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Comments on the EPA draft risk assessment for 

Libby asbestos – October 6, 2011

Suresh H. Moolgavkar, M.D., Ph.D.

Exponent, Inc.
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General Comments

 Clear that considerable time and effort has been devoted to 

this comprehensive report.

 Report is unevenly written and certain chapters omit critical 

information. For example, the Moolgavkar et al. (2010) study is 

given short shrift in chapter 4 on risk characterization, while it 

is discussed in some detail in chapter 5 for the cancer endpoints, but ignored in the discussion of non-cancer end-points.

 Literature review is uneven and references are dated.

 Sufficient attention has not been paid to critical evaluation of 

papers. Examples later.
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General Comments

 Extensive new analyses have been performed by EPA 

in this report. These, to the best of my knowledge, 

have not been peer-reviewed. Some of these are illconceived and yield biologically implausible results. 

Examples later.

 Important analyses to investigate temporal aspects of 

asbestos-associated lung cancer and mesothelioma 

risk not undertaken.

 Sufficient justification not provided for the new and 

unpublished data sets used for assessment of cancer 

and non-cancer risks.
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Fundamental Issues

 Temporal aspects of exposure and risk 

1. How does pattern of exposure to a carcinogen determine 

risk?

 Is the risk determined by cumulative exposure (Haber’s 

law)?

 Or are patterns of exposure (age at start and stop, 

intensity of exposure) important?

2. How does risk evolve with time since start of exposure? How 

long does the memory of exposure last? What is the 

temporal pattern of risk after exposure stops?
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Fundamental Issues

 Haber’s law is false. Cumulative exposure is generally a poor determinant 

of carcinogenic risk, but is often used as a measure of exposure because it 

is easily understood.

 Cancer risk depends on entire temporal history of exposure to carcinogen. 

 For most carcinogens, ‘memory’ of exposure fades with time.

 Currently used statistical methods for analyses of epidemiologic data, 

based on the Cox proportional hazards model, are not well-suited to 

investigating temporal aspects of exposure and risk. Epidemiologic studies 

only rarely address these issues.

 Methods based on biological ideas of multistage carcinogenesis are better 

suited to studying temporal aspects of cancer risk.

 Because the IUR for Libby asbestos is ultimately based on a life-table 

calculation it is important that temporal aspects of risk, e.g., changing RR 

with age be addressed.
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EPA’s 1986 IRIS Slope Factor for Asbestos

 Hybrid approach based on consideration of mesothelioma and lung cancer.

 Mesothelioma model – Absolute risk model: Hazard function is modeled 

explicitly as a function of both intensity of exposure and duration of 

exposure. Model is linear in concentration but non-linear in duration of 

exposure. Peto based the model on ideas of multistage carcinogenesis. In 

this model, risk CANNOT be expressed as a function of cumulative 

exposure.

 Lung cancer model – Relative risk model: Relative risk modeled as a linear 

function of cumulative exposure (Haber’s law !). 

 In the final step risk estimates for mesothelioma and lung cancer are 

combined in a life-table analysis to arrive at the IRIS Inhalation Unit Risk 

(IUR) for cancer.

 The 2008 EPA OSWER directive essentially endorsed the use of this 

approach with some refinements for exposure assessment.
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Lung Cancer - Cox Proportional Hazards 

Model

 Introduced in 1972 for analyses of clinical trials data. Trials 

rarely last more than a few years. Has now been widely 

adopted for analyses of epidemiologic studies spanning 

decades. Proportionality of hazards cannot be expected to 

hold over this long time period and is clearly often violated.

 EPA treats proportionality of hazards as a ‘law of nature’ and 

goes to great lengths (pages 5-76 – 5-77) to explain why this 

‘law’ is violated for lung cancer in the full Libby cohort. This is 

a principal reason given by EPA for choosing a sub-cohort for 

lung cancer risk assessment. 

 EPA wrongly believes that proportionality holds in the subcohort. Failure to reject proportionality is probably a power 

issue (number of lung cancer cases reduced from ~95 to 32).
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Rate ratios for all-cause mortality among smokers with a

a two-pack a day habit as a function of age. Rate ratios are from the

Cancer Prevention Study I (CPS I) cohort (Burns et al., 1997). The line is 

a smoother through the observed rate ratios.

RR if proportionality holds
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Rate ratios for lung cancer among smokers with a two-pack a

day smoking history in the CPS I cohort (Burns et al., 1997). The line represents

the predicted relative risks from a fit of the TSCE model to the data 

(Hazelton et al. 2005). 

RR if proportionality holds
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From Richardson (2009). Analysis of lung cancer in South Carolina Textile 

Workers’ Cohort using TSCE (MVK) model.

RR if proportionality holds
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From Richardson (2009). Analysis of lung cancer in South Carolina Textile 

Workers’ Cohort using Cox PH model.
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54 62 65 70 75 77 80 87

Time

-0.002 0.000 0.002 0.004 0.006 0.008 0.010

Beta(t) for cum.exp

Schoenfeld residuals – Libby lung cancer. P = 0.1, N = 95

Cum.exp:age interaction, p = 0.048
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Time

Beta(t) for cum.exp

53 59 63 65 71 74 78 82

0.0 0.002 0.004 0.006

Schoenfeld residuals – Libby lung cancer among those employed more than 1 year. P = 0.3, N = 53

Cum.exp:age interaction, p = 0.086
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Age

exp(logRR)

45 50 55 60 65 70 75

1.0000 1.0005 1.0010 1.0015

Libby Lung Cancer – Full Data

95 lung cancers. RR associated with

1 f/ml-yr.

RR if proportionality holds
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Age

exp(logRR)

45 50 55 60 65 70 75

0.9990 0.9995 1.0000 1.0005 1.0010 1.0015

Libby Lung Cancer – Employed longer than 1 year

53 lung cancers. RR associated with 1 f/ml-yr

RR if proportionality holds
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Mesothelioma

 References outdated. Hillerdal (1983) is really dated and based on opinion, not data. 

Background rates have been estimated in more contemporary references (Price & 

Ware, 2004; Moolgavkar et al, 2009). Substantial fraction of mesotheliomas not 

attributable to asbestos exposure (numerous references will be provided in written 

comments).

 Mischaracterizes analysis in Moolgavkar et al. (2010). Not Poisson regression. Full 

likelihood time-to-tumor analysis based on the Peto-Nicholson model. Likewise, 

McDonald (2004) does not utilize Poisson regression for mesothelioma contrary to 

the statement in the EPA document.

 Even if Poisson regression is used, why is it necessary to use Bayesian MCMC 

methods? Likelihood analyses appear to be straightforward. In either case, CIs are 

unlikely to be particularly informative with only 8 cases of mesothelioma. 

 Performing the multiple analyses reported in this document with only 8 cases of 

mesothelioma is overkill.
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Mesothelioma – contd.

 Clear for most carcinogens (in fact, ALL carcinogens I am aware of) that risk depends both 

upon concentration and on duration of exposure independently. The Peto-Nicholson model 

used in 1986 EPA document acknowledges this fact. Berman & Crump (2008) showed that the 

Peto-Nicholson model does a satisfactory job of describing mesothelioma mortality in many 

occupational cohorts. 

 Not clear that the Peto-Nicholson model was appropriately applied (equation 5-5) to the Libby 

data. The version with time-varying exposure concentration as in Berman & Crump (2008) 

should have been applied. Maximum likelihood estimate of KM(Moolgavkar et al. (2010)) should 

have been obtained. The exponent for duration of exposure (fixed at 3) could also have been 

estimated. {Moolgavkar et al. could not estimate the exponent because they had no information 

on which specific individuals actually had pleural cancer/mesothelioma.}

 By reverting to some measure of cumulative exposure without a proper exploration of the 

Peto-Nicholson model, the current EPA analysis takes a giant step backwards. Jettisoning the 

full Libby data set in favor of a smaller data set because the full data do not find a significant 

effect of concentration after taking duration into account on basis of the Poisson analysis is 

unwarranted. The sub-cohort used for this analysis reduces the number of mesotheliomas 

from ~15 to 8.

 The adjustment made for alleged under-estimation of mesothelioma in the cohort is 

inappropriate. Details in written report.
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Not at all clear that the risks reported in the last column are comparable. 

Moolgavkar et al. estimated KM from the Peto-Nicholson model, whereas

the current risk assessment reports risk associated with lagged cumulative exposure.
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Treatment of half-life is inappropriate

 What is the interpretation of half-life in this report?

 If deposition in the tissue is proportional to concentration (c) and deposited 

fibers are eliminated with linear kinetics (constant rate k). Then,

 CD(t) = const.*(c/k){1 – exp(-kt)} (CD = cumulative tissue dose)

 The appropriate way to model clearance would be to replace cumulative 

exposure by cumulative dose (CD) above in the Cox model or the hazard 

function of a multistage model.

 Just because half-life, as used in this report, has been used in previous 

publications does not mean its use is justified (literature should be critically

reviewed).

 The estimated half-lives (10 years for the lung cancer model and 5 years for 

the mesothelioma model) appear to be far too short.

 That the half-life in the pleura is much shorter than in the lung is 

biologically implausible.
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Non-cancer end-points

 Based on the Rohs et al. (2008) data. Localized pleural thickening taken as 

end-point of interest.

 Again, as is the case for cancer end-points, this EPA analysis discards 

most of the data from the Rohs data set and restricts analyses to workers 

with ‘good’ exposure data (57 cases among 250 participants reduced to 12 

among 119 participants). 

 These numbers are at odds with those reported by Rohs et al., which 

reports 80 cases among 280 participants total (first discrepancy) and 10 

cases among 90 participants with ‘good’ exposure data (second 

discrepancy). 

 The reasons for these discrepancies are not clear. Rohs reports ‘good’ 

exposure estimates after 1973; this report says ‘good’ exposure estimates 

were available after 1971. May explain second discrepancy.
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Non-cancer end-points – contd.

 The Agency fits numerous parametric models to the 

data with various lag structures, while failing to check 

whether a monotone increasing function of 

cumulative exposure is appropriate.

 Since the Rohs study reports on the prevalence of 

pleural abnormalities, the use of lags is totally 

inappropriate. There is NO information in the data on 

when the abnormality first appeared. 
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0 5 10 15

exp

-2 -1 0 1 2

ns(exp, 6)

Exposure Response for Pleural Abnormalities

in Rohs et al. (2008) data

-2

Cumulative Exposure

-1

0

1

2

3 f/cc-yr

Non-statistically 

significant doseresponse relationship

Response (beta for pleural abnormalities)

NOT 

significant 

when age 

considered

Interstitial Fibrosis and Diffuse 

Pleural Thickening NOT 

statistically significant
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CONCLUSIONS

 Risk assessments for both cancer and non-cancer end-points based on 

severely and inappropriately restricted data sets.

 Mesothelioma model is a giant step backwards because it ignores the 

important role played by pattern of exposure, rather than just cumulative 

exposure, in determining risk. Peto-Nicholson model should be more 

thoroughly explored.

 Lung cancer model should have been extended to examine the role of 

pattern of exposure, rather than just cumulative exposure. Proportionality 

of hazards is not a ‘law of nature’; nor does it have any intrinsic biological 

justification. 

 Treatment of half-life is unrealistic.

 The analysis of the Rohs data set is particularly simplistic. The use of lags 

for prevalent conditions is totally inappropriate.

 Detailed comments on these and other issues will be provided in the written 

document.