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This working document prepared by NIOSH’s Division of Compensation Analysis and Support (DCAS) or its contractor for use in discussions with

the ABRWH or its Working Groups or Subcommittees. Draft, preliminary, interim, and White Paper documents are not final NIOSH or ABRWH

(or their technical support and review contractors) positions unless specifically marked as such. This document represents preliminary positions

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that is protected by the Privacy Act 5 USC §552a and has been cleared for distribution.

Draft Criteria for the Evaluation and Use of Coworker Datasets 

J.W. Neton, Ph.D., CHP 

Revision 3 – October 30, 2014 

1.0 INTRODUCTION

Under the Energy Employees Occupational Illness Compensation Program Act (EEOICPA), 

NIOSH completes dose reconstructions for employees with cancer who are covered under the 

provisions of the Act. The methods used to complete these dose reconstructions are prescribed 

in 42 C.F.R. Part 82 (USHHS 2002). While the use of individual personnel monitoring is 

preferred in the completion of dose reconstructions, these data are oftentimes not available 

because either the worker was monitored and the data have been lost or the worker was 

potentially exposed and not monitored. In the latter case, NIOSH has observed that, in 

accordance with the practices in effect at the time, only workers with the highest exposure 

potential were monitored or, in some cases, monitoring was conducted on representative 

members of the exposed population. In the absence of individual monitoring data, 42 C.F.R. Part 

82 allows for the use other workers’ data to complete dose reconstructions. Section 82.2 (b) 

states: 

If individual monitoring data are not available or adequate, dose reconstructions may use 

monitoring results for groups of workers with comparable activities and relationships to the 

radiation environment. 

The groups of workers specified in §82.2(b) are generically known as coworkers. Coworkers are 

considered to be workers at the same site whose radiation monitoring measurements are 

considered to be representative or plausibly bounding of those received by one or more workers 

with no individual monitoring data. Depending on the amount and specificity of the available 

worker and workplace data, the level of detail available for a coworker model can vary greatly. 

For dose reconstructions under EEOICPA, it is often difficult to locate a worker in a specific job 

at a specific location. Because of this, NIOSH has chosen to develop coworker models that, 

whenever possible, cover a wide range of workers for a specific radionuclide at a specific time. 

2.0 CRITERIA FOR THE EVALUATION OF THE ADEQUACY AND 

COMPLETENESS OF COWORKER DATA 

As indicated above, coworker datasets should be established from monitored workers with 

comparable activities and relationships to the radiation environment. To accomplish this, one 

must carefully evaluate each coworker dataset to ensure that it is either representative of the 
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the ABRWH or its Working Groups or Subcommittees. Draft, preliminary, interim, and White Paper documents are not final NIOSH or ABRWH

(or their technical support and review contractors) positions unless specifically marked as such. This document represents preliminary positions

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that is protected by the Privacy Act 5 USC §552a and has been cleared for distribution.

distribution of exposures for the intended population or that it provides a plausible upper bound 

for those workers1. Additional guidance on how to establish this is provided later in this 

document. Prior to this, however, it is necessary to establish that the available internal or 

external monitoring measurements were technically capable of evaluating the monitored 

worker’s exposure environment. If the techniques used to monitor exposed workers were 

inadequate, they clearly cannot be used to assess exposures for unmonitored workers. Criteria to 

consider when determining the technical adequacy of a dataset are provided in section 2.1 below. 

2.1 Data Adequacy 

Co-worker models are developed using individual bioassay2 or personal dosimeter 

measurements. The measurement techniques employed must be evaluated to ensure that they are 

capable of quantitatively measuring the exposure of interest. When urine samples are used, this 

should include a review of the sample collection methods, any chemical processes employed, 

and the radiation counting equipment used. Among the items to be considered are: 1) 

representativeness of the bioassay sample collection method; 2) radiochemical recovery if 

chemical extraction techniques are used; 3) reduction in counting efficiency for alpha emitters 

due to self–absorption; and 4) reliability of the radiation counting equipment. If workers were 

exposed to a mixture of radionuclides (e.g., a combination of fission and activation products or 

actinides), and the samples were measured using a non-specific assay (beta, gamma, or alpha 

counting), the relative contribution of each radionuclide in the mixture must be evaluated. 

When in vivo measurements are used, the overall measurement program must be carefully 

reviewed to ensure that the data accurately represent the quantity of the radionuclide in the organ 

of interest. This includes the adequacy of the phantoms used to calibrate the partial or whole 

body measurement geometries and a review of the methodology used to quantify a 

measurement’s limit of detection. For the measurement of low energy photons (e.g., those below 

100 keV) emitted from the lungs, the program’s ability to correct for self-absorption due to 

varying chest wall thickness should be considered. Because certain radionuclides of interest do 

not emit photons that can be detected by an in in vivo measurement, facilities sometimes infer the 

radionuclide of interest based on the measurement of one of its progeny. In this case, it is 

                                                            

1 Under 42 CFR 83.13(c)(1), radiation doses are considered to be estimated with sufficient accuracy if NIOSH has 

established that it has access to sufficient information to estimate the maximum radiation dose, for every type of 

cancer for which radiation doses are reconstructed, that could have been incurred in plausible circumstances.

2

 While urinalysis is the bioassay method most often used for developing coworker models, in vivo measurements 

(e.g., lung or whole body counts) are sometimes used. If lapel breathing zone air samples are available, these may 

also be used, provided they have been determined to meet the data adequacy and completeness criteria outlined in 

this document. 
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(or their technical support and review contractors) positions unless specifically marked as such. This document represents preliminary positions

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that is protected by the Privacy Act 5 USC §552a and has been cleared for distribution.

important to verify the validity of the assumptions that were made regarding the degree of 

radionuclide equilibrium between the progeny and its parent. Similarly, if ratios are employed to 

infer the amount of a contaminant (e.g., quantifying the amount of 239Pu based on a measurement 

of 241Am in the lung), the ratio that is applied should be based on a well-established analysis of 

exposure conditions within the facility. 

For external exposure monitoring, it is important to consider the ability of the monitoring devices 

(e.g., film badges or thermoluminescent dosimeters) to detect the energies and types (beta, 

gamma, or neutron) of radiation that were present in the workplace. For those situations in 

which there is a technology shortfall, corrections to the measured readings must be established 

prior to use in a coworker model. In addition, a review of the adequacy of the calibration 

methods employed and the extent that fading is a factor should be addressed. 

The quality of the available data also needs to be considered. This would include a review of the 

appropriate collection and analysis of blank samples. When paired measurements are available, 

the precision between measurements should be examined. If widely different results from the 

same aliquot are observed, the effect this might have on the usefulness of the data should be 

considered. At facilities where chelation therapy may have been used (e.g., the administration of 

DTPA), the data should be reviewed to ensure that samples taken from personnel who were 

administered chelating agents are removed from the data set. 

Finally, the amount of dose that could have been received, but not detected by a routine 

monitoring program, must be evaluated to determine if the magnitude of this “missed” dose is 

within the plausible bounds of exposures received by the workers. In certain cases, where the 

monitoring frequency was low (e.g. one measurement per year) and the limit of detection of the 

measurement is high, the coworker model might predict exposures that are well above any 

credible scenarios for that facility. 

2.2 Data Completeness 

Once the measurement techniques have been found to be technically acceptable, the amount of 

available monitoring data must be evaluated to determine if there are sufficient measurements to 

ensure that the data are either bounding or representative of the exposure potential for each 

job/exposure category at the facility. This analysis should look, not only on the total amount of 

data that are available, but also consider any temporal trends in data availability. A useful 

technique to establish this is to conduct a gap analysis. That is, the available monitoring data 

should be reviewed against the number and types of workers that were involved in radiological 
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the ABRWH or its Working Groups or Subcommittees. Draft, preliminary, interim, and White Paper documents are not final NIOSH or ABRWH

(or their technical support and review contractors) positions unless specifically marked as such. This document represents preliminary positions

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that is protected by the Privacy Act 5 USC §552a and has been cleared for distribution.

activities over time at the facility. As part of this analysis, the number of monitoring samples for 

each identifiable job category should be compared to the total number of workers who were 

potentially exposed in that job category. For the purposes of this effort a job category need not 

be an individual job title. Instead, a job category could consist of several job titles if there is 

reason to believe that exposures in those job categories would be similar. 

If the number of potentially exposed workers in each category is unknown, a useful starting point 

is to look at the distribution of samples among the various categories of workers represented in 

the claimant population at that site. Table 1 provides an example of this for the categories of 

workers who were monitored for 239Pu at the Nevada Test Site. In this particular analysis, the 

radiation safety staff was monitored to a larger extent than workers directly involved in site 

activities. Thus, a coworker model based on these data would not necessarily reflect the 

exposure conditions of the unmonitored production/process workers. If, in fact, it can be 

established that the categories of workers were potentially exposed, yet inadequately monitored, 

it could preclude the development of a sufficiently accurate coworker model, unless it can be 

established that the exposures to another, adequately monitored category of workers reliably 

bounds the initial category’s exposures. 

Table 1. Number of Pu239 Samples (Percent of Samples) by Time Period and Job 

Category at the Nevada Test Site3

All JobSpecific 

Workers 

Radsafety 

Staff Laborers Welders Wiremen Miners Security 

Total Number of 

Samples 290 206 2 0 0 8 74 

1963–1967 30 (10.34%) 

28 

(13.59%) 

2 

(100.00%) - - 

0 

(0.00%) 

0 

0.00% 

1968–1970 34 (11.72%) 

31 

(15.05%) 0 (0.00%) - - 

2 

(25.00%) 

1 

1.35% 

1971–1980 79 (27.24%) 

76 

(36.89%) 0 (0.00%) - - 

3 

(37.50%) 0.00% 

1981–1992 147 (50.69%) 

71 

(34.47%) 0 (0.00%) - - 

3 

(37.50%) 

73 

98.65% 

Any identified gaps (i.e., periods of time where a small percentage of the workers were 

monitored) in the monitoring data should be reviewed to determine if there is a reasonable basis 

                                                            

3 Adapted from SC&A’s white paper on bioassay evaluation at the Nevada Test Site, dated October 21, 

2008
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the ABRWH or its Working Groups or Subcommittees. Draft, preliminary, interim, and White Paper documents are not final NIOSH or ABRWH

(or their technical support and review contractors) positions unless specifically marked as such. This document represents preliminary positions

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that is protected by the Privacy Act 5 USC §552a and has been cleared for distribution.

for the lack of monitoring data. For example, there may have been a temporary stoppage in the 

work at a facility due to the initiation of maintenance operations or facility upgrades. If the 

monitoring gap is found to exist during long periods of potentially elevated exposure, it may not 

be possible to develop representative coworker models during this time period. 

The number and types of discreetly identified activities will vary widely among covered 

facilities. At Atomic Weapons Employer (AWE) facilities that worked with uranium metal, 

there may only be one activity, while large Department of Energy (DOE) facilities will likely 

have multiple operations. One area that needs to be considered at each facility is the difference 

in exposure potential between maintenance and trades workers versus those involved in routine 

process operations. 

Facilities with the potential for internal and/or external exposure to a large percentage of the 

workforce would require many more samples than one in which the potential for exposure was 

limited to just a few workers. In addition, the variability of the exposure potential should be 

considered. It has been observed, for example, that some national laboratories conducted work 

under many different experimental configurations, resulting in a wide variety of exposure 

potentials. In this case, it might not be possible to generate a single coworker model that 

adequately captures all categories of unmonitored worker doses. 

Although there is no hard and fast rule for the minimum number of data points required to 

represent a given time interval, approximately fifteen values has been cited as a reasonable 

number for performing statistical tests on censored datasets (Singh et al. 2010). Because our 

program estimates parameters from the data, a default minimum of 30 values is recommended 

per each discrete time interval being evaluated (e.g., 30 samples per year if a co-worker model is 

being developed based on annual samples or measurements). The minimum number of samples 

should, of course, be considered in light of the number of workers potentially expose to the 

airborne source-term. For example, the number of samples necessary to be representative of the 

exposures at a uranium foundry, where airborne activity is generally widespread, will be greater 

than the number required of a small glove box operation where six workers were involved in the 

manipulation of plutonium parts. In the latter situation, it may be that samples for three out six 

workers could be used to bound exposures for the three who were not monitored. Where the 

distribution of the data has a large geometric means and/or standard deviations, the number of 

samples required will also be greater.

Finally, if electronic records or summary databases are used to develop the coworker model, 

these should be reviewed against a representative sampling of original data where possible to 
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(or their technical support and review contractors) positions unless specifically marked as such. This document represents preliminary positions

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that is protected by the Privacy Act 5 USC §552a and has been cleared for distribution.

verify that they contain a complete and unbiased listing of all the data collected by the site. If 

hard copy records are being used, the legibility of the information should be carefully examined. 

2.3 Applicability of Monitoring Data to Unmonitored Workers 

Prior to applying a site’s worker monitoring data to estimate the exposures of unmonitored 

workers, the type of personnel monitoring program employed at the covered facility must be 

established. In general, three types of monitoring programs have been employed at sites covered 

under EEOICPA. These programs, listed in hierarchical order of preference for use in coworker 

modeling are: 1) routine, representative sampling of the workers; 2) routine measurement of 

workers with the highest exposure potential; and 3) the collection of samples after the 

identification of an incident. Because they are not representative of the overall distribution of 

exposures, programs that rely on measurement of the highest exposed workers or are incidentbased require more careful consideration. 

For routine monitoring programs, a review of the program should be conducted to determine the 

basis for the selection of program participants. It must be established who was monitored and 

why they were monitored. This can most easily be established through a review of the site’s 

radiological control program documentation. In this evaluation there must be some 

demonstration that the monitored population consisted of: 1) a representative sample of the 

exposed population, or; 2) the workers with the highest exposure potentia1. In these cases, the 

assignment of a coworker dose from the distribution of measured values would either be 

representative of the worker’s exposure in the first case or claimant favorable in the second case. 

Even though the program documentation might indicate that routine sampling was conducted, it 

is important to verify that the site’s procedures were followed by a review of the samples that 

were actually collected. 

A variation of the routine monitoring program is one in which workers are intermittently 

monitored on an as needed basis (i.e. only when the potential for exposure existed). This would 

occur, for instance, in a short duration project that created an internal and/or external exposure 

potential. Based on the specifications in project work plans or radiological work permits, certain 

classes of workers would be monitored at end of the project to document that the exposure 

controls that were put in place were adequate. In these situations, it may be possible to use the 

monitoring data collected during the project close-out to place a plausible upper limit on the 

exposures of the unmonitored workers. 
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In some situations, sites have relied on incident-based sampling to monitor worker exposure. 

Because there are temporal gaps in the monitoring data, it is more difficult to demonstrate that 

this type of sampling can be used to develop representative or plausibly bounding coworker 

models. Prior to the use of incident-based sampling in a coworker model, the effectiveness of 

workplace controls must be demonstrated through the review of routine air monitoring samples 

and/or periodic contamination surveys. If one can demonstrate that the effectiveness of 

workplace administrative and/or engineering controls was adequate to prevent exposures, except 

during upset conditions, it may be possible to use incident-based sampling in a coworker model. 

It has been observed at a number of sites that different classes of workers during the same time 

period may have had monitoring programs that were conducted for different purposes. For 

example, construction and building trade workers, who worked intermittently in radiological 

areas, may have been monitored only when an incident was expected to have occurred, while 

those employees involved in routine process operations would have been routinely monitored on 

a frequency commensurate with their exposure potential. In this case, it would not be 

appropriate to combine the monitoring data for these two groups of workers into a single 

coworker model that assumes a chronic exposure pattern. Rather, the default in this case should 

be to consider separate coworker models. 

3.0 ANALYSIS OF MONITORING DATA 

If after review of the monitoring program data, it is established that: 1) there is sufficient data to 

construct a general coworker model, and; 2) the data can reasonably be represented by a 

statistical distribution (e.g., a log-normal or a Weibull distribution), the fitted distribution can be 

used to represent the exposures observed in the overall monitored population. For workers that 

are considered to have worked in environments with a potential for elevated exposure, the 95th

percentile of the distribution should be used as an upper bound of their exposure during the 

modeled time period. Although it could be argued that the job categories that fall under this 

criterion should be listed, any attempt to do so might be artificially restrictive. This decision is 

most accurately made using the information available in the site profiles, the claimant interview 

and other documents that might be in the worker’s records. For workers who were less likely to 

be highly and/or were intermittently exposed in the workplace, the full distribution (i.e., the 

geometric mean and its associated standard distribution if a lognormal fit is used) should be used 

as representative of their potential for exposure during the modeled period. 

When multiple bioassay samples are present during a monitoring period for a given individual, it 

is appropriate to average the values so that a single statistic can be computed for that individual. 
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that is protected by the Privacy Act 5 USC §552a and has been cleared for distribution.

The use of a single value for each monitored person in a given monitoring interval is appropriate 

because the desired coworker model represents a distribution of individual worker excretion 

results, as opposed to a distribution of all samples collected. The use of an average value for 

each worker has been called the One-Person-One-Statistic (OPOS) method. Rather than 

compute a simple average of the measured values, each individual bioassay result should be 

weighted by the fraction of the year it represents (i.e., a time-weighted OPOS). The details of 

how this calculation is performed is described in other program documentation (ORAU 2014) 

3.1 Time Interval of the Modeled Data

The amount of data that are available will directly influence the time intervals used in the 

coworker model. As stated in section 2.1, a minimum number of 30 samples per monitored 

interval is recommended. Based on a review of the currently available datasets, a modeled 

interval of one year strikes a good compromise between the availability of data and the need to 

ensure that the samples are contemporaneous with ongoing operations. In certain situations, 

there are sufficient data to develop quarterly models, but this is the exception rather than the 

norm. If, because of data limitations, it is necessary to consider time intervals beyond one year 

in the coworker model, any changes in site practices or operations should be evaluated to ensure 

that the data can be validly combined. In general, grouped time intervals should not exceed a 3 

year period, unless there is stringent justification for doing so.

4.0 EVALUATION OF STRATIFICATION 

If accurate job categories and/or descriptions can be obtained for the workers making up the 

general coworker dataset, and there is reason to believe that one of the categories is more highly 

exposed, the distribution of the potentially more highly exposed population should be evaluated 

as a separate standalone distribution. 

Once a dataset has been stratified, the analysis consists of a two-tiered evaluation where the 

stratified distributions are first compared on a year-by-year basis (or other selected monitoring 

interval) to determine if any of the individual distributions are significantly different. 

Significance in this case is evaluated using a Monte Carlo permutation test or a Peto-Prentice test 

at the 0.05 significance level. If a significant difference is observed in any of the modeled time 

intervals, then a test of practical significance is employed. This test compares the slopes of the 
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chronic intake models over the time periods where a statistically significant difference in the 

modeled distributions was observed (ORAU 2014). 

5.0 REFERENCES

ORAU 2014. Analysis of Stratified Coworker Datasets, ORAU-RPRT-0053, Rev- 02, Oak 

Ridge Associated Universities Team, Cincinnati, Ohio. October 2, 2014. 

Singh, A., N. Armbya, and A. K. Singh, 2010, ProUCL Version 4.1.00 Technical Guide (Draft), 

EPA/600/R-07/041, U.S. Environmental Protection Agency, Office of Research and 

Development, Washington, D.C., May. [SRDB Ref ID: 113464] 

U.S. Department of Health and Human Services. Methods for radiation dose reconstruction under 

the Energy Employees Occupational Illness Compensation Program Act of 2000, final rule. 

Washington, DC: U.S. Government Printing Office; 42 CFR Part 82; 2002. 

U.S. Department of Health and Human Services. Methods for radiation dose reconstruction 

under the Energy Employees Occupational Illness Compensation Program Act of 2000, final 

rule. Washington, DC: U.S. Government Printing Office; 42 CFR Part 83; 2002.