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Review of “Development of a CLL Risk Model for NIOSH-IREP” 

David Richardson 

Department of Epidemiology, School of Public Health, 

University of North Carolina at Chapel Hill 

August 25, 2009 
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BACKGROUND

The Energy Employees Occupational Illness Compensation Program Act of 2000 

(EEOICPA) is a compensation program for current and former employees in the U.S. nuclear 

weapons complex who developed cancer as a result of their occupational exposure to ionizing 

radiation. Under the EEOICPA, chronic lymphocytic leukemia (CLL) currently is regarded as a 

non-radiogenic form of cancer. 

NIOSH is reconsidering this classification of CLL as a non-radiogenic disease. SENES Oak 

Ridge, Inc., in collaboration with NIOSH, developed a prototype risk model for CLL that allows 

for non-zero calculations of probability of causation (Trabalka and Apostoaei, Development of a 

CLL Risk Model for NIOSH-IREP, June 30, 2009). This review comments on the proposed risk 

model for CLL, proposed radiation risk coefficients, and proposed latency assumption. 

Proposed Risk Model for CLL

Analyses of associations between estimated radiation doses and cancer incidence among 

Japanese atomic-bomb survivors in the Life Span Study (LSS) provide estimates of the excess 

relative risk (ERR) for most cancer types in NIOSH-IREP. Given the small number of CLL 

cases in this atomic bomb survivor cohort (reflecting the fact that CLL is very uncommon in the 

Japanese population), the draft report titled “Development of a CLL Risk Model for NIOSHIREP” proposes using a broader group of malignant diseases for development of a CLL risk 

model. Specifically, the draft report proposes a risk model for CLL derived by modifying the 

current NIOSH-IREP risk model for the group of non-Hodgkin’s lymphoma (NHL), Hodgkin’s 
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disease, and multiple myeloma (MM). The draft report asserts that “Based on the information 

reviewed to date ... we think that the risk model in IREP for NHL, Hodgkin’s disease 

(lymphoma), and multiple myeloma (lymphoma and multiple myeloma grouping; ICD-9 codes 

200−203) (Preston et al. 1994; Land et al. 2003) could be a reasonable surrogate for a CLL risk 

model.” 

As the draft report notes, there are major differences between NHL, Hodgkin’s disease, and MM; 

and, the argument for inclusion of Hodgkin’s disease in this group of diseases which are intended 

to serve as a “surrogate” for CLL is not compelling (pages 15-16). Contemporary lymphoma 

classification schemes consider B-cell CLL and small lymphocytic lymphoma to be a single 

disease entity, and a sub-type of non-Hodgkin’s lymphoma, in recognition of the biological and 

clinical similarities between these B-lymphocyte malignancies (1). The Life Span Study data 

encompass an adequate number of NHL cases with which to support a separate analysis of 

radiation-dose response associations for NHL. NIOSH should revise the proposed model for 

CLL to draw upon information derived from an analysis of the association between radiation 

dose and NHL in the LSS cohort. 

The draft report further contends that the proposed grouping of NHL, Hodgkin’s disease, and 

MM is justified given the obstacles to developing a new risk model based on analysis of 

incidence of NHL. This is an argument for doing what is convenient, rather than logical. The 

report notes that an analysis of NHL would require data access, use of specialized software, and 

an independent regression analysis of these data. These are relatively modest obstacles to the 

proper conduct of this federal compensation program. 
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Proposed Radiation Risk Coefficients

The radiation risk estimates for NHL, Hodgkin’s, and MM in IREP that are proposed for 

application to CLL analyses are derived via a model in which the ERR/Sv is time-constant for 

exposure ages greater than or equal to 30 years and attained ages greater than or equal to 50 

years (ERR/Sv=0.178; 95%CI: <0, 0.9465). Given that CLL is a disease of older ages and very 

rarely occurs at ages < 50 years, the modeled variation in ERR/Sv with attained age is of little 

consequence for claimants (i.e., terms in the expression shown on page 15 of the draft report for 

the modifier involving g(a) have little consequence since the attained age of CLL cases is 

typically greater than 50 years). In fact, for simplicity, a CLL model might be simplified by 

striking attained age effect modification. Similarly, given that few workers were exposed at ages 

<18 years, and the modeled variation in ERR/Sv is modest over the age range 18-<30 years, the 

proposed model may largely behave as a time-constant model for the ERR/Sv (with an ERR 

coefficient of approximately 0.18 per Sv). 

The radiation risk estimates for NHL, Hodgkin’s, and MM in IREP that are proposed for 

application to CLL analyses (i.e, an ERR/Sv=0.178 for exposure ages greater than or equal to 30 

years and attained ages greater than or equal to 50 years) is substantially smaller than the 

radiation risk estimate in Table 2 (page 16) for NHL among men (ERR/Sv=0.91). Moreover, 

Richardson et al. (2009) reported on the association between DS02 radiation dose estimates and 

mortality due to non-Hodgkin’s lymphoma among males aged 15-65 years in the Life Span 

Study cohort with mortality follow-up through 2000 (2). The estimated ERR/Sv (ERR/Sv=0.86; 

90%CI: 0.13, 2.03 under a minimal 5-year lag and ERR/Sv=1.12; 90%CI: 0.26, 2.51 under a 10-

year lag) is larger in magnitude than the time-constant ERR/Sv proposed for exposure ages 
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greater than or equal to 30 years and attained ages greater than or equal to 50 years, and similar 

in magnitude to the estimate reported for NHL among males in Table 2 of the report. Richardson 

et al. (2009) also noted that when the LSS data were limited to survivors with doses in the range 

0-<0.5 Sv (a dose range most comparable to the worker claimants in this compensation program) 

estimates of radiation-lymphoma mortality associations were of greater magnitude than when 

estimated over the entire dose range. Under a 5-year lag assumption (i.e., events occurring over 

the period 1950-2000), the estimated ERR/Sv for NHL was 2.86 (90% CI: 0.10, 7.24). This 

value is of much larger magnitude than the proposed time-constant ERR/Sv for exposure ages 

greater than or equal to 30 years and attained ages greater than or equal to 50 years. 

Proposed Latency Assumption

The preferred model for NHL, Hodgkin’s disease and MM used in the draft report allows the 

ERR/Sv to vary as a function of age at exposure, attained age, and their joint effect. Jointly these 

parameters describe the effect of time-since-exposure in the LSS cohort. It is therefore unclear 

what basis there would be for imposing a latency distribution that spans the period when there is 

empirical (i.e., observed) data on the evolution of lymphoma risk following irradiation in the 

LSS population. The draft report proposes allowing for age at exposure and attained age effects 

(and their interaction) as well as imposing a distribution of latency effects that only reaches its 

maximum approximately 25 years after exposure. The preferred LSS model for the grouping of 

lymphomas (as implied by its use in NIOSH IREP), allows for a time-constant ERR/Sv for 

exposure ages greater than or equal to 30 years and attained ages greater than or equal to 50 

years (once the interval between exposure and start of follow-up has transpired). This would 

imply that for exposure ages greater than or equal to 30 years and attained ages greater than or 
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equal to 50 years the exposure effect should be at its maximum once cancer incidence follow-up 

of the LSS cohort commences (approximately 13 years after exposure) and persist at that 

magnitude thereafter. Under the proposed model, the ERR/Sv only reaches its maximum value 

approximately 25 yr after exposure. 

Under the proposed prototype model for CLL, the latency function is notably different from that 

used for other cancers (including the current model for lymphoma and multiple myeloma). Data 

collection for incident cancers for the atomic bomb survivor studies commenced 13 years after 

the bombings (with a special leukemia registry providing leukemia incidence data commencing 

in 1950). The LSS risk models describe the temporal evolution of the ERR/Sv (with age at 

exposure and attained age) in the period 1958 forwards (1950 forwards for leukemia). Rather 

than allow an abrupt transition from 0 excess risk to the modeled ERR/Sv in the LSS for a given 

attained age and age at exposure (or, extrapolating using the exponential age functions that are 

modeled), a smooth latency function is used in NIOSH-IREP. Under the current IREP model, 

for lymphoma and multiple myeloma (and all solid cancers except thyroid and bone cancer), an 

S-shaped (sigmoid) function was used to describe a smooth transition in the ERR/Sv over the 

latent interval. The nominal value of the midpoint of the sigmoid latency function is assumed to 

be 7.5 yrs, and the shape parameter for this function is set so that the latency adjustment attains 

values of approximately 0.01 and 0.99 at 4 and 11 yrs, respectively. Given empirical data for 

lymphomas in the period approximately 13 years or more after exposure, it would make sense to 

employ a model that gives full weight to fitted ERR/Sv estimates in the period when there is 

empirical data available. As noted above, the inclusion of model terms for effect modification 
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by attained age, age at exposure, and their joint effect, is used to describe variation in effect with 

time since exposure in the period where empirical data are available. 

 Epidemiological findings not covered in the draft report

Richardson et al. (2009) reports on estimated radiation dose-lymphoma mortality associations 

among male workers at the SRS. Positives associations between radiation dose and NHL 

mortality were observed in that cohort as well under 5- and 10-year lag assumptions 

(ERR/Sv=6.45; 90%CI: 0.48, 17.95; and, ERR/Sv=7.62; 90%CI: 0.93, 20.77). 

Given that CLL cases may be asymptomatic for many years, and that incidence data may have 

greater diagnostic accuracy than mortality data, there has been interest in results of incidence 

studies of CLL following radiation exposures. Some recent incidence studies of CLL among 

radiation workers have noted positive associations, including a study reporting an increase in 

CLL among Czech uranium miners who had gamma radiation exposures (3), and studies 

reporting positive associations between estimated radiation dose and CLL incidence in the 

Ukrainian-American study of leukemia and related disorders among Chernobyl recovery workers 

(ERR/Gy = 4.09; 95% CI < 0-14.41) (4), and in the study of hematological malignancies was 

conducted among Chernobyl liquidators (accident recovery workers) from Belarus, Russia and 

Baltic countries (ERR/100 mGy= 0.47; 90%CI: nd, 7.61)(5). In contrast, Muirhead et al. 

reported no evidence of an association between radiation dose under a 10 year lag and CLL 

incidence (ERR per Sv=-0.337, 90% CI: -1.72, 3.1) in their analysis of workers in the National 

Registry for Radiation Workers (6). 
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Comment on the calculation of expected CLL counts for the LSS population

It is unclear what contribution to this report is made by the calculation of the expected incidence 

of CLL in the LSS cohort (and Appendix A). The interpretation of the findings appears onesided; the authors contend that clear interpretation of the findings is only possible if the evidence 

suggests that ionizing radiation is protective for CLL. “If the estimated number of CLL cases in 

the absence of exposure had been greater than 4 [the observed number] then we might have been 

able to conclude that the dose-response for CLL in the LSS cohort should be negative.” 

The authors should clarify why they have tabulated the expected count over the period 1945-

1987 (rather than over the period Oct 1, 1950-1987). The report currently includes “estimates for 

the number of cases of CLL expected in the LSS cohort over the 42-y period from 1945−1987.” 

In contrast, the cited LSS analysis spans the period Oct 1, 1950-1987; it is unclear why the report 

includes expected counts spanning a non-comparable period (and longer period). In Preston et 

al. (1994) cancer cases ascertained by the Leukemia registry prior to Oct 1, 1950 were excluded. 

The estimated number of expected cases should be somewhat reduced when re-tabulated over the 

period Oct 1, 1950-1987. 

The observed number of CLL cases (4 cases observed) is 4.44 times the number expected based 

on the Japanese population (0.9 cases expected) and 3.33 times the number expected based on 

incidence rates for Hiroshima and Nagasaki (1.5 cases expected), leaving aside the problem in 

derivation of the expected counts noted above. The report should revise the calculations of 

expected counts to correspond to the period of CLL ascertainment in the cited LSS report, 
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include an estimate of the ratios of observed to expected counts (i.e., SIRs), and report associated 

90% confidence intervals for these ratios. 

 

 Conclusion

This report provides a useful starting point for consideration of a risk model for CLL to apply via 

NIOSH-IREP. This model may be strengthened by several suggested revisions, described 

below. 

Firstly, NIOSH should revise the proposed model for CLL to draw solely upon information for 

NHL. 

Secondly, the proposed ERR/Sv for exposure ages greater than or equal to 30 years and attained 

ages greater than or equal to 50 years (ERR/Sv=0.178; 95%CI: <0, 0.9465) is substantially 

smaller than the radiation risk estimate in Table 2 (page 16) for NHL among men 

(ERR/Sv=0.91) and substantially smaller than values reported for the association between 

radiation dose and mortality due to non-Hodgkin’s lymphoma among males aged 15-65 years in 

the Life Span Study cohort with mortality follow-up through 2000 (ERR/Sv=0.86; 90%CI: 0.13, 

2.03 under a minimal 5-year lag and ERR/Sv=1.12; 90%CI: 0.26, 2.51 under a 10-year lag). 

Given that the LSS data suggests a significant excess risk for males (2, 7), sex-averaged 

estimates of association may understate the excess risk for male claimants. The revised NIOSH 

model should incorporate information regarding the magnitudes of radiation-lymphoma 

associations observed in these analyses of male LSS survivors. 

 
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Thirdly, the proposed risk model allows ERR/Sv to vary as a function of age at exposure, 

attained age, and their joint effect. However, the model in the draft report further includes a 

distribution of latency intervals. Under the proposed model the ERR/Sv reaches 0.999 of the 

maximum 25 years after exposure. The revised NIOSH model should follow the current IREP 

model for lymphoma and multiple myeloma by employing a sigmoid latency function with a 

midpoint of the sigmoid latency function at 7.5 yrs and shape parameter set so that the latency 

adjustment attains values of approximately 0.01 and 0.99 at 4 and 11 yrs, respectively. Such a 

model would allow the terms for effect modification by attained age and age at exposure to 

describe latency functions in the period for which there is empirical data. 

The general approach for evaluation of compensation claims for CLL described in the report 

“Development of a CLL Risk Model for NIOSH-IREP” fits well within the approach currently 

implemented under the EEOICPA. However, workers would be better served, in terms of 

fairness and in terms of the soundness of the scientific basis for evaluating CLL claims, by 

addressing the issues raised above. 
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1999;17:3835-49. 

Richardson DB, Sugiyama H, Wing S, et al. Positive associations between ionizing radiation and 

lymphoma mortality among men. American journal of epidemiology 2009;169:969-76. 

Rericha V, Kulich M, Rericha R, et al. Incidence of leukemia, lymphoma, and multiple myeloma 

in Czech uranium miners: a case-cohort study. Environmental health perspectives 2006;114:818-

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Romanenko AY, Finch SC, Hatch M, et al. The Ukrainian-American study of leukemia and 

related disorders among Chornobyl cleanup workers from Ukraine: III. Radiation risks. 

Radiation research 2008;170:711-20. 

Kesminiene A, Evrard AS, Ivanov VK, et al. Risk of hematological malignancies among 

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Muirhead CR, O'Hagan JA, Haylock RG, et al. Mortality and cancer incidence following 

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British journal of cancer 2009;100:206-12. 

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