1



## Comments on the EPA draft risk assessment for Libby asbestos

– October 6, 2011 

## Suresh H. Moolgavkar, M.D., Ph.D. Exponent, Inc.2


## 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 end 

### points, 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.3


## 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 ill conceived 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 Sufficient justification not provided for the new and unpublished data sets used for assessment of cancer and non - cancer risks.4


## 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?5


## 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.6 

## 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.7 

## 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 sub 

### cohort. Failure to reject proportionality is probably a power issue (number of lung cancer cases reduced from ~95 to 32).8

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 holds9

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 holds10

From Richardson (2009). Analysis of lung cancer in South Carolina Textile Workers’ Cohort using TSCE (MVK) model. RR if proportionality holds11

From Richardson (2009). Analysis of lung cancer in South Carolina Textile Workers’ Cohort using Cox PH model.12

Schoenfeld residuals

– Libby lung cancer. P = 0.1, N = 95 Cum.exp:age interaction, p = 0.04813

Schoenfeld residuals

– Libby lung cancer among those employed more than 1 year. P = 0.3, N = 53 Cum.exp:age interaction, p = 0.08614

Libby Lung Cancer

– Full Data 95 lung cancers. RR associated with 1 f/ml - yr.

RR if proportionality holds15

Libby Lung Cancer

– Employed longer than 1 year 53 lung cancers. RR associated with 1 f/ml - yr

RR if proportionality holds16


## 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.17


## 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 K M (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.18

Not at all clear that the risks reported in the last column are comparable. Moolgavkar et al. estimated K M from the Peto - Nicholson model, whereas the current risk assessment reports risk associated with lagged cumulative exposure.1920


## 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.2122


## 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.23


## 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.24 

## 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 dose response relationship Response (beta for pleural abnormalities)

NOT significant when age considered

Interstitial Fibrosis and Diffuse Pleural Thickening NOT statistically significant25


## 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.