shown in Fig. 6. The relationship is modelled as linear on a log scale for each variable, and therefore has the form P,,, = AplXr where Ppi is the percent excess mortality from pleural cancer, X is cumulative exposure and A# and r are regression parameters. The corresponding predicted number of pleural cancers for a given cohort is ApiX'E^/lOO (where Adj is expected all cause deaths adjusted to an age at exposure of 30). The parameters were esti mated by minimising the residual deviance between the observed and predicted numbers of pleural cancer for each (pure fibre) cohort. It is clear that a wide range of slopes (r) are statisti cally consistent with the data. With independent fits to each fibre type the slopes are 0.62, 1.2 and 0.72 for crocidolite, amosite and chrysotile respectively. HWBUI0009376 378 I, T. Hodgson and A. Damton Table 4. Joint Poisson regression (structure model I of relationship between pleural anti peritoneal mesotheliomas Operitoneal-A.'/fpleuraf') MmJcl A b Residua) deviance Degrees ni V freedom l. Ail duU 2. Bv Hbre. common slope 1.7 a Overall 3. Fit excluding Leyland and Canadian sas ma*k daiu Bv fibre l> a Overall 0.21 0.00X9 0.26 0.00074 0.17 1.2 3.4 3.4 3.3 3.1 31.6 12.0 M !3 S 0.1 1.0 11 \ 1 <0.001 6 0.06 5 0.95 If) 0.12 3 0.93 4 0.91 7 0.`N Fig. 6. Excess mortality from pleural mesothelioma against cumulative exposure, showing fibre type. Regression lines fit ted to pure fibre cohort. Bold lines indicate fits with slope con strained to be common across fibre types, narrow lines are unconstrained fits. (The fit for amosite is of course completely determ ined- since there are only two observations.) The total residual deviance is 3.93. Moving to a model in which the three slopes are constrained to be equal, the residual deviance increases marginally to 4.53, an increase of 0.6 with a corresponding increase of 2 degrees of freedom (df), clearly not a statistically sig nificant change in overall fit (P=0.74), nor for any individual fibre type. The best fitting common slope is 0.75. Using deviance differences to construct a 95% confidence limits for the common slope gives esti mated upper and lower limits of 0.27 and 1.3. Peritoneal mesothelioma and cumulative exposure Figure 7 and Table 6 show similar regression analyses for peritoneal cancer. Again the crocidolite and amosite points align themselves on two parallel lines. The small numbers of observed events means that the statistical uncertainties are quite wide. There 10 100 100 Cumulative exposure (flml.yr) Fig. 7. Excess mortality from peritoneal mesothelioma against cumulative exposure, showing fibre type. Regression lines fit ted to pure fibre cohorts. Bold lines indicate fits with slope constrained to be common across fibre types, narrow lines are unconstrained fits (the slopes are identical for crocidolite). is very little difference between the slopes {/) for the two fibres, and the best common slope is 2.1. with a deviance based 95% confidence interval from 1.2 to 2.9.. The single peritoneal mesothelioma among die Carolina men, together with zero cases in the other chrysotile cohorts generates a negative value of t. If a common slope is imposed over all three fibres the best estimate is 1.6. but with significant heterogeneity (P=O.0O25--data not shown). Only the amphibole cohorts have enough data to draw valid conclusions on peritoneal mesotheliomas. The comparison of pleural and peritoneal slopes independent of exposure levels suggested a ratio of slopes between 2.4 and 3.2. If the ratio of the esti- HWBUI0009377 .j'._ .. ,.u . ,, . aiHSSttr, ,\..... ;. . Quantitative risks of mesothelioma and lung cancer Table 5. Possion regression of pleural cancer against cumulative exposure by fibre type Fit/fibre type ^P! r 959r Cl for r Residual Degrees of deviance freedom 1. Independent fits 0 1.4 a 0.02 y Overall 0.0057 2. Best common slope 0 0.93 a 0.(3 V 0.0047 Overall 3. Common slope, amphiboles only o a Overall 0.88 0.120 0.62 1.2 0.72 0.75 0.77 (-0.54. 1.43) (-0.32. 3.5) (0.17. 1.79) (0.27.1.3) (-0.069. 1.62) 0.25 3.68 3.93 0.36 0.49 3.68 4.53 0.39 0.44 0.83 1 0 4 5 2 i 6 7 2 1 > 579 P 0.62 0.45 0.56 0.84 0.48 0.72 0.72 0.82 0.51 0.66 Table 6. Possion regression of peritoneal cancer against cumulative exposure by fibre type Fit/fibre type f 957c Cl for r Residual Degrees of deviance freedom p 1. Independent fits 0 0.0022 2.1 (0.93. 2.9) 0.10 l 0.75 a 0.00018 2.4 (0.41,6.4) y Overall 1.4 -1.7 (-22. 0.91) 2.60 4 0.63 2.70 5 0.75 2. Common slope, amphiboles only O 0.0022 0.10 2 0.95 2.1 (1.2.2.9) i 0.76 a 0.0006 0.09 -> 0.91 Overall 0.19 S males of the peritoneal and pleural slopes is con strained to be 2.4, the best fit pleural and peritoneal slopes are : 0.86 (95%CI 0.51-1.15) and 2.1 (95%CI 1.2-3.6). If the ratio of slopes, is constrained to be 3.2. the estimated values are r=0.67 (95%CI 0.400.90) and r=2.1 (95%Ci 1.3-2.9). Support for a convex (r<l) increase of pleural mesothelioma risk with exposure can be found in the detailed dose-specific analyses of the Witienoom mesotheliomas by Berry (1991). Most of these cases 162 of 72) were pleural. Figure 8 plots the constant terms in the four exposure categories of Berry's analysis against their mean cumulative exposure. The slope is very close to 0.5. In addition, Coggon et a!, (1995). concluded from a comparison of the ranking of occupations by mortality from pleural and perito neal cancers and from asbestosis that "a more plaus ible explanation (of the different rankings] is that the exposure response relations for mesothelioma and asbestosis are non-linear, with the risk of pleural mesothelioma rising relatively more steeply at low exposures, but less steeply at high exposures". A non-linear relationship between exposure and the rales of pleural and peritoneal mesothelioma means that the percent excess mortality per f/ml.yr (/?M) will Fig. 8. Scaling constant in the four exposure groups of Berry (1991) analysis of the Witienoom crocidolite cohort, plotted against the mean cumulative exposure in each group. The plot ted line is proportional to the square root of cumulative exposure. not provide a consistent summary of the effect for mesothelioma at the two sites considered individu ally. Each additional unit of exposure will add--pro gressively--less risk for pleural turnouts, and more for peritoneal tumours. The point at which the absol- HWBUI0009378 580 (a) 10000 J. T. Hodgson and A. Dam Ion amphibole and mixed cohorts, fit to amphibole data (b) chrysotile and mixed cohorts, fits to chrysotile Co 1000 0s- <n in aus 2 100 <u uc as u D) 10 -I y y y (yo| ------yj y< y^ y*o y*o zero 10 100 1000 Cumulative exposure (f/ml.yrs) (Y (Vl 10 100 Cumulative exposure (f/ml.yrs) 1000 Fig. 9. Percent excess lung cancer by cumulative exposure, showing fibre type, with regression lines fitted to.Jure fibre cohorts (A: combined amphibole data. (I) slope free. (2) slope fixed=l: Y: chrysutile data. (I) all data, slope free (2) exd. Carolina. slope free. ()) exd. Carolina, slope fixed=l). me risks for tumours at the two sites are predicted to be equal is around 90f/ml.yr for crocidolite, around 55f/ml.yr for amosite. Below these values pleural tumours are more common, and at higher levels per itoneal tumours dominate. It happens that across the scale of cumulative exposure values in the reviewed cohorts (from about 10 to nearly 1000 f/ml.yr), the relationship between exposure and total mesotheli oma risk is not far from linear, so the summary index ffM does provide a reasonable index of the overall mesothelioma risk over this range. Lung cancer If pleural and peritoneal mesothelioma have a non linear relationship with asbestos exposure, the ques tion arises as to whether the relationship for lung can cer is linear. Figure 9 shows a plot of percent excess lung cancer against cumulative exposure and Table 7 summarises regression results for lung cancer by cumulative exposure. There is no significant differ ence between the regressions for crocidolite and amosite points, so these are treated together. Using all the data, independent fits for amphibole fibres gives a concave relationship (r=l.6). and for chrysotile a negative slope (n=-0.25). These are clearly inconsist ent with each other, and both depart very significantly from linearity (P<0.00i). The negative slope for chrysotile depends entirely on the Carolina data, and if this is removed the slope is just positive (r=A.039) with a Cl that just includes 1. Clearly the data for chrysotile-only cohorts do not provide a coherent basis for direct estimation of the exposure-response slope, and some appeal to the evi dence provided by cohorts with mixed exposure is necessary (as in the discussion of Table 2 and Fig. 3). The concave slope for amphibole cohorts is largely dependant on the two extreme points, the Massachu setts and SA amosite cohorts. The lung cancer excess in the SA amosite cohort is quite small and statisti- Tablc 7. Poisson regression of lung cancer against cumulative exposure by fibre type Fit/fibre type r 95% CT for r Residual Degrees deviance of freedom P Combined amphibole 0.49 ...excluding Massachusetts and SA amosite: 1.1 Chrysotile ...excluding Carolina (95 215 1.6 1.4 -0.27 0.030 (1.1, 2.1) (0.89, 2.0) (-0.44. -0.07) (-0.26. 1.1) 2.35 0.83 19.8 0.91 3 0.50 1 0.36 4 <0.001 2 0.63 J HWBUI0009379 o ) Quantitative risks of mesothelioma and lung cancer 581 cally unstable, and (he exposure estimate for the Mas sachusetts cohort is based on fairly slender evidence. If these two cohorts are removed (he best fit slope becomes 1.4, with a confidence interval that includes 1. The Massachusetts cohort with its very high levels of excess mortality, and as cohort with the highest estimated mean exposure to crocidolite, has an important--though not determining--impact on the estimates. It is unfortunate that the exposure estimates are somewhat speculative (see Appendix B). Ai the same time it should be noted that in relation to a prior expectation of a linear dose response the effects of this observation on the pleural and lung cancer esti mates are opposite: the pleural slope is flattened and the lung slope is steepened. This does not of course prove that the exposure estimate is correct, but if it is materially in error then either the pleural or the lung slope is even further from linear than suggested by the present analyses. DEVELOPMENT OF NON-LINEAR RISK ESTIMATES Mesothelioma The data in Tables 5 and 6 and Figs. 6 and 7 sug gest the following model with separate components for pleural and peritoneal tumours: PM=Ap,X' + ArJC where PM is the percent excess mortality, r and t are the pleural ,and peritoneal slopes of the exposure response ort% log-log scale. Apl and are constants of proportionality for the pleural and peritoneal elements of the risk respectively, and X is cumulative exposure in f/ml.yr. If the information about the ratio of r and t from the non-quantified cohorts is ignored, the best fit values using all the data are r=0.75 and r=2.1. With out the chrysotile data, the estimate of r is essentially the same (0.77). Analysis of the ratio Ur including the non-quantified cohorts (Table 4) indicates values for this ratio around 2.4 with all the data, around 3.2 excluding the two outlying cohorts. If a simultaneous fit is made to the full data with the ratio of pleura! and peritoneal slopes fixed at 2.4, the resulting esti mates (using only the amphiboie data) are r=0.86 and f=2.1. If the ratio of slopes is constrained to be 3.2, the estimated values are r=0.67 and r=2.1. There is little to choose between values of r from 0.67 to 0.86. We will use a slope of 0.75 as our best estimate for r. The estimates for r are less variable, and in any case have no bearing on risk estimates at low levels. We will take r=2.l as the best estimate. How wide a margin of uncertainty should be allowed on these slopes? On purely statistical criteria, values of r between 0.4 and 1.2 could be chosen. However a slope as low as- 0.4 seems unlikely on physical grounds. Berry's analysis of Wittenoom data using individual doses implies a slope of about 0.5. but the uncertainties of individual dose assignment are likely to have biased this estimate downwards. The argument above suggests that the lower end of range should be set at 0.67 or lower. We will take 0.6 to represent the lower end of the plausible slope range. There are quite strong a priori reasons for using a slope of 1. It is the value that all previous risk esti mations have used, and represents a natural assump tion (effect is proportional to cause) in the absence of evidence to the contrary. A linear relationship is also (in most models) consistent with the data. We therefore take r= I as the upper end of the slope range. Different slopes imply different best fit values for Ap, and Ap,. These estimates and their 95% confidence intervals for the three fibre types are shown in Table 8, Effects of exposure duration and age at first exposure This formulation does not take duration of exposure or age at first exposure into account. The HE1 (and similar) risk models (see Appendix A) imply that for equivalent cumulative exposures, short exposure times produce larger risks than long exposure times, (in other words 10 fi'ml for 1 yr is worse than 1 f/ml for 10 yr); and that exposure at younger ages will produce higher excess mortality rates. All the amphiboie cohorts considered here had short exposures (averaging about 2 yr). The suggested risk model for amphiboles is therefore appropriate for short exposures, but will overstate the risk from extended exposure periods. The chrysotile coef ficients are effectively determined by the Quebec cohort, where the average exposure durations were quite long (averaging about 10 yr). A given cumulat ive exposure accrued over 2 yr (starting at age 30) produces about 40% more deaths as the same exposure accrued over 10 yr. For general risk assess ment purposes, where short exposures are more likely to be at issue, the chrysotile coefficient should be increased by a factor of 1.4. Reductions in the exposure accrual time below 2 yr have very little impact on the risk. The risk estimates summarised above apply to exposure starting at age 30. Table 9 shows adjustment factors derived from the HE! model to convert risk estimates for an age at exposure of 30 to other exposure ages. Predicted effects at very long follow up It can reasonably be questioned whether a given asbestos exposure will continue to generate a constant excess mesothelioma mortality beyond 40 or 50 yr follow up. The evidence from cohorts with long fol low up is that the incidence eventually falls. In the Paterson cohort a significant fall is seen for follow up beyond 35 yr. in (he US/Canada insulators there HWBUI0009380 582 J- T. Hodgson and A. Danuoi Table S. Estimated coefficient with 95% confidence intervals lor constants in the risk prediction equation fur three levels of the slope coefficient r Slupe/Fibre A,., 9y,'r Cl 95% Cl at Best estimate slope (r0.75 r=O.U Ctocidolite Amosite Chrysotile High slope (r=l. r=2.3) Crocidolite Amosite Chrysotile Low slope (r=0.6. 1=1.1) Crocidolite Amosite Chrysotile 0.94" 0.I30.01)47" 0.43 0.052 0.000971) 1.5 0.24 0.012 10.71.1.2) (0.060.0.25) 10.0030.0.0069) (0.33-.0.54) r0.022-.0.099) 10.00064''.0.0014) (1.1.1.9') (0.11.0.44') (0.007S.0.013') 0.0022 0.001)6 0.00053 0.O0S3 0.003 (0.11011.0.0039) (0.00025.0.0012) 10.00029.0.00087) 10.01)0049.0.00024) (0.0043.0.014) (0.0013.0.0058) "Coeflicients used for risk extrapolation at low doses shown in bold: "best estimate, lowest arguable, `highest arguable (see Table i I). Numbers of peritoneal mesotheliomas at low doses are . negligible. For short exposure, chrysotile coefficients should be multiplied by 1.4. Table 9. Adjustment factors to convert estimates of meso thelioma mortality due to asbestos exposure starting at age 30 to other exposure start ages . Age Factor 20 25 35 40 2.1 1.5 0.6 0.4 Table 10. Estimated coefficients with 95% confidence intervals for constants in the risk prediction equation for PL for chosen levels of the slope coefficient r Flbre/model Al 95% Cl Amphibole ' Linear (r-l) Best (r=l.3) Steepest (r=l.6) Chrysotile" Best (r=l.3) Cautious model-max of: Linear (r=l) Steepest (r=t.6) 4.8 1.6 0.49 0.028 0.5 0.039 --* (1.2. 1.9) (0.37,0.62) Jk "A linear model is not strictly statistically consistent with the observed data. The line with /tu=4.8 is the single best fit. ''Non-statistical uncertainties dominate choice of chrysotile models, 95% confidence intervals cannot be properly calcu lated. See text for discussion. is a fail beyond 50 yr. Qualitatively it seems clear that the risk does not increase indefinitely, but there is insufficient evidence on very long follow up to fix the risk profile in this period. A rough and ready way of limiting the predicted risk at very long- follow up periods is to truncate the predictions at some age. The Doll and Peto and HE1 reports both truncated their predictions at age 80, and we will follow this conven tion. It is likely that this would still overstate the risk from exposure at ages below 20. and truncation of the predicted effect at 60 yr follow up might then be appropriate. Lung cancer The data in Table 7 and Fig. 9 suggest that the relation between lung cancer and cumulative exposure may be concave--i.e. that the excess lung cancer risk is proportional to a powet^greater than I of cumulative exposure. Statistically' the range of powers consistent with all the amphibole data is from I.l to 2.1. Without the two extreme cohorts the range becomes 0.89-2.0 with a central estimate of 1.4. No previous analysis of the epidemiological data has sug gested a concave relationship, though experimental data for a wide range of carcinogens (Hoel and Fort ier, 1995) suggest they may be quite common. Across the range of exposures in a single study, and given the uncertainties in individual exposure estimation, a moderate degree of non-linearity will be difficult to detect. The reasonably arguable values for r fall in the interval I to 2: a degree on conservatism and some doubts about the two extreme cohorts lead us to prefer the lower end of this interval. We will take r=l (a linear relationship) and r=l.6 to represent the fiattest and steepest slopes for risk assessment, and the mid point of this range (r=1.3) as our best estimate assumption. The estimates and 95% confidence limits for the constant term AL in a model for lung cancer FV = AiXr with r=l (linear) 1.3, and 1.6 based on amphibole data are shown in Table 10. As already discussed, the inconsistencies in the pure chrysotile data rule out a direct estimate of the exposureresponse slope based on this data. The dominant uncertainties for chrysotile are the reasons for the observed differences in exposure-specific lung .cancer risk, rather than the statistical uncertainties in estimat ing this risk level. This uncertainty is already reflected in the five-fold difference between our `best' and `cautious' estimates of RL (0,1 and 0.5 respectively). In the absence of a better approach we will assume HWBUI0009381 Quantitative risks of mesothelioma and ltm cancer 5 S3 the same range of possible slopes for the chrysotile lung cancer relationship as for the amphiboles, and determine the scaling constant by fixing the predicted excess mortality at the median exposure for chrysotile cohorts (70 f/ml.yr) to 0.1% for the best estimate and 0.5% for the cautious estimate. The resulting values are shown in Table 10. The pattern of excess lung cancer--broadly con stant relative excess from 10 to 40 (perhaps more) years from exposure (see Appendix A) implies that for exposure starts between 20 and 40 yr of age there is very little difference in the predicted risk. There .may be some decline for very long follow up, but the rate of decline is unknown. As for mesothelioma we address this possibility approximately by truncating the predicted excess at age 80. IS THERE A THRESHOLD? Another question with important implications for risk at low levels of exposure is whether there is a threshold for cancer initiation by asbestos. The HSE's recent Review offibre toxicology (Meldrom, 1996). presents arguments mainly on a toxicological basis for believing that there may be a threshold for asbes tos induced lung cancer. The argument is essentially based on a view of the carcinogenic process induced by asbestos as being an extension of the chronic inflammatory processes producing fibrosis. It is widely agreed that heavy doses of chrysotile are required to produce lung fibrosis. And some evidence has been delved from the New Orleans cohort sug gesting a threshold dose of about 30 f/ml.yr for radio logical fibrosis (Weill. 1994). Analysis of necropsy material from the Carolina cohort also shows a dis tinct step increase in fibrosis score for cumulative exposures around 20-30 f/ml.yr (Green et al,, 1997). This does not apply to amphibole exposure: radiologi cal fibrosis which progressed after -the cessation of exposure has been documented (Sluis-Cremer, 1991). in South African amphibole miners under medical surveillance and with cumulative doses less than 5 f/ml.yr. This suggests that if a threshold applies to the lung cancer effect of amphibole asbestos, it is very low. The adoption of a slightly concave exposure response slope entails a moderately ihreshold-like behaviour. Several lines of argument also suggest that any threshold for mesothelioma is at a very low level. Some cohorts (Neuberger and Kundi, 1990: Newhouse and Sullivan, 1989; McDonald and McDonald. 1978; Thomas et at., 1982; Rossiter and Coles. 1980). have produced mesotheliomas in conditions where no excess lung cancer was.seen. Occupational PMRs for British men suggest that the range of jobs for which mesothelioma rates are above background levels is very wide (Hutchings et a!.. 1995; Hodgson et a!.. 1997). Also the proportion of mesothelioma cases in population studies for whom no likely source of asbestos exposure can be identified is often quite, high. All these observations suggest (hat relatively brief exposures may carry a low, but non-zero, risk of causing mesothelioma. Some authors (Jlgren and Browne. 1991; Liddell. 1993) have argued for a mesothelioma threshold, or threshold-like behaviour of the dose-response. Such arguments are fraught with statistical and logical dif ficulties. The attempt (Ilgren and Browne, 1991) to deduce a `threshold' by identifying the lowest esti mated dose received by any observed case is a logical nonsense. Furthermore, the existence of zero cases in a dose category (human or animal) should not be automatically interpeled as zero risk. Direct statistical confirmation of a threshold from human data is vir tually impossible. One would need accurate assess ment of very low doses across a large population with long term follow up. Case-control studies with lung content measures of exposure (McDonald et /.. 1989; Rodelsperger et at.. 1999; Rogers et at., 1991) do not suggest any threshold, or downward inflexion of the dose response at the lower end of their exposure scales. Some of the animal data cited by Ilgren and Browne are suggestive of a threshold-- particularly that from inlta-pleural and intra-periloneal injection--but it is not clear how this would translate into a estimated human effect threshold for exposure by inhalation. Taking this evidence together we do not believe there is a good case for assuming any threshold for mesothelioma risk. quantified risk assessment Under current conditions, the main interest in the health risks of asbestos relates to exposure circum stances well outside the range for which we have direct observations. The statements we can make about risk therefore incorporate two kinds of uncer tainty. First there is the usual statistical uncertainty of inferring underlying risk from observations in particular groups. This kind of uncertainty depends essentially on the number of events (in this case can cer deaths) observed. The uncertainty can thereforegiven some assumptions--be quantified: the more observed events, the less the statistical uncertainty. Statistical uncertainty is expressed as a confidence interval (a range of values with--conventionally--a 95% probability of covering the true value). The second kind of uncertainty relates to the ques tion whether the relationship between exposure and outcome seen in the observed range continues to hold outside that range. This kind of uncertainty cannot be quantified statistically. Qualitatively one can reason ably argue that the agreement will be better for exposures close to the observed range, but with increasing distance from the observed range our con fidence that we know whai to expect decreases. For example, previous assessments of cancer risk from asbestos have ail assumed that the effect is linear. HWBUI0009382 ) 5SJ i. T. Hodgson and A. Damion This review has presented evidence suggesting that this may not be the case. Uncertainty about the slopes of exposure-response lines has an increasing impact with increasing distance from the observed range. Also the strength of qualitative arguments such as those advanced in the HSE review (Meldrum. 1996). in favour of a threshold for the lung cancer effect increase as exposure falls. All the above implies that simply to present a table of risk estimates--or even risk ranges--for different cumulative exposures cannot capture the changing balance of the different kinds of uncertainty. Table 11 gives a verbal assessment of risk at a range of representative cumulative exposures. No estimates have been given for lifetime risks lower than 1 in 100000, and this level is referred to as 'insignificant'. A lifetime risk of 1 in 100 000 corresponds to an annual risk well below 1 in a million, which HSE has suggested (Health and Safety Executive. 1999) as a "guideline for the boundary between the broadly acceptable and tolerable regions [of fatal risk to an individual]." It is also well below the level at which it is suggested that mesothelioma would occur in the absence of asbestos exposure: a clear majority of the very few mesotheliomas that would occur at this level would not be caused by asbestos. Mesothelioma risks in the observed cohorts have been expressed as a percentage (PM) of total expected mortality in order to standardise observations from different follow up configurations. To make predic tions of risk this measure must be converted back into absolute terms, and this is done using the average male life table discussed in Appendix A. For exposures starting at age 30 the excess mortality esti mate PM is applied to the total expected mortality from age 40 to age 79 (allowing a 10 yr minimum latency, and truncating risk at age 80). The life table predicts that about 70% of survivors to age 30 will die between the ages of 40 and 80. Absolute risk esti mates can therefore be derived from the PM value for a given exposure by multiplying by a factor of 0.7. Lung cancer risks have been expressed as a percent age excess of expected lung cancer mortality. The major determinant of this underlying lung cancer risk is smoking--especially cigarette smoking--and the number of asbestos-related lung cancers will be affec ted by the prevalence of smoking in the exposed population. Currently (in 1997) about 9.5% of male deaths between the ages of 40 and 79 ate due to lung cancer. For women the figure is 7%, reflecting differ ences in past smoking. Total survival to age 80 is lower in men than in women, and combining data for survival and proportionate mortality from lung cancer it can be predicted that for 1000 30-yr-old men 54 will die of lung cancer between the ages of 40 and 79. For women the number is 28. Thus for a popu lation with the past smoking habits of British men aged 60+ (the ages at which most lung cancers occur), the lung cancer risk from asbestos exposure is given by 0.054P,. For women with typical past smoking habits the figure would be 0.02SP,. Table 11 makes statements about the lifetime risks ot exposures accumulated over short (up to 5 yr) per iods from age 30. The factors given in Table 10 can be used to apply the mesothelioma estimates to other ages at exposure. The lung cancer estimates are based on 1997 male lung cancer rates. They are not sensi tive to age at exposure. For the lung cancer risk due to chrysolite two prin cipal figures are given: a best estimate and a cautious estimate. A risk estimate derived from the Carolina cohort is also given, with the qualification that this might be arguable in 'exceptional circumstances'. These exceptional circumstances cannot be defined with any certainty since the features of exposure at this plant responsible for the very high lung cancer risks there are not known. Exposure to textile grade (i.e. long fibre) chrysotile is presumably necessary, but does not seem to be sufficient, since other textile plants have recorded much lower exposure-specific risk (even with additional exposure to amphibote fibre). The spraying of the raw fibre with mineral oil (as a dust suppression measure) has been suggested as a possible explanation. This hypothesis seems to be supported by a case-control study of lung cancers at Carolina (though the relevant results have not been fully reported), and by observations from another asbestos textile plant (Rochdale), where men first employed after oil spraying was introduced had three times the exposure-specific risk of those first employed in earlier periods (though still lower than the Carolina risk). The main uncertainties in this picture relate to the effects of chrysotile. particularly at low doses. The application of these estimates in the assessment of a particular risk situation will depend on the purposes of that particular assessment, and the extent to which a precautionary approach is appropriate. DISCUSSION There have been a number of papers (Cullen, 1998: Stayner et at., 1996; Nicholson and Landrigan, 1996; Smith and Wright, 1996), in the literature recently which directly or indirectly consider whether there are differences in potency between the fibre types as causes of mesothelioma and lung cancer. The claim that there are important differences is often described as `the amphibole hypothesis'. In its strongest form this has been said to claim that pure chrysotile (i.e. without any associated tremoiite fibre) would pyesent little or no carcinogenic risk. At the other extreme, it has been argued (Smith and Wright, 1996), that there is virtually no difference between die risks presented by the different fibre types. Most commentators (e.g. Doll and Pelo. 1985: Hughes and Weill, 1986; Health Effects Institute, 1991) have considered that the amphibole fibre types are more dangerous, parti- HWBUI0009383 ) ' i) Quantitative risks of mesothelioma and lung cancer 585 SU IJs &s a t-1 2 H si- E c o SUB & 5-o -= 3o 'T| U 3co3o I1 -1 t* JI fa fu lfc. uccj O* gI % o *8* -g*c S 1^ 5.8*-. |=?" 3 g2| I! 5 .2, ? us-id o >R. Sec? <=M .=2 .2c? =P VI I-- IOIc '3 |- i SS.2 --, Jn uot Uu ^ g-S S go 3o sSo'T -3=2 2 .C2 3~ ~2 I lit " g-3 s 3ZS ,E = S S fl o' a *<> t<r5. l!i vs rt O `s: S -g .sS-g?p= ffj III > ?F 8 o-g ?.2 = l *.| IS** 8.f! 0 E3 i3 'II Illl exposure, Best estii exposure Best estii exposure SU, "Ifl 8 * i jfOc. O~.OKtJOX! Xe0 00 --w- t0n 52 c03 .=: 1 la If SO T' M5 = 0 B `-2 w .`S S X " u 1 E46 5 8 r 50 < 0 s 3 8 *_ 8. S. 2> .2 = js= ---2y 2> xa II 3b 2 i* & J= I E3 Sgroa ?o Eu EE IP o~-> Ifl 1~ i-1 vi C So I' u HWBUI0009384 5Hf i, T. Hodgson and A. Oomton t II sJ- ") jis sS x 1 ~ -- a _ = r~ ; s s.J! g.i = I3 5t 02 "si lit "H: a.s s =* 6 '= eft / II &i - - = = rz u = I S> il Is li ill^ C5 J5 5= ui " 3s* 3 ^ n * s ,A ?i ,,vJw*5 sf'i-i *3 Si-------H ~v. ^. ss H3 s^ 53 j s ^ -3 >*-r ^gfg K% >-sSs^fci-i ^ r 7 '- `-S=1I- as ad .-3? s a r I > 2-5 1 pi 4= <a1=1*af 1~= Jfil* JI1i 2 2-e'.: "3 rial's-s 2 5 1 2? esf t> 2 * = 1W! s = i 2 " 3 .3 mil "illII Jlr 2 3 r, 5 C3 = O S y " = *5 =" r5" - -22 .2 ?s:^6 s>i i| ii a;-H; 5 i .M 2S3 .2>- 3<3. Cj? ==.=- *^n 5: 2 5 ri u * 53 -=> s - s nuns* -S Lt *> Jill- 333?^ .>i2f *U 3HU ^-O"J *w8 v 32 -oa &-1S Sj - 1 l --8 --V ^1-=88 ^ -a -- = 22^5i "o|i *s o c 3 o ;i5o>^ :Sp:|l xanm IlPlS s-lliP *S J S\ fcs II! li 2" .8 O v. _ i 3l I e=a = 2 v* *2 ** as 8" 1 S * lg>ii=^ f as 2 3 a = is :-.S jil i i <u 8 CJ SO3 .t1prt 23 "-Cts .22aS2 3 g 3w i r^li^ csili a ~ 2 g 5JJ g S-g 5 2?1 Ie.2-3 ..) HWBUI0009385 Quamiuilive risk.* of mesoiheliomo and lung cancer 587 cularly for mesothelioma, but some (Cullen, 1993: Stayner et at., 1996) have regarded the extent of these differences as unimportant, particularly since chryso lite has been overwhelmingly the most commonly used fibre. The interpretation of the whole body of evidence depends importantly on the interpretation of results from cohorts with predominantly chrysotile exposure together with a minority contribution--usually a few per cent--from amphiboles. As long as the difference in potency is not extreme these cohorts can be reason ably interpreted as indicating the risk of chrysotile exposure. But if the differences in potency are very substantial this is no longer the case. Furthermore, in this situation an additional source of error in the estimation of exposure will be introduced, since the measured exposure (mainly of chrysotile) will often be a poor proxy for the relevant exposure. The data in this review suggest that order of magni tude differences in potency may indeed apply for mesothelioma, and probably also for lung cancer. The main reason this review differs from earlier similar reviews is in its use of the information from the amphibote mining cohorts in South Africa and Aus tralia. The publication of mortality results from the South African mines seems to have gone almost unnoticed. The Australian cohort has been the subject of a series of publications with varying analytical approaches and varying results. One of these analyses gave a lung cancer risk from the cohort of around I % per fibre/ml.yr. and this is the value that, has been most usually quoted, but this is probably an underesti mate due to incomplete follow up at older ages. This review is also the only one to have exploited the (admittedly uncertain) quantitative exposure infor mation in the Massachusetts cohort. Implications of the non-lineur exposure response for mesothelioma A non-linear relationship between the rates of pleu ra! and peritoneal mesothelioma is more readily explicable if the cancer risk is proportional to some function of the concentration of fibres in the target tissue, rather than the simple number burden. if concentration rather than number burden is the relevant parameter, then the possibility of a threshold type relationship becomes much more plausible, since if the effect depends on fibres acting together, there must presumably be some point at which individual fibres are simply loo far apart to exert any joint effect. Of course, if the mechanisms of distribution of fibres within the lung and pleura are such that fibres tend to be delivered preferentially to particular areas--and there is evidence that this is the case in the pleura (Boutin et at.. 1996)--the effective threshold level may be very low. In any case such a threshold is unlikely to he a sharp cut-off. Random variations in the distribution of fibres in particular lungs, and dif ferences in individual susceptibility will mean that the exposure response curve simply starts to descend more steeply from some point on the cumulative exposure scale. Also, fibre concentration is the more plausible exposure metric for the production of fibrosis, so this interpretation is consistent with the link suggested by the HSE fibre review (and by other authors) between the two processes. It should be noted that the sugges tion is not that tumours arise directly from fibrosis, but that both are products of an underlying inflamma tory process. If fibre concentration in tissue is the key risk meas ure, the extreme sensitivity in animal experiments to imra-peritoneal and intra-tracheal instillation of mass ive fibre doses is also readily explicable. Combined with the knowledge of the much greater solubility of chrysotile in the lung, this may also explain why asbestos related diseases have only been clearly seen with heavy chrysotile exposures. If exposures are heavy and sustained a sufficient con centration of fibre in the lung may be maintained to trigger both fibrosis and malignancy. The extreme rar ity of peritoneal mesothelioma in cohorts exposed to chrysotile alone may also be explained. If the route by which asbestos reaches the peritoneum is from the pleural cavity, it may well be that chrysotile fibres do not survive long enough in body tissues to make the journey in sufficient numbers. Chrysotile and asbestos related malignancy Smith and Wright (1996), showed a ranking of 25 cohort studies by proportional mortality from pleural mesothelioma and argued that since chrysotile was the primary exposure for two of the top 10 cohorts and present as part of the mix in six of them, and that the picture for crocidolite in terms of its presence in the mix was similar, while amosite was less evident than either of the other two fibre types, that chrysotile must therefore be similarly potent as a cause of pleu ra! mesothelioma. What this argument ignores is any quantification of exposure. Without quantification it is very difficult to draw any conclusion about relative risk from a simple ranking by mesothelioma rate. In relation to the 25 cohorts identified in this review an equally pertinent observation might be that all of them involved exposure to one or other of the amphibole fibres. Smith and Wright also present arguments based on the relative levels of mortality from pleural mesothelioma and from excess lung cancer to suggest that there is only moderate difference between the potency of chrysotile and the amphibole fibres for causing mesothelioma--they suggest a factor of three or four. However this argument is based on the assumption that all fibre types are equally potent for lung cancer. If this review is correct in suggesting that this is not the case, these arguments are not valid. Nicholson and Landrigan 11996), present similar HWBUI0009386 5S8 /. T. Hodgson and A. Danuon arguments based on the assumed equivalence of the fibre types to cause lung cancer. They also show an analysis of the mesothelioma mortality of a small sub set of the US insulators study which shows that the pattern of deaths over time implies that members of this cohort were exposed to a pleural carcinogen before 1935. Since, reportedly, amosite was first used from around 1935, and prior to this date only chryso lite was used, some of these deaths must have been due to chrysotile. The authors do not mention the possible role of crocidolile. but if we accept that no amphibole fibre was used before 1935 by US insu lation workers, these observations do show that some of the cases in this cohort must have been caused by exposure to chrysotile prior to 1935. These exposures will often have been heavy, in fact the contemporary US trade journal `Asbestos' (published monthly from July 1919) makes it clear that both amosite and cro cidolile were used in the US through the 1920s. though probably in limited quantities, since the US industry seems to have been resistant to their use on technical grounds, and chrysotile was the fibre of choice for most applications. Stayner el al.'s review of the issues (1996), sets out similar arguments, and also points to the evidence that all three commercial fibre types have produced a similar level of lung tumours in animal inhalation experiments. This is the most problematic evidence to reconcile with the human evidence that amphibole fibres are substantially more potent lung carcinogens. However, the time periods needed to induce cancer in humans (yr) and in tats (months) are very different, and it is at least plausible that all fibres are equally potent ih rats because none of them are materially cleared from the rat lung over the months needed to initiate a rat lung tumour. By contrast, in humans chrysotile (cleared in months) might have less effect than the amphibole fibres (cleared in years). A detailed elaboration of this argument has recently been published by Berry (1999). It may also be rel evant that the animal experiments were made with exposure concentrations massively in excess of those represented in the human data. The differences in the human data summarised in this review seem reason ably clear (certainly in respect of mesothelioma), and are based on a range of independent data. In the end, if a choice has to be made between animal and human evidence as a basis for assessing human risk, adequate human data must be given priority. Many of the arguments presented against the `amphibole hypothesis' in connection with mesotheli oma are variants on the basic theme that it is simply unbelievable that such a small component of the exposure could be responsible for the observed risk. If it is true that the mesothelioma risk is proportional to a less than unit power of exposure, then these argu ments are correct in their basic perception that a dis proportionate effect of the amphibole component was required to explain the data. Low levels of amphibole do have a disproportionate effect. REFERENCES Acheson, E. D.. Gardner. M. Pippard. E. C. and Grime. L. P. (1982) Mortality of two groups of women who manufac tured jas masks from chrysotile and crocidolile asbestos: a 40-year follow-up. British Journal of Industrial Medicine 39. 344-348. .Acheson. E. D.. Gardiner. M. 1.. Winter. P. D. and Bennett. C. (1934) Cancer in a factory using amosite asbestos. Inter national Journal of Epidemiology 13, 3--10. Albin. M,. Jacobson. K.. Aitawell. R.. lohannson. L. and vellindcr. H. (1990aI Mortality and cancer morbidity in cohorts of asbestos eemenl workers and referents. British Jourtud of Industrial Medicine 47. 602-610. Albin, M.. Johansson. L.. Pooley. F. D-. Jakobsson. K.. Attawell, R. and MUha, R. (1990b) Mineral fibres, fibrosis and asbestos products in the lungs from deceased asbestos cement workers. British Journal of Industrial Medicine 47. 747-774. Armstrong. B. K,, de Klerk, N. H-, Musk. A. W. and Hobbs. M. S. T. (1989) Cancer mortality in relation to measures of occupational exposure to crocidolile at Witteroom Gorge in Western Australia. British Journal ofIndustrial Medicine 46. 529-536. Berry G. ft991) Prediction of mesothelioma,ffctng cancer, and asbestosis in former Wiuenoom asbestos workers. British Journal of Industrial Medicine. 48. 793-802. Berry. G. (1995) Environmental mesothelioma incidence, time since exposure to asbestos and level of exposure. Environmetrics 6, 221-228. Berry. G. (1999) Models for mesothelioma incidence following exposure to fibres in terms of timing and duration of exposure and the biopersistence of the fibres. Inhalation Toxicology 11, 101-120. Berry, G. and Newhouse, M. L. (1983) Monality of workers manufacturing friction materials using asbestos. British Journal of Industrial Medicine 40. 1-7. Berry. G., Newhouse, M. L. and Antonis. P. (1985) Combined effect of asbestos and smoking on mortality from lung cancer and mesothelioma in factory workers. British Journal of Industrial Medicine 42, 12-18. Boutin. C.. Dumortier, F.. Rey. F., Viallat, R. and DeVuyst. P. (1996) Black spots concentrate oncogenic asbestos fibres in the parietal pleura-thorascopic and mineralogical study. American Journal ofRespiratory and Critical Care Medicine 153. 444-449. Coggon. D.. Inskip. H.. Winter, P. and Panned, B. (1995) Dif ferences in occupational mortality from pleural cancer, per itoneal cancer and asbestosis. Occupational and Environ mental Medicine 52, 775--777. Cullen, M. R. (1998) Chrysotile asbestos: enough is enough. Lancet 351. 1377-1378. de Klerk. N. H., Musk, W. A.. Armstrong, B. K. and Hobbs. M. S. T. (1991) Smoking, exposure to crocidolile, and the incidence of lung cancer and asbestosis, British Journal of Industrial Medicine 48, 412-417. de Klerk N. H., Musk. A. W. and Armstrong. B. K. (1992) The epidemiology of asbestos and mesothelioma. In Malignant Mesothelioma, eds D. W. Henderson, K. B. Shilkin; S. L. Langlois and D. Whitaker, pp. 223-250. Hemisphere Pub lishing Cotp, New York. de Klerk N. IL, Musk. A. W,, Armstrong, B. K. and Hobbs, M. S. T. (1994) Diseases in miners and millers of crocidolile from Wittenoom. Western Australia: a further follow-up to December 1986. Annals of Occupational Hygiene 38(supp I), 647-655. Demem. J. M. (1991) Carcinogenicity of chrysotile asbestos: HWBUI0009387 Quantitative risks of mesothelioma and lung cancer 589 evidence from cohort studies. Annals of Nets York Academy of Science 643. 15-23. Dement. J. M,, Brown. D. P. and Okun. A. (1994) Follow-up Study of chrysotile asbestos textile workers: cohort mortality and .case-control analyses. American Journal of Industrial Medicine 26. 431-447. Doll. R. and Peto, J. (1985). Effects an Health Exposure to Asbestos. HMSO. London. Enterline. P. E. Harley, I. and Henderson. V. (1987) Asbestos and cancer: a cohort followed up to death. British Journal of Industrial Medicine 44, 396-401. Finkelsiein, M. M. (1984) Mortality among employees of an Ontario asbestos-cement factory. American Revittr of Res piratory Disease 119. 750-761. Finkelsiein. M. M. and Vingilis. J. J. (1984) Radiographic abnormalities among asbestos cement workers: and exposure response study. American Review of Respiratory Disease 129. 17-22. Finkelsiein MM. (1989) Mortality among employees of an Ontario factory manufacturing insulation materials from amosite asbestos. American Journal of Industrial Medicine. 15. 477-481. Green. F. H. Y., Harley. R.. Vallyathan, V.. Althouse. R., Ficfc. G., Dement. J.. Mitha, R. and Pooley. F. (1997) Exposure and mineralogies! correlates of pulmonary fibrosis in chryso lite asbestos workers. Occupational and Environmental Medicine 54, 549-559. Health and Safety Executive (1999). Reducing Risks. Protect ing People--Discussion Document. Health and Safety Executive. Health Effects Institute (1991), Asbestos in Public and Com mercial Buildings: A Literature Review and Synthesis of Current Knowledge. Health Effects Institute--Asbestos Research, Cambridge. MA. Hill B. Rosenberg I. Langard S. (198!) Occurrence of cancer in a small cohort of asbestos-exposed workers. Scandanavian Journal of Work, Environment and Health. 7. 185-189. Hodgson, J. T.^Peto. J., Jones. 1. R. and Matthews. F. E. (1997) Mesotheiiunii) mortality in Britain: patterns by birth cohort and occupation. Annols of Occupational Hygiene 41(supp l). 129-133. Hoel. D. J. and Portier. C. J. (1995) Non-linearity of doseresponse functions for carcinogenicity. Environmental Health Perspectives 102(supp I), 109-113. Hughes, J. M. and Weill. H. (1986) Asbestos exposure--quan titative assessment of risk. American Review ofRespiratory Disease 133, 5-13. Hughes, J. M., Weiil, H. and Hammad. Y. Y. (1987) Mortality of workers employed in two asbestos cement manufacturing plants. British Journal of Industrial Medicine 44. 161-174. Ilgren. E. B. and Browne. K. (1991) Asbestos related meso thelioma: evidence for a threshold in animals and humans. Regulatory toxicology and pharmacology 13. 116-132. INSERM (1996) Effets sur la same des prinetpaux types d'ex- Dosiiion a t'amuinic. Paris. Jones, J. S. K Gibbs. A. R.. MacDonald, J. C and Pooley. F. D. (1996) Mesothelioma following exposure to crocidolite (blue asbestos). A 50 year follow up study In Proceedings of second international congress on lung cancer, pp. 407- 411. Mondttzzi Editore SpA, Bologna. Italy. Langer, A. M. and Nolan. R. P. (1989) Fibre type and burden in parenchymal tissues of workers occupationally exposed to asbestos in the United States. In Non-occupational Exposure to Mineral Fibres. Scientific Publication no. 90, eds J. Bignon and J. Peto, pp. 330-335. IARC, Lyon. Lash. T. L.. Crouch. E. A. C. and Green, L. C. (1997) A meta- analysis of the relation between cumulative exposure to asbestos and relative rusk of lung cancer. Occupational uittl Environmental Medicine 54, 254-263. l^vin. J. L.. McLarty. J. W.. Hurst. G. A.. Smith, A. N. and Frank, A. L. (1998) Tyler asbestos workers: mortality experi ence in a cohort exposed to amosite. Occupational and Environmental Medicine 55. 155-160. Liddell, F. D. K. (1993) Exposure-response: asbestos and mesothelioma. European respiratory review 3, 98-99. Liddell, F. D. K.. McDonald. A. D. and McDonald. J. C. (19971 The 1891-1920 cohort of Quebec chrysotile miners and mil lers: development from 1904 and mortality to 1992. Annals of Occupational Hygiene 41. 13-36. Liddell. F. D. K., McDonald. A. D,, McDonald. J. C. (1998) Dust exposure and lung cancer in Quebec chrysolite miners and millets. Annals of Occupational Hygiene. 42. 7-20. McDonald. A. D.. Fry. J. S., Woolley. A. J. and McDonald. J. (1983) Dust exposure and mortality in an American chryso lite textile plant British Journal of Industrial Medicine 40. 361-367. McDonald. A. D,, Fry. J. S-. Woolley. A. J. and McDonald.). C. (1983) Dust exposure and mortality in an American fac tory using chrysotile, amosite. and crocidotile in mainly tex tile manufacture. British Journal of Industrial Medicine 39. 368-374. McDonald. A. D.. Fry. J. S.. Woolley, A. J. and McDonald. J. C. (1984) Dust exposure and mortality in an American chrysotile asbestos friction products plant British Journal of Industrial Medicine 46. 151-157. McDonald, A. D. and McDonald, J. C, (1978) Mesothelioma after crocidolite exposure during gas mask manufacture. Environmental Research 17. 340-346. McDonald, ). C. Armstrong, B,, Case, B.. Doell. D.. McCaughey. W. T. E,, McDonald, A. D. and Sebastien. P. (1989) Mesotheliama and asbestos fibre type: evidence from lung tissue. Cancer 63. 1544-1547. Meldrum. M. (J996) Review ofFibre Toxicology. HSE Books. Sudbury. UK. Miller. B. G,, Jones, A. D.. Start, A., Buchanan, D-. Cullen. R. T,, Sourer, C. A., Davis. J. M. G. and Donaldson. K. (1999) Influence of characteristics of inhaled fibres on the development of tumours in the rat lung. Annals of Occu pational Hygiene 43, 167-179. Neuberger. M. and Kundi. M. (1990) Individual asbestos exposure: smoking and mortality--a cohort study into the asbestos cement industry. British Journal ofIndustrial Medi cine 47. 615-620. Neuberger. M. and Kundi, M. (1991) Zum Berufskrebsrisiko durch Asbesl in Osieraieh. In 2. Kolloquium cur der Krebsgefahr durch Asbesl Bundesgesunheitsamt, Berlin. Neuberger, M. and Kundi, M. (1993) Cancer in asbestos cement production and implication for risk management In: Proceedings ofthe 24th Congress iffthe International Com mission on Occupational Health. 26 September-1 October. Nice, 1993. International Commission on Occupational Health and French Ministry of Labour, Employment and Professional Training. Ncwhouse. M. L. and Sullivan, K. R. (1989) A mortality study of workers manufacturing friction materials: 1941-86. Bri tish Journal of Industrial Medicine 46. 176-179, Nicholson, W. J. and Landrigan, P. J. (1996) Asbestos: a status report. Current Issues in Public Health 2. 118-123. Hutchings, S-, Jones, J. and Hodgson, J. (1995) Asbestos related disease. In Occupational Health Decennial Sup plement Office of Population Censuses and Surveys/Hcahh and Safetv Executive tseries OS no. 101. ed. F. Drever HMSO, London. Paralari. G., Metier. E,, Bcrtazzi, P.A., Zocchelti. C. Carnev- ale. F.. Berrino. F. (1987) Cli effetti delt'esposizkme ad amiantu amosite ira gli ex lavotatori di una fabbrica di coibenti: 2. lndagine epidemiologies suite cause di mode. Pages 123134 in Paralari. G,, Gherson, G,, Cristofolini. A., Merler. E (Eds) II rischio neopluslico da amianto neu luoghi di lavoro e nell'ambienie di vita. Verona. Bi & Gi Editore. Peto. J.. Doll, R., Herndon, C,, Binns. W.. Clayton, R. and Gaffe. T. (1985) Relationship of mortality to measures of HWBUI0009388 590 J. T. Hudson ;tnd A. Darntnn environmental asbestos pollution in on ashcxlt*. textile laclory, Annuls iff Occupational Hygiene 29. 305-3.25. Appendix A Piolatto. G.. Neyri. E.. La Vocclsin. C-. Pint. E.. Decarli. A. and Peso. J. (1990) An update of cancer mortality among EXTRACTED COHORT DATA chrysotile asbestos miners in Balangcro. northern Italy. Bri tish Jounuit of Industrial Medicine 47. J41 *>--8 14. The details of extracted data is shown in Tables 12 Rtklelsperger. K,, Wonowitz. H. BrUckel. B.. Arhelgcr. R.. and 13. with explanatory notes in Tables 14 and 15. Pohlabein. H. and Jik'kel. K. -H. (1999) Dose response relotioaship between amphibole fiber lung burden and meso thelioma. Cancer Detection and Pretention 23. 183-193. Rogers. A. 11990) Cancer mortality and exposure to crucidolitc. British Journal rtj Industrial Medicine 47. 236. Rogers. A.. Leigh. L. Berry. G., Ferguson. D. A.. Mulder. H, B. and Ackad. M. (1991) Relationship between lung asbestos SUMMARISING MORTALITY AND EXPOSURE MEASURES THE CHOICE OF FOLLOW UP PER(00 IN RELATION TO EXPOSURE PERIOD fibre type and concentration and relative risk of mesotheli oma. Crnicer 67. 1912-1920. LUNG CANCER Rossiter. C. E. and Coles, R. M. (1930) HM Dockyard. Devon- For most of (he studies considered in this review, port: 1947 mortality study. In Biological Effects of Mineral Fibres. Scientific PubUcutum no. JO. ed. J. C. Wager, pp. 713-721.1ARC. Lyon. Sibastien. P.. McDonald. 1. C,, McDonald. A. D.. Case, B. and Harley, R.(l 989) Respiratory cancer in chrysolite textile and mortality was reported for the period 20 or more years from first exposure. The reason for (his is that it is assumed Chat this represents a period in which the effect of exposure will be fully expressed. Deaths mining industries: exposure inferences from lung analysis. observed in the period immediately following first British Journal of Industrial Medicine 46. 180-137. Seidman. H. and Selikofl. I. J. (1990) Decline in death rates among asbestos insulation wokets 1967-1986 associated with diminuation of work exposure to asbestos. Annuls of New York Academy of Science 609. 300-318. exposure will be unaffected by Chat exposure, and the effect of exposure will become progressively more apparent as follow up time increases. Comparisons of observed and expected deaths that include periods Seidman. H.. Selikoff, I. J. and Gelb, S. K. (1986) Mortality immediately after first exposure wi(^introduce some experience of amosite asbestos factory workers: doseresponse relationships 5 to 40 years alter onset of short-term work exposure. American Journal ofIndustrial Medicine 10. 479-514. Sluis-Cremer. G. K. (1991) Asbestos disease at low exposures downward bias to the assessed risk'level. Evidence on (he levels of excess in different per iods after exposure suggests that between 10 or 20 and about 40 yr from exposure the lung cancer risk after long residence limes. Annals of New York Academe of is reasonably stable. Beyond 40 or 45 yr follow up Science 643. 182-193. there may be sdme decline in risk, but the extern to ) Sluis-Cremer. G. K.. Liddell. F. D. K.. Logan. W. P. D. and Bezuidenhout. B. N. (1992) The mortality of amphibole miners in South Africa. 1946-30. British Journal of Indus which this may have diluted recorded lung cancer ris1 in these cohorts seems limited (for example, there L trial Medicine 49. 566-575. very little difference between the US/Canada insu Smith, A. H. and Wright. C. C. (1996) Chrysolite asbestos is lators lung cancer SMR calculated over all follow up the main cause of pleural mesothelioma. American Journal of Industrial Medicine 30. 252-266. Stanton. M. F.. Layard. M.. Tegeris. A.. Miller. E.. May. M,. Morgan, E. and Smith. A. (1981) Relation of panicle dimen sion to carcinogenicity in amphibole asbestos and other from 20 yr and one restricted to the period between 20 and 40 yr). No adjustment for differences in maxi mal follow up between cohorts has therefore been applied. fibrous materials. Journal of the National Cancer Institute The impact of follow up less than 10 yr being 67. 965-975. Stayner. L.T.. Dankovic. D. A. and Lenten. R. A. (1996) Occu pational exposure to chrysotiie asbestos and cancer risk: a review of the amphibole hypothesis. American Journal of Public Health 86. 179-186. included in the reported results, and the related prob lem of choosing an average exposure appropriate to the observed mortality needs to be considered. If observed and expected mortality from observations Stayner. L..'Smith. R., Bailer. J.. Gilbert. S.. Steenland. K.. less than 10 yr from first exposure are uninformative Dement J- Brown. D. and Lenten, R. (1997) Exposure- of the possible effects of the exposure the inclusion response analysts of risk of respiratory disease associated with occupational exposure to chrysotiie asbestos. Occu pational and Environmental Medicine 54. 646-665. Talcoit. i. A., Thurber, W. A.. Knntor. A F-. Gaensler. E. A., of such observations in the reported results for a cohort, will dilute any actually occurring effect Pro vided there is a reasonable amount of informative fol Danahy. J. F,, Amman. K. A. and Li, F. P. ((989) Asbestos- low up on every cohort member this dilution effect associated diseases in a cohort of cigarette-filter workers. can probably be ignored, since the observed and New England Journal of Medicine 321, 1220-1223. Thomas. H. F,, Benjamin. 1. T.. Elwood, P. C. and Sweelnam. P. M. (1982) Further follow-up study of workers from an asbestos cement factory. British Journal ofIndustrial Medi expected deaths generated from the early (uninformative) follow up will be outweighed for all individuals by tbeir later observations. But if a high cine 39. 273-276. proportion of a cohort generates mainly uninforma Vainio. H. and Sofetfa. P. (1994) Mechanisms of the combined tive follow up, reported overall results may be seri effect of asbestos and smoking in the aetiology of lung can cer. Scandinavian Journal of Work Environment and Health 20, 235-242. Weill. H. (1994) Biological effects: asbestos cement manufac ously distorted. This can happen if recruitment to a cohort continues to the end of follow up. Subjects starting within 10 yr of the end of follow up will con turing. Annuls of Occupational Hygiene 38. 533-538. tribute no informative mortality data. HWBUI0009389 f) Quantitative risks of mesothelioma and lung cancer s #5 3. S -S K S .5 & M|| 2 6 ^ *-s ^ 'O > 21 ^ eo rt ^ sc C d dC rS ^ r" c 'S - Sr v"rJf~> NN oo ^ f*. *n v*. 2 <** tit 2 O' eo -- 'ij ^ rJ 2g s ?O' 3r; fs p$ g -- ~ 5 S dS g ?5 <-> .:!: nrJ. (o--ns vvr,..i;;.; ss g ; r- r~ > C- ^ H ro oo n O t? fc r H O2 {T2^3^ eoS^f^r- n v*. 8 P3 O^^2 If: = : S3 , ~3 g ! * s = 3k S't n3 *5 if SOI o * gg e= s-o 25 2 d *sr n r-f p*. kA -- -- rr*-evci n%is - O Cg. -rr!*cBScC->C- OSC' - do - --' ^ - c c c: r -- o -- *- =3 " O . 3% v. :ddd 591 - a =S S == S I ?,=, g, >, | *? > S Ve 51- - uu u so -sfc-s s o <J u- U- s 2 -?I. "1 :> 15 Klll^ S~: *^1= *t 21 U=J = ,,J =1 "11 So 3= ^ w ""5 SS'-*- St s3 U. 'a -*.h& "2 -- o ' ; j 4=5 e: *a V := ilflsSlIli 12 5 & *5 C* s .2 4 *5 2 jZ Cw-to, 2cw '=2S I Is-- Sc .c ^,, X) 5cp0> <s z r. S S 8*g o-ct `i% .SS ' 5111 So I e > _ as3 1lilltil 5 |l-3 ;;<=< D 5 eJ oj e, e. w coo 5y-=c - w. ?:s side uS S" f'i " > SI II S Os- c*5 -T vr-. W O'--------------- -- 3Za =,'f0M !ce tc.-st. HWBUI0009390 59: J. T. Hodgson and A. Damton Notes on starred entries in Table 15. V t. " DC: Death certificate; BE: Best evidence; CulAv: Exposure category mid points weighted by expected mortality; ind: Mean of individual duse distribution (person weighted); 1/1): Mean exposure level times mean exposure duration; text: Value given in text, assumed to be mean of individual dose distribution (person weighted); con: Menu exposure of controls matched to lung cancer cases. * , 3 3| 5 !8 . 3 R - -a =: g 2 _ ; f 5 , '3 g. | as c it if ".l CJ 31 e-2 h 5 ac ,ouS*! f l i iss8j-.* "-*233 sii =,,> >5 SO ~ = >v s s& auo tS i c; - n Z S S TM =; p' !*w3!.:555^ = o ~ - -s - ~ O SS O - * i J I ( -- I 1 i t 1 I ! J O ! jg ^-H* 1 '2* *.5 ' 1 ' ' * * r)Tt'Cp t>"o>* r<r w'`s!0SO *S? iOPSi^*5i'1'0!o^''!l; >>> >>> >>>> Muorlur^^ri^-JaOOO " "" r=. 3 i^icai^nS^'oOsr'-rT-S^^iS^nSOjO'CsC^n^^oo^ n--or*.; 5'S2[rt2S'0'C"'S*"OWrSv"<)"0tsd 3o dS --^c'icl^o -- -- -- ri -- e 2 as 5 v 2 vo 2 -- d QQ rr*>^ -- 3p M * ^ cS - ..-_ .-ia" uuuumyuuuttiuooiuouuuiuu QQQQfflQQQQaQOQiQQOQffiaQ 2-ootnrf-- E>K^-2:5>0'c>S>~aoo!C!2r e cN<*~(. Ncn^rav>ovi'Or^eeo_^ a-- ./ ,) HWBUI0009391 D Quantitative risks of mesothelioma and lung cancer 59? i-g I! --w t vt " >^ iaa* ES * zEta SSs fa >o u I'l6z V3 HWBUI0009392 1. T. Hndgsun and A. Damton as 3 S~ : ?o r= * iH| Llf i"- 1 i. 3 - s - 5 I ^ a 3 $ s'iSlv ' 2 B >. 2 ^ u 3 y ill'!-^ o -- C :* -- t g ^ S "5 H -- -- 3 >H-5 2 5|sg|ga-; H.2-<0 = oS .3 3|gU2| ISs sfif|P =lls .3^2-i-g =! JS O Q--- U *< *J "'I / \ g I o g e u c %5C>. 8 a S s 2 w ra 5 a, to J3t H6 1 U n ^ >n I o O HWBUI0009393 Quantitative risks of mesothelioma and lung cancel 595 g e 8 8 8 1 = ,,5 Ooon C 0 2e 1 S S u I ill ' tsc"_S s? | v oc s s "<*s . f& S. Mjea 2 S. 2-S e c s a, 11-- 5 `I =< si Ms lP,iS*s "T>>s E .5 ,, .H .b = ~ = II* 5 " o o >26`- cc S tSS- .3I 3e & Xw 2. f 3 l.s ? no is IP ll I i o CO CA eS "3 11 1=2 11 I _I^' a s c*i s o 2 <= = 5l5Si k -n.= -S 2 " S " s =S = ey^=_ S/i ,^, V c ~- S = s ,u5 .f .~Sc= Kau>.C-T- 0=0-.2J * _; o g ^IpJSts. U"3 f= Bf l-~ig 13 g <3= i>d2 (3*\ iI!I 2 .5 t m 2 C in a "_ 3*1* I ff-S1= a| yi."1S S s S.2 -- w J3 S2n = 52c.= g=S|u ^ g.H5S = 5|S .2.J>Sj>e^=ag_3o!-u5olSS?oo'2Si l|2|cC a 3= f*c>--,SS>->="o =tiS 3"s&|ie-sg -ISl^S.i IC2.ee t" '" ^ S e > 2 2 o 8.5 > 31 HWBUI0009394 ) 0 596 J. T. Hodgson and A. Damion w 'J 2X JZ o 6-g 1.1 li SO IS 1g 1"1 5 O.S - 1 5 III > I 12 ss Bc B 3tfi a M .a 3a 3 O C& S 5 J3 > CSS < 5OS WI a gs^s 3" ySo(3S- ^g.3g gac? EcC||gsc^^>rS* ok. 1 I lil! s 8.^1 "2 :a Sg I8 |* <| " gl 3 | i.l'5'3 <= -a " a 2 si 83Ea| Jill1 oo-o 5 vo 3 o SJS3 j-'. g 3 m S.6 SSuE Op9 C 4)0 c If Ou> g 5 5 IgjH :~^l! _ * "o .t: *c ".HI3-2 HI!!?.; i O s = o? 3 - ! | 3 g 2'5 ss-g &f a =fr|g5=a f-Jlflif, H-alHi!! I^ssl-g 8.-- a .= g o .2 o 5|53 g.i'-s s 5Ji~^S5' " _J-fll 2 S3 1-2 Ez Si " gl-i sf: & e_ < ss. S-8. ae sg *e &o .JEJ 8c 50 e -s 32 51 <2 awO 3JSa5Si sJovIC -- O* 5 in HWBUI0009395 Quamiiaiivt risks of mesothelioma and lung cancer 597 DEFINITION OF AVERAGE EXPOSURE The inclusion of large numbers of cohort members who contribute no informative follow up may also bias the average exposure. An appropriately weighted average exposure will give zero weight to individual exposures in this group. If only a simple mean is used, and if this late entrant group is large and has-- as is likely--systematically, lower exposures, the apparent cohort exposure will be too low in relation to the observed mortality, and the estimated risk per unit exposure will be exaggerated. REVIEW OF COHORTS WITH POTENTIAL EFFECT DILUTION OR BIASSED EXPOSURE AVERAGES The potential biases discussed in the preceding paragraphs will not apply where the reported mor tality excludes observations before the tenth year of follow up (or a later year), and where the average exposure has been weighted by expected lung cancer mortality. This leaves the following cohorts as poten tially affected: Wittenoom, Ontario. Vocklabruck, US/Canada insulators, Balangero. Paterson, SA mines, Massachusetts, Albin and Ferodo. Table 16 summarises the relevant data. The possibility of dilution due to uninformative fol low up needs to'be considered for the SA mines and for the Massachusetts and Paterson cohorts. This can certainly be ignored for Massachusetts and Paterson cohorts, because of their combination of limited recruitment period with long follow up. It cannot be dismissed "for the SA mines, and an adjustment will be developed below (Appendix C). The possibility that a simple mean of individual exposures (the available figure) will be a poor proxy for the desired average weighted by expected lung cancer mortality needs to be considered for all the cohorts listed in Table 16. For all but one there are reasons (summarised individually below) for believ ing that the available figure is an acceptable proxy. For Wittenoom, the measure of excess mortality used has been truncated at subjects' 65th birth days. The effect of this is broadly to equalise the follow up durations (and therefore the expected mortality weights) of different first exposure groups. The recruitment period of the Ontario cohort is relatively short, and no mention is made of major variations in exposure conditions. Only 18% of the Vocklabruck cohort started their exposure after 1969: described as "the decisive year in improving the dust situation'. The basis for the `mean' exposure in the US/Canadian insulators cohort (drawn from pre vious reviews) is very uncertain. It is not based on averaging known or estimated individual exposures. It is plausible that conditions may not have changed greatly over the relevant period (up to 1966). The narrow range of first exposure dates for the Massachusetts cohort implies limited scope for changes in average levels, and the long minimum follow up also means that even if there were such changes, the weighting applied to early and late entrants would be similar. Comparison of the most recent follow up report on the Balangero mine cohort with a previous report (recruitment to 1965, follow up to 1975), suggests that only a relatively small proportion of the latest cohort were first exposed after 1965; though it is not entirely clear how the two cohorts relate to each other, and the minimum employment quali fication time was more restrictive (1 yr) for the later report than for the earlier (1 month), so the comparison is not straightforward. There was reportedly little change in exposure conditions between 1946 and 1960. Some downward bias in the derived exposure average is possible, but the extent of this is difficult to quantify. Even on an unadjusted basis, the derived risk per unit exposure for this cohort is one of the lowest seen. Table ! 6. Recruitment and follow up configurations for cohorts with potential effect dilution or biassed exposure averages Recruitment follow up Cohort Numbers of From To From To Maximum Follow up years follow up on latest latency (yr) entrants (yr) Wittenoom Ontario Vocklabruck US/Canada insulators Balangero Paterson SA mines Massachusetts Albin Ferodo 10 20 20 20 20 5 0 0 20 10 1943 1948 1907 1907 1930 194! 1925 1951 1907 1920 1966 1959 1979 1966 1986 1945 1980 1953 1977 1977 1943 1948 1950 1967 1946 1941 1946 1953 1927 1942 1986 1977 1990 1986 1987 1982 1980 1988 1986 1979 44 30 84 80 58 42 56 38 80 60 20 18 11 20 1 37 0 35 9 2 HWBUI0009396 598 1. T. Hodgson am) A, Dumion * For the Alhin cohort, major exposure changes started to apply only from the late 1960s. the last 10 yr of 70 yr of intake. The scope for bias is therefore limited. The average exposure used for the Ferodo cohort is that of controls matched to lung cancer cases. It is therefore--indirectly--weighted in the appro priate way. The one exception is the SA mines cohort. The report on this cohort shows that a large proportion of the cohort (amosite and crocidolite workers combined) were first exposed less than 10 yr from the end of follow up. Illustrative exposure data is also shown which implies that these workers were exposed to levels 4-6 times lower than those which applied before about 1950. Some adjustment to the reported individual mean exposure is therefore indi cated. This adjustment, and the related adjustment to exclude observed and expected mortality arising from uninformative follow up are described in detail in Appendix C. Briefly, we conclude that both the observed excess lung cancer and the associated cumu lative exposure should be adjusted upwards, the exposure by rather more than the mortality excess. The implied dose specific risk is reduced by about a quarter. SUMMARY MEASURES FOR MESOTHELIOMA Mesothelioma incidence rate rises very steeply with time since exposure, and this complicates the choice of a summary measure that will be properly comparable across cohorts. Comparisons between cohorts with different follow up times (or different mixes of follow up times) should be adjusted to allow for the impact of those differences on the observed mesothelioma mortality. One solution is to fit a stat istical model. The following formulation was used in the HEI report, and is fairly typical: where L is exposure level expressed in f/nd, D is exposure duration in yr and the contents of the curly brackets {) are set to zero if<0. However not all cohorts have the data needed to fit the HEI (or similar) models. A pragmatic way of making an equivalent adjustment is to express observed mesothelioma numbers as a percentage of expected mortality from oil causes, since this too is a measure which increases steeply with follow up time. The expected mortality from all causes has one drawback as a denominator for mesothelioma risk: it is dependent on age at first exposure. This would not be a serious problem if the mean age at first exposure was similar in different cohorts, but this is not the case. For those cohorts lor which the mean age at first exposure is given or can be estimated, it ranges from 23 lor Quebec to 37 for Paterson, with a mean across cohorts of about 30. We have therefore standardised the expected all cause mortality figure given for each cohort to an assumed mean age at first exposure of 30. The amount of adjustment applied has been calcu lated using the following formula: ,wi = FA.Vfv/M,, / Where AJ| is the adjusted expected all cause mor tality to be used as denominator for the observed mesothelioma mortality; a is the mean age at first exposure for the cohort in question; A is the actual expected all cause mortality from the person years in which the mesotheliomas arose; Afw.and M,, are proportional expected all cause mortality estimates for the `typical' follow up duration for the cohort (the follow up duration that divides the observation field beyond the minimum latency into two equal areas) from ages 30 and a respectively. The schedule of all cause death rates used to calculate M:,,, and Mu. was rate=exp(-9.61+.Q936a)--where a is age in yr-- which provides a close fit to male alljfcause mortality in Australia, Austria, USA and Great Britain (using data taken from the mid 1970s). The fit is less good for South African and for Swedish death rates, but the adjustment depends on the ratio /MJ of expected deaths in different--and quite wide--age ranges, a measure that is not sensitive to the precise underlying life table. So for convenience the same life table: , approximation was used for all cohorts. For the two ^ cohorts where mean age at first exposure was not available (Rochdale and Albin), a mean age of 30 was assumed. A similar argument to that set out above in relation to the effect of uninformative follow up on recorded lung cancer mortality in the SA mines cohorts also applies to the excess mortality from mesothelioma. Ail the recorded mesotheliomas in these cohorts occurred more than 10 yr from first exposure. An esti mate of the expected all cause mortality arising from follow up less than 10 yr from first exposure has been subtracted from the reported total expected all cause mortality, and this adjusted figure used as the denomi nator for excess mesothelioma mortality in these cohorts. Details of .this calculation are given in Appendix C. COMPARISON OF COHORT AVERAGE RISK MEASURES WITH ALTERNATIVES BASED ON INTERNAL COMPARISONS For reasons explained in the main report, this review has taken cohort level measures of exposure and outcome as the basic units of observation. In the next two sections these cohort-level measures ate compared to the corresponding internal analyses for those cohorts where both are available. HWBUI0009397 Quantitative risks of mesothelioma and lung cancer 599 COMPARISON OF RISK MEASURES-- MESOTHELIOMA For cohorts where details of mesothelioma deaths and person years by time from first exposure were given, the HEI model filled to these rates. The model was fitted using values for individual calendar years of time from first exposure aggregated to give the reported latency categories. Best fit was assessed by maximum likelihood methods assuming a Poisson distribution, and the resulting estimates of Ku are shown in Table 12. Figure 10 compares the two alternative measures of mesothelioma risk: the HEI coefficient KM and the percent excess mortality per f/ml.yr index /?M. There is good agreement between these measures. The most discrepant point relates to the Quebec cohort (code 6), though this is on either measure clearly the lowest value. It may be relevant chat the HEI parameter KM for the Quebec cohort was calculated using data based on age at death as a proxy for time since exposure, since this will have introduced additional inaccuracy. The HEI formula may be preferred for the purposes of risk projection, but the alternative measure seems to provide an equally valid summary of the relative levels of mesothelioma risk in these cohorts. COMPARISON OF RISK MEASURES--LUNG CANCER Where exposure response regressions were reported by authors, the regression slope has been noted: this provides the 'regression slope' estimate of the lung cancer risk. For cohorts where dose-specific SMR data had been reported, but no regression analy sis was reported, a Poisson regression fit was calcu lated. The association between the cohort average esti mate of f?L with the regression slope estimate, for studies where both measures were available is shown in Fig. 11. There is a clear overall relationship, viewed across the whole risk scale. The discrepant points are those with substantial statistical uncer tainty. either because they are based on small differ ences between observed and expected cases (5a-- New Orleans, plant 1; and 17--Ferodo) or because of uncertainties deriving from the small size of the reference population (15--Albin). The most discrepant point relates to the Albin cohort, which was analysed as an unmatched casecontrol study in relation to a control cohort of non asbestos exposed industrial workers from the same area. The overall RR for respiratory cancer excluding mesothelioma was 1.8 (though with a wide confi dence interval: 0.9-3.7) and the mean cumulative exposure was 13 f/ml.yr, giving a cohort average esti mate of ftL of 6.2% per f/ml.yr (with an even wider confidence interval: --0.8-21). The value of the internal regression slope in relation to exposure is not reported, though we are told that it was not statisti cally significant (P=0.5). Inspection of the RRs for the three exposure categories implies that the slope would have been about 0.05. Whether this discrep ancy reflects inaccuracy in the baseline, or in the exposure measurements (or a mix of these) is difficult to say. The high mesothelioma risk in this cohort tends to suggest that the cohort average measure is nearer the truth, but substantial uncertainty must remain. A further discrepant point relates to women in the Carolina cohort (2f), where the regression implies /?L=1, while the cohort average gives L=6.7. The authors suggest that the low regression slope may reflect uncertainties in women's employment histories (which would tend to flatten the regression slope). There is some tendency for the cohort average esti mate to be larger than the corresponding regression Fig. 10. Comparison of alternative measures of mesothelioma Fig. 11. Comparison of alternative measures of lung cancer mortality. mortality. HWBUI0009398 600 J. T. Hodgson and A. Damlon slope for those cohorts with clearly positive results. This might be predicted from the flattening of regression slopes by inaccuracies in exposure esti mates. But it can also reflect inadequacies in baseline rales. For example, the two-fold difference between the cohort average and regression slope measures for the Quebec cohort reflects the SMR of about 1.3 seen in all the low dose subgroups, and which the authors interpret as non-asbestos related. Nevertheless, the broad agreement between the two measures across studies suggests that valid conclusions can be drawn from the cohort average measure. ably present assuming some exhaust control on the bag opening, carding and mixing areas. Unfortunate!' no mention of (he control system is made. Also it is probably an average value that has been given for both the wet and dry methods as both were in use. It is probable given that the sampling locations are unknown that higher concentrations occurred in the dry areas: around 100 f/ml as measured by the current method. Of course this is very approximate, but 100 f/ml looks to be a good maximum exposure with TWA of 60 f/ml. - } Appendix 8 AppenUlx C FIBRE-PARTICLE CONVERSION FOR CROCIDOLITE CIGARETTE FILTER COHORT NOTE BY DR G. BURDETT The measurements in 1952 which gave an average of 80 particles per tnl, within the Massachusetts stan dard of 175 particles per ml. almost certainly refer to tmpinger measurements, which were frequently made for insurance company purposes. The norma! units are millions of particles per cubic foot (mppcf). As one cubic foot is equivalent to 28 316.8 ml the value of 80 particles per ml is equiv alent to 2.265 mppcf and 175 particles per ml is equi valent to 5 mppcf. Five mppcf was the threshold value in force from the 1930s to the 1960s (maybe even until 1972) when it was replaced by a membrane filter limit of 10 f/ml, which has been falling ever since. As the units suggest, the method only counted par ticles using relatively low powered microscopy and would overlook many of the respirable fibres and is a very indirect measurement of the fibre level. It should also be remembered that impingers have poor capture efficiency below 1 fim. It is also noted that cotton and acetate fibres were mixed, carded and deposited on crepe paper under dry conditions. This would suggest that fibres made up many of the particles but I have not referred to the patent to work out quantities used to estimate the fibre percentage. My best guesstimate is that 30% of the particles were fibres but only about 10% of. the fibres seen would be crocidolite (it is more dusty, but has very few >1 pun fibres compared to the other dusts). This would mean about 3% of the count was cro cidolite fibres or about 2.5 f/ml>l pun wide. To con vert to the current index we generally find one can assume only some 4% of the >5 pm long crocidolite fibres were visible as compared with the current index. This is equivalent to a concentration of about 60 crocidolite fibres per ml using a modem version of the membrane filter method. This is several times higher than the better factories at this time but not too far away from what was prob- DEVELOPMENT OF ADJUSTMENTS TO THE SOUTH AFRICAN MINES COHORT DATA The starting point for the adjustment of the reported results from this cohort is the data given in Tables 1 and 2 of the published paper, which give illustrative data on exposure levels in different per iods (Table 1) and a breakdown of the whole cohort by year of birth and date of first exposure (Table 2). The average age at first exposure'of the groups rep resented by the cells of Table 2 can be estimated using the mid points of the year of birth and year of first employment categories (1900 and 1935 were assumed for the earliest birth and employment categ ories respectively). Age specific all cause and lung cancer rates for white South African men in 195" 1965 and 1975 were then used to calculate the dist button of expected lung cancer deaths by time sinecfirst exposure in each cell. The rates for 1955 were also used for the cells relating to first employment between 1941 and 1950, but the expected number was reduced by a factor of 0.64 to allow for the fact that cause specific follow up was only recorded from 1949. The total expected lung cancers calculated in this way (39.3) agrees quite closely to the value reported in the paper (36.6) and the proportion of expected lung cancer deaths arising from follow up less than 10 yr from first exposure is 0.23. The reported observed and expected lung cancers in the two pure fibre subcohorts have therefore been reduced by 0.23 times the expected numbers given. The data reported in Table 1 was used to estimate approximate relative exposure levels at'ten year inter vals from 1945. Taking 1945 as 1, the numbers used were l, 0.6, 035, 0.25 for amosite; and 1, 0.5, 0.25, 0.15 for crocidolite. Exposures in the 1930s were assumed to be the same as in die 1940s. To derive an expected lung cancer weighting for this relative exposure pattern, the expected lung cancers in each birth-start cell from the 10th anniversary of first employment to the end of follow up in 1980 was cal culated in a similar way to that described for the first 10 yr of follow up. The resulting distribution of , '[ HWBUI0009399 Quantitative risks of mesothelioma and lung cancer 601 Table 17. Derivation of correction factor for reported mean exposure using assumed relative exposure and weighting factors by year of first employment in asbestos mines Data item Year of first employment Mean weighted by Before 19-10 1941-50 1951-60 1961-70 1971-80 Persons Expected lung cancer Relative exposure Crocidolite Amosite Weighting factors Persons Expected lung cancers >10 yr from 1st exposure 1 1 62 1.68 1 0.5 0.25 l 0.6 0.35 404 2355 2408 6.75 17.59 4.36 0.15 0.25 2088 0 0.35 0.44 Totals 7317 30.38 0.6 0.68 ); expected lung cancers in the five date of start groups is shown in Table 17. Table 17 also shows the numbers of individuals in each group, and the assumed relative levels of exposure. Mean exposures are calculated using the two alternative weightings. The expected lung cancer weighted means are larger than the corresponding person weighted means by a factor of 1.71 for crocidolile and 1.55 for amosite. The reported mean exposures have been adjusted using these factors for use in this review. Similar calculations for all cause deaths imply that the proportion of expected all cause deaths falling in the first 10 yr of follow up is 33%. The all cause mortality denominator for the observed mesotheliomas in the two subcohorts has therefore been reduced by a factor of 0.67. r HWBUI0009400