Draft 

ADVISORY BOARD ON 

RADIATION AND WORKER HEALTH 

National Institute for Occupational Safety and Health

 

DRAFT REVIEW OF ORAUT-RPRT-0044: 

ANALYSIS OF BIOASSAY DATA WITH A 

SIGNIFICANT FRACTION OF LESS-THAN RESULTS 

Contract No. 200-2009-28555 

SCA-TR-PR2010-0009, Revision 0 

Prepared by 

Joyce Lipsztein, PhD 

Harry Chmelynski, PhD 

S. Cohen & Associates 

1608 Spring Hill Road, Suite 400 

Vienna, VA 22182 

November 2010 

Disclaimer 

This document is made available in accordance with the unanimous desire of the Advisory Board on 

Radiation and Worker Health (ABRWH) to maintain all possible openness in its deliberations. However, 

the ABRWH and its contractor, SC&A, caution the reader that at the time of its release, this report is predecisional and has not been reviewed by the Board for factual accuracy or applicability within the 

requirements of 42 CFR 82. This implies that once reviewed by the ABRWH, the Board’s position may 

differ from the report’s conclusions. Thus, the reader should be cautioned that this report is for 

information only and that premature interpretations regarding its conclusions are unwarranted.

 
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Document No. 

SCA-TR-PR2010-0009 

S. COHEN & ASSOCIATES: 

Technical Support for the Advisory Board on

Radiation & Worker Health Review of 

NIOSH Dose Reconstruction Program 

Effective Date: 

Draft – November 12, 2010 

DRAFT REVIEW OF ORAUT-RPRT-0044: 

ANALYSIS OF BIOASSAY DATA WITH A 

SIGNIFICANT FRACTION OF LESS-THAN 

RESULTS 

Page 2 of 21 

Task Manager: 

________________________ Date: ___________ 

Stephen F. Marschke 

Supersedes: 

N/A 

Project Manager: 

________________________ Date: ___________ 

John Mauro, PhD, CHP 

Reviewers: 

 Arjun Makhijani, PhD 

 Stephen F. Marschke 

Record of Revisions 

Revision 

Number 

Effective 

Date Description of Revision 

0 (Draft) 11/12/2010 Initial issue 
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TABLE OF CONTENTS 

Abbreviations and Acronyms ..........................................................................................................4 

Executive Summary.........................................................................................................................5 

1.0 Introduction..........................................................................................................................7 

2.0 Review of Section 2 of ORAUT-RPRT-0044 .....................................................................9 

2.1 General Comments...................................................................................................9 

2.2 Technical Comments .............................................................................................11 

2.3 Issues of Importance to SRS Construction Workers..............................................12 

3.0 Review of Section 3 of ORAUT-RPRT-0044 ...................................................................15 

3.1 Comments Specific to SRS....................................................................................16 

4.0 Review Checklist ...............................................................................................................18 

5.0 Conclusions........................................................................................................................20 

References......................................................................................................................................21 
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ABBREVIATIONS AND ACRONYMS 

CL Censoring Level 

CW Construction Worker 

dpm disintegrations per minute 

EPA U.S Environmental Protection Agency 

GSD Geometric Standard Deviation 

HLN hybrid lognormal 

L Liter 

LD detection limits 

MDA Minimum Detectable Activity 

NCW Non-Construction Worker 

NIOSH National Institute for Occupational Safety and Health 

NOCTS NIOSH OCAS Claims Tracking System 

NRC U.S. Nuclear Regulatory Commission 

ORAUT Oak Ridge Associated Universities Team 

OTIB ORAUT Technical Information Bulletin 

PIC Pocket Ionization Chamber 

POC Probability of Causation 

PROC Procedure 

RPRT Report 

SEC Special Exposure Cohort 

SRS Savannah River Site 

TLD Thermoluminescent Dosimeter 
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EXECUTIVE SUMMARY 

This report presents SC&A’s initial review of ORAUT-RPRT-0044, Analysis of Bioassay Data 

with a Significant Fraction of Less-Than Results (ORAUT 2009), which describes the National 

Institute for Occupational Safety and Health’s (NIOSH’s) proposed methods for reconstructing 

exposures to unmonitored workers when essentially all or most1 of the available data from 

monitored workers are below the limit of detection. The proposed methods complement and 

supplement the recommendations contained in ORAUT-PROC-0095, Generating Summary 

Statistics for Coworker Bioassay Data (ORAUT 2006a). This report also addresses potential 

issues related to the application of the proposed methods to construction workers at the Savannah 

River Site (SRS), especially those issues that might be considered SEC-related. This report does 

not contain a detailed analysis of the issues, and is intended solely for use by the designated 

working groups. 

SC&A has identified the following four findings. 

Finding No. 1: The statistical methods proposed in ORAUT-RPRT-0044 are based on sound 

statistical methodologies, and the material is well presented. The proposed methods are an 

improvement over the regression methods proposed in ORAUT-PROC-0095 when essentially all 

or most of the data are less-than results, the limit of detection was the same for all samples in the 

dataset, and the samples above the limit of detection are randomly spread across workers, 

job types, and work areas. 

The work location and work assignments of the workers with positive results are not considered 

in the NIOSH approach. Before these methods are used in a coworker model, further analysis of 

the positive results is required. In particular, identification of the workers, work areas, and 

processes accounting for the positive results in the datasets is required to reveal possible patterns 

that may explain the occurrence of positive results. 

NIOSH does not offer any consideration relating to the pattern or time distribution of the positive 

results. For example, the positive results could be present x times per year, during defined 

periods of time, or during a specific campaign. It is possible that the same subgroup of workers 

accounted for most of the positive readings year after year. 

Finding No. 2: ORAUT-RPRT-0044 does not address the representativeness of the dataset for 

workers in all work areas and job types. No individual worker analysis was performed, as the 

report concentrates only on analysis of a collection of analytical results. 

Finding No. 3: The methods proposed in RPRT-0044 are illustrated using several examples. 

One example uses simulated data from two distributions. In this situation, the maximum 

likelihood method performs as expected. In a second example involving analysis of SRS Pu-239 

urinalyses, detection limits thought to reflect conditions before the 1980s are applied 

retrospectively to a 1997 dataset that contained no less-than results. The 1997 dataset contains 

1

 SC&A interprets “essentially all” as greater than 95% of the results below the censoring level, and “most” 

as between 90% and 95% of the results below the censoring level. 
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NOTICE:

no less-than results, due to the capability of modern laboratory procedures. The 1997 SRS data 

selected for the example appear to be a mixture of two different distributions. Although no lessthan results are found in the 1997 dataset, the method of RPRT-0044 is illustrated by selecting a 

hypothetical detection limit higher than the lower of these two distributions. Since the data used 

in this example do not reflect working conditions at SRS prior to 1980, the use of SRS urinalysis 

data in this example is largely an academic exercise, providing no useful information about pre1980 exposures at SRS or the appropriateness of using the two-distribution model for any type of 

worker in time periods before 1997. 

Finding No. 4: The methods proposed in RPRT-0044 for datasets with essentially all or most of 

the less-than results are based on samples obtained from all workers, regardless of job type or 

location. No attempt was made to determine the work areas, processes, or job types of workers 

with positive results. This approach is not claimant favorable for construction workers for three 

reasons: 

(1) In many cases, construction workers were not monitored as frequently as nonconstruction workers,2

 hence the dataset may not be representative of the 

distribution of construction worker exposures. 

(2) Because constructions workers were sampled less frequently, a higher percentage 

of these workers will require use of the coworker model. 

(3) The positive samples may come from very few workers or restricted time periods, 

which may not be representative of the worker population.3

The work assignments of the workers with samples in the upper tail of the mixture distribution 

may have an unexpectedly high number of construction workers when compared with their 

degree of monitoring. The work assignments of workers with samples in the two populations 

should be inspected and categorized by job type to look for such disparities. 

2

 For instance, in the data complied by NIOSH for addressing SRS CW vs. NCW plutonium exposures for 

ORAUT-OTIB-0052, there were 612 NCW (roll numbers 1 to 3) and 888 CW (roll numbers 4 to 6), yet only about 

30% of the samples were from CWs. Moreover, there were no CW samples in the 1950s and few in the 1960s and 

1970s. Most of the samples were in the 1980s. In contrast, the NCW samples were spread throughout the period 

from 1953 to the mid-1990s (see SC&A 2010, Figure 1). The numbers of CWs and NCWs were determined from 

the NIOSH spreadsheet where the data were compiled. See also Figure 3 below, which shows that CWs at SRS 

were less frequently monitored for plutonium in most of the years prior to 1990. 3

 Both were true of the CW plutonium bioassay dataset used by NIOSH for its SRS analysis in ORAUTOTIB-0052 (see SC&A 2010, pp. 5 to 8). 
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1.0 INTRODUCTION 

This report presents methods for analyzing datasets dominated by censored results. Methods are 

presented for two types of datasets; (1) those that have a large fraction (from 90% to 95%) of 

censored data, and (2) datasets that consist entirely (>95%) of censored data. Censored data is a 

statistical term for measurements below the limit of detection, i.e., the “Less-Than Results” 

noted in the title of the report. This report is intended to correct/complement ORAUT-PROC0095, Generating Summary Statistics for Coworker Bioassay Data (ORAUT 2006a). The 

methods recommended in PROC-0095 for estimating parameters of a lognormal distribution are 

applicable when less-than results are present in the data, but there must be a sufficient number of 

measurements above the detection limit to fit a regression line to the data. 

The PROC-0095 approach assumes that there is a single lognormal distribution applicable to the 

entire dataset that is being analyzed. In the case where there are no values above the detection 

limit, PROC-0095 cannot be applied. If there are only a few values above the detection limit, the 

regression method in PROC-0095 may yield erratic results. If the dataset contains more than one 

population with possibly different distributions, PROC-0095 is no longer applicable. RPRT0044 addresses these situations where the procedure recommended in PROC-0095 is not 

applicable, cannot be applied, or may yield erratic results. 

The deficiencies of ORAUT-PROC-0095 when there is a large number of less-than results were 

noted by SC&A in Finding ORAUT-PROC-0095-03 in Findings from 3rd set of Procedures. In 

NIOSH Responses to Selected Findings from 3rd set of Procedures, NIOSH explains that a new 

report (ORAUT-RPRT-0044) has been developed to better address the issue of censored data. 

The statistical treatment of the data in RPRT-0044 is correct and well explained. If the dataset 

contains urine results for which most samples do not have analyte in the urine, but a small 

fraction do, the methods presented in RPRT-0044 are an improvement over the PROC-0095 

procedure. 

RPRT-0044 consists of two separate methods, to be applied depending on the relative fraction of 

less-than results in the dataset. The first method is described in Section 2 of the report. It relates 

to datasets consisting of results that are essentially all below the censoring level (CL). The 

statistical treatment of the data is correct and well explained. The general approach is to 

determine a normal distribution to represent the measurements that lie below the CL. The upper 

tail of this normal distribution is then approximated by a lognormal distribution for use in dose 

modeling. 

 

The second method presented in RPRT-0044 (Section 3 of the report) addresses the case when 

the number of results above the CL is larger, but a large majority of results are less than the CL. 

The approach in this case is to assume that there are two distinct populations; one group of 

samples has no measureable analyte in the urine, but a small fraction of the samples do. The 

samples in the former group are assigned a normal distribution of “background” readings, and 

the samples in the latter group are assigned a lognormal distribution. Maximum likelihood 

methods are used to estimate the parameters of the mixed model. 
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NOTICE:

Section 4 of RPRT-0044 contains an example based on the use of 1997 SRS Pu-239 urinalysis 

data. The use of this dataset to illustrate the proposed methods is largely an academic example, 

because the dataset actually contains no less-than results, due to the advanced capabilities of 

modern analytical techniques and advances in process technology and worker protection. It is 

likely that the few positive results in this dataset are incident-related. 

The “two-population” method proposed in RPRT-0044 is illustrated by assuming a high CL 

thought to be reflective of analytical techniques used in the years before the 1980s. Since the 

data used in this example are 1997 data, it is unlikely that the data reflect working conditions at 

SRS in the earlier years. The two-distribution approach may only be useful in the analysis of the 

most recent datasets. RPRT-0044 provides no evidence of the existence of a “two-population” 

model in datasets from earlier years. 
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2.0 REVIEW OF SECTION 2 OF ORAUT-RPRT-0044 

This section of RPRT-0044 presents a method that can be applied to datasets consisting of results 

that are essentially all below the CL. The general approach is to determine a normal distribution 

to represent the measurements that lie below the CL. The upper tail of this normal distribution is 

then approximated by a lognormal distribution for use in dose modeling. 

 

2.1 GENERAL COMMENTS 

In the application of the procedure recommended in RPRT-0044, the issue of completeness of 

the available data has not been addressed. Some workers were not monitored, otherwise there 

would be no need for a coworker model. The underlying assumption seems to be that the 

workers with the most exposure potential were monitored; but we have seen in a number of cases 

that this was not necessarily true. So, if essentially all known results are below the MDA, then 

can the whole population (including the unmonitored workers) be represented by a distribution 

that is almost entirely below MDA, except for some scattered people in a lognormal tail fit to the 

same distribution? If the unmonitored workers are from a different population, the applicability 

of a coworker model derived from monitored workers would be in question. 

Another limitation of the approach recommended in RPRT-0044 is that it assumes a single 

detection limit for all measurements in the dataset. Hence, the method is best applied when all 

measurements were made using the same type of equipment and analytical techniques for the 

same radionuclides. The method described and exemplified in the report uses only 1 year of 

results. While the use of 1-year results is consistent with the NIOSH coworker modeling effort, 

which generates dose estimates for each year, in most cases, the limit of detection of the 

equipment for the specific analyte and solubility type in question is not known and must be 

assumed or inferred from other sources. 

 

Many NIOSH datasets contain results noted as “less-than” with no detection limit specified. 

Often there is no indication of a “less-than” result; only an entry of 0, which is usually 

interpreted as a less-than result. In these and other cases, NIOSH has determined detection limits 

retrospectively. For example, the following detection limits are recommended for use at Y-12 

(ORAUT 2006b): 

5A.3 IN VITRO DETECTION LIMITS 

Tables 5A-1 and 5A-2 summarize information developed in Section 5.2. The 

tabulated values for urinalysis results represent laboratory detection limits LD

and do not include uncertainties introduced by sample collection or conversion 

from submitted volumes to daily void volumes. As noted in Section 5.2, LD values 

for some historical techniques remain to be identified, and will be reported in 

subsequent revisions as available. 
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Table 5A-1. Uranium urinalysis detection limits.

Detection Detection

Method Period limit (mass) limit (activity)

 Fluorometry 1950–9/1989 7 μg/d 11 dpm/d 

 Gross alpha 1950–1964 47 dpm/d 

 Gross alpha 1965–9/1989 26 dpm/d 

 Alpha spectrometry 10/1989–present 0.15 dpm/d 

Table 5A-2. Other in vitro detection limits.

Detection limit

Analyte/solubility type Method Period (activity)

 Tritium (HTO) Liquid scintillation 10/1988–present 2,000 dpm/da 

 Isotopic plutonium/M, S Alpha spectrometry 10/1989–present 0.025 dpm/sample 

 Am-241/M Alpha spectrometry 10/1989–present 0.050 dpm/sample 

 Th-228/M, S Alpha spectrometry 10/1989–present 0.150 dpm/sample 

 Th-232/M, S Alpha spectrometry 10/1989–present 0.070 dpm/sample 

 Np-237/M Alpha spectrometry 10/1989–present 0.100 dpm/sample 

a. Estimate 

At SRS, detection levels are described in terms of reporting levels. The reporting level is 

described as follows: 

The reporting level is distinguished from the MDA. As described in WSRC-IM90-139 [WSRC 1990], the reporting level is the minimally acceptable decision 

level; the bioassay laboratory was required to achieve decision levels below the 

reporting level. SRS technical documentation provided no quantitative 

relationship between the reporting level and MDA (p. 65, ORAUT 2005). 

Recommendations for reporting levels for plutonium and americium at SRS are shown in the 

following table, extracted from Appendix D of ORAUT-TKBS-0003 (ORAUT 2005): 

Table D-1. Limits of detection for urinalysis

Radionuclide Period MDA (pCi/L) Reporting Level 

238, 239, 240Pu 1954 to 1962 0.05dpm/1.5L 

238, 239, 240Pu 1963 to 1981 0.1dpm/1.5 L 238Pu 1981 to 1988d 0.05dpm/1.5L 238Pu 1988 to present 0.07 dpm 239Pu, 240Pu 1981 to 1988d 0.07 dpm/1.5 L 239Pu, 240Pu 1988 to present 0.06 dpm 241Am Mid 1960s to 1971 3 dpm/1.5 L 241Am 1971 to 1990 0.3 dpm/1.5 L 241Am 1990 to 1994 0.1 dpm/L 241Am 1994 to present 0.029 

NOTE: Only the Pu and Am data in Table D-1 are reproduced here. 
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When the procedure recommended in RPRT-0044 is applied to samples collected in a single year 

and analyzed using the same equipment, the disparity in limits of detection across samples may 

be reduced, but a large degree of uncertainty remains in the assignment of a specific value for the 

limit in that year. In most cases, the limit of detection of the equipment for the specific analyte 

and solubility type in question is not known, and must be assumed or inferred from other 

sources. It is unlikely that the detection limits remained constant for long time periods of several 

decades, as suggested by the tables above. When the dataset does have limits of detection 

associated with the less-than results, these limits may have been added to the dataset at a later 

date during a retrospective analysis, such as the one above. Hence, the mere fact that the 

detection limits in a dataset are all the same for a given radionuclide may indicate only that they 

were imposed by assumption. 

In the early years, with higher limits of detection, there may also be a relatively smaller 

proportion of measurements above the limit. Lack of detailed information on the type and limit 

of detection of the equipment in use in these earlier years serves to compound this problem. 

2.2 TECHNICAL COMMENTS 

A normal distribution is symmetric, while a lognormal distribution is asymmetric and has a 

longer tail on the right. For sample sizes that are not too large, the samples that fall in the upper 

tail of a normal distribution may appear to be lognormally distributed. Figure 1 shows a case 

with N = 1,000 samples from a standard normal distribution with n = 34 of the samples occurring 

in the upper 5% tail of the distribution (× >1.645 standard deviations). The vertical scale is 

logarithmic and the regression line fits relatively well, indicating that the samples in the tail of 

the normal distribution appear to be a sample from a censored lognormal distribution. 

N=1,000 and n=34

y = 0.4969x - 0.3876

R2

 = 0.9795

0.4

0.6

0.8

1

1.2

1.4

1.6

1 1.5 2 2.5 3 3.5 4

z-score

Ln(x)

ln(x>1.645), x~N(0,1) Fitted Lognormal Distribution

Figure 1: Fitting a Lognormal Distribution to 34 Samples in the Upper Tail of 1,000 

Samples from a Standard Normal Distribution N(0,1) 
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It is well known that the lognormal distribution has “fatter” tails than the normal distribution, but 

at a moderate sample size of 1,000, the two distributions appear very similar. When the sample 

size is increased to N = 10,000 with n = 468, then the difference in the upper tails of the two 

distributions becomes apparent, as shown in Figure 2. 

The slope of the fitted regression lines in Figures 1 and 2 are σ = 0.4969 and σ = 0.4494, 

respectively. The geometric standard deviations (GSD = eσ

) of the two lognormal distributions 

are 1.64 and 1.57, respectively. When the upper 5% tail of a sample from the standard normal 

distribution is fit to lognormal distribution, the expected GSD is 1.55 (σ = 0.438). This curious 

relationship between the normal and lognormal distributions forms the gist of the approach 

recommended for the case of essentially all less-than results: use a lognormal distribution with a 

GSD of 1.55 and then to match the CL. 

N=10,000 and n=468

1.6

1.4

y = 0.4494x - 0.2308

2 R = 0.9794

1.2

1

Ln(x)

0.8

0.6

0.4

1 1.5 2 2.5 3 3.5 4

z-score

ln(x>1.645), x~N(0,1) Fitted Lognormal Distribution

Figure 2: Fitting a Lognormal Distribution to 468 Samples in the Upper Tail of 

10,000 Samples from a Standard Normal Distribution N(0,1) 

2.3 ISSUES OF IMPORTANCE TO SRS CONSTRUCTION WORKERS 

The methods proposed in RPRT-0044 for analyzing datasets that consist almost entirely of lessthan results are statistically sound, but use of this approach in the coworker model requires 

additional qualification that the dataset is representative of all groups of workers. The job types 

of the workers in the SRS 1997 Pu dataset were not analyzed in RPRT-0044. In SC&A’s study 

of construction versus non-construction worker claimants in NOCTS, the relative frequency of 

Pu urinalysis samples were compared for these two groups of claimants. Job types are known 

for most NOCTS claimants. 
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The time series plot in Figure 3 shows the percentage of claimants that were sampled by year for 

these two groups of workers and for all workers combined. In this plot, the percentage of CW 

claimants with at least one sample was lower than for NCW claimants up to approximately 1990. 

The lower frequency of CW monitoring is particularly marked in some periods, such as the 

1950s and early 1960s. 

Figure 4 shows a plot of the average number of samples per year for these two groups of 

workers. In this figure, the number of samples for each NCW exceeded the average number of 

samples from CWs, except from 1980 to 1985. In some years, NCWs were over-sampled by a 

margin of over 2 to 1. If the sampling frequency in the general population of SRS workers was 

the same as for NOCTS claimants analyzed by SC&A, then the dataset used for analysis of SRS 

Pu urinalyses is not representative of SRS CWs, due to the higher sampling frequency for 

NCWs. Note that in both figures and for almost all years, the plot for all workers combined is 

approximately equal to the NCW plot. This fact again shows that a coworker model based on 

datasets obtained from NCWs and CWs combined would not be representative of CWs. 

In the case of the SRS CWs who were monitored less frequently than NCWs, it is likely that this 

group of workers forms a separate exposure population. If this were the case, the applicability of 

a coworker model derived from NCW data would be in question. The problem for the CWs is 

two-fold; (1) The use of a coworker model would be required for a larger percentage of these 

workers than for the NCW, and (2) the coworker model would be based on data obtained with an 

under-sampling of construction workers. 

Percentage of Workers Monitored for Pu by Year

0%

10%

20%

30%

40%

50%

60%

70%

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

Percentage Monitored

Construction Workers

NonConstruction Workers

All Database Workers

Figure 3: Percentage of Workers Monitored for Plutonium at SRS 
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Average Number of Samples per Worker Monitored for Pu in Year

0.00

1.00

2.00

3.00

4.00

5.00

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

Samples

Construction Workers

NonConstruction Workers

All Database Workers

Figure 4: Average Number of Samples per Worker Monitored for Plutonium at SRS

NOTICE:
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3.0 REVIEW OF SECTION 3 OF ORAUT-RPRT-0044 

The second part of the report addresses datasets consisting of results that are mostly below the 

CL. The authors of the report postulate that the distribution of results is not a single lognormal 

distribution, but a mixture of two distinct distributions, one normal and the other lognormal. In 

this model, there are two populations of samples; most samples have no measureable level of 

analyte in the urine, but a small fraction of the samples do. In this mixed model, the former 

group of samples with less-than results is assigned a normal distribution representing 

“background” exposures, and the latter group of samples is assigned a lognormal distribution of 

exposures.4

 

The statistical treatment of the data is correct and well explained. Maximum likelihood 

techniques are used to estimate the parameters of the mixed model. Since the method 

recommended in ORAUT-PROC-0095 for parameter estimation is based on the assumption of a 

single lognormal distribution, the method recommended there is no longer applicable in this 

situation. 

If a dataset contains urine results for which most of the workers do not have analyte in their urine 

but a small fraction of the workers do, then the method presented in RPRT-0044 is an 

improvement over the PROC-0095. Although there are other procedures that may be applied to 

these datasets, like the Bayesian approach described by Miller et al. (2003), maximum likelihood 

methods are widely used and are acceptable. This is a valid method to be applied for coworker 

statistics when the analytical results above the CL are randomly spread across workers, job 

types, and work areas. 

The method described and exemplified in the report uses only 1-year results. While the use of 

1-year results is consistent with the NIOSH coworker modeling effort, which generates dose 

estimates for each year, NIOSH does not offer any consideration relating to the pattern or time 

distribution of the positive results. It is necessary to know if the positive results occur every 

year, and if those results are related to a particular procedure. For example, the positive results 

could be present x times per year, during defined periods of time, or during a specific campaign. 

The work location and assignments of the workers with positive results are not considered in the 

NIOSH approach. The method described in the report also does not consider the fraction of the 

worker population that had positive results. For example, did one sub-group of workers account 

for most of the positive results? NIOSH does not analyze all the years in which workers were 

exposed in the facility, where the exposures occurred, or the processes being carried out in the 

facility. Information of this type is useful to distinguish rare events from ones that appear 

routinely every year in one part of the facility. 

4

 Mixtures of normal and lognormal distributions have been applied by other authors to characterize annual 

worker exposure. One example is the hybrid lognormal (HLN) distribution proposed by Kumazawa and Numakunai 

(1981). This model was used by the EPA Office of Radiation Programs to model individual and collective 

occupational exposures to U.S. radiation workers. In that application, the normal distribution component was used 

to model exposures in the extreme upper tail, near and above NRC individual exposure limits, while the lognormal 

distribution component characterized the less-exposed group of workers. 
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3.1 COMMENTS SPECIFIC TO SRS 

The issues of work location and assignment are fundamental in the use of coworker models for 

CWs at SRS. The work assignments of the workers in the upper tail of the mixture distribution 

may have an unexpectedly high number of CWs when compared with their degree of monitoring. 

The work assignments of workers in the two populations should be inspected and categorized by 

job type to look for such disparities. 

Section 4 of RPRT-0044 contains an example that uses 1997 SRS Pu-239 urinalysis data. The 

use of this dataset to illustrate the proposed methods is largely an academic example, because the 

dataset actually contains no less-than results, due to the advanced capabilities of modern 

analytical techniques and recent advances in process technology and worker protection. It is 

likely that the few positive results in this dataset are incident related. 

Plutonium-239 data collected in 1997 at the SRS is used as an example of the “two-population” 

model. None of this dataset was censored data, using 1997 analytical procedures. When the 

cumulative distribution of the data is plotted using the graphical technique described in PROC0095, there appear to be two populations of results. The method of RPRT-0044 is applied 

retrospectively, by postulating a CL of 0.1 dpm/L and imposing this CL on data collected much 

later in 1997. This CL is proposed as being representative of analytical procedures for plutonium 

before the 1980s. This value exceeds the reporting level for Pu-239 of 0.1 dpm/1.5 liters 

recommended by NIOSH in Table D-1 of ORAUT 2005, reproduced in Section 2.1 of this 

report. 

The example selected has little relation to actual exposures that may have occurred at SRS before 

the 1980s. In 1997, the data reflect different conditions and processes and many advances in 

worker safety than before the 1980s. In the 1997 dataset, the relatively few high exposures may 

have resulted from unusual incidents, while many workers experienced much safer conditions. 

The clear success of the two-population model for 1997 data does not imply that the same shape 

of distribution is applicable to SRS before the 1980s. 

The two-population model for the 1997 SRS data is described as follows in RPRT-0044: 

The lower-level distribution represents the normally distributed analytical 

background. ... The higher-level data above the CL of 0.1 dpm/L represent a 

second population of data in addition to the analytical background. This higherlevel population is referred to here as the “exposure population” because it 

represents results that are unlikely to have come from fluctuations in the 

analytical background. 

There are several ways to interpret the terms “population” and “analytical background” as they 

are used in RPRT-0044. The “population” is not a distinct population of workers, but a 

population of sample results. An individual worker may have samples in both populations. 

As noted in Section 2.3 of this report, the dataset used in this example may not be representative 

of CW exposures at SRS. The difference in the number of samples per worker noted in that 
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section apply equally to the two-population model. Figure 4 showed a plot of the average 

number of samples per year for these two groups of workers. In this figure, the number of 

samples for each NCW exceeded the average number of samples from CWs in 1997 by a margin 

of over 2 to 1. If the sampling frequency in the general population of SRS workers was the same 

as for NOCTS claimants analyzed by SC&A, then the dataset used for analysis of SRS Pu 

urinalyses is not representative of SRS CWs, due to the higher sampling frequency for NCWs. 
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4.0 REVIEW CHECKLIST 

Table 1. Procedure Review Outline/Checklist 

Document No.: ORAUT-RPRT-0044 Effective Date: 05/22/2009

Document Title: Analysis of Bioassay Data with a Significant Fraction of Less-Than Results

Reviewer: J. Lipsztein/H. Chmelynski

No. Description of Objective Rating

1-5* Comments

1.0 Determine the degree to which the procedure supports a process that is expeditious and 

timely for dose reconstruction.

1.1 Is the procedure written in a style that is clear 

and unambiguous? 

5 

1.2 Is the procedure written in a manner that 

presents the data in a logical sequence? 

3 Only one year of data is analyzed 

for SRS. 

1.3 Is the procedure complete in terms of required 

data? 

1 See comment 1.2. 

1.4 Is the procedure consistent with all other 

procedures that are part of the hierarchy of 

procedures employed by NIOSH for dose 

reconstruction? 

N/A 

1.5 Is the procedure sufficiently prescriptive in 

order to minimize the need for subjective 

decisions and data interpretation? 

3 Variation of MDAs over time is not 

addressed. 

2.0 Determine whether the procedure provides adequate guidance to be efficient in instances 

where a more detailed approach to dose reconstruction would not affect the outcome. 

2.1 Does the procedure provide adequate guidance 

for identifying a potentially high probability of 

causation as part of an initial dose evaluation of 

a claim? 

2 Several critical issues, such as job 

types and variation of MDAs, are 

not addressed. 

2.2 Conversely, for claims with suspected 

cumulative low doses, does the procedure 

provide clear guidance in defining worst-case 

assumptions? 

3 

3.0 Assess the extent to which the procedure accounts for all potential exposures and ensures 

that resultant doses are complete and based on adequate data in instances where the POC 

is not evidently clear. 

3.1 Assess quality of data sought via interview: ---- 

3.1.1 Is scope of information sufficiently 

comprehensive? 

NA 

3.1.2 Is the interview process sufficiently flexible to 

permit unforeseen lines of inquiry? 

NA 

3.1.3 Does the interview process demonstrate 

objectivity and is it free of bias? 

NA 

3.1.4 Is the interview process sensitive to the 

claimant? 

NA 
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3.1.5 Does the interview process protect information 

as required under the Privacy Act? 

NA 

3.2 Assess whether the procedure adequately 

addresses generic as well as site-specific data

pertaining to: 

---- 

3.2.1 Personal dosimeters (e.g., film, TLD, PICs) NA 

3.2.2 In vivo/In vitro bioassays 

3.2.3 Missing dosimetry data 

3.2.4 Unmonitored periods of exposure 

4.0 Assess procedure for providing a consistent approach to dose reconstruction regardless of 

claimants’ exposures by time and employment locations.

4.1 Does the procedure support a prescriptive 

approach to dose reconstruction? 

3 

4.2 Does the procedure adhere to the hierarchical 

process as defined in 42 CFR 82.2? 

N/A 

5.0 Evaluate procedure with regard to fairness and giving the benefit of the doubt to the 

claimant.

5.1 Is the procedure claimant favorable in instances 

of missing data? 

2 Does not distinguish work in 

different areas or job types. 

5.2 Is the procedure claimant favorable in instances 

of unknown parameters affecting dose 

estimates? 

2 See comment 5.1. 

5.3 Is the procedure claimant favorable in instances 

where claimant was not monitored? 

3 

6.0 Evaluate procedure for its ability to adequately account for the uncertainty of dose 

estimates.

6.1 Does the procedure provide adequate guidance 

for selecting the types of probability 

distributions (i.e., normal, lognormal)? 

5 

6.2 Does the procedure give appropriate guidance in 

the use of random sampling in developing a 

final distribution? 

5 

7.0 Assess procedure for striking a balance between the need for technical precision and 

process efficiency.

7.1 Does the procedure require levels of detail that 

can reasonably be accounted for by the dose 

reconstructor? 

2 Variation of MDAs over time is a 

necessity, but there is no procedure 

as to how this information is to be 

obtained. 

7.2 Does the procedure avoid levels of detail that 

have only limited significance to the final dose 

estimate and its POC? 

5 

7.3 Does the procedure employ scientifically valid 

protocols for reconstructing doses? 

2 Statistical approach is valid, but 

essential technical details are not 

addressed. 

______________________ 

* Rating System of 1 through 5 corresponds to the following: 1=Never, 2=Infrequently, 

3=Sometimes, 4=Frequently, 5=Always. N/A indicates not applicable 
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5.0 CONCLUSIONS 

In conclusion, the statistical methods proposed in ORAUT-RPRT-0044 are based on sound 

statistical methodologies, and the material is well presented. The proposed methods are an 

improvement over the regression methods proposed in ORAUT-PROC-0095 when essentially all 

or most of the data are less-than results, the limit of detection was the same for all samples in the 

dataset, and the samples above the limit of detection are randomly spread across workers, 

job types, and work areas. Further analyses are needed to evaluate the pattern, time 

distribution, and worker distribution of the positive results, before a generic coworker daily 

intake is assigned to the unmonitored worker. 

ORAUT-RPRT-0044 does not address the representativeness of the dataset for workers in all 

work areas and job types. No individual worker analysis was performed, as the report 

concentrates only on analysis of a collection of analytical results. 

The “two-population” method proposed in RPRT-0044 is illustrated by assuming a high CL 

thought to be reflective of analytical techniques used in the years before the 1980s. Since the 

data used in this example are 1997 data, it is unlikely that the data reflect working conditions at 

SRS in the earlier years. The two-distribution approach may only be useful in the analysis of the 

most recent datasets. RPRT-0044 provides no evidence of the existence of a “two-population” 

model in datasets from earlier years. 

This approach is not claimant favorable for CWs. In general, CWs were not sampled as 

frequently as NCWs, hence the dataset may not be representative of the distribution of CW 

exposures. Also, because CWs were sampled less frequently than NCWs, a higher percentage of 

CWs are expected to require use of the coworker model for dose reconstruction.
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REFERENCES 

Kumazawa, S. and T. Numakunai, 1981. “A New Theoretical Analysis of Occupational Dose 

Distributions Indicating the Effect of Dose Limits,” Health Physics, Vol. 31, No. 3, pp. 465–475. 

1981. 

Miller, G., T. Little, and R. Guilmette, 2003. “The Application of Bayesian Techniques in the 

Interpretation of Bioassay Data,” Radiation Protection Dosimetry Vol. 105, Nos. 1–4, pp. 333–

338. 2003. 

ORAUT 2005. Technical Basis Document for the Savannah River Site, ORAUT-TKBS-0003, 

Rev. 03, Oak Ridge Associated Universities Team, Cincinnati, Ohio. April 5, 2005. 

ORAUT 2006a. Generating Summary Statistics for Coworker Bioassay Data, ORAUT-PROC0095, Oak Ridge Associated Universities Team, Cincinnati, Ohio. June 5, 2006. 

ORAUT 2006b. Y-12 National Security Complex – Occupational Internal Dose, ORAUTTKBS-0014-5, Rev. 02, Oak Ridge Associated Universities Team, Cincinnati, Ohio. August 3, 

2006. 

ORAUT 2007. Parameters to Consider When Processing Claims for Construction Trade 

Workers, ORAUT-OTIB-0052, Rev. 00 PC-1, Oak Ridge Associated Universities Team, 

Cincinnati, Ohio. January 16, 2007. 

ORAUT 2009. Analysis of Bioassay Data with a Significant Fraction of Less-Than Results, 

ORAUT-RPRT-0044, Rev. 00-A, Oak Ridge Associated Universities Team, Cincinnati, Ohio. 

2009. 

SC&A 2010. Comparison of ORAUT-OTIB-0052 Plutonium Data for Construction and NonConstruction Workers and the Implications for Coworker Models at Savannah River Site, 

SC&A, Inc., Vienna, Virginia, and Saliant, Inc., Jefferson, Maryland. April 2010. 

WSRC 1990. Internal Dosimetry Technical Basis Manual, WSRC-IM-90-139, Westinghouse 

Savannah River Company, Aiken, SC. December 20, 1990.