The Quantitative Risks of Mesothelioma and Lung Cancer in ... · PDF fileQuantitative risks of mesothelioma and lung cancer 567 this error is very small, but in an asbestos exposed

Ann. occup. Hyg., Vol. 44, No. 8, pp. 565–601, 2000Crown Copyright 2000

Published by Elsevier Science Ltd on behalf of British Occupational Hygiene SocietyAll rights reserved. Printed in Great Britain.

0003-4878/00/$20.00PII: S0003-4878(00)00045-4

The Quantitative Risks of Mesothelioma and LungCancer in Relation to Asbestos ExposureJOHN T. HODGSON* and ANDREW DARNTONEpidemiology and Medical Statistics Unit, Health and Safety Executive, Magdalen House, StanleyPrecinct, Bootle L20 3QZ, UK

Mortality reports on asbestos exposed cohorts which gave information on exposure levelsfrom which (as a minimum) a cohort average cumulative exposure could be estimated werereviewed. At exposure levels seen in occupational cohorts it is concluded that the exposurespecific risk of mesothelioma from the three principal commercial asbestos types is broadlyin the ratio 1:100:500 for chrysotile, amosite and crocidolite respectively. For lung cancerthe conclusions are less clear cut. Cohorts exposed only to crocidolite or amosite recordsimilar exposure specific risk levels (around 5% excess lung cancer per f/ml.yr); but chryso-tile exposed cohorts show a less consistent picture, with a clear discrepancy between themortality experience of a cohort of chrysotile textile workers in Carolina and the Quebecminers cohort. Taking account of the excess risk recorded by cohorts with mixed fibreexposures (generally,1%), the Carolina experience looks uptypically high. It is suggestedthat a best estimate lung cancer risk for chrysotile alone would be 0.1%, with a highestreasonable estimate of 0.5%. The risk differential between chrysotile and the two amphibolefibres for lung cancer is thus between 1:10 and 1:50.

Examination of the inter-study dose response relationship for the amphibole fibres suggestsa 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 ofcumulative exposure, lung cancer risk lies between a linear and square relationship andpleural mesothelioma seems to rise less than linearly with cumulative dose. Although thesenon-linear relationships provide a best fit to the data, statistical and other uncertainties meanthat 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 ofquantified risk summary statements for different levels of cumulative exposure are presented.Crown Copyright 2000 Published by Elsevier Science Ltd on behalf of British OccupationalHygiene Society. All rights reserved

Keywords: asbestos; amphibole hypothesis; exposure-response; lung 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. Existingpublished quantitative risk assessments have mostlynot 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: [email protected]

565

has produced quantified estimates of the risk fromamphiboles (a collective mineralogical term coveringcrocidolite and amosite). A review commissioned bythe HSE in the 1980s from Professors Richard Dolland Julian Peto (1985) gave estimates for chrysotilealone; more recently a review by the Health EffectsInstitute (1991) produced estimates for an unspecifiedmixture of fibre types. An INSERM review (1996)also ignored differences in fibre type, and drew heav-ily on the HEI review.

The studies included in this review were selectedby reviewing the material referenced in the Doll andPeto, HEI and INSERM reports and identifying allcohort mortality reports for which quantified data on

566 J. T. Hodgson and A. Darnton

exposure was available either as an average for thecohort as a whole, or for individual subgroups. Seven-teen such cohorts were identified (Albinet al., 1990a;de Klerk et al., 1994; Dementet al., 1994; Enterlineet al., 1987; Finkelstein, 1984; Hugheset al., 1987;Liddell et al., 1997; McDonaldet al., 1983b, 1984;Neuberger and Kundi, 1990; Newhouse and Sullivan,1989; Petoet al., 1985; Piolattoet al., 1990; Seidmanet al., 1986; Seidman and Selikoff, 1990; Sluis-Cremeret al., 1992; Talcottet al., 1989). Three ofthe selected cohorts have been split into sub-cohortswhich have been separately treated in this review: theSouth African crocidolite and amosite mining cohortshave been treated separately; the New Orleans asbes-tos cement cohort has been split into the two separateplants covered, since the mix of fibres used in the twoplants was different; and the Carolina textile cohorthas been split by sex, since the results for men andwomen were rather different. The cohorts have beenreferred to by their geographical location except forcohorts 3 (Enterlineet al., 1987) and 17 (Newhouseand Sullivan, 1989) which are identified by a com-pany name, and cohort 15 (Albinet al., 1990a) wherethe name of the principal author on the cohort hasbeen used.

Information extractedInformation was extracted from the identified

reports on the following:

O The number of deaths in the cohort from all causesand from lung cancer, and the correspondingSMRs;

O Dose specific lung cancer SMRs (or rates),where available;

O The number of mesothelioma deaths in the cohort(for pleural and peritoneal mesotheliomaseparately);

O The rates of mesothelioma by categories of timesince first exposure;

O The process/type of work being carried out;O Cohort recruitment period and duration of follow

up;O Average age at first exposure, when available;O The type(s) of asbestos fibre used in the process;O The average fibre levels for the entire cohort and

the average employment duration for workers inthe cohort, or simply the average cumulativeexposure for the entire cohort;

O Information about the smoking habits of the work-ers in the cohort where available;

O The sex of the workers.

Some general issues on the summary of outcomeand exposure measures are discussed below. A moredetailed discussion on some of these points is givenin Appendix A, and the extracted data is shown infull in Tables 12 and 13.

Excess lung cancer measureExcess overall lung cancer mortality has been

expressed as a percentage excess of expected lungcancer mortality per unit of cumulative exposure.

RL = 100(OL2EL)/(EL.X)

WhereOL and EL are the numbers of observed andexpected lung cancers, respectively andX is cohortmean exposure. This estimate of the lung cancer riskis described as the ‘cohort average’ estimate. 95%confidence limits for the cohort average estimateRL

have been calculated assuming a Poisson distributionfor OL.

Mesothelioma measureMesothelioma mortality was expressed as a per

cent of expected mortality from all causes (adjustedto an age of first exposure of 30) per unit of cumulat-ive exposure.

RM = 100OM/(EAdjX)

Where OM is the number of mesothelioma deaths,EAdj the total expected deaths from all causes adjustedto an age of first exposure of 30, andX the meancumulative exposure. (See Appendix A for a dis-cussion of this measure, and the calculation ofEAdj).When the expected all causes mortality was not avail-able, the denominator was taken to be the totalobserved deaths less the total of asbestos-relateddeaths (mesothelioma, asbestosis and any excess lungcancer deaths). A 95% confidence interval forRM wascalculated assuming a Poisson distribution forOM.

Treatment of ‘best evidence’ cause of death dataIn some studies causes of death have been assigned

in two ways, one based purely on data given on thedeath 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 majorityof other studies. For mesothelioma however, the BEdata 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 arederived from DC data (as in the SMR analyses inthis report) the observed deaths on a DC basis shouldalways be used. The argument is not as clear cut asit seems. The coding of death certificates is subjectto a range of errors, and the net error in the count ofdeaths coded to lung cancer on national death certifi-cates will be determined by the balance of these errorsacross the whole population. One of these errors isthe tendency of pleural mesothelioma deaths to becoded to lung cancer. In the population as a whole,

567Quantitative risks of mesothelioma and lung cancer

this error is very small, but in an asbestos exposedcohort it may have a substantial effect. Leaving themiscoded mesotheliomas in the lung cancer count willoverstate the true lung cancer SMR. Excluding themwill in theory understate it, but only to the smallextent that this error affects the population as a whole.The best available approximation to a true estimateof the risk is therefore to exclude the miscoded meso-theliomas, and this has been done for this review.

Derivation of cohort mean exposure estimatesMean 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 lungcancer in the group. On the assumption that excessrisk is proportional to cumulative exposure, thisweighting preserves the same proportionality whenthe results from subgroups with different exposuresare aggregated, it is therefore the optimal statisticalmeasure of aggregate exposure.

Where mean exposure values for individual dosecategories were not given, the midpoints were used.The top exposure category was usually given as anopen interval (e.g. exposures>100 f/ml.yr): in thesecases a value was chosen based on a view of the high-est likely exposure and the distribution of individualsacross all exposure categories. It was assumed thatwhere the highest category contains a relatively smallproportion of the population, the category mean willbe a smaller multiple of the lower band than other-wise.

For cohorts where results for exposure specific sub-groups were not given, the cohort mean was eithergiven directly (cohorts 4, 13 and 15); derived frominformation given on the distribution of individualdoses (cohorts 1 and 7), or on the exposure of internalcontrols (cohort 17), or by multiplying a meanexposure level by mean exposure duration (cohorts 8and 14).

Exposure estimates given in particle counts wereconverted to counts of ‘regulated fibres’ (fibres withan aspect ratio greater than 3:1, and length>=5microns), using conversion factors calculated by thereport authors where possible. The most commonlyused conversion was 1 mppcf (million particles percubic foot)=3 f/ml (fibres per millilitre), and this wasthe value adopted for the Johns Manville cohort,where a conversion was not given. For the Massachu-setts cohort, where the fibre involved was crocidolite(rather than chrysotile as in the other cohorts withparticle counts), an independent expert hygienist wasasked for an assessment (see Appendix B).

The exposure estimates for Wittenoom have beenquestioned by Rogers (1990) who has suggested—having re-examined some of the original samplesusing modern light and electron microscopy—that thelevels may have been 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 anadjustment to the published estimates, and if so byhow much. Similar comments may of course apply toother cohorts and introducing a correction might thendistort rather than correct the overall picture. de Klerkand colleagues, developing estimates of environmen-tal risk at Wittenoom (1992) use a factor of 4 withoutdetailed discussion. The effect of using this adjustedexposure level is examined as a variant of the mainanalyses.

Exposure-specific risk estimatesIt is generally assumed that the most reliable guide

to dose-specific risk is provided by exposure analysesusing estimates of individual exposure. This is clearlythe case when these individual exposure values canbe accurately determined. However this assumptionis 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 recentmeasurements; there are also problems of convertingthe most usual historic measurements (in terms ofparticle counts) to the more relevant measure of fibrecounts. Direct fibre counting only became generallyused in the 1970s.

In these circumstances it is at least arguable thatglobal assessments of average exposure, set againstoverall mortality outcomes, should be preferred.Exposure–response regressions with inaccurate indi-vidual exposure assignments will produce a slopeestimate biased downwards. Use of an overall assess-ment will also minimise the error introduced by con-version from particle counts to fibres, since theseaverage conversion factors will represent a moreaccurate conversion for the totality of exposure thanfor a particular individual.

However, the arguments are not all one way. Over-all mortality outcomes can only be assessed againstsome outside reference—usually the regional ornational population—and this may not represent atrue baseline level for the exposed population in ques-tion. Assessment of an internal exposure responsegives some check on this issue. A complete absenceof exposure response must cast some doubt on anyoverall excess being counted as a measure of risk (theAlbin and Connecticut cohorts are examples of this).

Cohort-level risk measures were chosen for thisreview both because these allow a wider range of datato be assessed than if attention is restricted to internalexposure response analyses and since (as arguedabove) cohort-level exposure estimates are likely tobe more accurate than individual exposures.

SmokingThe evidence on the joint effect of smoking and

asbestos exposure on lung cancer has been reviewed


recently (Vainio and Bofetta, 1994) who concludethat the overall evidence indicates an interaction inthe multiplicative region. This implies that the rela-tive risk of lung cancer due to asbestos exposure willbe the same for smokers and non-smokers alike. ThusSMRs for lung cancer based on a reference popu-lation with the same smoking habits as the cohortmembers should only reflect the effect on mortalitydue to asbestos exposure. An earlier review by Berryet al. (1985) estimated that the effect of asbestosexposure was about 1.8 times greater in non-smokersthan in smokers (though with confidence limits whichdid not exclude a simple multiplicative interaction).If this is the case the observed effect of asbestos onlung cancer rates will be greater in populations withlower smoking prevalence. However, given the rela-tive lung cancer risks typical of smoking (about 15-fold) and asbestos exposure (about 2-fold) togetherwith the generally high prevalence of smoking in theobserved populations, the scope for bias—if there isindeed a differential effect of the scale suggested—is limited. In either case, a problem arises when thesmoking habits of the cohort members differ fromthose of the reference population, which is the casefor some of the cohorts reviewed. For this reason, anyinformation about smoking given in the studies wassummarised. The amount of information given wasvery variable, and could be categorised as follows:

1. No information given, (Ferodo, US Insulators, Pat-erson, South Africa, Johns Manville, Albin).

2. The percentage of the cohort that smoked, usuallybased on a cross sectional survey conducted in aparticular year, (Connecticut, Balangero, Quebec,Pennsylvania, Rochdale, Wittenoom).

3. Comparison of the prevalence of smoking in thecohort and the reference population, (New Orle-ans, Massachusetts, Carolina).

4. Estimation of the effect of any differences inprevalence—for example calculation of smokeradjusted lung cancer SMRs, (Vocklabruck)

5. Data on prevalence of smoking within exposurecategories—but with no external comparison(Ontario).

Most studies fell within the first two of the abovecategories. In these cases only subjective judgementscould 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 thecohort and may not be very representative. For moststudies which addressed the issue the authors con-cluded that there was no major difference in smokingprevalence or that the slight differences in prevalencewere not likely to change the expected number oflung cancer deaths in a substantial way. Of the studieswhere comparative smoking data were given, theVocklabruck cohort showed the largest difference incohort smoking habits and those of the general popu-

lation, and this was the only study where an explicitadjustment for smoking was made. Unadjusted datawas used for all other studies.

Fibre type and industry processFor the purpose of summarising the information

given in the studies, each cohort was given a fibretype classification of 1, 2 or 3 letters according to thetype of fibre used, with the letters y, a and o rep-resenting chrysotile, amosite and crocidoliteexposures respectively. For example:

‘yao’ means all three commercial asbestos typeswere used in the cohort

‘yo’ means chrysotile and crocidolite were used‘a’ means only amosite was used

The order of the letters indicates the relative impor-tance of the fibres used. Very small quantities of fibrewere ignored in some cohorts (Carolina, New Orleansplant 1, Connecticut), the reasoning for this in eachcase is set out in Appendix A (Table 14). In a similarway, for display in tabular and graphical data sum-maries, industry process was coded as follows.

M MinesC CementT TextilesI Insulation ProductsF Friction ProductsL Lagging and work with insulationO Other

Meta-analytic issuesThe aim of a meta-analysis is to identify where evi-

dence from different studies is discrepant; ideally, toexplain the reasons for the discrepancies; and wheredata from different studies are coherent to combinethem into a common summary which will be moreprecise and soundly based than the estimate from anysingle study. For this review the coherence of esti-mates ofRL and RM from different studies has beenassessed in a Poisson regression framework, fitting acommon value of the parameter of interest across agroup of studies and testing the residual deviancebetween the observed and predicted numbers ofevents (mesothelioma or lung cancer deaths) in thestudies in the group. Confidence limits around thegroup estimates were calculated by profile likelihoodmethods. Confidence limits are not shown for themeans 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 themean itself has any natural meaning. Faced withclearly discrepant data, purely statistical criteria can-not be used to decide on a ‘correct’ summary orcompromise estimate.

The statistical analyses in this report only takeaccount of the statistical variability of the mortality


outcomes. The statistical variability in expected mor-tality levels and cohort average exposures areignored. This means that calculated confidence inter-vals will be narrower and statistical distinctionssharper than they would be if these variabilities wereknown and allowed for. This needs to be borne inmind in the interpretation of these analyses.

RESULTS

OverviewFigure 1 shows a graphical comparison of the

mesothelioma and lung cancer risk coefficients. Inorder to plot zero values (which convert to minusinfinity on the log scale), convenient nominal positivevalues smaller than any real non-zero value in the(relevant) data have been used. These are in the range0.001–0.002 forRL and between 0.0001 and 0.0003for RM. The three panels of Fig. 1 display the samedata, with each cohort represented by its cohort code,fibre type and process. Cohorts which did not showa statistically significant excess of lung cancer (RL)are shown in brackets.

Both risk measures cover about three orders ofmagnitude. For the bulk of the data risk estimates forlung cancer and mesothelioma are strongly correlatedwith RL, very roughly equal to 100RM. This hetero-geneity seems more readily explicable in terms offibre type than process. For example there are miningand asbestos cement cohorts at both extremes of therisk scale, while all the amphibole cohorts are at thehigh risk end of the scale. But there are not reallyenough examples within each category statistically todraw definitive conclusions of this type.

Total mesotheliomaThe summarised data for total (pleural and

peritoneal) mesothelioma mortality are shown inTable 1 and Fig. 2. The estimates ofRM for crocido-lite cohorts are closely grouped around an averagevalue of 0.51. Similarly, the two amosite cohortsshow results statistically consistent with their averageof 0.10. The results from mixed fibre cohorts covera wide range from a value close to that seen for thecrocidolite cohorts (RM=0.59 for Ontario) to valuesnearly three orders of magnitude lower, close to thoseseen in the chrysotile mining cohorts. The test for het-erogeneity is very clearly significant (P,0.001). Theranking of mixed cohorts by mesothelioma risk doesnot appear to correspond either to process or fibremix.

If the exposure estimate for Wittenoom is increasedby a factor of 4, the summary value ofRM falls to0.15, and the consistency of the three crocidolitevalues is completely lost (P,0.001).

Three of the six chrysotile cohorts had no observedmesothelioma deaths. The rates in the two chrysotilemining cohorts are similar at around 0.0015, while the

Fig. 1. Comparison of exposure-specific risks of mesotheliomaand lung cancer (% per f/ml.yr), with cohorts labelled by cohortcode, fibre type and process. [Note: the two coincident cohortsin the top right of the chart are Ontario (4, yo, C) and SAcrocidolite mines (13o, o, M). Symbols in brackets indicate a

non-significant lung cancer excess].


Tab

le1.

Sum

mar

yof

mes

othe

liom

am

orta

lity

data

and

expo

sure

-spe

cific

risk

estim

ates

Coh

ort

Coh

ort

nam

eP

roce

ssF

ibre

Mes

othe

liom

ade

aths

Tot

alA

djus

tmen

tA

vera

geM

esot

helio

ma

risk

expr

esse

das

P-v

alue

for

num

ber

expe

cted

fact

orfo

rcu

mul

ativ

epe

rcen

tage

tota

lex

pect

edm

orta

lity

per

hete

roge

neity

mor

talit

yag

efir

stex

posu

ref/m

l.yr

(R

M)

expo

sed

(f/m

l.yr)

Tot

alN

umbe

rU

nadj

uste

dA

djus

ted

for

95%

CI

num

ber

perit

onea

lag

eat

first

expo

sure

14M

assa

chus

etts

Oo

53

8.3

0.74

120

0.50

0.68

(0.2

2,1.

6)1

Witt

enoo

mM

o72

1060

1.8

1.08

230.

520.

48(0

.38,

0.60

)13

oS

Acr

ocid

olite

min

esM

o20

222

3.2

a0.

9316

.40.

550.

59(0

.36,

0.91

)T

otal

–C

roci

dolit

e97

150.

51(0

.41,

0.61

)0.

6co

hort

s12

Pat

erso

nI

a17

935

5.9

0.63

650.

073

0.12

(0.0

68,0

.19)

13a

SA

amos

item

ines

Ma

41

305.

7a

0.93

23.6

0.05

60.

060

(0.0

16,0

.015

)T

otal

–am

osite

coho

rts

2110

0.10

(0.0

62,0

.15)

0.2

4O

ntar

ioC

yo17

862

.20.

7760

0.46

0.59

(0.3

4,0.

9)15

Alb

inC

yao

130

493.

31

130.

20.

2(0

.11,

0.35

)7

Voc

klab

ruck

Cyo

51

530.

21.

0525

0.03

80.

036

(0.0

12,0

.084

)8

US

/Can

ada

insu

lato

rsL

yao

453

282

3170

.61.

0950

00.

029

0.02

6(0

.024

,0.0

29)

11P

enns

ylva

nia

TF

ya14

482

1.1

1.08

600.

029

0.02

7(0

.014

,0.0

44)

9R

ochd

ale

Tyo

100

602.

51

740.

022

0.02

2(0

.011

,0.0

41)

17F

erod

oF

yo13

026

46.3

135

0.01

40.

014

(0.0

075,

0.02

4)5o

New

Orle

ans

(pla

nt2,

yo)

Cyo

30

217

b1.

2693

0.01

50.

012

(0.0

024,

0.03

4)5a

New

Orle

ans

(pla

nt1)

Cya

10

294.

50.

8579

0.00

40.

005

(0.0

001,

0.02

8)3

John

sM

anvi

llere

tiree

sI

yao

82

762.

51

750

0.00

10.

001

(0.0

005,

0.00

28)

0.02

1P

,0.

001

2mC

arol

ina

(men

)T

y1

141

0.1

1.34

280.

017

0.01

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.001

6,0.

047)

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alan

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My

20

225.

41.

230

00.

003

0.00

25(0

.000

3,0.

009)

6Q

uebe

cM

y33

059

12.7

160

00.

001

0.00

09(0

.000

6,0.

0013

)2f

Car

olin

a(w

omen

)T

y0

-29

9.2

1.34

260

0(0

,0.0

35)

5yN

ewO

rlean

s(p

lant

2,y)

Cy

0-

397.

1b

1.26

220

0(0

,0.0

33)

16C

onne

ctic

utF

y0

-55

0.7

0.93

460

0(0

,0.0

16)

Poo

led

chry

sotil

ees

timat

esT

otal

370

0.00

10(0

.000

7,0.

0014

)0.

11–

excl

udin

gC

arol

ina

350

0.00

10(0

.000

7,0.

0013

)0.

69m

en–

excl

udin

gm

ines

700

0.00

33(0

.000

6,0.

010)

0.14

a Red

uced

bya

fact

orof

0.67

toex

clud

eex

pect

edde

aths

less

than

10yr

from

first

expo

sure

(see

App

endi

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).bE

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ted

all

caus

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orta

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inpl

ant

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.


Fig. 2. Exposure-specific mesothelioma mortality (RM) 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.

two cases seen in among men in the Carolina cohortproduce an estimate, with wide confidence limits, of0.013—about an order of magnitude higher than forthe mines cohorts. The very wide confidence limitsfor the three cohorts where no cases were observedare statistically consistent with either end of thisrange. Indeed there is no significant heterogeneitybetween RM estimates in the chrysotile group,although the total shows some tendency to heterogen-eity (P=0.11). If the mines cohorts are excluded, thecentral combined estimate ofRM increases to 0.0033,but with wide confidence limits (0.0006–0.01) andwith a similar level of heterogeneity (P=0.14). Withthe Carolina men excluded, the remaining data arecoherent (P for heterogeneity=0.69), and the meanestimate ofRM is 0.001 (95% CI 0.0007 to 0.0013)No summary estimate ofRM has been calculated forthe mixed fibre cohorts, since these are so clearly stat-istically heterogeneous. This heterogeneity is plausi-bly explicable by variations in the mix of fibresencountered. The estimates from the pure fibrecohorts suggest a difference in potency approachingtwo orders of magnitude between chrysotile andamosite, and a further five-fold difference betweenamosite and crocidolite. If these gross differences areeven approximately correct, quite small variations inthe fibre mix in the cohorts exposed to several fibretypes 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. Thiswill be particularly true where the history of usage ofdifferent fibre types has varied over time.

Lung cancerThe summary data for lung cancer is shown in

Table 2 and Fig. 3. The pure fibre groupings are lesscoherent forRL than forRM, although the general pic-ture is similar, with higher values for the amphibolecohorts, lower values for most of the chrysotilecohorts and intermediate values for the mixedexposure groups. The Carolina cohort is the one clearexception to this pattern. The mean estimate for thethree crocidolite cohorts is 4.2% per f/ml.yr (95% CI2.8–5.8). The two amosite cohorts give somewhat dif-ferent results, and despite the wide confidence limitson the South African data they are not statisticallyconsistent (P=0.022). Their joint mean is 5.2% perf/ml.yr (95% CI 4.0–6.5). The five amphibole cohortstaken together are also not a statistically consistentgroup (P=0.027), with a joint mean of 4.8% perf/ml.yr (95% CI 3.9–5.8). The heterogeneity is mainlydue to the SA amosite cohort, and if this is set asidethe remaining four amphibole cohorts are just statisti-cally consistent (P=0.072) with a joint mean of 5.1%per f/ml.yr (95% CI 4.1–6.2). If the exposure estimatefor Wittenoom is increased by a factor of 4, the sum-mary value ofRL falls to 2 for the combined amphi-bole cohorts and to 1.1 for the three crocidolite


Tab

le2.

Sum

mar

yof

lung

canc

erm

orta

lity

data

and

expo

sure

-spe

cific

risk

estim

ates

Coh

ort

Coh

ort

nam

eP

roce

ssF

ibre

Lung

canc

erde

aths

Ave

rage

Lung

canc

erris

k(%P-v

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Fig. 3. Exposure-specific excess lung cancer mortality (RL) 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 (P,0.001).

Among the mixed cohorts, two stand out with parti-cularly high values (Ontario and Albin). Both areasbestos cement cohorts, and both also had high lev-els of mesothelioma mortality. The values forRL forthese two cohorts are both more than six times thelevel of the next highest observation.

The heterogeneity among the mixed fibre cohortsis driven principally by three of them: Ontario,US/Canada Insulators and the Johns Manvilleretirees. Other reviewers (Doll and Peto, 1985;Hughes and Weill, 1986), have remarked on theunusually high risk estimate implied by the Ontariocohort and have suggested that the exposure estimatesfor this group may have been underestimated.Another potential contribution to the high risk of lungcancer in this cohort is exposure to silica: 8 out of26 workers with post mortem examinations showedsigns of silicosis (Finkelstein and Vingilis, 1984).There are clearly considerable uncertainties in theestimation of average exposures for the US/CanadaInsulators cohort, since this is averaged over a verylarge cohort with no doubt very variable exposureexperiences and over a long time period. The size ofthis group means that the value adopted for it willdetermine statistically the average risk in this group.The study of retirees from the Johns Manville asbes-tos products company is unusual in basing its esti-mates exclusively on follow-up of retired individuals

from the age of 65. There is no obvious theoreticalreason why this should produce a seriously biasedestimate of risk, though asbestos related mortality atages below 65 will be missed. This cohort has beenfollowed up almost to extinction, and if the impactof asbestos exposure on mortality eventually declinesafter the cessation of exposure, then cohorts with nearcomplete lifetime follow up will tend to show ratherlower excess mortalities than those where survivorsform a substantial proportion of the cohort. Inaddition, the Johns Manville cohort was one wherethe authors had not suggested a conversion factorfrom particles to fibres, and this review has used themost commonly used value of 3 f/ml=1 mppcf. If thisconversion implies higher exposure than in fact tookplace (the recent review by Lashet al. (1997), useda value of 1.4 borrowed from the New Orleanscohort), then the risk coefficient implied here wouldbe too low. If these three cohorts are excluded fromthe group the remaining eight are just statisticallyconsistent (P=0.056), and their joint mean is 0.32(95% CI 0.16–0.50).

The six chrysotile cohorts fall into two groups: thetwo Carolina cohorts give values around 6% perf/ml.yr; the other four, including the two minescohorts and dominated by the large Quebec cohort,are consistent with a jointRL estimate of 0.06% perf/ml.yr (95% CI 0.043–0.079). The Connecticut andNew Orleans (chrysotile only) cohorts give centralestimates ofRL substantially above this value, (0.80


and 1.3 respectively) but both confidence intervals arevery wide. Even if the mines cohorts are excludedthere is still very clear statistical inconsistencybetween the Carolina results and those from Con-necticut and New Orleans (P=0.0013). The Carolinaresults are also out of line with the two other (mixedfibre) textile cohorts—Rochdale and Pennsylvania—whose 95% confidence intervals forRL have no over-lap with those for Carolina.

RISK ASSESSMENT AT MODERATE AND HIGHERCUMULATIVE EXPOSURES

MesotheliomaThe quantified risk for mesothelioma at the kinds

of cumulative exposure levels recorded in thereviewed cohorts—say, from 10 f/ml.yr upwards—presents a reasonably coherent picture, with values ofRM, in round figures, of 0.5, 0.1 and 0.001 (at most0.003) for crocidolite, amosite and chrysotile respect-ively (see Fig. 2).

Lung cancerIt 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 chrysotilecohorts (see Fig. 3). The amphibole estimates arereasonably consistent. In round figures the estimatesfall in the range 2–10% per f/ml.yr. The mean for thecrocidolite group is rather lower (4.2) than that forthe 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), butwith some evidence of heterogeneity (P=0.027). If theSA amosite cohort data are set aside, the remainingdata are reasonably consistent (P=0.072), and themean estimate becomes 5.1 (95%CI 4.1–6.2). Inround figures, a value of 5% per f/ml.yr would rep-resent a reasonable risk estimate for both amphibolefibre types.

The pure chrysotile cohorts produce estimates ofRL spanning two orders of magnitude, from a valueof 6.7 for the Carolina women to 0.03 for Balengeromine. How should this very wide range ofRL esti-mates be interpreted? As far as evidence from ‘pure’exposure goes there are only two strongly informativecohorts: Quebec and Carolina. The differencesbetween these two has been studied and discussedextensively but, finally, inconclusively. The hypoth-esis that mineral oil used to suppress dust in the Caro-lina plant may have contributed to the lung cancerexcess has been addressed by an internal case-controlanalysis of this factor reported by Dementet al.(1994) and Dement (1991)). The most recent report(Dementet al., 1994), shows that the odds ratios fordifferent cumulative asbestos exposure categories areessentially unchanged by the addition of a variablerepresenting subjects’ typical level of exposure to

mineral oil (slight, moderate, high). The coefficientsfor these categories in the joint model were notreported, but were as follows, expressed as oddsratios relative to ‘slight’ exposure:

Mineral oil Odds ratio 95%exposure Confidence

intervalModerate 1.12 0.57–2.21High 1.47 0.8–2.75(Dement, personal communication)

Although these ORs are not statistically significant(and do not form a statistically significant trend),there is some suggestion that mineral oil may have arole in enhancing the asbestos effect, particularlysince all the effect of exposureduration is absorbedin the asbestos measure (workers were assigned tooil exposure categories according to the assessed oilexposure level at which they had spent the longestproportion of their employment in the plant). Earlyresults from this case control study showed a crosstabulation of cases and controls by asbestos exposureand mineral oil category (Dement, 1991), without for-mal modelling. Crude odds ratios on this data suggestthat the asbestos response is progressively steeperwith increasing mineral oil category. If mineral oildoes have an enhancing effect, the anomalousincrease in estimated exposure specific lung cancerrisk for men in the Rochdale cohort first exposed after1950 could be explained, since dust suppression usingmineral oil was introduced from that date (Petoet al.,1985). The regression slope estimate ofRL for themen first exposed after 1950 is 1.3 (95%CI 0.37–2.6),three times the value for men first exposed between1930 and 1950.

The plausible suggestion that the longer fibre usedin textile processes are responsible seems to be con-tradicted by the comparative analyses of lung fibreburdens in Quebec and Carolina cohorts reported bySebastien et al. (1989). They found that the pro-portionate distribution of fibres by length was verysimilar in Quebec and Carolina lungs. Nevertheless,the notion that the longer fibres used in textile pro-cesses do represent a higher risk, is consistent withexperimental evidence that longer fibres are more car-cinogenic (Meldrum, 1996; Stantonet al., 1981;Miller et al., 1999). Greenet al. (1997) have shownthat the mean length and aspect ratio of chrysotilefibres in the lungs of Carolina workers are greaterthan in a local population control series; and than inthe lungs of workers from the Albin cohort (Albinetal., 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 atmuch lower levels than for chrysotile and its associa-ted tremolite. Se´bastienet al. (1989 report that amphi-bole fibres at concentrations >0.1 f/µg (fibres >5microns long) were only found in the lungs of work-


ers hired before 1940, which conflicts with the periodof known use of crocidolite yarn (in very small quan-tities—see Appendix A) in the plant after 1950. Thisraises the possibility that some amphibole formed partof the exposure mix in this cohort in an early period.Greenet al. (1997) show that the levels of amphiboleare higher in Carolina workers than in local controls(2-fold difference in geometric mean,P=0.031) butmuch less strikingly than for chrysotile (5-fold,P,0.0001) or tremolite (14-fold,P,0.0001). Theyalso report that amphibole at levels >1.0 f/µg (all fibrelengths) were found in only one of the ten lung cancercases for whom this datum was available. This lastobservation limits the extent to which amphiboleexposure—perhaps unrecognised—might play a rolein this cohort. Whatever mechanism is in play doesnot appear to apply—to the same extent, at least—to the other two textile cohorts reviewed. As alreadypointed out, the Pennsylvania and Rochdale cohorts(with mixed fibre exposures) both give substantiallylower estimates ofRL.

If it is accepted that some such feature of the pro-cessing in the Carolina cohort has genuinely produceda much higher risk than seen in other chrysotilecohorts the question can be asked how typical thesefeatures are of the bulk of applications? Looked at inthe wider context of cohorts with mixed fibreexposure, theRL value for Carolina looks untypicallyhigh. Setting aside the possibility that amphibolepresents a higher risk of lung cancer, the observationsof RL from mixed fibre cohorts can be taken asinformative of theRL level for chrysotile. This sug-gests that in typical applications (including other tex-tile processes)RL for chrysotile is generally lowerthan the value derived from the Carolina cohort. ThemedianRL for the 16 cohorts with some chrysotileexposure is 0.5, compared to 4.5 for Carolina menand 6.7 for Carolina women. All but two of the mixedfibre cohorts give anRL estimate less than 1, and ofthe two exceptions one (Albin) has a confidence limitincluding zero, and the other (Ontario) shows featuressuggestive of significant exposure to crocidolite (seebelow, Fig. 4 and related text).

To the extent that amphibole fibres make a dispro-portionate contribution to the lung cancer risk in themixed exposure cohorts—and the evidence presentedhere suggests that they do—the typical risk of lungcancer from chrysotile exposure would be even lower.In most circumstances a value of 0.5% per f/ml.yrshould probably be regarded as an upper limit to thelung cancer risk from pure (commercial) chrysotile.The meanRL 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.yris not as far out of line with the Carolina observationsas it might seem. The ‘cohort average’ risk estimatefrom this cohort (6.7 for women, 4.7 for men) prob-

Fig. 4. Comparison of excess mortality from pleural and perito-neal mesothelioma, showing fibre type.

ably overestimates the risk, which from internalanalysis is 1 for women and 3 for men (Dementetal., 1994, p. 439). The exposure response regressionson this cohort give an intercept close to zero excessrisk 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 isalso the possibility of inaccuracies in the conversionof particle counts to fibre counts. One early report onthis cohort (McDonaldet al., 1983a) suggested thatthe average conversion factor should be about 6 f/mlto 1 mppcf. If this were true, the risk per f/ml.yrwould be halved.

A ‘best estimate’ of the lung cancer risk would belower than 0.5% per f/ml.yr. Noting that the meanrisk of the mixed fibre cohorts (excluding the threementioned above) is 0.32% per f/ml.yr, and that theamphibole risk is over 10 times higher, it is possiblethat virtually all the observed risk could be explainedby rather less than 10% of amphibole in the mixedexposures. However there is no direct evidence onwhich an estimate of the risk of ‘pure’ chrysotilecould be based. Apart from the Balangero cohort, allthe chrysotile evidence considered here effectivelyrelates to Canadian chrysotile, since this was thedominant source of fibre for the other chrysotilecohorts. 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 producesome additional risk, the best estimate should be sethigher than the mines level, say at 0.1% per f/ml.yr.The overall risk, of a mixture of 96% chrysotile witha risk of 0.1, and 4% amphibole with a risk of 5.1would be 0.3% per f/ml.yr.

EXTRAPOLATION TO LOW EXPOSURES

All these cohort observations reflect the effect ofexposure to high levels of asbestos. The main interest


in quantitative risk assessment in current conditionsis to apply this evidence to the estimation of the risksassociated with exposure levels 100–1000 timeslower. The standard assumption is that, other thingsbeing equal, the risk will be proportional to dose; butthis is more a cautious default assumption than any-thing more soundly based. To quote from the HEIreview: “The assumption of dose-linearity for low-dose assessment purposes is thus a widely acceptedand scientifically reasonable compromise rather thanan established scientific principle of carcinogenesis”.

However, if the true relationship between exposureand response was not linear, the impact on low doseextrapolations could be dramatic. There is some indi-cation in the present data suggesting a non-linearexposure response, particularly for peritoneal meso-thelioma, and the next sections examine this question.

Relationship of pleural and peritoneal mesotheliomaFigure 4 plots the percentage excess mortality from

peritoneal mesothelioma against that from pleuralmesothelioma. Cohorts with no mesothelioma casesof either kind are excluded. Cohorts with no perito-neal mesotheliomas are plotted on the peritoneal scaleon or close to the 0.01 ordinate. The positioning ofthe cohort points strongly suggests a pattern of twoalignments, one defined by the pure crocidolitecohorts, the other by the two pure amosite cohorts.Four mixed exposure cohorts lie very close to theamosite line: the US/Canada Insulators, New Orleansplant 1, the Johns Manville retirees and the Albincohorts. All but the last of these clearly had amositeas the main amphibole fibre. The point representingthe Ontario cohort lies very close to the crocidoliteline, suggesting perhaps that the anomalous resultsfrom this cohort may be explained by underestimatedexposure to crocidolite.

The position of the (male) Carolina cohort seemssomewhat anomalous. The single peritoneal meso-thelioma in this group is the only one in a cohortwithout material amphibole exposure, and the equal-ity between pleural and peritoneal numbers (one ofeach) is only otherwise seen in cohorts with muchhigher levels of mesothelioma (and substantial amphi-bole exposure). The possibility of unrecognisedamphibole exposure again suggests itself, but toomuch should not be read into this single peritonealcase. It is clear that the three fibre types produce dif-ferent mesothelioma responses overall. The questionof differential responses by mesothelioma site canreally only be addressed for the amphibole fibres.

This relationship does not depend on quantifiedexposure data, and if it is real it should be reproducedin other cohorts with predominant amphiboleexposure. The most informative cohorts will be thosewith crocidolite or amosite exposure, but not both.A Medline search identified eight such cohorts. Therelevant data are summarised in Table 3, and a plotof the percent excess mortalities from these cohorts

(and the pure fibre quantified cohorts) is shown inFig. 5.

There is still an apparent separation between cro-cidolite and amosite cohorts, though the segregationis now less clear cut (as might be expected given thesmall numbers often involved). There is, of courseconsiderable statistical uncertainty in both of thesevariables, and a simple regression (in which uncer-tainty about ‘x’ values is ignored) would be mislead-ing. Table 4 summarises the results of regressions inwhich the fit is optimised in both variables simul-taneously (fit being measured by deviance, assumingPoisson variation for the numbers of mesotheliomasat each site).

Fitting a single line through all the data producesa line with a slope (on the log–log scale) of 1.2, butthe overall fit is unsatisfactory (P,0.001). Allowingthe two fibres to have separate fits makes a very sig-nificant improvement to the fit (P,0.001), and bothfits have steeper slopes (2.3 for crocidolite and 3.1for amosite — not shown in table). These slopes arenot very precisely determined, and constraining themto be equal does not materially degrade the fit(P=0.75). The central estimate for this common slopeis 2.4.

This model provides a very close statistical fit toall but two of the cohorts. The two exceptions are thegas mask cohorts in Canada (McDonald and McDon-ald, 1978) and in Leyland (Achesonet al., 1982),which contribute 6.1 and 4.5 respectively to the totaldeviance. Possible reasons for these cohorts to beuntypical can be identified. The Leyland cohort wasnot ascertained from employment records, but fromoccupational details recorded on the wartime popu-lation register compiled in September 1939. If thenumbers directly involved with gas mask assemblyhave been over estimated the percentage excess mor-talities will be proportionately under estimated. If, forexample, only 2/3rds of the identified women werein fact exposed, the expected mortality denominatorwould fall to around 120, and the residual falls from6.1 to 4.3—still an outlier, but materially less extreme(P=0.038 instead of 0.014). The overall excess mor-tality from mesothelioma recorded in the Leylandcohort is much lower than in the Nottingham cohortengaged on the same process: 2.7% at Leyland and16.5% at Nottingham, again suggesting the possibilityof underestimation (eg by dilution of the exposedpopulation), perhaps substantial.

The assessment of mesothelioma in the Canadiangas mask cohort was particularly exhaustive, involv-ing review of pathological data for all cancer cases.Three of the six peritoneal cases were only identifiedafter this review. If the number of peritoneal meso-theliomas is reduced by three, the residual for thiscohort falls from 4.5 (P=0.034) to 2.0 (P=0.16).

However these are post-hoc rationalisations, and itis not clear whether it is better to remove thesecohorts from the model or not. Despite the large


Table 3. Additional data on pleural and peritoneal mesothelioma from cohorts with predominant exposure to crocidoliteor amosite (but not both), and without reported quantified cumulative exposures

Cohort Process Fibre Sex Expected all Pleural Peritonealcause

moralityNo. Reference No. % Excess No. % Excessmortality mortality

18 Joneset al. (1996) o f 400a 53 13 14 3.519 Achesonet al. (1982) Gas masks o f 185 3 1.6 2 1.1

(Leyland group)20 McDonald and oy mf 41a 3 7.3 6 14.6

McDonald (1978)21 Hilt et al. (1981) O o m 5b 1 20 1 2022 Levin et al. (1998) I a m 133.6 4 3 2 1.523 Parolariet al. (1987) I a mf 115.1 2 1.7 1 0.8724 Finkelstein (1989) I a m 1.89 2 10625 Achesonet al. (1984) I ay m 298.8 4 1.3 1 0.33

aEstimated as observed deaths less asbestos related deaths.bEstimated assuming 25% mortality from age 31 to 68.

Fig. 5. Joint distribution of excess mortality from pleural andperitoneal mesothelioma, showing fibre type. [Note: Label size(area) roughly proportion to total mesothelioma numbers in

each cohort].

residuals for these two cohorts, the overall residualdeviance for the inclusive data (model 2) indicates asatisfactory fit (P=0.22). If the two outliers areremoved, the separate fibre model fits the data almostexactly, and the slopes for the two fibres are verysimilar (model 3) and higher (around 3.2) than the2.4 for the fit including them. In either case the singleline model is rejected in favour of separate fits to thetwo fibre types, with similar slopes. The peritonealrate is proportional to at least the square—perhaps asmuch as the cube—of the pleural rate.

The form of the relationship is unusual and some-what surprising, since both outcomes reflect the effectof the same carcinogenic insult to the same type of

tissue. If true, it is presumably related to the dynamicscontrolling the distribution of asbestos fibres aroundthe body. Note that this relationship does not dependon 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.

Pleural mesothelioma and cumulative exposureTo examine this question more closely, Fig. 6

shows a plot of excess mortality from pleural meso-thelioma against cumulative exposure with cohortsrepresented by their fibre type code. Figure 7 showsa similar plot for peritoneal mesothelioma. The pointsfor the pure amphibole cohorts show a clear patternof 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 Poissonregression fits to the relationship between percentageexcess mortality from pleural cancer and cumulativeexposure, and the observed data points and selectedregression lines are shown in Fig. 6. The relationshipis modelled as linear on a log scale for each variable,and therefore has the formPpl = AplXr where Ppl isthe percent excess mortality from pleural cancer,Xis cumulative exposure andApl and r are regressionparameters. The corresponding predicted number ofpleural cancers for a given cohort isAplXrEAdj/100(whereEAdj is expected all cause deaths adjusted toan age at exposure of 30). The parameters were esti-mated by minimising the residual deviance betweenthe observed and predicted numbers of pleural cancerfor each (pure fibre) cohort.

It is clear that a wide range of slopes (r) are statisti-cally consistent with the data. With independent fitsto each fibre type the slopes are 0.62, 1.2 and 0.72for crocidolite, amosite and chrysotile respectively.


Table 4. Joint Poisson regression (structure model) of relationship between pleural and peritoneal mesotheliomas(%peritoneal=A.%pleuralb)

Model A b Residual deviance Degrees of Pfreedom

1. All data 0.21 1.2 31.6 11 ,0.0012. By fibre, common slopeo 0.0089 2.4 12.0 6 0.06a 0.26 2.4 1.1 5 0.95Overall 13.1 10 0.223. Fit excluding Leyland and Canadian gasmask dataBy fibreo 0.00074 3.3 0.1 3 0.98a 0.17 3.1 1.0 4 0.91Overall 1.1 7 0.99

Fig. 6. Excess mortality from pleural mesothelioma againstcumulative 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 totalresidual deviance is 3.93. Moving to a model in whichthe three slopes are constrained to be equal, theresidual deviance increases marginally to 4.53, anincrease of 0.6 with a corresponding increase of 2degrees of freedom (df), clearly not a statistically sig-nificant change in overall fit (P=0.74), nor for anyindividual fibre type. The best fitting common slope is0.75. 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 exposureFigure 7 and Table 6 show similar regression

analyses for peritoneal cancer. Again the crocidoliteand amosite points align themselves on two parallellines. The small numbers of observed events meansthat the statistical uncertainties are quite wide. There

Fig. 7. Excess mortality from peritoneal mesothelioma againstcumulative exposure, showing fibre type. Regression lines fit-ted to pure fibre cohorts. Bold lines indicate fits with slopeconstrained to be common across fibre types, narrow lines are

unconstrained fits (the slopes are identical for crocidolite).

is very little difference between the slopes (t) for thetwo fibres, and the best common slope is 2.1, witha deviance based 95% confidence interval from 1.2to 2.9.

The single peritoneal mesothelioma among theCarolina men, together with zero cases in the otherchrysotile cohorts generates a negative value oft. Ifa common slope is imposed over all three fibres thebest estimate is 1.6, but with significant heterogeneity(P=0.0025—data not shown). Only the amphibolecohorts have enough data to draw valid conclusionson peritoneal mesotheliomas.

The comparison of pleural and peritoneal slopesindependent of exposure levels suggested a ratio ofslopes between 2.4 and 3.2. If the ratio of the esti-


Table 5. Possion regression of pleural cancer against cumulative exposure by fibre type

Fit/fibre type Apl r 95% CI for r Residual Degrees of Pdeviance freedom

1. Independent fitso 1.4 0.62 (20.54, 1.43) 0.25 1 0.62a 0.02 1.2 (20.32, 3.5) 0y 0.0057 0.72 (0.17, 1.79) 3.68 4 0.45Overall 3.93 5 0.562. Best common slopeo 0.93 0.36 2 0.84a 0.13 0.75 (0.27,1.3) 0.49 1 0.48y 0.0047 3.68 6 0.72Overall 4.53 7 0.723. Common slope, amphiboles onlyo 0.88 0.39 2 0.82a 0.120 0.77 (20.069, 1.62) 0.44 1 0.51Overall 0.83 2 0.66

Table 6. Possion regression of peritoneal cancer against cumulative exposure by fibre type

Fit/fibre type Apr t 95% CI for t Residual Degrees of Pdeviance freedom

1. Independent fitso 0.0022 2.1 (0.93, 2.9) 0.10 1 0.75a 0.00018 2.4 (0.41,6.4)y 1.4 21.7 (222, 0.91) 2.60 4 0.63Overall 2.70 5 0.752. Common slope, amphiboles onlyo 0.0022 0.10 2 0.95

2.1 (1.2,2.9) 1 0.76a 0.0006 0.09 2 0.91Overall 0.19

mates of the peritoneal and pleural slopes is con-strained to be 2.4, the best fit pleural and peritonealslopes are : 0.86 (95%CI 0.51–1.15) and 2.1 (95%CI1.2–3.6). If the ratio of slopes is constrained to be3.2, the estimated values arer=0.67 (95%CI 0.40–0.90) andt=2.1 (95%CI 1.3–2.9).

Support for a convex (r,1) increase of pleuralmesothelioma risk with exposure can be found in thedetailed dose-specific analyses of the Wittenoommesotheliomas by Berry (1991). Most of these cases(62 of 72) were pleural. Figure 8 plots the constantterms in the four exposure categories of Berry’sanalysis against their mean cumulative exposure. Theslope is very close to 0.5. In addition, Coggonet al.(1995), concluded from a comparison of the rankingof occupations by mortality from pleural and perito-neal cancers and from asbestosis that “a more plaus-ible explanation [of the different rankings] is that theexposure response relations for mesothelioma andasbestosis are non-linear, with the risk of pleuralmesothelioma rising relatively more steeply at lowexposures, but less steeply at high exposures”.

A non-linear relationship between exposure and therates of pleural and peritoneal mesothelioma meansthat the percent excess mortality per f/ml.yr (RM) will

Fig. 8. Scaling constant in the four exposure groups of Berry(1991) analysis of the Wittenoom crocidolite cohort, plottedagainst 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 formesothelioma at the two sites considered individu-ally. Each additional unit of exposure will add—pro-gressively—less risk for pleural tumours, and morefor peritoneal tumours. The point at which the absol-


Fig. 9. Percent excess lung cancer by cumulative exposure, showing fibre type, with regression lines fitted to pure fibre cohorts(A: combined amphibole data, (1) slope free, (2) slope fixed=1; Y: chrysotile data, (1) all data, slope free (2) excl. Carolina,

slope free, (3) excl. Carolina, slope fixed=1).

ute risks for tumours at the two sites are predicted tobe equal is around 90f/ml.yr for crocidolite, around55f/ml.yr for amosite. Below these values pleuraltumours are more common, and at higher levels per-itoneal tumours dominate. It happens that across thescale of cumulative exposure values in the reviewedcohorts (from about 10 to nearly 1000 f/ml.yr), therelationship between exposure and total mesotheli-oma risk is not far from linear, so the summary indexRM does provide a reasonable index of the overallmesothelioma risk over this range.

Lung cancerIf 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 excesslung cancer against cumulative exposure and Table7 summarises regression results for lung cancer by

Table 7. Poisson regression of lung cancer against cumulative exposure by fibre type

Fit/fibre type AL r 95% CI for r Residual Degrees Pdeviance of

freedom

Combined amphibole0.49 1.6 (1.1, 2.1) 2.35 3 0.50

%excluding Massachusetts and SA amosite:1.1 1.4 (0.89, 2.0) 0.83 1 0.36

Chrysotile%excluding Carolina 195 20.27 (20.44, 20.07) 19.8 4 ,0.00127.5 0.030 (20.26, 1.1) 0.91 2 0.63

cumulative exposure. There is no significant differ-ence between the regressions for crocidolite andamosite points, so these are treated together. Using allthe data, independent fits for amphibole fibres givesa concave relationship (r=1.6), and for chrysotile anegative slope (r=20.25). These are clearly inconsist-ent with each other, and both depart very significantlyfrom linearity (P,0.001).

The negative slope for chrysotile depends entirelyon the Carolina data, and if this is removed the slopeis just positive (r=0.039) with a CI that just includes1. Clearly the data for chrysotile-only cohorts do notprovide a coherent basis for direct estimation of theexposure–response slope, and some appeal to the evi-dence provided by cohorts with mixed exposure isnecessary (as in the discussion of Table 2 and Fig. 3).

The concave slope for amphibole cohorts is largelydependant on the two extreme points, the Massachu-setts and SA amosite cohorts. The lung cancer excessin the SA amosite cohort is quite small and statisti-


cally unstable, and the exposure estimate for the Mas-sachusetts cohort is based on fairly slender evidence.If these two cohorts are removed the best fit slopebecomes 1.4, with a confidence interval thatincludes 1.

The Massachusetts cohort with its very high levelsof excess mortality, and as cohort with the highestestimated mean exposure to crocidolite, has animportant—though not determining—impact on theestimates. It is unfortunate that the exposure estimatesare somewhat speculative (see Appendix B). At thesame time it should be noted that in relation to a priorexpectation of a linear dose response the effects ofthis observation on the pleural and lung cancer esti-mates are opposite: the pleural slope is flattened andthe lung slope is steepened. This does not of courseprove that the exposure estimate is correct, but if itis materially in error then either the pleural or thelung slope is even further from linear than suggestedby the present analyses.

DEVELOPMENT OF NON-LINEAR RISK ESTIMATES

MesotheliomaThe data in Tables 5 and 6 and Figs. 6 and 7 sug-

gest the following model with separate componentsfor pleural and peritoneal tumours:

PM = AplXr + AprXt

wherePM is the percent excess mortality,r and t arethe pleural and peritoneal slopes of the exposureresponse on a log–log scale,Apl andApr are constantsof proportionality for the pleural and peritonealelements of the risk respectively, andX is cumulativeexposure in f/ml.yr.

If the information about the ratio ofr and t fromthe non-quantified cohorts is ignored, the best fitvalues using all the data arer=0.75 andt=2.1. With-out the chrysotile data, the estimate ofr is essentiallythe same (0.77). Analysis of the ratiot/r including thenon-quantified cohorts (Table 4) indicates values forthis ratio around 2.4 with all the data, around 3.2excluding the two outlying cohorts. If a simultaneousfit is made to the full data with the ratio of pleuraland peritoneal slopes fixed at 2.4, the resulting esti-mates (using only the amphibole data) arer=0.86 andt=2.1. If the ratio of slopes is constrained to be 3.2,the estimated values arer=0.67 andt=2.1.

There is little to choose between values ofr from0.67 to 0.86. We will use a slope of 0.75 as our bestestimate forr. The estimates fort are less variable,and in any case have no bearing on risk estimates atlow levels. We will taket=2.1 as the best estimate.

How wide a margin of uncertainty should beallowed 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 onphysical grounds. Berry’s analysis of Wittenoom data

using individual doses implies a slope of about 0.5,but the uncertainties of individual dose assignmentare likely to have biased this estimate downwards.The argument above suggests that the lower end ofrange should be set at 0.67 or lower. We will take0.6 to represent the lower end of the plausible sloperange.

There are quite stronga priori reasons for using aslope 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 absenceof evidence to the contrary. A linear relationship isalso (in most models) consistent with the data. Wetherefore taker=1 as the upper end of the slope range.Different slopes imply different best fit values forApl

and Apr. These estimates and their 95% confidenceintervals for the three fibre types are shown inTable 8.

Effects of exposure duration and age at first exposureThis formulation does not take duration of

exposure or age at first exposure into account. TheHEI (and similar) risk models (see Appendix A)imply that for equivalent cumulative exposures, shortexposure times produce larger risks than longexposure times, (in other words 10 f/ml for 1 yr isworse than 1 f/ml for 10 yr); and that exposure atyounger ages will produce higher excess mortalityrates. All the amphibole cohorts considered here hadshort exposures (averaging about 2 yr). The suggestedrisk model for amphiboles is therefore appropriate forshort exposures, but will overstate the risk fromextended exposure periods. The chrysotile coef-ficients are effectively determined by the Quebeccohort, where the average exposure durations werequite 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 sameexposure accrued over 10 yr. For general risk assess-ment purposes, where short exposures are more likelyto be at issue, the chrysotile coefficient should beincreased by a factor of 1.4. Reductions in theexposure accrual time below 2 yr have very littleimpact on the risk.

The risk estimates summarised above apply toexposure starting at age 30. Table 9 shows adjustmentfactors derived from the HEI model to convert riskestimates for an age at exposure of 30 to otherexposure ages.

Predicted effects at very long follow upIt can reasonably be questioned whether a given

asbestos exposure will continue to generate a constantexcess mesothelioma mortality beyond 40 or 50 yrfollow up. The evidence from cohorts with long fol-low up is that the incidence eventually falls. In thePaterson cohort a significant fall is seen for followup beyond 35 yr. In the US/Canada insulators there


Table 8. Estimated coefficientsa with 95% confidence intervals for constants in the risk prediction equation forPM atthree levels of the slope coefficientr

Slope/Fibre Apl 95% CI Apr 95% CI

Best estimate slope (r=0.75, t=2.1)Crocidolite 0.94a (0.71,1.2) 0.0022 (0.0011,0.0039)Amosite 0.13b (0.060,0.25) 0.0006 (0.00025,0.0012)

Chrysotile 0.0047a (0.0030,0.0069)High slope (r=1, t=2.5)

Crocidolite 0.43 (0.33b,0.54) 0.00053 (0.00029,0.00087)Amosite 0.052 (0.022b,0.099) 0.00012 (0.000049,0.00024)

Chrysotile 0.000970 (0.00064b,0.0014)Low slope (r=0.6, t=1.7)

Crocidolite 1.5 (1.1,1.9c) 0.0083 (0.0043,0.014)Amosite 0.24 (0.11,0.44c) 0.003 (0.0013,0.0058)

Chrysotile 0.012 (0.0078,0.018c)

aCoefficients used for risk extrapolation at low doses shown in bold:abest estimate,blowest arguable,chighest arguable (see Table 11). Numbers of peritoneal mesotheliomas at low doses arenegligible. 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 20 25 35 40Factor 2.1 1.5 0.6 0.4

Table 10. Estimated coefficients with 95% confidenceintervals for constants in the risk prediction equation for

PL for chosen levels of the slope coefficientr

Fibre/model AL 95% CI

AmphiboleLinear (r-1) 4.8 –a

Best (r=1.3) 1.6 (1.2, 1.9)Steepest 0.49 (0.37,0.62)(r=1.6)

Chrysotileb

Best (r=1.3) 0.028 –b

Cautious model-max of:Linear (r=1) 0.5 –b

Steepest 0.039 –b

(r=1.6)

aA linear model is not strictly statistically consistent withthe observed data. The line withAL=4.8 is the single best fit.bNon-statistical uncertainties dominate choice of chrysotilemodels, 95% confidence intervals cannot be properly calcu-lated. See text for discussion.

is a fall beyond 50 yr. Qualitatively it seems clearthat the risk does not increase indefinitely, but thereis insufficient evidence on very long follow up to fixthe risk profile in this period. A rough and ready wayof limiting the predicted risk at very long follow upperiods is to truncate the predictions at some age. TheDoll and Peto and HEI reports both truncated theirpredictions at age 80, and we will follow this conven-tion. It is likely that this would still overstate the riskfrom exposure at ages below 20, and truncation ofthe predicted effect at 60 yr follow up might thenbe appropriate.

Lung cancerThe data in Table 7 and Fig. 9 suggest that the

relation between lung cancer and cumulativeexposure may be concave—i.e. that the excess lungcancer risk is proportional to a power greater than 1of cumulative exposure. Statistically the range ofpowers consistent with all the amphibole data is from1.1 to 2.1. Without the two extreme cohorts the rangebecomes 0.89–2.0 with a central estimate of 1.4. Noprevious analysis of the epidemiological data has sug-gested a concave relationship, though experimentaldata for a wide range of carcinogens (Hoel and Port-ier, 1995) suggest they may be quite common. Acrossthe range of exposures in a single study, and giventhe uncertainties in individual exposure estimation, amoderate degree of non-linearity will be difficult todetect.

The reasonably arguable values forr fall in theinterval 1 to 2: a degree on conservatism and somedoubts about the two extreme cohorts lead us to preferthe lower end of this interval. We will taker=1 (alinear relationship) andr=1.6 to represent the flattestand steepest slopes for risk assessment, and the midpoint of this range (r=1.3) as our best estimateassumption.

The estimates and 95% confidence limits for theconstant term AL in a model for lung cancerPL = ALXr with r=1 (linear) 1.3, and 1.6 based onamphibole data are shown in Table 10. As alreadydiscussed, the inconsistencies in the pure chrysotiledata rule out a direct estimate of the exposure–response slope based on this data. The dominantuncertainties for chrysotile are the reasons for theobserved differences in exposure-specific lung cancerrisk, rather than the statistical uncertainties in estimat-ing this risk level. This uncertainty is already reflectedin the five-fold difference between our ‘best’ and‘cautious’ estimates ofRL (0.1 and 0.5 respectively).In the absence of a better approach we will assume


the same range of possible slopes for the chrysotilelung cancer relationship as for the amphiboles, anddetermine the scaling constant by fixing the predictedexcess mortality at the median exposure for chrysotilecohorts (70 f/ml.yr) to 0.1% for the best estimate and0.5% for the cautious estimate. The resulting valuesare 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 thatfor exposure starts between 20 and 40 yr of age thereis very little difference in the predicted risk. Theremay be some decline for very long follow up, but therate of decline is unknown. As for mesothelioma weaddress this possibility approximately by truncatingthe predicted excess at age 80.

IS THERE A THRESHOLD?

Another question with important implications forrisk at low levels of exposure is whether there is athreshold for cancer initiation by asbestos. The HSE’srecentReview of fibre toxicology(Meldrum, 1996),presents arguments mainly on a toxicological basisfor believing that there may be a threshold for asbes-tos induced lung cancer. The argument is essentiallybased on a view of the carcinogenic process inducedby asbestos as being an extension of the chronicinflammatory processes producing fibrosis. It iswidely agreed that heavy doses of chrysotile arerequired to produce lung fibrosis. And some evidencehas been derived from the New Orleans cohort sug-gesting a threshold dose of about 30 f/ml.yr for radio-logical fibrosis (Weill, 1994). Analysis of necropsymaterial from the Carolina cohort also shows a dis-tinct step increase in fibrosis score for cumulativeexposures around 20–30 f/ml.yr (Greenet al., 1997).This does not apply to amphibole exposure: radiologi-cal fibrosis which progressed after the cessation ofexposure has been documented (Sluis-Cremer, 1991),in South African amphibole miners under medicalsurveillance and with cumulative doses less than 5f/ml.yr. This suggests that if a threshold applies to thelung cancer effect of amphibole asbestos, it is verylow. The adoption of a slightly concave exposureresponse slope entails a moderately threshold-likebehaviour.

Several lines of argument also suggest that anythreshold for mesothelioma is at a very low level.Some cohorts (Neuberger and Kundi, 1990; New-house and Sullivan, 1989; McDonald and McDonald,1978; Thomaset al., 1982; Rossiter and Coles, 1980),have produced mesotheliomas in conditions where noexcess lung cancer was seen. Occupational PMRs forBritish men suggest that the range of jobs for whichmesothelioma rates are above background levels isvery wide (Hutchingset al., 1995; Hodgsonet al.,1997). Also the proportion of mesothelioma cases inpopulation studies for whom no likely source of

asbestos exposure can be identified is often quitehigh. All these observations suggest that relativelybrief exposures may carry a low, but non-zero, riskof causing mesothelioma.

Some authors (Ilgren and Browne, 1991; Liddell,1993) have argued for a mesothelioma threshold, orthreshold-like behaviour of the dose–response. Sucharguments are fraught with statistical and logical dif-ficulties. The attempt (Ilgren and Browne, 1991) todeduce a ‘threshold’ by identifying the lowest esti-mated dose received by any observed case is a logicalnonsense. Furthermore, the existence of zero cases ina dose category (human or animal) should not beautomatically interpeted as zero risk. Direct statisticalconfirmation of a threshold from human data is vir-tually impossible. One would need accurate assess-ment of very low doses across a large population withlong term follow up. Case-control studies with lungcontent measures of exposure (McDonaldet al.,1989; Rodelspergeret al., 1999; Rogerset al., 1991)do not suggest any threshold, or downward inflexionof the dose response at the lower end of theirexposure scales. Some of the animal data cited byIlgren and Browne are suggestive of a threshold—particularly that from intra-pleural and intra-perito-neal injection—but it is not clear how this wouldtranslate into a estimated human effect threshold forexposure by inhalation. Taking this evidence togetherwe do not believe there is a good case for assumingany threshold for mesothelioma risk.

QUANTIFIED RISK ASSESSMENT

Under current conditions, the main interest in thehealth risks of asbestos relates to exposure circum-stances well outside the range for which we havedirect observations. The statements we can makeabout risk therefore incorporate two kinds of uncer-tainty. First there is the usual statistical uncertaintyof inferring underlying risk from observations inparticular groups. This kind of uncertainty dependsessentially on the number of events (in this case can-cer deaths) observed. The uncertainty can therefore—given some assumptions—be quantified: the moreobserved events, the less the statistical uncertainty.Statistical uncertainty is expressed as a confidenceinterval (a range of values with—conventionally—a95% probability of covering the true value).

The second kind of uncertainty relates to the ques-tion whether the relationship between exposure andoutcome seen in the observed range continues to holdoutside that range. This kind of uncertainty cannot bequantified statistically. Qualitatively one can reason-ably argue that the agreement will be better forexposures close to the observed range, but withincreasing distance from the observed range our con-fidence that we know what to expect decreases. Forexample, previous assessments of cancer risk fromasbestos have all assumed that the effect is linear.


This review has presented evidence suggesting thatthis may not be the case. Uncertainty about the slopesof exposure–response lines has an increasing impactwith increasing distance from the observed range.Also the strength of qualitative arguments such asthose advanced in the HSE review (Meldrum, 1996),in favour of a threshold for the lung cancer effectincrease as exposure falls.

All the above implies that simply to present a tableof risk estimates—or even risk ranges—for differentcumulative exposures cannot capture the changingbalance of the different kinds of uncertainty. Table11 gives a verbal assessment of risk at a range ofrepresentative cumulative exposures. No estimateshave been given for lifetime risks lower than 1 in100 000, and this level is referred to as ‘insignificant’.A lifetime risk of 1 in 100 000 corresponds to anannual risk well below 1 in a million, which HSE hassuggested (Health and Safety Executive, 1999) as a“guideline for the boundary between the broadlyacceptable and tolerable regions [of fatal risk to anindividual].” It is also well below the level at whichit is suggested that mesothelioma would occur in theabsence of asbestos exposure: a clear majority of thevery few mesotheliomas that would occur at this levelwould not be caused by asbestos.

Mesothelioma risks in the observed cohorts havebeen expressed as a percentage (PM) of total expectedmortality in order to standardise observations fromdifferent follow up configurations. To make predic-tions of risk this measure must be converted back intoabsolute terms, and this is done using the averagemale life table discussed in Appendix A. Forexposures starting at age 30 the excess mortality esti-mate PM is applied to the total expected mortalityfrom age 40 to age 79 (allowing a 10 yr minimumlatency, and truncating risk at age 80). The life tablepredicts that about 70% of survivors to age 30 willdie between the ages of 40 and 80. Absolute risk esti-mates can therefore be derived from thePM value fora 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. Themajor determinant of this underlying lung cancer riskis smoking—especially cigarette smoking—and thenumber of asbestos-related lung cancers will be affec-ted by the prevalence of smoking in the exposedpopulation. Currently (in 1997) about 9.5% of maledeaths between the ages of 40 and 79 are due to lungcancer. For women the figure is 7%, reflecting differ-ences in past smoking. Total survival to age 80 islower in men than in women, and combining data forsurvival and proportionate mortality from lung cancerit can be predicted that for 1000 30-yr-old men 54will die of lung cancer between the ages of 40 and79. For women the number is 28. Thus for a popu-lation with the past smoking habits of British menaged 60+ (the ages at which most lung cancers occur),the lung cancer risk from asbestos exposure is given

by 0.054PL. For women with typical past smokinghabits the figure would be 0.028PL.

Table 11 makes statements about the lifetime risksof exposures accumulated over short (up to 5 yr) per-iods from age 30. The factors given in Table 10 canbe used to apply the mesothelioma estimates to otherages at exposure. The lung cancer estimates are basedon 1997 male lung cancer rates. They are not sensi-tive to age at exposure.

For the lung cancer risk due to chrysotile two prin-cipal figures are given: a best estimate and a cautiousestimate. A risk estimate derived from the Carolinacohort is also given, with the qualification that thismight be arguable in ‘exceptional circumstances’.These exceptional circumstances cannot be definedwith any certainty since the features of exposure atthis plant responsible for the very high lung cancerrisks 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 textileplants have recorded much lower exposure-specificrisk (even with additional exposure to amphibolefibre). The spraying of the raw fibre with mineral oil(as a dust suppression measure) has been suggestedas a possible explanation. This hypothesis seems tobe supported by a case-control study of lung cancersat Carolina (though the relevant results have not beenfully reported), and by observations from anotherasbestos textile plant (Rochdale), where men firstemployed after oil spraying was introduced had threetimes the exposure-specific risk of those firstemployed in earlier periods (though still lower thanthe Carolina risk).

The main uncertainties in this picture relate to theeffects of chrysotile, particularly at low doses. Theapplication of these estimates in the assessment of aparticular risk situation will depend on the purposesof that particular assessment, and the extent to whicha precautionary approach is appropriate.

DISCUSSION

There have been a number of papers (Cullen, 1998;Stayneret al., 1996; Nicholson and Landrigan, 1996;Smith and Wright, 1996), in the literature recentlywhich directly or indirectly consider whether thereare differences in potency between the fibre types ascauses of mesothelioma and lung cancer. The claimthat there are important differences is often describedas ‘the amphibole hypothesis’. In its strongest formthis has been said to claim that pure chrysotile (i.e.without any associated tremolite fibre) would presentlittle or no carcinogenic risk. At the other extreme, ithas been argued (Smith and Wright, 1996), that thereis virtually no difference between the risks presentedby the different fibre types. Most commentators (e.g.Doll and Peto, 1985; Hughes and Weill, 1986; HealthEffects Institute, 1991) have considered that theamphibole fibre types are more dangerous, parti-


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high

est

argu

able

estim

ate

4de

aths

per

100

000

Exc

ess

lung

canc

erde

aths

prob

ably

insi

gnifi

cant

.C

autio

uses

timat

e3

per

100

000.

expo

sed.

Inex

cept

iona

lci

rcum

stan

ces

(see

note

c)it

isar

guab

leth

atan

estim

ate

of10

per

100

000

mig

htbe

just

ified

.T

heca

sefo

ra

thre

shol

d—ie

zero

,or

atle

ast

very

low

risk—

isst

rong

lyar

guab

le.

Ris

ksu

mm

arie

sfo

rcu

mul

ativ

eex

posu

res

of0.

01f/m

l.yrs

Cro

cido

lite

Bes

tes

timat

eab

out

20de

aths

per

100

000

expo

sed.

Hig

hest

argu

able

estim

ate

100,

Ris

kis

prob

ably

insi

gnifi

cant

(ran

ge,

1to

3ex

cess

lung

canc

erde

aths

per

low

est

2.10

000

0ex

pose

d).

Mes

othe

liom

ais

now

the

dom

inan

tris

k,so

prec

ise

estim

atio

nof

the

lung

canc

erris

kis

not

criti

cal.

Am

osite

Bes

tes

timat

eab

out

3de

ath

per

100

000

expo

sed.

Hig

hest

argu

able

estim

ate

20,

low

est

insi

gnifi

cant

.C

hrys

otile

Ris

kpr

obab

lyin

sign

ifica

nt,

high

est

argu

able

estim

ate

1de

aths

per

100

000

Ris

kof

exce

sslu

ngca

ncer

very

prob

ably

insi

gnifi

cant

exce

pti

nex

cept

iona

lex

pose

d.ci

rcum

stan

ces

(see

note

c)w

hen

itis

argu

able

that

anes

timat

eof

1de

ath

per

100

000

mig

htbe

just

ified

.T

heca

sefo

ra

thre

shol

d—ie

zero

,or

atle

ast

very

low

risk—

isst

rong

lyar

guab

le.

(Co

ntin

ue

do

nn

ext

pa

ge)


Tab

le11

.(co

ntin

ue

d)

Fib

reM

esot

helio

ma

Lung

canc

er

Ris

ksu

mm

arie

sfo

rcu

mul

ativ

eex

posu

res

of0.

005

f/ml.y

ran

dlo

wer

At

thes

ele

vels

only

mes

othe

liom

ane

edbe

cons

ider

ed.

The

abso

lute

risk

islo

w—

,bu

tqu

antit

ativ

eun

cert

aint

ies

are

very

cons

ider

able

.C

roci

dolit

eB

est

estim

ate

abou

t10

deat

hspe

r10

000

0ex

pose

d.H

ighe

star

guab

lees

timat

e55

,In

sign

ifica

nt,

poss

ibly

zero

low

est.

Bes

tes

timat

efa

llsto

insi

gnifi

cant

leve

lat

0.00

02f/m

l.yr,

and

high

est

argu

able

risk

beco

mes

insi

gnifi

cant

at6

×102

6f/m

l.yr

Am

osite

Bes

tes

timat

eab

out

2de

aths

per

100

000

expo

sed

high

est

argu

able

lifet

ime

risk

15,

falli

ngto

,1

(ie.

insi

gnifi

cant

)at

7×102

5f/m

l.yr

Chr

ysot

ileIn

sign

ifica

ntIn

sign

ifica

nt,

very

poss

ibly

zero

a Exp

osur

eas

sum

edto

beac

cum

ulat

edov

ersh

ort—

upto

5yr

perio

dsst

artin

gat

age

30.

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expo

sure

atot

her

ages

adju

stth

epr

edic

ted

mes

othe

liom

afig

ure

sus

ing

the

fact

ors

inT

able

9.E

stim

ates

for

long

erpe

riods

ofex

posu

reca

nbe

appr

oxim

ated

bym

akin

gse

para

tees

timat

esfo

rsu

cces

sive

5-ye

arpe

riods

and

addi

ngth

ere

sulti

ngr

isks

(thi

sw

illsl

ight

lyov

eres

timat

eris

k).

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imat

esha

vebe

enro

unde

dto

near

est

5in

seco

ndsi

gnifi

cant

digi

t(o

rto

one

sign

ifica

ntdi

git

whe

nle

ssth

an10

).bT

helu

ngca

ncer

risk

isba

sed

onB

ritis

hm

ale

mor

talit

yin

1997

whe

n9.

5%of

mal

ede

aths

atag

es40

–79

wer

edu

eto

lung

canc

er.

Thi

sre

pres

ents

anav

erag

ef

ora

popu

latio

nw

itha

past

patte

rnof

smok

ing

sim

ilar

toth

atof

olde

rB

ritis

hm

en.

In19

9623

%of

men

aged

60+

had

neve

r(o

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25%

wer

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with

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.


cularly for mesothelioma, but some (Cullen, 1998;Stayneret al., 1996) have regarded the extent of thesedifferences as unimportant, particularly since chryso-tile has been overwhelmingly the most commonlyused fibre.

The interpretation of the whole body of evidencedepends importantly on the interpretation of resultsfrom cohorts with predominantly chrysotile exposuretogether with a minority contribution—usually a fewper cent—from amphiboles. As long as the differencein potency is not extreme these cohorts can be reason-ably interpreted as indicating the risk of chrysotileexposure. But if the differences in potency are verysubstantial this is no longer the case. Furthermore,in this situation an additional source of error in theestimation of exposure will be introduced, since themeasured exposure (mainly of chrysotile) will oftenbe a poor proxy for the relevant exposure.

The data in this review suggest that order of magni-tude differences in potency may indeed apply formesothelioma, and probably also for lung cancer. Themain reason this review differs from earlier similarreviews is in its use of the information from theamphibole mining cohorts in South Africa and Aus-tralia. The publication of mortality results from theSouth African mines seems to have gone almostunnoticed. The Australian cohort has been the subjectof a series of publications with varying analyticalapproaches and varying results. One of these analysesgave a lung cancer risk from the cohort of around 1%per fibre/ml.yr, and this is the value that has beenmost usually quoted, but this is probably an underesti-mate due to incomplete follow up at older ages. Thisreview is also the only one to have exploited the(admittedly uncertain) quantitative exposure infor-mation in the Massachusetts cohort.

Implications of the non-linear exposure response formesothelioma

A non-linear relationship between the rates of pleu-ral and peritoneal mesothelioma is more readilyexplicable if the cancer risk is proportional to somefunction of the concentration of fibres in the targettissue, rather than the simple number burden.

If concentration rather than number burden is therelevant parameter, then the possibility of a thresholdtype relationship becomes much more plausible, sinceif the effect depends on fibres acting together, theremust presumably be some point at which individualfibres are simply too far apart to exert any joint effect.Of course, if the mechanisms of distribution of fibreswithin the lung and pleura are such that fibres tendto be delivered preferentially to particular areas—andthere is evidence that this is the case in the pleura(Boutin et al., 1996)—the effective threshold levelmay be very low. In any case such a threshold isunlikely to be a sharp cut-off. Random variations inthe distribution of fibres in particular lungs, and dif-

ferences in individual susceptibility will mean that theexposure response curve simply starts to descendmore steeply from some point on the cumulativeexposure scale.

Also, fibre concentration is the more plausibleexposure metric for the production of fibrosis, so thisinterpretation is consistent with the link suggested bythe HSE fibre review (and by other authors) betweenthe 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 tointra-peritoneal and intra-tracheal instillation of mass-ive fibre doses is also readily explicable.

Combined with the knowledge of the much greatersolubility of chrysotile in the lung, this may alsoexplain why asbestos related diseases have only beenclearly seen with heavy chrysotile exposures. Ifexposures are heavy and sustained a sufficient con-centration of fibre in the lung may be maintained totrigger both fibrosis and malignancy. The extreme rar-ity of peritoneal mesothelioma in cohorts exposed tochrysotile alone may also be explained. If the routeby which asbestos reaches the peritoneum is from thepleural cavity, it may well be that chrysotile fibres donot survive long enough in body tissues to make thejourney in sufficient numbers.

Chrysotile and asbestos related malignancySmith and Wright (1996), showed a ranking of 25

cohort studies by proportional mortality from pleuralmesothelioma and argued that since chrysotile wasthe primary exposure for two of the top 10 cohortsand present as part of the mix in six of them, and thatthe picture for crocidolite in terms of its presence inthe mix was similar, while amosite was less evidentthan either of the other two fibre types, that chrysotilemust therefore be similarly potent as a cause of pleu-ral mesothelioma. What this argument ignores is anyquantification of exposure. Without quantification itis very difficult to draw any conclusion about relativerisk from a simple ranking by mesothelioma rate. Inrelation to the 25 cohorts identified in this review anequally pertinent observation might be that all ofthem involved exposure to one or other of the amphi-bole fibres. Smith and Wright also present argumentsbased on the relative levels of mortality from pleuralmesothelioma and from excess lung cancer to suggestthat there is only moderate difference between thepotency of chrysotile and the amphibole fibres forcausing mesothelioma—they suggest a factor of threeor four. However this argument is based on theassumption that all fibre types are equally potent forlung cancer. If this review is correct in suggestingthat this is not the case, these arguments are not valid.

Nicholson and Landrigan (1996), present similar


arguments based on the assumed equivalence of thefibre types to cause lung cancer. They also show ananalysis of the mesothelioma mortality of a small sub-set of the US insulators study which shows that thepattern of deaths over time implies that members ofthis cohort were exposed to a pleural carcinogenbefore 1935. Since, reportedly, amosite was first usedfrom around 1935, and prior to this date only chryso-tile was used, some of these deaths must have beendue to chrysotile. The authors do not mention thepossible role of crocidolite, but if we accept that noamphibole fibre was used before 1935 by US insu-lation workers, these observations do show that someof the cases in this cohort must have been caused byexposure to chrysotile prior to 1935. These exposureswill often have been heavy. In fact the contemporaryUS trade journal ‘Asbestos’ (published monthly fromJuly 1919) makes it clear that both amosite and cro-cidolite were used in the US through the 1920s,though probably in limited quantities, since the USindustry seems to have been resistant to their use ontechnical grounds, and chrysotile was the fibre ofchoice for most applications.

Stayneret al.’s review of the issues (1996), setsout similar arguments, and also points to the evidencethat all three commercial fibre types have produceda similar level of lung tumours in animal inhalationexperiments. This is the most problematic evidenceto reconcile with the human evidence that amphibolefibres are substantially more potent lung carcinogens.However, the time periods needed to induce cancerin humans (yr) and in rats (months) are very different,and it is at least plausible that all fibres are equallypotent in rats because none of them are materiallycleared from the rat lung over the months needed toinitiate a rat lung tumour. By contrast, in humanschrysotile (cleared in months) might have less effectthan the amphibole fibres (cleared in years). Adetailed elaboration of this argument has recentlybeen published by Berry (1999). It may also be rel-evant that the animal experiments were made withexposure concentrations massively in excess of thoserepresented in the human data. The differences in thehuman data summarised in this review seem reason-ably clear (certainly in respect of mesothelioma), andare based on a range of independent data. In the end,if a choice has to be made between animal and humanevidence as a basis for assessing human risk, adequatehuman 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 simplyunbelievable that such a small component of theexposure could be responsible for the observed risk.If it is true that the mesothelioma risk is proportionalto 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 amphiboledo have a disproportionate effect.

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Appendix A

EXTRACTED COHORT DATA

The details of extracted data is shown in Tables 12and 13, with explanatory notes in Tables 14 and 15.

SUMMARISING MORTALITY AND EXPOSUREMEASURES - THE CHOICE OF FOLLOW UP

PERIOD IN RELATION TO EXPOSURE PERIOD

LUNG CANCERFor most of the studies considered in this review,

mortality was reported for the period 20 or more yearsfrom first exposure. The reason for this is that it isassumed that this represents a period in which theeffect of exposure will be fully expressed. Deathsobserved in the period immediately following firstexposure will be unaffected by that exposure, and theeffect of exposure will become progressively moreapparent as follow up time increases. Comparisonsof observed and expected deaths that include periodsimmediately after first exposure will introduce somedownward bias to the assessed risk level.

Evidence on the levels of excess in different per-iods after exposure suggests that between 10 or 20and about 40 yr from exposure the lung cancer riskis reasonably stable. Beyond 40 or 45 yr follow upthere may be some decline in risk, but the extent towhich this may have diluted recorded lung cancer riskin these cohorts seems limited (for example, there isvery little difference between the US/Canada insu-lators lung cancer SMR calculated over all follow upfrom 20 yr and one restricted to the period between20 and 40 yr). No adjustment for differences in maxi-mal follow up between cohorts has therefore beenapplied.

The impact of follow up less than 10 yr beingincluded in the reported results, and the related prob-lem of choosing an average exposure appropriate tothe observed mortality needs to be considered. Ifobserved and expected mortality from observationsless than 10 yr from first exposure are uninformativeof the possible effects of the exposure the inclusionof such observations in the reported results for acohort, will dilute any actually occurring effect. Pro-vided there is a reasonable amount of informative fol-low up on every cohort member this dilution effectcan probably be ignored, since the observed andexpected deaths generated from the early(uninformative) follow up will be outweighed for allindividuals by their later observations. But if a highproportion of a cohort generates mainly uninforma-tive follow up, reported overall results may be seri-ously distorted. This can happen if recruitment to acohort continues to the end of follow up. Subjectsstarting within 10 yr of the end of follow up will con-tribute no informative mortality data.


Tab

le12

.S

umm

ary

ofco

hort

data

(par

t1)

a

Coh

ort

iden

tifica

tion

Pro

cess

Fib

reS

exA

vera

geT

ypic

alP

redi

cted

mor

talit

yN

umbe

rD

eath

sby

all

caus

esM

esot

helio

ma

age

atfo

llow

for

typi

cal

follo

wup

ofye

ars

deat

hsfir

stup

bfr

omav

g.ag

efir

stla

tenc

yex

posu

reex

pose

dc

Cod

eN

ame

Ref

eren

ceP

ropo

rtio

nR

elat

ive

Obs

Exp

SM

RT

otal

%E

xces

sto

age

atnu

mbe

rm

orta

lity

first

expo

sure

of30

1W

itten

oom

deK

lerk

et

al.

(199

4)M

om

2935

0.24

1.08

1071

960

1.8*

1.19

72*

122m

Car

olin

aD

emen

teta

l.(1

994)

Ty*

m26

410.

321.

3415

607

410.

11.

482

0.49

(men

)2f

Car

olin

aT

y*f

2641

0.32

1.34

1536

229

9.2

1.21

00

(wom

en)

3Jo

hns

Ent

erlin

eet

al.

(198

7)I

yao

m37

651

1ag

e>65

944

762.

51.

248

1M

anvi

llere

tiree

s4

Ont

ario

Fin

kels

tein

(198

4)C

yom

3327

0.17

0.77

2010

862

.21.

7317

275a

New

Orle

ans

Hug

heset

al.

(198

7)C

ya*

m32

350.

30.

8520

259

294.

50.

881

0.34

(pla

nt1)

5oN

ewO

rlean

sC

yom

2738

0.27

1.26

2060

361

4.1

0.98

30.

49(p

lant

2)6

Que

bec

Lidd

elle

ta

l.(1

997)

My

m23

721

1ag

e>55

6587

5912

.7*

1.11

33*

0.56

7V

ockl

abru

ckN

eube

rger

and

Kun

diC

yom

f24

650.

951.

0520

540

530.

2*–

50.

94(1

990)

8U

S/C

anad

aS

eidm

anan

dS

elik

off

Lya

o*m

2462

0.9

1.09

2046

2631

70.6

1.46

453

14in

sula

tors

(199

0)9

Roc

hdal

eP

etoe

ta

l.(1

985)

Tyo

*m

3042

0.45

120

727

602.

5*1.

2110

1.7

10B

alan

gero

Pio

latto

et

al.

(199

0)M

ym

2747

0.51

1.2

2031

722

5.4

1.41

20.

8911

Pen

nsyl

vani

aM

cDon

aldet

al.

(198

3a,b

)T

Fya

*m

2940

0.35

1.08

2089

582

1.1

1.09

141.

712

Pat

erso

nS

eidm

anet

al.

(198

6)I

am

3740

0.6

0.63

559

335

5.9

1.67

174.

813

aS

Aam

osite

Slu

is-C

rem

eret

al.

(199

2)M

am

3140

0.4

0.93

064

845

6.3*

1.42

40.

88m

ines

13o

SA

Mo

m31

400.

40.

930

423

333.

1*1.

2720

6cr

ocid

olite

min

es14

Mas

sach

uset

tsT

alco

tteta

l.(1

989)

Oo

m34

370.

40.

740

288.

33.

375

6015

Alb

inA

lbin

et

al.

(199

0a,b

)C

yao*

m30

620.

981

2059

249

3*1.

213

2.6

16C

onne

ctic

utM

cDon

alde

ta

l.(1

984)

Fy*

m31

400.

410.

9320

557

550.

71.

010

017

Fer

odo

New

hous

ean

dS

ulliv

anF

yo*

mf

3045

0.56

110

2577

2646

.30.

9713

0.49

(198

9)

a Not

eson

star

red

entr

ies

inT

able

14.

bF

ollo

wup

dura

tion

whi

chdi

vide

sth

eob

serv

atio

nfie

ldbe

yond

the

min

imum

late

ncy

into

two

equa

lar

eas.

a See

text

.


Tab

le13

.S

umm

ary

ofco

hort

data

(par

t2)

a

Coh

ort

Lung

canc

erde

aths

Ave

rage

Bas

isfo

rLu

ngca

ncer

risk

expr

esse

das

%M

esot

helio

ma

risk

expr

esse

das

code

cum

ulat

ive

aver

age

expe

cted

lung

canc

erpe

rf/m

l.yr

expo

sure

expo

sure

(f/m

l.yr)

estim

ate

Obs

erve

dD

iagn

ostic

Exp

ecte

dS

MR

Exc

ess

%E

xces

sC

ohor

tS

MR

Reg

ress

ion

%T

otal

expe

cted

Est

imat

ed(s

eeba

sis

Ave

rage

regr

essi

onsl

ope

mor

talit

ype

rf/m

l.yr

(R

M)

KM

inco

des

(see

(RL)

slop

ein

tern

alH

EI

belo

wb)

code

sm

odel

Una

djus

ted

Adj

uste

dfo

rbe

lowb

)ag

eat

first

expo

sure

187

DC

48.7

1.79

38.3

7923

*in

d3.

43.

1*0.

7to

5.4*

0.52

0.48

13.8

2m74

DC

32.2

2.3

41.8

130

28C

atA

v4.

63*

–0.

017

0.01

3–

2f38

DC

13.8

2.75

24.2

175

26C

atA

v6.

71*

–0

0–

373

DC

28.4

2.57

44.6

157

750

Cat

Av

0.21

0.18

–0.

0014

0.00

14–

422

BE

5.3

4.14

16.7

314

60te

xt5.

2–*

–0.

530.

6813

5a21

DC

22.5

0.93

21.

52

6.7

79C

atA

v0

0.03

–0.

0024

0.00

28–

5o73

DC

50.1

1.46

22.9

4647

Cat

Av

0.97

0.76

–0.

0069

0.00

55–

658

7*D

C43

1.6

1.36

155.

436

600

Cat

Av

0.06

0*0.

037*

0.01

2*0.

0009

0.00

090.

011

747

*D

C42

.21.

114.

811

25*

ind

0.45

––

0.03

80.

036

–8

934*

BE

256.

83.

6467

7.2

264

500

L*D

0.53

––

0.02

90.

027

1.7

956

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.11.

5118

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138*

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Av

0.37

0.51

–0.

022

0.02

20.

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300*

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Av

0.03

–*–

0.00

30.

0025

–11

50*

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33.8

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16.2

4860

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Av

0.80

–*–

0.02

90.

027

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98*

BE

20.5

4.78

77.5

378

65C

atA

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82.

7*–

0.07

30.

117

3.0

13a

21*

DC

14.5

*1.

456.

545

23.6

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xt1.

9–

–0.

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0.06

2.7

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19*

DC

10.2

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868.

886

16.4

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xt5.

2–

–0.

550.

5934

148

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13.1

7.4

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120*

L*D

10–

–0.

50.

68–

1535

BE

19.4

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815

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13te

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2–

00.

180.

18–

1649

DC

35.8

1.37

13.2

3746

Cat

Av

0.80

–*–

00

–17

241*

DC

242.

50.

992

1.5

20.

635

*co

n0

–0.

058*

0.01

40.

014

–

a Not

eson

star

red

entr

ies

inT

able

15.

bD

C:

Dea

thce

rtifi

cate

;B

E:

Bes

tev

iden

ce;

Cat

Av:

Exp

osur

eca

tego

rym

idpo

ints

wei

ghte

dby

expe

cted

mor

talit

y;in

d:M

ean

ofin

divi

dual

dose

dist

ribut

ion

(per

son

wei

ghte

d);

L*D

:M

ean

expo

sure

leve

ltim

esm

ean

expo

sure

dura

tion;

text

:V

alue

give

nin

text

,as

sum

edto

bem

ean

ofin

divi

dual

dose

dist

ribut

ion

(per

son

wei

ghte

d);

con:

Me

anex

posu

reof

cont

rols

mat

ched

tolu

ngca

ncer

case

s.


Tab

le14

.E

xpla

nato

ryno

tes

onco

hort

data

Coh

ort

iden

tifica

tion

Gen

eral

coho

rtno

tes

Fib

rety

peD

eath

sB

yA

llC

ause

sM

esot

helio

ma

mor

talit

y

Cod

eN

ame

Obs

Exp

1W

itten

oom

Dea

ths

toag

e65

(evi

denc

eof

inco

mpl

ete

deat

has

cert

ainm

ent

aol

der

ages

)2m

Car

olin

a(m

en)

Ver

ysm

all

amou

nts

ofcr

ocid

olite

yarn

wer

eus

ed,

but

raw

croc

idol

itefib

rew

asno

tpr

oces

sed.

The

quan

tity

ofcr

ocid

olite

used

was

abou

t0.

002%

ofth

eto

tal

2fC

arol

ina

(wom

en)

3Jo

hns

Man

ville

retir

ees

4O

ntar

ioP

rodu

ctio

nan

dm

aint

enan

cew

orke

rson

ly5a

New

Orle

ans

(pla

nt1)

Exc

l.m

enw

ith,3

mon

ths

Mai

nly

chry

sotil

e,am

osite

used

from

early

1940

s;cr

ocod

olite

used

occa

sion

ally

Tw

oad

ditio

nal

empl

oym

ent

from

1962

(too

rece

ntto

affe

ctob

serv

edm

orta

lity)

mes

othe

liom

asat

,20

yr5o

New

Orle

ans

(pla

nt2)

Exc

l.m

enw

ith,6

mon

ths

Mai

nly

chry

sotil

e,cr

ocid

olite

used

inpi

pepr

oduc

tion

proc

ess

late

ncy.

Afu

rthe

rfo

urem

ploy

men

toc

curr

edaf

ter

end

offo

rmal

follo

wup

6Q

uebe

cE

xclu

ding

Asb

esto

sfa

ctor

y,bu

tin

clud

ing

case

sat

Tab

le9

and

text

ages

,55

and

the

smal

ler

The

tford

min

es7

Voc

klab

ruck

Obs

erve

dle

ss5

mes

othe

liom

asan

d4.

8ex

cess

lung

canc

ers

8U

S/C

anad

ain

sula

tors

Nic

hols

onan

dLa

ndrig

an(1

996)

estim

ate

the

expo

sure

toha

vebe

en60

%ch

ryso

tile

and

40%

amos

ite,

base

don

publ

ishe

dpr

oduc

tco

mpo

sitio

ns.

Ana

lyse

sof

lung

burd

enin

lung

sfr

om16

US

insu

latio

nw

orke

rsgi

ven

byLa

nger

and

Nol

an(1

989)

foun

dam

osite

inal

l16

,ch

ryso

tile

inha

lfof

them

and

croc

idol

itein

thre

e.9

Roc

hdal

eP

redo

min

antly

chry

sotil

e,E

xpec

ted

all

caus

ede

aths

cann

otbe

rest

ricte

dto

men

with

>1

yrem

ploy

men

tbu

tfr

omm

id19

30s

aex

cess

mes

othe

liom

a-

and

mea

nex

posu

re-

ther

efor

eex

pres

sed

for

who

leco

nsite

nt5%

orso

was

coho

rtcr

ocid

olite

.10

Bal

ange

ro11

Pen

nsyl

vani

aA

bout

10%

ofth

efib

repr

oces

sed

was

amos

ite.

Ver

ysm

all

amou

nts

ofcr

ocid

olite

wer

eal

sous

ed,

but

little

was

hand

led

asra

wfib

re(C

on

tinu

ed

on

ne

xtp

ag

e)


Tab

le14

.(co

ntin

ue

d)

Coh

ort

iden

tifica

tion

Gen

eral

coho

rtno

tes

Fib

rety

peD

eath

sB

yA

llC

ause

sM

esot

helio

ma

mor

talit

y

Cod

eN

ame

Obs

Exp

12P

ater

son

13a

SA

amos

item

ines

Rep

orte

dex

pect

ednu

mbe

rre

duce

dto

estim

ate

the

num

ber

rela

ting

to10+

yrfr

omfir

stex

posu

re(d

etai

lsin

App

endi

xC

)13

cS

Acr

ocid

olite

min

es14

Mas

sach

uset

ts15

Alb

inM

ainl

ych

ryso

tile

(>95

%),

Exp

ecte

dnu

mbe

res

timat

edfr

omob

serv

edan

dal

lca

use

RR

with

smal

ler

amou

nts

ofcr

ocid

olite

and

amos

ite.

Rec

ent

croc

idol

iteus

e,

1%,

neve

r>

3-4%

.U

pto

18%

ofam

osite

used

brie

flydu

ring

the

1950

s.It

isno

tcl

ear

whi

cham

phib

ole

shou

ldbe

seen

asth

em

ost

impo

rtan

t-

amos

itebe

caus

eof

the

rela

tivel

yhi

ghpe

rcen

tage

used

durin

ga

limite

dpe

riod

orcr

ocid

olite

beca

use

ofits

regu

lar

hist

oric

use

atlo

wle

vels

.16

Con

nect

icut

Dat

aon

men

expo

sed

,1

year

excl

uded

Chr

ysot

ilew

asth

eon

lyty

peof

asbe

stos

used

until

1957

whe

nso

me

anth

ophy

llite

was

adde

dfo

rso

me

prod

uct

lines

.A

bout

400

lbs

ofcr

ocid

olite

was

hand

led

expe

rimen

tally

ona

few

occa

sion

sin

the

labo

rato

ry,

but

only

betw

een

1964

and

1972

.G

iven

the

smal

lsc

ale

and

timin

gof

this

amph

ibol

eus

e,th

eco

hort

has

been

trea

ted

asch

ryso

tile

only

17F

erod

oA

part

from

two

perio

dsbe

fore

1944

,w

hen

croc

idol

iteas

best

osw

asus

edon

one

part

icul

arco

ntra

ct,

only

chry

sotle

asbe

stos

has

been

used

.


Tab

le15

.E

xpla

nato

ryno

tes

onco

hort

data

a

Coh

ort

Lung

canc

erm

orta

lity

Ave

rage

cum

ulat

ive

expo

sure

Lung

canc

erris

kco

effic

ient

Mes

othe

liom

aris

kco

effic

ient

code

(f/m

ly)

KM

inH

EI

mod

el

SM

Rre

gres

sion

slop

eR

egre

ssio

nS

lope

—in

tern

al

1F

rom

Ber

ry(1

995)

Coe

ffici

ent

for

expo

sure

grou

pIn

ferr

edfr

oman

alys

esin

Fro

mde

Kle

rke

ta

l.(1

992)

cove

ring

coho

rtm

ean

inB

erry

Arm

stro

nget

al.

(198

9)an

dde

(199

1)K

lerk

et

al.

(199

1)2m

Tab

le5

Giv

enin

text

ofm

ain

refe

renc

e;S

tayn

ereta

l.(1

997)

give

anov

eral

les

timat

eof

2.19

2f 3E

xcl.

4m

esot

helio

mas

Tab

le2

Rep

orte

dva

lue

of0.

56,

conv

ersi

onfa

ctor

34

Exc

l.an

estim

ated

4E

xpos

ure

spec

ific

rate

sm

esot

helio

mas

inco

nsite

nt,

wid

era

nge

ofsl

opes

can

befit

ted.

5aE

xlud

ing

1m

esot

helio

ma

Tab

les

8,10

and

115o 6

Dat

afo

ras

best

osfa

ctor

yca

nnot

Tab

le8

Poi

sson

regr

essi

on(d

ata

inT

able

Fro

mLi

ddel

le

ta

l.(1

998)

Fitt

edva

lue×3

3/38

toad

just

for

beco

nsis

tent

lyex

clud

ed,

but

8)(T

able

3)fa

ctor

yca

ses

impa

cton

risk

estim

ate

ism

inor

7S

mok

ing

adju

sted

data

for

20+E

stim

ated

from

Fig

.4

inN

eube

rger

yrla

tenc

yfr

omN

eube

rger

and

and

Kun

di(1

991)

Kun

di(1

993)

8E

xclu

ding

anes

timat

ed42

The

rang

eof

expo

sure

cond

ition

slik

ely

toha

vebe

enm

etby

this

grou

p,an

dth

elo

ngtim

epe

riod

over

mes

othe

liom

as(h

alf

the

whi

chth

ese

expo

sure

sw

ere

accr

ued

mea

nsth

atan

yes

timat

eof

the

leve

lsof

expo

sure

will

beve

rydi

ffere

nce

betw

een

DC

and

BE

appr

oxim

ate.

Oth

erre

view

sha

vead

opte

dno

tiona

lva

lues

ofav

erag

eex

posu

rele

vel

as15

–30

f/ml

and

mes

othe

liom

anu

mbe

rs)

aver

age

expo

sure

dura

tion

as25

yr,

impl

ying

am

ean

cum

ulat

ive

expo

sure

arou

nd50

0f/m

l.yr,

this

islik

ely

tobe

very

unce

rtai

nes

timat

e.9

Exc

ludi

ngw

orke

rsw

ith,

1yr

Ove

rall

mea

nis

73f/m

l.yr

(Tab

le16

);m

ean

expo

sure

dura

tion

7.9

yrem

ploy

men

t(T

able

7);

thus

mea

nle

vel

arou

nd10

f/ml;

half

the

man

year

sco

ntrib

uted

bysh

ort

term

men

(Tab

le8)

;as

sum

ing

expo

sure

sof

5f/m

l.yr

for

shor

tte

rmm

enim

plie

sex

posu

reof

138

f/ml.y

rfo

rm

enw

ith>

1yr

expo

sure

.10

Fro

mT

able

3:th

ere

sult

isse

nsiti

veR

epor

ted

dose

spec

ific

data

has

not

been

used

here

,si

nce

com

paris

onof

the

man

-yea

rsin

the

dose

toth

ech

oice

ofm

ean

dose

inth

esp

ecifi

cca

tego

ries

ofT

able

3of

Pio

latto

et

al.

(199

0)w

ithth

enu

mbe

rof

men

inth

ese

cate

gory

high

est

cate

gory

(whi

chco

vers

30%

stro

ngly

sugg

ests

that

all

the

man

-yea

rsof

follo

w-u

pfo

rm

enin

each

cate

gory

has

been

assi

gned

toof

men

).W

ithth

eto

pdo

sem

ean

the

dose

cate

gory

they

even

tual

lyac

hiev

e,th

eex

posu

rere

spon

seis

thus

bias

eddo

wnw

ards

byan

set

at50

0f/m

l.yr

the

over

all

mea

nun

know

nam

ount

.is

250,

incr

easi

ngto

400

ifth

eto

pdo

sem

ean

isse

tat

1000

f/ml.y

r.A

valu

eof

300

has

been

adop

ted

for

this

revi

ew.

(Co

ntin

ue

do

nn

ext

pa

ge)


Tab

le15

.(co

ntin

ue

d)

Coh

ort

Lung

canc

erm

orta

lity

Ave

rage

cum

ulat

ive

expo

sure

(f/m

lLu

ngca

ncer

risk

coef

ficie

ntM

esot

helio

ma

risk

coef

ficie

ntco

dey)

KM

inH

EI

mod

el

SM

Rre

gres

sion

slop

eR

egre

ssio

nS

lope

—in

tern

al

11E

xclu

ding

thre

e(M

cDon

ald,

Tab

le5

The

dose

cate

gorie

sof

the

thre

em

esot

helio

mas

code

das

pers

onal

com

mun

icat

ion)

resp

irato

ryca

ncer

not

know

n,so

anad

just

edex

posu

re-r

espo

nse

mes

othe

liom

as.

Exp

ecte

dca

nnot

befit

ted.

mul

tiplie

dby

repo

rted

byS

MR

for

low

est

expo

sure

cate

gory

(0.6

69)

12E

xclu

ding

anes

timat

edfo

urT

able

XV

IP

oiss

onre

gres

sion

(Tab

leV

I)m

esot

helio

mas

13a

Obs

erve

dan

expe

cted

redu

ced

Rep

orte

dm

eans

adju

sted

tore

flect

wei

ghin

gof

expe

cted

mor

talit

y>

10yr

from

first

expo

sure

(see

13o

byth

ees

timat

edex

pect

edA

ppen

dix

C)

num

bers

with

in10

yrfr

omfir

stex

posu

re(S

eeA

ppen

dix

C).

Obs

erve

dnu

mbe

rsal

sore

duce

dby

the

estim

ated

num

ber

ofm

esot

helio

mas

code

dto

lung

canc

er(1

in13

a,5

in13

o)14

Bur

dett,

pers

onal

com

mun

icat

ion

(see

App

endi

xB

)15

Lung

canc

erta

ken

tobe

resp

irato

ryca

ncer

excl

udin

gpl

eura

lm

esot

hlio

mas

,R

Rin

terp

rete

das

SM

R16

Tab

le3

Mor

talit

yda

taw

ere

repo

rted

bycu

mul

ativ

eex

posu

re,

but

high

mor

talit

yin

shor

tte

rmw

orke

rsm

akes

inte

rpre

tatio

nof

this

data

diffi

cult.

The

high

est

resp

irato

ryca

ncer

mor

talit

yis

seen

inth

elo

wes

tex

posu

reca

tego

ry.

Thi

sre

mai

nsth

eca

seif

shor

tte

rmw

orke

rsar

eex

clud

ed:

low

-dos

em

enin

all

empl

oym

ent

dura

tion

cate

gorie

sha

dhi

ghle

vels

ofre

spira

tory

canc

erm

orta

lity

(Tab

le8)

.T

hem

ost

likel

yex

plan

atio

nse

ems

tobe

that

expo

sure

sha

vebe

enw

rong

lyas

sign

edat

the

indi

vidu

alle

vel.

The

avai

labl

ejo

bde

tails

gene

rally

only

allo

wed

wor

ker

hist

orie

sto

bede

scrib

edby

depa

rtm

ent,

rath

erth

anpr

oces

s.B

ecau

seof

this

the

expo

sure

–res

pons

eda

tais

not

used

here

.17

Tot

alob

serv

edlu

ngca

ncer=lun

gF

rom

Ber

ryan

dN

ewho

use

(198

3)F

rom

Ber

ryan

dN

ewho

use

(198

3)(c

ase

cont

rol,

anal

ysis

)an

dpl

eura

lca

ncer

sle

sspl

eura

l(m

ean

expo

sure

oflu

ngca

ncer

mes

othe

liom

asco

ntro

ls)

a Tab

les

orfig

ures

refe

rred

toar

eth

ose

foun

din

the

mai

nre

fere

nces

liste

din

Tab

le12

,un

less

othe

rwis

esp

ecifi

ed.


DEFINITION OF AVERAGE EXPOSURE

The inclusion of large numbers of cohort memberswho contribute no informative follow up may alsobias the average exposure. An appropriately weightedaverage exposure will give zero weight to individualexposures in this group. If only a simple mean isused, and if this late entrant group is large and has—as is likely—systematically lower exposures, theapparent cohort exposure will be too low in relationto the observed mortality, and the estimated risk perunit exposure will be exaggerated.

REVIEW OF COHORTS WITH POTENTIAL EFFECTDILUTION OR BIASSED EXPOSURE AVERAGES

The potential biases discussed in the precedingparagraphs will not apply where the reported mor-tality excludes observations before the tenth year offollow up (or a later year), and where the averageexposure has been weighted by expected lung cancermortality. This leaves the following cohorts as poten-tially affected: Wittenoom, Ontario, Vocklabruck,US/Canada insulators, Balangero, Paterson, SAmines, Massachusetts, Albin and Ferodo. Table 16summarises the relevant data.

The possibility of dilution due to uninformative fol-low up needs to be considered for the SA mines andfor the Massachusetts and Paterson cohorts. This cancertainly be ignored for Massachusetts and Patersoncohorts, because of their combination of limitedrecruitment period with long follow up. It cannot bedismissed for the SA mines, and an adjustment willbe developed below (Appendix C).

The possibility that a simple mean of individualexposures (the available figure) will be a poor proxyfor the desired average weighted by expected lungcancer mortality needs to be considered for all thecohorts listed in Table 16. For all but one there arereasons (summarised individually below) for believ-ing that the available figure is an acceptable proxy.

Table 16. 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 upyears follow up on latest

latency (yr) entrants (yr)

Wittenoom 10 1943 1966 1943 1986 44 20Ontario 20 1948 1959 1948 1977 30 18Vocklabruck 20 1907 1979 1950 1990 84 11US/Canada insulators 20 1907 1966 1967 1986 80 20Balangero 20 1930 1986 1946 1987 58 1Paterson 5 1941 1945 1941 1982 42 37SA mines 0 1925 1980 1946 1980 56 0Massachusetts 0 1951 1953 1953 1988 38 35Albin 20 1907 1977 1927 1986 80 9Ferodo 10 1920 1977 1942 1979 60 2

O For Wittenoom, the measure of excess mortalityused has been truncated at subjects’ 65th birth-days. The effect of this is broadly to equalise thefollow up durations (and therefore the expectedmortality weights) of different first exposuregroups.

O The recruitment period of theOntario cohort isrelatively short, and no mention is made of majorvariations in exposure conditions.

O Only 18% of theVocklabruck cohort started theirexposure after 1969: described as ‘the decisiveyear in improving the dust situation’.

O The basis for the ‘mean’ exposure in theUS/Canadian insulatorscohort (drawn from pre-vious reviews) is very uncertain. It is not based onaveraging known or estimated individualexposures. It is plausible that conditions may nothave changed greatly over the relevant period (upto 1966).

O The narrow range of first exposure dates for theMassachusettscohort implies limited scope forchanges in average levels, and the long minimumfollow up also means that even if there were suchchanges, the weighting applied to early and lateentrants would be similar.

O Comparison of the most recent follow up report onthe Balangeromine cohort with a previous report(recruitment to 1965, follow up to 1975), suggeststhat only a relatively small proportion of the latestcohort were first exposed after 1965; though it isnot entirely clear how the two cohorts relate toeach other, and the minimum employment quali-fication time was more restrictive (1 yr) for thelater report than for the earlier (1 month), so thecomparison is not straightforward. There wasreportedly little change in exposure conditionsbetween 1946 and 1960. Some downward bias inthe derived exposure average is possible, but theextent of this is difficult to quantify. Even on anunadjusted basis, the derived risk per unitexposure for this cohort is one of the lowest seen.


O For the Albin cohort, major exposure changesstarted to apply only from the late 1960s, the last10 yr of 70 yr of intake. The scope for bias istherefore limited.

O The average exposure used for theFerodo cohortis that of controls matched to lung cancer cases.It is therefore—indirectly—weighted in the appro-priate way.

The one exception is theSA mines cohort. Thereport on this cohort shows that a large proportion ofthe cohort (amosite and crocidolite workerscombined) were first exposed less than 10 yr from theend of follow up. Illustrative exposure data is alsoshown which implies that these workers wereexposed to levels 4–6 times lower than those whichapplied before about 1950. Some adjustment to thereported individual mean exposure is therefore indi-cated.

This adjustment, and the related adjustment toexclude observed and expected mortality arising fromuninformative follow up are described in detail inAppendix C. Briefly, we conclude that both theobserved excess lung cancer and the associated cumu-lative exposure should be adjusted upwards, theexposure by rather more than the mortality excess.The implied dose specific risk is reduced by abouta quarter.

SUMMARY MEASURES FOR MESOTHELIOMA

Mesothelioma incidence rate rises very steeplywith time since exposure, and this complicates thechoice of a summary measure that will be properlycomparable across cohorts. Comparisons betweencohorts with different follow up times (or differentmixes of follow up times) should be adjusted to allowfor the impact of those differences on the observedmesothelioma mortality. One solution is to fit a stat-istical model. The following formulation was used inthe HEI report, and is fairly typical:

r = KM·L·[{ t210}32{ t2102D} 3]

where L is exposure level expressed in f/ml,D isexposure duration in yr and the contents of the curlybrackets {} are set to zero if,0.

However not all cohorts have the data needed tofit the HEI (or similar) models. A pragmatic way ofmaking an equivalent adjustment is to expressobserved mesothelioma numbers as a percentage ofexpected mortality from all causes, since this too is ameasure which increases steeply with follow up time.

The expected mortality from all causes has onedrawback as a denominator for mesothelioma risk: itis dependent on age at first exposure. This would notbe a serious problem if the mean age at first exposurewas similar in different cohorts, but this is not the

case. For those cohorts for which the mean age at firstexposure is given or can be estimated, it ranges from23 for Quebec to 37 for Paterson, with a mean acrosscohorts of about 30. We have therefore standardisedthe expected all cause mortality figure given for eachcohort to an assumed mean age at first exposure of30. The amount of adjustment applied has been calcu-lated using the following formula:

EAdj = EAM30/Ma

Where EAdj is the adjusted expected all cause mor-tality to be used as denominator for the observedmesothelioma mortality;a is the mean age at firstexposure for the cohort in question;EA is the actualexpected all cause mortality from the person yearsin which the mesotheliomas arose;M30 and Ma areproportional expected all cause mortality estimatesfor the ‘typical’ follow up duration for the cohort (thefollow up duration that divides the observation fieldbeyond the minimum latency into two equal areas)from ages 30 anda respectively. The schedule of allcause death rates used to calculate M30 and Ma, wasrate=exp(29.61+.0936a)—where a is age in yr—which provides a close fit to male all cause mortalityin Australia, Austria, USA and Great Britain (usingdata taken from the mid 1970s). The fit is less goodfor South African and for Swedish death rates, but theadjustment depends on the ratio (M30/Ma) of expecteddeaths in different—and quite wide—age ranges, ameasure that is not sensitive to the precise underlyinglife table. So for convenience the same life tableapproximation was used for all cohorts. For the twocohorts where mean age at first exposure was notavailable (Rochdale and Albin), a mean age of 30was assumed.

A similar argument to that set out above in relationto the effect of uninformative follow up on recordedlung cancer mortality in the SA mines cohorts alsoapplies to the excess mortality from mesothelioma.All the recorded mesotheliomas in these cohortsoccurred more than 10 yr from first exposure. An esti-mate of the expected all cause mortality arising fromfollow up less than 10 yr from first exposure has beensubtracted from the reported total expected all causemortality, and this adjusted figure used as the denomi-nator for excess mesothelioma mortality in thesecohorts. Details of this calculation are given inAppendix C.

COMPARISON OF COHORT AVERAGE RISKMEASURES WITH ALTERNATIVES BASED ON

INTERNAL COMPARISONS

For reasons explained in the main report, thisreview has taken cohort level measures of exposureand outcome as the basic units of observation. In thenext two sections these cohort-level measures arecompared to the corresponding internal analyses forthose cohorts where both are available.


COMPARISON OF RISK MEASURES—MESOTHELIOMA

For cohorts where details of mesothelioma deathsand person years by time from first exposure weregiven, the HEI model fitted to these rates. The modelwas fitted using values for individual calendar yearsof time from first exposure aggregated to give thereported latency categories. Best fit was assessed bymaximum likelihood methods assuming a Poissondistribution, and the resulting estimates ofKM areshown in Table 12.

Figure 10 compares the two alternative measuresof mesothelioma risk: the HEI coefficientKM and thepercent excess mortality per f/ml.yr indexRM. Thereis good agreement between these measures. The mostdiscrepant point relates to the Quebec cohort (code6), though this is on either measure clearly the lowestvalue. It may be relevant that the HEI parameterKM

for the Quebec cohort was calculated using data basedon 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 purposesof risk projection, but the alternative measure seemsto provide an equally valid summary of the relativelevels of mesothelioma risk in these cohorts.

COMPARISON OF RISK MEASURES—LUNGCANCER

Where exposure response regressions werereported by authors, the regression slope has beennoted: this provides the ‘regression slope’ estimate ofthe lung cancer risk. For cohorts where dose-specificSMR 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 RL with the regression slope estimate, for

Fig. 10. Comparison of alternative measures of mesotheliomamortality.

studies where both measures were available is shownin Fig. 11. There is a clear overall relationship,viewed across the whole risk scale. The discrepantpoints 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 becauseof uncertainties deriving from the small size of thereference population (15—Albin).

The most discrepant point relates to the Albincohort, which was analysed as an unmatched case-control study in relation to a control cohort of non-asbestos exposed industrial workers from the samearea. The overall RR for respiratory cancer excludingmesothelioma was 1.8 (though with a wide confi-dence interval: 0.9–3.7) and the mean cumulativeexposure was 13 f/ml.yr, giving a cohort average esti-mate ofRL of 6.2% per f/ml.yr (with an even widerconfidence interval:20.8–21). The value of theinternal regression slope in relation to exposure is notreported, though we are told that it was not statisti-cally significant (P=0.5). Inspection of the RRs forthe three exposure categories implies that the slopewould have been about 0.05. Whether this discrep-ancy reflects inaccuracy in the baseline, or in theexposure measurements (or a mix of these) is difficultto say. The high mesothelioma risk in this cohorttends to suggest that the cohort average measure isnearer the truth, but substantial uncertainty mustremain.

A further discrepant point relates to women in theCarolina cohort (2f), where the regression impliesRL=1, while the cohort average givesRL=6.7. Theauthors suggest that the low regression slope mayreflect 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. 11. Comparison of alternative measures of lung cancermortality.


slope for those cohorts with clearly positive results.This might be predicted from the flattening ofregression slopes by inaccuracies in exposure esti-mates. But it can also reflect inadequacies in baselinerates. For example, the two-fold difference betweenthe cohort average and regression slope measures forthe Quebec cohort reflects the SMR of about 1.3 seenin all the low dose subgroups, and which the authorsinterpret as non-asbestos related. Nevertheless, thebroad agreement between the two measures acrossstudies suggests that valid conclusions can be drawnfrom the cohort average measure.

Appendix B

FIBRE–PARTICLE CONVERSION FORCROCIDOLITE CIGARETTE FILTER COHORT

NOTE BY DR G. BURDETT

The measurements in 1952 which gave an averageof 80 particles per ml, within the Massachusetts stan-dard of 175 particles per ml, almost certainly refer toimpinger measurements, which were frequently madefor insurance company purposes.

The normal units are millions of particles per cubicfoot (mppcf). As one cubic foot is equivalent to28 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 fromthe 1930s to the 1960s (maybe even until 1972) whenit 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 andwould overlook many of the respirable fibres and isa very indirect measurement of the fibre level. Itshould also be remembered that impingers have poorcapture efficiency below 1µm.

It is also noted that cotton and acetate fibres weremixed, carded and deposited on crepe paper under dryconditions. This would suggest that fibres made upmany of the particles but I have not referred to thepatent to work out quantities used to estimate thefibre percentage.

My best guesstimate is that 30% of the particleswere fibres but only about 10% of the fibres seenwould be crocidolite (it is more dusty, but has veryfew >1 µm fibres compared to the other dusts).

This would mean about 3% of the count was cro-cidolite fibres or about 2.5 f/ml>1µm wide. To con-vert to the current index we generally find one canassume only some 4% of the >5µm long crocidolitefibres were visible as compared with the currentindex. This is equivalent to a concentration of about60 crocidolite fibres per ml using a modern versionof the membrane filter method.

This is several times higher than the better factoriesat this time but not too far away from what was prob-

ably present assuming some exhaust control on thebag opening, carding and mixing areas. Unfortunatelyno mention of the control system is made.

Also it is probably an average value that has beengiven for both the wet and dry methods as both werein use. It is probable given that the sampling locationsare unknown that higher concentrations occurred inthe dry areas: around 100 f/ml as measured by thecurrent method.

Of course this is very approximate, but 100 f/mllooks to be a good maximum exposure with TWA of60 f/ml.

Appendix C

DEVELOPMENT OF ADJUSTMENTS TO THESOUTH AFRICAN MINES COHORT DATA

The starting point for the adjustment of thereported results from this cohort is the data given inTables 1 and 2 of the published paper, which giveillustrative data on exposure levels in different per-iods (Table 1) and a breakdown of the whole cohortby 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 estimatedusing the mid points of the year of birth and yearof first employment categories (1900 and 1935 wereassumed for the earliest birth and employment categ-ories respectively). Age specific all cause and lungcancer rates for white South African men in 1955,1965 and 1975 were then used to calculate the distri-bution of expected lung cancer deaths by time sincefirst exposure in each cell. The rates for 1955 werealso used for the cells relating to first employmentbetween 1941 and 1950, but the expected number wasreduced by a factor of 0.64 to allow for the fact thatcause specific follow up was only recorded from1949.

The total expected lung cancers calculated in thisway (39.3) agrees quite closely to the value reportedin the paper (36.6) and the proportion of expectedlung cancer deaths arising from follow up less than10 yr from first exposure is 0.23. The reportedobserved and expected lung cancers in the two purefibre subcohorts have therefore been reduced by 0.23times the expected numbers given.

The data reported in Table 1 was used to estimateapproximate relative exposure levels at ten year inter-vals from 1945. Taking 1945 as 1, the numbers usedwere 1, 0.6, 0.35, 0.25 for amosite; and 1, 0.5, 0.25,0.15 for crocidolite. Exposures in the 1930s wereassumed to be the same as in the 1940s. To derivean expected lung cancer weighting for this relativeexposure pattern, the expected lung cancers in eachbirth-start cell from the 10th anniversary of firstemployment to the end of follow up in 1980 was cal-culated in a similar way to that described for the first10 yr of follow up. The resulting distribution of


Table 17. Derivation of correction factor for reported mean exposure using assumed relative exposure and weightingfactors by year of first employment in asbestos mines

Data item Year of first employment Mean weighted by

Before 1940 1941–50 1951–60 1961–70 1971–80 Persons Expectedlung cancer

Relative exposureCrocidolite 1 1 0.5 0.25 0.15 0.35 0.6Amosite 1 1 0.6 0.35 0.25 0.44 0.68Weighting factors TotalsPersons 62 404 2355 2408 2088 7317Expected lung 1.68 6.75 17.59 4.36 0 30.38cancers >10 yr from1st exposure

expected lung cancers in the five date of start groupsis shown in Table 17.

Table 17 also shows the numbers of individuals ineach group, and the assumed relative levels ofexposure. Mean exposures are calculated using thetwo alternative weightings. The expected lung cancerweighted means are larger than the correspondingperson weighted means by a factor of 1.71 for cro-cidolite and 1.55 for amosite. The reported mean

exposures have been adjusted using these factors foruse in this review.

Similar calculations for all cause deaths imply thatthe proportion of expected all cause deaths falling inthe first 10 yr of follow up is 33%. The all causemortality denominator for the observed mesotheli-omas in the two subcohorts has therefore beenreduced by a factor of 0.67.

The Quantitative Risks of Mesothelioma and Lung Cancer in ... · PDF fileQuantitative risks of mesothelioma and lung cancer 567 this error is very small, but in an asbestos exposed

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