National Institute for Occupational Safety and Health

Co-exposure Model Implementation Guide: SRS Co-exposure Model - Example

Timothy D. Taulbee, PhD, CHP

Associate Director for Science

ABRWH Meeting

Oakland, California | December 11, 2019
Overview

 Name change to be more specific and improve communication 

clarity.

– Co-exposure Model instead of Coworker model

 Background leading to development of co-exposure Model Criteria

 Draft Criteria for the Evaluation and Use of Coworker Datasets

 SRS co-exposure Model Example

 Summary

2
Background leading to development of coexposure model criteria
Co-exposure Model Background

 2010: Concern that some co-exposure models using raw 

bioassay were dominated by few individuals 

– ORAUT-RPRT-0053 One Person One Statistic (OPOS) 

 2014: Multiple SEC Issues Workgroup meetings discussing 

OPOS, stratification, statistical comparison methodology, etc

 The 2014 discussions promulgated the development of the 

Draft Criteria for the Evaluation and Use of Coworker Datasets

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Co-exposure Model Implementation Guide

 Timeline

– June 2, 2014 - Rev 1

– September 30, 2014 - Rev 2

– October 30, 2014 – Rev 3

– February 26, 2015 – Rev 4

– March 12, 2015 – Rev 4.1

– July 6, 2015 – Rev 4.1.1

 SEC Issues Workgroup requested a 

demonstration or pilot example

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Draft Criteria for the Evaluation and Use of 

Coworker Datasets (rev 4.1.1)

July 6, 2015 – By J. Neton
Co-exposure Model Implementation Guide - Elements

 Evaluation of Stratification

 Data Adequacy

 Data Completeness and Validation

 Applicability to Unmonitored Workers

 Analysis and Application to Unmonitored Population

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Data Adequacy

 Review of sampling methods and laboratory analysis, 

consideration should be given to:

– Representativeness of bioassay collection methods

– Radiochemical recovery

– Counting efficiency (self absorption)

– Reliability of measurement method 

8
Data Completeness

 Evaluate whether the data are either sufficiently 

representative or bounding of the exposure potential

– Recommended minimum 30 person measurements per year 

– Assess temporal trends (gap analysis)

– Assess data quality

• Accuracy of the data (transcription errors) 

– Evaluation of potentially missing data

• Compare to claimant files (NOCTS data)

9
Applicability to Unmonitored Workers

 Hierarchical Order

1. Routine, representative sampling

2. Routine measurement of highest exposure potential

3. Collection of samples after the identification of an 

incident

 Representative sample of exposed population OR workers 

with the highest potential for exposure

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Analysis and Application to the Unmonitored Population

 Sufficient data to construct a representative co-exposure

model

– Recommend use of 30 workers per interval, however, less data 

can be used if the data fit a distribution reasonably well

 Data can be reasonably represented by a statistical 

distribution

 Time-Weighted One Person One Statistic (TWOPOS)

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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Evaluation of Stratification

 Should be evaluated where:

1. Accurate job categories or descriptions can be obtained 

for all workers

2. There is reason to believe that one job category is more 

highly exposed

3. There are unmonitored workers in this job category

Note: Stratification by individual job categories was never our 

intention from the standpoint of co-exposure models 

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Co-exposure Model Implementation Guide – Pilot

 ORAUT-OTIB-0081 Rev 3 – November 22, 2016

– 3 Radionuclides

• (Americium, Curium, Californium), Tritium, and Thorium 

– Subsequent discussion of stratification and applicability to 

subcontractor Construction Trades Workers (CTWs)

– General Workgroup consensus needed the full model to 

evaluate all aspects

 ORAUT-OTIB-0081 Rev 4 – March 13, 2019

– Contained models for all radionuclides

13
SRS Co-exposure – Stratification Decision

 NIOSH decided to a priori to stratify based on differences in 

exposure potential between routine and non-routine 

operations. 

– We found it difficult to make the argument that the exposure 

potential was similar for the two types of workers

– For example, consider when a glovebox is purposely breached 

• Loss of engineering control used to protect operations 

workers vs. after breach respiratory protection used to 

protect non-routine workers

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SRS Co-exposure Models – Stratification cont.

 In reality, the initial CTW vs. non-CTW stratification of the coexposure model was the hard part

 We have demonstrated that we have sufficient data to stratify 

the workforce

 What remains unclear, based on mixed comments, is the 

recommendation as to how we stratify from Workgroups

– No Stratification needed

– CTWs and non-CTWs

– Subcontractors vs. non-Subcontractors (all DuPont)

15
What SRS Co-exposure Models Are Needed?

 ORAUT-OTIB-0018 bounding approach actually takes care of a 

large number of the claimants who would need a co-exposure 

model

 Goal is to supplement ORAUT-OTIB-0018 with a best estimate 

co-exposure model

 Need co-exposure model for all major radionuclides at SRS

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SRS Co-exposure Models – Radionuclides

4.1 Americium/Curium/Californium (Trivalent radionuclides)

4.2 Tritium

4.3 Plutonium

4.4 Uranium

4.5 Fission Products (Strontium)

4.6 Cobalt-60

4.7 Cs-137

4.8 Neptunium

4.9 Thorium 

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Individual Radionuclide Discussion / Format Closely 

Follows Co-exposure Implementation Guide Criteria

1. Data Adequacy

– Discussion of Personnel Monitoring

– Applicability to Unmonitored Workers

– Bioassay Analysis Technique

2. Data Validation 

– Data Completeness and Quality

– Data Interpretation 

– Data Exclusion

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Individual Radionuclide Discussion / Format Closely 

Follows Co-exposure Implementation Guide Criteria

3. Statistical Analysis

– Development of the TWOPOS 

4. Intake modeling

– Fitting TWOPOS bioassay distribution in IMBA to obtain intakes

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SRS Plutonium Co-exposure Models – Data Adequacy

 Personnel Monitoring (who was monitored)

– Bioassay Control procedures starting in 1968 (attachment C) 

identify types of workers and frequency of monitoring within 

specific areas

– Construction Trades Workers monitored every 3 years

 Applicability to Unmonitored Workers

– Number of workers monitored relatively constant over time

– No temporal gaps in data

– Workers with highest exposure potential monitored more 

frequently 

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SRS Pu Co-exposure Models – Monitoring Frequency

21
SRS Plutonium Monitoring – Frequency

22

Exposure 

potential Area Sample Frequency

Low Tritium facilities, 100 Area, 305-M, 773-A (Select 

personnel, Reactor Engineering), 320-M, 777-M

1 every 3 

years

Medium 221-FH, A-Line, 235-F (non-process), 772-F (non-process), 

321-M 1 per year

High

221-HB Line, 221-FB Line

221-JB Line, 235-F (process area), 772-F (process area), 

773A (Select personnel)

4 per year
SRS Pu Co-exposure Models – Analysis Method

 Bioassay Analysis Techniques

– 1954 bismuth phosphate and lanthanum fluoride coprecipitation

– 1959 nitric acid/hydrogen peroxide dissolution and ion exchange

– 1966 tri-iso-octylamine (TIOA) liquid extraction

– 1981 coprecipitation technique with alpha spectrometry

 Reporting / Censoring Level = 0.1 dpm/day

– (This is a reporting level NOT necessarily the LOD or the MDA)

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Plutonium Logbooks – Censored Data (SRDB# 51887)

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SRS Pu Co-exposure Models – Data Interpretation

 Most measurements were gross alpha

 During the 1980s 238Pu and 239Pu reported separately

– Merged into gross alpha, assumed to be 12% 10-year aged 

plutonium (chosen to be claimant favorable) 

 Data exclusions

– Chelation or indication of DTPA use

– LIP (lost in process) samples

– Insufficient identifying information

– Samples given per unit mass (likely fecal samples)

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SRS Plutonium Co-exposure Models – Data Validation

 NOCTS In Vitro Dataset 

– (which contains Pu, U, EU, FP)

 Critical Fields (1%) 

– Isotope, “<“, and Result

– 11 errors / 4386 checked = 0.25% 

(0.13%-0.45%)

 All Fields (5%)

– Last Name, First Name, Middle Name, 

Payroll ID, Date, Units, Area

– 4 errors / 874 checked = 0.46%

(0.13%-1.17%)

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Statistical Analysis

 Time-Weighted One Person One Statistic (TWOPOS) 

Methodology 

– ORAUT-RPRT-0053, Analysis of Stratified co-exposure Datasets

– TWOPOS data are fit to lognormal distributions during the statistical 

analysis

 Most of the bioassay data is censored (data reported as “less 

than” some value)

– Analysis method uses multiple imputation for censored data

– ORAUT-RPRT-0096, Multiple Imputation Applied to Bioassay coexposure Models

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Multiple Imputation Methodology

28

1969 Imputation Model 1969 First TWOPOS Imputation
TWOPOS Pu Plots – After Multiple Imputation

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Statistical Analysis – TWOPOS Data

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Example from Table 4-4. Calculated 50th- and 84th-percentile urinary excretion rates 

of plutonium based on a lognormal fit to the TWOPOS data, 1955 to 1990 (dpm/d).

Year

non-CTW 

50th

percentile

non-CTW 

84th

percentile

non-CTW

GSD

non-CTW 

# of 

individuals

CTW 

50th

percentile

CTW 

84th

percentile

CTW

GSD

CTW 

# of 

individuals

1967 0.00629 0.0387 6.14 358 0.00375 0.0263 7.00 152

1968 0.01186 0.0608 5.13 414 0.00957 0.0530 5.54 146

1969 0.03617 0.1136 3.14 296 0.03434 0.1188 3.46 108

1970 0.02776 0.0894 3.22 290 0.02591 0.0872 3.37 98
Steps of Co-exposure Intake Model Development

 Intake modeling for each of the nine radionuclide categories

– 50th and 84th percentiles for each year and solubility type 

are used for intake modeling 

– Selection of time intervals of similar results

• Internal Dosimetry professional judgement

– Assume a chronic intake scenario for each time interval to 

determine intake

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SRS Plutonium Intake Modeling – Time Interval #1

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Figure F-17. Predicted plutonium bioassay results calculated using IMBA-derived 

plutonium intake rates (line) compared with measured bioassay results (dots), 

50th percentile, non-CTW 1955 to 1960, type M.
SRS Plutonium Intake Modeling – Time Interval #2

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Figure F-18. Predicted plutonium bioassay results calculated using IMBA-derived 

plutonium intake rates (line) compared with measured bioassay results (dots), 

50th percentile, non-CTW 1961 to 1966, type M.
SRS Plutonium Intake Modeling – Time Interval #3

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Figure F-19. Predicted plutonium bioassay results calculated using IMBA-derived 

plutonium intake rates (line) compared with measured bioassay results (dots), 

50th percentile, non-CTW 1967 to 1970, type M.
SRS Plutonium Intake Modeling – Time Interval #4

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Figure F-20. Predicted plutonium bioassay results calculated using IMBA-derived 

plutonium intake rates (line) compared with measured bioassay results (dots), 

50th percentile, non-CTW 1971 to 1981, type M.
SRS Plutonium Intake Modeling – Time Interval #5

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Figure F-21. Predicted plutonium bioassay results calculated using IMBA-derived 

plutonium intake rates (line) compared with measured bioassay results (dots), 

50th percentile, non-CTW 1982 to 1990, type M.
Complete SRS Co-exposure Plutonium Intake Model

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Table F-3. Summary of plutonium non-CTW intake rates (dpm/d) and dates, type M.

Start End 50th

percentile

84th

percentile

GSD Adjusted 

GSD

95th

percentile

01/01/1955 12/31/1960 3.265 9.742 2.98 3.00 19.90

01/01/1961 12/31/1966 1.606 6.453 4.02 4.02 15.83

01/01/1967 12/31/1970 5.778 20.170 3.49 3.49 45.17

01/01/1971 12/31/1981 1.692 7.678 4.54 4.54 20.37

01/01/1982 12/31/1990 0.724 5.03 6.94 6.94 17.5
SRS Plutonium Intake Modeling – Full Interval

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Figure F-57. Predicted plutonium bioassay results calculated using IMBA-derived 

plutonium intake rates (line) compared with measured bioassay results (dots), 

non-CTW 50th percentile, all years, type M.
SRS Plutonium Intake Modeling – Full Interval

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Figure F-58. Predicted plutonium bioassay results calculated using IMBA-derived 

plutonium intake rates (line) compared with measured bioassay results (dots), 

non-CTW 84th percentile, all years, type M.

Censoring Level
SRS Plutonium Intake Modeling – Full Interval

40

Figure 4-7. Plutonium type M non-CTW TWOPOS data box 

and whisker plot beginning in 1955.
Americium Intake Results

41
Tritium Dose Results

42
Uranium Intake Results

43

Type F Type M

Type S
Cesium Intake Results

44
Neptunium Intake Results

45

urinalysis

Whole Body Count
Application of Co-exposure Models to Unmonitored 

Workers

 Normally, the 50th percentile with full lognormal distribution

will be assigned to workers who may have been exposed to

greater than environmental levels but less than a typical

operations worker

 Workers considered to have a high potential for exposure may

be assigned the 95th percentile of the co-exposure distribution

on a case by case basis as determined by the Dose

Reconstructors (Professional Judgement)

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Co-exposure Model Summary

 This example co-exposure model demonstrates how the Draft 

Criteria for the Evaluation and Use of co-exposure Datasets 

will be implemented 

– NIOSH believes the intent of the Draft Criteria for the 

Evaluation and Use of co-exposure Datasets has been met

– NIOSH believes the co-exposure models presented are 

claimant friendly, reasonable (best estimate), and 

adequately bound the potential doses for compensation 

purposes

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Co-exposure Model – Next Steps

 Now that the workgroup has approved the methodology

 Change the name and update to our standard implementation 

guide format and post on our website

 Start implementing the method across all of the sites where 

co-exposure models are needed

– The implementation of this methodology is going to take 

some significant time (years) to complete

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SC&A Review of Co-exposure Model - Pilot

(ORAUT-OTIB-0081) SRS Model

Bob Barton, Ron Buchanan, Harry Chmelynski, Rose Gogliotti, 

and Joyce Lipzstein, SCA, Inc.
SC&A Finding #1 – Bioassay Variability

 Finding 1: Although SC&A recognizes that incident-based sampling 

involving chelation is not considered in final coworker modeling, the 

removal of DTPA-influenced samples from consideration in the analysis of 

the high variability observed in trivalent actinide bioassay results has not 

been justified sufficiently. Evidence suggests the variation among DTPA 

and non-DTPA samples is nearly identical. Furthermore, OTIB-0081 has not 

provided any reference to justify the assumption that DTPA causes 

heterogeneity among a single urinalysis voiding.

 Status – Open, SC&A Action:

– Review bioassay methods and provide feedback to workgroup 

on issue of data adequacy

50
SC&A Finding #2 – Multiple Imputation

 Finding 2: Use of imputed values that are less than one-half of the MDA 

raises a fundamental fairness issue in that monitored workers who have 

bioassay results that are less than the MDA are assigned a missed dose in 

accordance with ORAUT-OTIB-0060, “Internal Dose Reconstruction.”

– Per that guidance, bioassay values that are censored are assumed to be equal 

to one-half of the MDA rather than the use of an alternate imputed value. In 

order to further address this issue, SC&A performed scoping calculations using 

imputed values, numerical values reported less than MDA, and missed dose 

approaches.

– Scoping calculations are illustrative and not all encompassing.

 Status – Open, SC&A Action:

– Review ORAUT-RPRT-0096 Multiple Imputation Method

51
SC&A Observation 1 – Multiple Imputation

 Observation 1: While the multiple imputation method is 

mathematically correct, it has the potential to result in biasing the 

simulated bioassay results unnecessarily low. Alternate approaches, 

such as the maximum possible mean method, which replaces 

censored data with the actual censoring limit (or alternately onehalf the censoring limit), would solve the issues associated with 

datasets containing a large number of censored values in a 

claimant-favorable manner.

 Status – Open, SC&A Action:

– Review ORAUT-RPRT-0096 Multiple Imputation Method

52
SC&A Finding 3 – Multiple Imputation

 Finding 3: The sample comparison of coworker intakes to a missed 

dose method for uranium showed that the coworker model derived 

intakes were a factor of 4 or more higher than the missed dose 

approach. This illustrates the potential for inequity between the 

treatment of unmonitored workers assigned coworker intakes and 

monitored workers with results less than the detection limit in 

some situations.

 Status – Open, SC&A Action:

– Review ORAUT-RPRT-0096 Multiple Imputation Method

53
SC&A Finding 4 – Data Completeness (Additional Data)

 Finding 4: The coworker analysis uses the internal monitoring for 

claimants for which data were available to NIOSH in approximately 

August 2011 (~4,000 claims). Since that time, approximately 2,000 

additional claims have been submitted that could be used to 

augment the coworker dataset. Inclusion of these data would be 

especially important for the two contaminants that required a 

combination of multiple years for analysis due to lack of a sufficient 

number of data points (uranium and cesium)..

 Status – Closed

– Workgroup decided not to pursue inclusion of additional data

54
SC&A Observation 3 – Data Completeness (Trivalent 

Logbooks)

 Observation 3: Available trivalent logbook data show notable 

differences with the number of reported samples taken in 1980 and 

1982. These years, and any changes in operations, are not discussed 

specifically in OTIB-0081. However, it is noted that a future NIOSH 

report on americium exposure potential at SRS is pending that may 

address the apparent gaps in the data.

 Status – Open, NIOSH Action

– NIOSH to provide reference indicating a backlog of bioassay analyses 

during these years explain the noted differences.

55
SC&A Observation 4 – Stratification Evaluation

 Observation 4: OTIB-0081 does not provide a statistical 

comparison of the two stratified groups as prescribed in the 

coworker implementation guide. The various coworker 

models were stratified based on the a priori assumption that 

exposure potential between CTWs and nonCTWs was 

different.

 Status – Statistical comparison not necessary at SRS, workers 

are stratified a priori. No Action Required.

56
SC&A Observation 5 – Stratification Evaluation

 Observation 5: SC&A believes a quantitative assessment of 

available job plans, rather than a qualitative basis, is 

appropriate to determine that prime contractor and 

subcontractor CTWs are part of the same exposure strata. 

Such an assessment has been performed by NIOSH, and a 

report of their findings has recently been issued.

 Status – To be determined

– Issue is discussed via alternate white paper, no action 

required at this time

57
SC&A Finding 5 – Strata Misclassification

 Finding 5: Classification of a “Machinist” as a nonCTW in OTIB0081 is inconsistent with its classification in OCAS-PER-014, 

“Construction Trades Workers.”

 Status – Closed

 Workgroup discussed this issue at length and concluded that 

that since the misclassification rate is less than 5% it would 

have minor impact on the co-exposure models

58
SC&A Finding 6 – Misclassification Sensitivity Analysis

 Finding 6: A targeted sampling comparing the OTIB-0081 

strata designation (CTW or nonCTW) against two alternate 

sources for identifying worker job classification indicated that 

just over 9 percent of the entries appear to be in conflict 

when comparing the NIOSH and SC&A analyses.

 Status – Closed

 Workgroup discussed this issue and decided not to pursue the 

sensitivity analysis since the misclassification rate is less than 

5%

59
SC&A Observation 6 – Scoping Analysis

 Observation 6: SC&A acknowledges that there are inherent difficulties in 

correctly associating individual workers with the correct CTW/nonCTW

strata. This is particularly true for job titles that could potentially be 

included in either stratum.... SC&A suggests a scoping analysis in which 

such borderline job titles are removed to ascertain the effect on the 

resulting distributions. Such an analysis would help determine whether 

current strata designations are sufficient or a more rigorous approach to 

individual job classification is warranted.

 Status – Closed

 Workgroup discussed this issue and decided not to pursue the 

sensitivity analysis since the misclassification rate is less than 5%

60
SC&A Observation 7 – Quality Assurance Assessment

 Observation 7: The results shown in Attachment A of OTIB0081 demonstrate a high degree of confidence that the 

acceptable error rates are within the goals established for 

each test. However, this conclusion is dependent on the 

assumption that payroll ID issues identified would not affect 

the resulting coworker distributions.

 Status – Closed

 NIOSH explained the payroll ID issue with the workgroup and 

the workgroup concurred that this should not impact the QA 

assessment

61
Status of ORAUT-RPRT-0092: Evaluation of 

Subcontractor monitoring at the SRS
ORAUT-RPRT-0092: Update Subcontractor Monitoring 

 Discussions are ongoing:

– June 2019, ORAUT-RPRT-0092 Evaluation of Bioassay Data for 

Subcontracted Construction Trade Workers at the Savannah 

River Site was submitted to Workgroup

– November 2019, SC&A provided comments on the report 

– December 2019, Both NIOSH and SC&A presented their 

respective views to the SRS and SEC Issues Workgroups.

– Status: NIOSH to provide responses to SC&A’s comments

63
Major topics needing further discussion to resolve

 Stratification (non-CTW, Dupont CTW, subCTW)

– June 2019, NIOSH submitted a white paper entitled Savannah River 

Site Plutonium Construction Trade Worker Stratification Refinement

– November 2019, SC&A provided comments on the white paper

– Status: NIOSH to provide response to SC&A comments in 2020 

 Americium Monitoring at the Savannah River Site (SRS) 

– June 2019: ORAUT-RPRT-0091 Rev 00, Evaluation of Savannah River 

Site Americium-241 Source Terms Between 1971 and 1999 Using 

Bioassay Frequency Tables

– Status: SC&A to provide comments on this report in 2020

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