OCAS-IG-002

INTERNAL DOSE RECONSTRUCTION 

IMPLEMENTATION GUIDELINE

Rev. 0

August 2002

Department of Health and Human Services (DHHS)

Centers for Disease Control and Prevention (CDC)

National Institute for Occupational Safety and Health (NIOSH)

Office of Compensation Analysis and Support (OCAS)

4676 Columbia Parkway 

Mail stop R-45

Cincinnati, Ohio 45226
Preface

The purpose of this guide is to provide basic information on the methods employed in 

reconstructing doses under the Energy Employees Occupational Illness Compensation 

Program Act of 2000. The intent of this guide is to assist a qualified health physicist in 

determining annual organ dose from exposure to various sources of internal radiation. 

Because not all possible exposure scenarios can be foreseen, this guide does not provide 

step by step instructions for how the dose reconstruction should be performed. It is 

recognized there will be situations for which the methods outlined in this guide result in 

underestimates or overestimates of a claimants actual dose. In these cases, care must be 

exercised that the doses are conservative (claimant friendly) but reasonable for the 

claimant’s exposure scenario. 

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4.2 Radionuclide Present in the Work Area........................................................... 14

4.2.1 Primary Radionuclide ............................................................................... 14

4.2.2 Radionuclide Impurities............................................................................ 14

4.2.3 Radionuclide Progeny............................................................................... 15

4.2.4 Radon ........................................................................................................ 15

4.3 Solubility Class.................................................................................................... 15

4.4 Particle Size ......................................................................................................... 16

5.0 COLLECTION OF THE INDIVIDUAL DOSIMETRY DATA.16

5.1 Bioassay Data ...................................................................................................... 17

5.2 Workplace Monitoring Data.............................................................................. 18

5.3 Source Term Evaluation..................................................................................... 19

6.0 PRELIMINARY DOSE ESTIMATES........................................20

6.1 Preliminary Dose Estimate – Low Dose Potential ........................................... 21

6.2 Preliminary Dose Estimate – High Dose Potential........................................... 22

6.3 Modifications to the Preliminary Dose Estimate Process............................... 23

6.4 The Probability of Causation Calculation........................................................ 25

6.5 Refining Preliminary Estimates......................................................................... 25

7.0 DETAILED DOSE ESTIMATES ...............................................26

7.1 Estimate of Intake Date ...................................................................................... 26

7.2 Uncertainty .......................................................................................................... 27

7.3 Missed Dose ......................................................................................................... 30

7.4 Radon ................................................................................................................... 32

8.0 EXAMPLE DOSE ESTIMATES................................................33

8.1 Scenario................................................................................................................ 33

8.2 Case Evaluation................................................................................................... 34

8.3 High Dose Potential Preliminary Estimate ....................................................... 35

8.4 Low Dose Potential Preliminary Estimate........................................................ 36

8.5 Detailed Dose Reconstruction............................................................................ 38

8.6 Missed Dose ......................................................................................................... 43

8.7 Uncertainty .......................................................................................................... 45

9.0 REFERENCES............................................................................46

APPENDIX A – IREP-EXCEL INPUT FORMAT...............................48
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1.0 INTRODUCTION

The purpose of this document is to provide guidance on the methods and approaches that 

can be used to reconstruct occupational radiation dose from internally deposited 

radionuclides in support of the Energy Employees Occupational Illness Compensation 

Program Act (EEOICPA, 2000). The responsibilities of NIOSH toward this goal are 

included in the Act and Executive Order 13179 (2000). The end result of the internal 

dose reconstruction will be the dose, expressed in cSv (rem), received in individual 

calendar years to the organ of interest along with the uncertainty associated with the dose. 

42 CFR part 82 (2002) governs the process of reconstructing doses to individuals. This 

dose will be used as input into the NIOSH-IREP program to determine the Probability of 

Causation (PC) that the cancer was contracted as a result of the individual’s radiation 

exposure from DOE sources. 42 CFR part 81 (2002) governs the process of determining 

the individual’s Probability of Causation. 

This process differs from traditional internal dosimetry in a number of aspects. Some of 

the more important aspects include:

1. Internal dosimetry has traditionally been concerned with radiological 

protection. As such, only the most exposed organs and effective whole body 

doses are of concern. Under EEOICPA only the dose to a specific organ is 

calculated. That organ is often not one of the most exposed organs. This 

means that the approach to identifying “worst-case” conditions will differ 

from that used in traditional radiological protection programs.

2. Traditionally, analytical sensitivity is a program issue which affects the design 

of a dosimetry program. No dose is assigned unless the results are detectable. 

In reconstruction for compensation, analytical sensitivity must be treated on 

an individual basis in order to determine the amount of intake (and thus dose) 

that may not have been measured. This missed dose must be quantified and 

applied to the specific organ dose.

3. Current radiological protection practices determine the “committed” dose 

received from internally deposited radionuclides. The committed dose is the 

dose the organ will receive over the 50-year period following an intake. 

Under EEOICPA, annual doses are calculated. This is necessary to allow the 

appropriate relative risk to be used based on the time between exposure and 

diagnosis.

Section 2 of this guide provides background information pertaining to the internal 

dosimetry models that will be utilized by NIOSH in reconstructing internal doses. 

Sections 3 through 7 describe the actual dose reconstruction process itself. Sections 3, 4, 

and 5 pertain to the information-gathering phase from the various potential sources of 

information. Section 6 provides details for utilizing the efficiency process described in 

42 CFR part 82. This process allows for limited research and analysis to be performed 

for cases in which the Probability of Causation (PC) is clearly greater than or less than 

50%. This guide demonstrates how NIOSH intends to utilize that process in 
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reconstructing interna l doses as well as the necessary coordination with the individual’s 

external dose. Section 7 describes the detailed internal dose estimation process. Since 

this process is very individualized and specific to each claim being reconstructed, no set 

procedure was described, only additional considerations are provided. 

Section 8 provides an example of an internal dose reconstruction. The example includes 

preliminary estimates for two different organs. The example is then expanded into a 

detailed dose estimate. 

2.0 MODELS

The most current models and recommendations, as deemed appropriate for dose 

reconstruction purposes, from the International Commission on Radiological Protection 

(ICRP) will be used to assess dose from internally deposited radionuclides. These 

recommendations describe how various internally deposited radionuclides enter, transfer, 

and leave the body. From this information, the dose to specific organs over specific time 

frames can be determined. 

2.1 General Models

The ICRP has recommended different biokinetic models for various radionuclides. 

While these models vary, they all derive from the general model depicted in Figure 1. As 

indicated, the primary routes of entry to the body are inhalation, ingestion, absorption and 

injection (wounds). The ICRP has separately published more detailed models that govern 

inhalation and ingestion. These models describe the rate and amount that enters the 

transfer compartment. For the purposes of dose reconstruction, the transfer compartment 

shall be considered the blood stream. It is a compartment that transfers radionuclides 

from the entry point to various organs and eventually out of the body. 

The deposition and clearance of inhaled radioactive material is governed by the lung 

model. The current lung model, described in ICRP Publication 66 (ICRP 66, 1994), 

accounts for deposition of the inhaled radionuclide into various regions of the lung 

(Figure 2). The size of a particulate is the primary variable in determining the extent of 

deposition. Once deposited, the chemical form of the radionuclide determines the rate 

that the material is cleared from the various regions of the lung. Physiological clearance 

mechanisms are also considered. The normal physiological lung clearance function 

results in some of the material being swallowed, at which point, the material is treated as 

an ingestion intake.

The gastrointestinal (GI) tract model (Figure 3) predicts how ingested material is 

incorporated into the body and how a portion is eventually eliminated. This model also 

describes the dose to the various regions of the GI tract from an ingestion intake. 

Material can enter the GI tract by direct ingestion, indirectly by transfer from the 

respiratory tract, or by transfer from other body organs via the transfer compartment. The 

current GI tract model is described in ICRP Publication 30 (ICRP, 1979). 
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Figure 1

General Biokinetic Model

Transfer

Compartment

Respiratory

tract

Lymph nodes

Liver

Kidney

Urinary

Bladder

Other

organs

Subcutaneous

tissue

Gastrointestinal

tract

Skin

Skin

Wound

Absorption

Urine

Inhalation Exhalation Ingestion

Extrinsic removal

Feces
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AI = Alveolar-interstitial region

bb = Bronchiolar region

BB = Bronchial region

ET1 = Anterior Nasal Passage

ET2 = Posterior Nasal Passage, Pharynx, Larynx

LNET =Lymph nodes associated with the Extrathoracic region

LNTH =Lymph nodes associated with the Thoracic region

Figure 2

ICRP 66 Lung Model

LNET ETseq

ET1

ET2

BBseq BB2 BB1

bbseq bb2 bb1

AI3 AI2 AI1

LNTH

Environment

GI tract

Extrathoracic

Thoracic

Excretion

Inhalation
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Figure 3

ICRP 30 Gastrointestinal Tract 

Stomach (ST)

Small Intestine (SI)

Upper Large Intestine (ULI)

Lower Large Intestine (LLI)

Body Fluids

Excretion

Ingestion
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Absorption and injection are both considered to directly enter the transfer compartment. 

In the case of injection, it is possible that material will lodge in the subcutaneous tissues 

and then be cleared to the transfer compartment over time. 

2.2 Specific Models

Since the publication of ICRP 30, the ICRP has issued updated biokinetic models for 

selected radionuclides. Table 1 indicates the model that will be used for reconstructing 

doses for each of these selected radionuclides. The remaining radionuclides will be based 

on the models contained in ICRP Publication 30 (1979).

TABLE 1. ICRP Publication Containing the Models for Selected Radionuclides

Element ICRP publication Element ICRP publication

Tritiated water 56 I 56

H

3

 or organically 

bound tritium

56 Ba 67

Fe 69 Ce 67

Zn 67 Pb 67

Se 69 Po 67

Nb 56 Ra 67

Sr 67 Th 69

Zr 67 U 69

Mo 67 Np 67

Ag 67 Pu 67

Sb 69 Am 67

Te 67

This table was recreated from ICRP Publication 68, Table 5

3.0 COLLECTION OF DATA FOR THE INDIVIDUAL CASE

The first step in any internal dose reconstruction under EEOICPA is to collect the data 

associated with the case. The primary sources of this information are the case file sent 

from the Department of Labor, pertinent information on dose from the Department of 

Energy and the interview conducted with the claimant.

3.1 Covered Employment

The location that the individual worked is obviously important. Dates as well as location 

are important since processes changed through the years at a number of sites. It is also 

not unusual for an individual to be employed at more than one site throughout his career. 

Some individuals were employed by one site but worked at another. Lastly, employment 

location is not limited to the site or company at which the individual worked. 

Employment location can often be determined on a building or area basis from the 

claimant interview.
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3.2 Incidents

Incidents are often recorded in the claimant interview. These are very important to 

internal dosimetry for three reasons. First, they can document the date of an intake. This 

is often a critical piece of information when evaluating bioassay samples. Second, an 

incident report often documents many of the details associated with the event including, 

radionuclides present, concentrations in the air, and even dosimetry data. This 

documentation can be very useful in determining the individual’s dose for a period when 

the dose potential may have been very high. Third, even without an incident report, the 

claimant’s report of an incident documents an unusual exposure condition. This is 

important when data is lacking and an interpolation technique is considered during that 

time frame.

3.3 Organ of Interest

The organ (or tissue) of interest is the organ that developed a primary cancer. This will 

be the organ for which the radiation dose is calculated. Documentation from the 

Department of Labor (DOL) will include a verification of the organs or tissues with 

primary cancer. Only the DOL verified organs can be used in the dose reconstruction. If 

inconsistencies are noted between the DOL documentation and the clamant interview, 

DOL must be contacted to verify any additional primary cancers.

The Department of Labor will normally classify the cancer by the ICD-9 code 

(International Classification of Diseases, Clinical Modification 9th revision) associated 

with the cancer (Department of HHS, 1991). This code is a classification system that 

groups related diseases and procedures for the reporting of statistical information. This 

code will be provided for each claimant on the Department of Labor referral summary 

sheet. However, some of the codes can be too specific, while others are not sufficiently 

specific. For example, the ET2 compartment of the lung model is used to calculate dose 

to the posterior nasal passages, however, the ICD codes divide this region into a number 

of types of cancer such as numerous surfaces and structures of the tongue, salivary gland, 

lips, and gums. On the other hand, ICD-9 code 159.0 describes malignant neoplasm of 

the intestinal tract without more detail but the ICRP GI tract model specifies 3 separate 

regions of the intestinal tract, each of which will have a separate dose calculation.

The first case, when the ICD codes are too specific, can be addressed by assigning the 

dose to the more general region for each of the ICD-9 cancers specified. For example, 

the organ dose calculated for the ET2 region can be used to describe the dose to a cancer 

identified as ICD-9 code 141.2, which is specific for cancer of “the tip and lateral border 

of the tongue”. 

The second case requires a review of the medical records submitted with the claim. If the 

records indicate a more exact description of the cancer location, then use this description 

to choose the appropriate region for which to calculate the dose. If no specific location 

can be determined, use the highest organ dose among the possible regions associated with 

the cancer. As discussed above, ICD code 159.0 is described as the “intestinal tract” 
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while the ICRP GI tract model calculates a dose for 3 separate regions of the intestinal 

tract. In this case, if the medical records describe the cancer as being in the small 

intestine, the dose should be calculated for the small intestine. However, if the 

description does not mention the exact location, the dose to all three regions should be 

calculated and the region with the highest dose assigned to the claimant.

3.1.1 Organs Not Included in ICRP Models

An additional problem arises when the organ that developed cancer is clearly specified 

but the ICRP model does not calculate a dose to that specific organ. For example, there 

is no ICRP model for ICD-9 code 190.5, which describes cancer of the retina. In these 

situations, the dose assigned to the organ should be the highest dose among the other 

organs that are not part of the ICRP metabolic model. The ICRP metabolic models 

always calculate doses to several different regions of the lung, and the GI tract, but the 

metabolically modeled organs vary with radionuclide. Organs that do not metabolically 

concentrate a radionuclide will, however, receive photon exposure because of their 

proximity to a source of radiation (the concentrating organs). The newer ICRP biokinetic 

models also consider exposure from beta and alpha radiation to these other organs by 

defining them as a “soft tissue” compartment and describing uptake and clearance rates 

for this compartment. Using these techniques, many of these other organ doses are 

calculated. Since these organs are all considered soft tissue, and thus are all similarly 

exposed, all these doses are relatively equal. This implies that choosing the highest of 

these doses is claimant friendly. However, it is possible for one of the organ doses to be 

much higher than the others due to a close proximity to a concentrating organ emitting 

photon radiation. In this case, the location of the cancer must be evaluated to ensure the 

estimate is not unrealistically high. If it is, the next highest organ dose should be used. 

As a final note, the only lymph node dose specifically calculated by the ICRP models is 

that for the lymph nodes associated with the lungs. A number of ICD codes describe 

cancers of the lymph system without specifically describing the location in the body. It 

might appear reasonable to assign this calculated lymph node dose to the individual 

without further consideration. However, insoluble compounds often cause the lymph 

nodes associated with the lungs to receive high doses, often the highest dose of any 

organ. Because lymph nodes in the lung are considered to retain radioactive material 

almost indefinitely, the material is not transferred throughout the lymphatic system. It 

would be a gross exaggeration to assign this dose to lymphatic cancer associated with a 

lymph node located in a different part of the body. This means that lymphatic cancers 

not associated with lymph nodes in the lungs must be treated in the manner described 

above. That is, the dose to the highest exposed organ that is not described by the ICRP 

metabolic models should be assigned as the appropriate dose.

Table 2 summarizes the decision process discussed in this section.
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TABLE 2. Correlation of ICD-9 Codes to ICRP Models

Scenario Resolution

1 More than one ICD code describes 

organs associated with only one 

region calculated by ICRP models.

Calculate the dose to the ICRP described 

region and assign that dose to the organ.

2 One ICD code describes organs 

associated with more than one 

region calculated by ICRP models.

Attempt to reconcile the location from medical 

records; if not possible, assign the highest dose 

from the appropriate ICRP regions.

3 Organ described by ICD code is not 

described by ICRP models.

Use the dose from the highest exposed organ 

not associated with the ICRP metabolic model.

4 ICD code describes a type of 

lymphatic cancer.

Use lymph node dose calculated from ICRP 

lung model only for lymph cancer associated 

with these lymph nodes. Otherwise, use same 

resolution as number 3 above.

4.0 COLLECTION OF WORK AREA DATA

Collecting work area data pertains to evaluating the material to which the individual 

could have been exposed. Much of this data can be obtained in the interviews conducted 

with the claimant or co-workers. In addition, the DOE site profile databases assembled 

by OCAS can also provide information useful to characterize the workplace exposure 

conditions. The various parameters important to internal dose estimates include:

• Routes of entry

• Radionuclide

• Solubility class

• Particle size (for inhalation exposures)

4.1 Routes of Entry

The route of entry is the path taken by the radionuclide into the individual’s body. The 

route of entry of a radionuclide into the body has a substantial effect on the manner in 

which the body transfers and eliminates the deposited material. This in turn affects the 

dose to individual organs. All intakes of radionuclides can be generally categorized into 

one of four categories.

• Inhalation

• Ingestion

• Injection

• Absorption

4.1.1 Inhalation

In the workplace, inhalation is perhaps the most common route of internal exposure to 

radionuclides. This is an important route of entry since almost any operation with 

uncontained radioactive material involves some chance of the material becoming 
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airborne. Once the material is airborne, a worker in the vicinity is susceptible to inhaling 

it. ICRP 66 (Figure 2) describes the deposition and clearance processes that take place in 

the lung and how they are modeled. This publication also describes the method for 

determining the dose to various regions of the respiratory system.

Exposure from inhalation depends on a number of parameters such as particle size and 

solubility. Unless evidence to the contrary exists, default values from the International 

Commission on Radiological Protection Publication 66 (ICRP 66, 1994) will be used. 

4.1.2 Ingestion

Exposure by ingestion is generally not a significant route of entry. Ingestion and 

clearance of insoluble compounds through the gastrointestinal tract (GI) delivers a dose 

for only a few days, and soluble compounds that are readily absorbed are eliminated 

fairly quickly. Also, loose material that could be accidentally ingested could also be 

inhaled. Unless the fraction that was inhaled or ingested can be determined, the most 

conservative (i.e. claimant favorable) approach that yields the highest dose (inhalation) 

should be used. For these reasons, ingestion generally does not need to be considered 

during a dose reconstruction unless there is some evidence of an unusual event.

While the ingestion pathway typically does not produce a significant dose compared to 

other pathways, it can play a useful role in the dose reconstruction process. While the 

fraction of material ingested often results in relatively minimal dose, it can produce 

bioassay data comparable to a larger inhalation dose. This implies that the erroneous 

assignment of a fraction of the bioassay data to ingestion can significantly bias the 

assigned dose. In some cases, this effect can result in doses that are several orders of 

magnitude low. Because of this, caution must be used before assuming any bioassay data 

is the result of ingestion. However, what appears to be conflicting bioassay data must be 

evaluated. For example, a fecal sample for Th-232 indicated a large dose when assumed 

to be the result of inhalation while an in vivo measurement indicates no detectable Th232 in the lungs. If both samples are valid, and some evidence exists that indicates 

ingestion is possible, this dose can be assigned, at least in part, as ingestion since that is 

the only way to reconcile the two valid measurements.

When evaluating ingestion exposures (or potential exposures) the current IRCP 

recommendations are to be used. Any evidence that would produce more accurate results 

than the ICRP default values may be substituted, provided that documentation is 

available and all assumptions are clearly stated.

4.1.3 Injection

Injection exposures are a result of radioactive material that is taken up directly into the 

body. These types of exposures normally occur as a result of some sort of accident, such 

as a plutonium metal splinter being stuck in a hand. Most often, these types of exposures 

are isolated incidents and there is usually no need to evaluate injection exposure 

scenarios except in the case of a reported event. When such an event occurs, there is 

normally some monitoring data to support a dose reconstruction.
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The uptake occurs as the material dissolves and is taken up by the bloodstream. Once the 

uptake is determined, the ICRP model can be used to calculate the dose by assuming the 

uptake goes directly to the transfer compartment. However, particular attention needs to 

be paid to the exposure duration. In many of these cases, a portion of the material is 

physically removed but not always completely eliminated. This can leave the individual 

with a high rate of uptake initially followed by a step decrease, but not to zero. Some of 

these cases result in the excision of tissue at a later date causing yet another step change 

in the rate of uptake. The events, including any medical procedures, should have been 

documented, so there is normally reasonable data for reconstructing this dose and its 

subsequent effect on bioassay samples. For cases involving uptake by injection, every 

effort should be made to obtain all incident reports, associated monitoring data and 

medical procedure reports.

4.1.4 Absorption

Absorption through the skin is another potential route of entry. Since absorption occurs 

with exposure to soluble compounds, the material is usually eliminated relatively quickly 

from the body. However, if the quantity of material to which the individual is exposed is 

large, the resultant doses can be significant.

Absorption is limited to only a few compounds. Tritium compounds (and gas) are the 

most likely encountered in the weapons complex, however, other exotic chemical 

compounds have been produced in national laboratories that could result in an absorption 

hazard. If the individual was working with any of these soluble compounds, absorption 

dose must be considered. Current ICRP recommendations should be used when 

determining a dose from absorption. Credit can be given to protective clothing, however 

good documentation must exist to evaluate its effectiveness.

4.2 Radionuclide Present in the Work Area

Obviously knowledge of the radionuclides that are present in the work area is important 

to internal dosimetry. The primary radionuclides are generally well known for most 

areas, however, consideration must be given to other aspects of the particular facility. 

4.2.1 Primary Radionuclide

While the primary radionuclides are generally well known, this knowledge is based on 

exposure to the most exposed organs or the effective dose to the whole body. Under 

EEOICPA, organ doses must be calculated to organs that often are not the most exposed. 

This may change the evaluation as to which radionuclides present are the primary source 

of exposure for a particular case.

4.2.2 Radionuclide Impurities

Many materials handled at weapons complex facilities have additional radionuclide 

impurities associated with them. While these normally account for minimal doses when 

compared to the material itself, some chemical processes can concentrate these 

impurities. Familiarization with these processes is an important part of gathering work 

area data.
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4.2.3 Radionuclide Progeny

Some of the radionuclides encountered in DOE facilities decay to progeny which have 

long residence times in the body. Because of this, it is important to account for the buildup of these progeny in the body. While this is performed under the current ICRP 

recommendations, this only accounts for the progeny that grow in after an intake of 

parent radionuclide. When inhaled as a mixture, the intake of progeny must be accounted 

for separately. Many internal dosimetry programs concentrate on the ma jor radionuclides 

only and the progeny are considered to be negligible or are assumed to be related to the 

primary intake by some factor. These evaluations while useful, cannot be universally 

accepted since most of these programs were designed under eithe r the ICRP Publication 

30 (ICRP, 1979) or ICRP 2 (ICRP, 1959) dosimetry models. Progeny deemed as having 

negligible contribution to internal dose in 1955 may not be so evaluated under current 

models. Also, historical air sample data are largely based on gross alpha or beta 

measurements and will often assign all of the activity to the most dosimetrically 

significant radionuclide. Prior to using air sample data, any historical program 

assumptions should be reviewed to ensure they consider decay series progeny. 

4.2.4 Radon

Occupational exposure to radon and its progeny presents a number of unique issues. A 

discussion of these issues and their effect on assessing radon exposure is included in 

section 7.4.

4.3 Solubility Class

Solubility of a given radionuclide is one of the most important parameters in determining 

the internal radiation dose. This parameter is highly dependent on the chemical form of 

the material. The current ICRP recommendations for these solubility classes will be used 

as the default values. Some solubility studies have been done by various facilities that 

may provide more process specific data. Where available, these studies should be 

evaluated and, if appropriate in the context of the dose reconstruction, applied to the dose 

reconstruction.

The most accurate means of evaluating the solubility class is by examining multiple 

bioassay samples after an intake. This has the potential of providing an accurate 

determination of the solubility for the particular material. However, inhaled material 

often exhibits more than one solubility class. A plot of multiple bioassay samples can 

produce a curve that appears to show a soluble compound when in fact it is only the 

soluble portion of the inhaled material that is actually being followed. The slowly 

changing insoluble portion may not be noticeable. Therefore, consideration must be 

given to the potential presence of more insoluble compounds whenever bioassay samples 

are used to determine solubility. Figure 4 demonstrates this effect. As can be seen, a 

mixture of solubility class S and M plutonium produces a clearance curve with virtually 

the same slope as that of pure class M material.
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Pu-239 Urine Concentration After Acute Exposure

0 200 400 600 800 1000 1200 1400 1600

Days Post Exposure

Log Urine Concentration

Class S

Class M

30% Class S, 70% Class M

Figure 4. Example Urine Concentrations for Mixtures of Solubility Classes

4.4 Particle Size

Particle size is an important parameter in determining internal dose from inhalation of 

radionuclides. Particle size is typically material and process related. A number of 

facilities have measured particle sizes for various processes in the past. Some of these 

measurements may be transferable to similar processes at other facilities utilizing similar 

material. In the absence of any measurements or studies, default values from the 

International Commission on Radiological Protection Publication 66 (ICRP 66) will be 

used.

5.0 COLLECTION OF THE INDIVIDUAL DOSIMETRY DATA

Once the exposure pathways and workplace characteristics are evaluated, the individual’s 

dosimetry record should be reviewed. The internal dosimetry information is normally in 

the form of urinalysis and in vivo counts, however, other types of information may also 

be present, including personal air sample results and incident reports. In general, the 

individual’s dosimetry data can be categorized into three categories: bioassay data, workplace monitoring data, and source term data. The last two types of information are 

typically not in the individual’s dosimetry record and may require the dose 

reconstructionist to revisit the work area data.
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5.1 Bioassay Data

Bioassay measurements are generally the most reliable data available for assessing 

internal exposures. This is the result of the fact that all other methods must estimate the 

actual intake of radionuclides based on an assessment of the environmental conditions. 

To insure accuracy, however, intake assessments based on bioassay measurements must 

also consider some of the environmental exposure conditions (particle size, solubility, 

etc.) but not all (airborne concentration, breathing rate). As such, the dose reconstruction 

process will rely on these data when available. These data must, however, be evaluated 

to ensure that they are valid.

Bioassay data is applicable to all routes of entry and almost all radionuclides. For the 

purposes of this implementation guide, bioassay is considered to be any means of 

measuring the actual intake or uptake of radionuclides by an individual. These measures 

include but are not limited to:

• Urinalysis;

• fecal samples;

• In Vivo measurements; and,

• breath radon and or thoron results.

From these measurements, the appropriate biokinetic model is used to determine the 

actual intake or uptake based on the amount and rate of elimination. Using multiple 

points, the rate of elimination can also be used to help determine when the intake 

occurred, whether the intake was acute or chronic and, possibly, the solubility of the 

material.

One of the most important considerations when evaluating bioassay data is the extent to 

which the sampling scheme would detect the radionuclide of concern. While bioassay 

programs are typically designed to detect a particular radionuclide, exposure to mixtures 

of radionuclides is not uncommon. The bioassay program must be evaluated for its 

ability to detect each radionuclide being considered.

When a dose reconstruction is performed using this data, the detection limits and 

uncertainty of the analyses will be used. If this information is available in the dosimetry 

record, it is important to note that. However, this will normally be information that will 

have to be obtained from the work area data in the form of a site dosimetry technical 

manual or other documentation. 

Assuming an adequate bioassay program exists, the next step is the evaluation of any 

positive results. Positive results in this context are results indicating the presence of the 

radionuclide above the detection limit. If an analysis of these positive results establishes 

a dose that results in a probability of causation of ≥ 50%, there is no reason to further 

refine the dose estimate. 

The one caveat to this is that the positive results must be valid. In any type of analysis 

there is a potential for a false positive result. False positive bioassay samples can be due 
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to a number of factors, including contamination of the sample from contamination on the 

individual’s hands or clothing. An attempt should be made to verify the reasonableness 

of any unusually high bioassay results. This does not have to be a quantitative 

verification, but simply a check to determine if the result was anomalous. As one 

example, a very high air sample or a report of some sort of unusual release during the 

time frame that yielded the positive bioassay result would verify the data. In the absence 

of a valid reason for a very high result, an evaluation of co-worker data, air sample data, 

or any other reliable data should be used to determine if the sample is anomalous or 

reasonably realistic. Follow-up samples can be very useful in this determination. If a 

very high result is followed the next day by a non-detectable result, one of the samples 

must be considered suspicious and anomalous. It is important to keep in mind that the 

emphasis of this evaluation is not to produce small refinements of the estimated dose but 

to identify gross errors in sampling, analysis, or transcription of data.

5.2 Workplace Monitoring Data

If bioassay data is not adequate to evaluate the individual’s internal dose, workplace 

monitoring data can be used. Workplace monitoring data consists of any type of sample 

that assesses the conditions in the workplace. Some examples of this type of data 

include:

• Breathing zone air samples

• General area air samples

• Surface contamination surveys

While workplace monitoring may be useful in the evaluation of ingestion or absorption 

cases, it is primarily applicable to the inhalation route of entry. 

When used appropriately, workplace monitoring data is a viable alternative when 

bioassay data is not available. This type of data tends to be less reliable than bioassay, 

since it is an indirect measurement of an individual’s uptake. However, with due care, 

this data can be a substitute for bioassay data (Ritter et al, 1984). 

The general approach to using workplace monitoring data is to determine as closely as 

possible the airborne radioactivity concentration in the individual’s breathing zone. This 

concentration, along with the associated exposure time, particle size, solubility, respirator 

use, etc. can be used to estimate the individual’s intake of radionuclides. The best data 

for determining airborne concentrations are from job specific air samples. Since the 

individual’s breathing zone is the location of interest, lapel type breathing zone air 

samples are preferred. In the absence of breathing zone samples, general area air samples 

can be used, but consideration must be given to any factors that could create a difference 

between general area and breathing zone concentrations (NRC, 1992). Some of the 

factors that should be considered are: the amount and direction of ventilation, the location 

of the airborne sources in relation to the individual and the air sampler, and whether the 

individual is mobile or stationary in the course of the work.
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In the absence of air sample measurements, contamination surveys can provide a 

quantitative indication of the amount of dispersible material in the work area available to 

create airborne contamination. Consideration should be given to types of work activities, 

the type of material, and ventilation or other forces that could cause the material to be 

suspended in air. Resuspension factors can be used, provided enough information is 

available to properly classify the material and conditions. Some references for 

resuspension factors are available (NRC, 1993) but the basis of these references must be 

reviewed to ensure the factors apply to the particular situation.

Once the radionuclide concentration in the breathing zone is established, the individual’s 

intake and deposition of radionuclides must be estimated. When no other information is 

available, the ICRP 66 defaults for a “reference worker” will be used for deposition 

fractions, particle size, etc. It will also be necessary to estimate the individual’s exposure 

time. For a normal workday, the average airborne concentrations and average worker 

exposure time should be acceptable with minimal error. For unusual or abnormal 

conditions that created much higher than normal airborne concentrations, a more rigorous 

examination of the exposure time should be conducted. Typical exposure time and 

abnormal events can often be obtained from the claimant interview.

An additional factor that must be considered is respiratory protection. Measured and 

documented fit factors should not be used since they are not typically indicative of the 

protection afforded in the work environment. Prior to giving credit for respiratory 

protection, the respirator program should be evaluated to determine its protection 

effectiveness. This is not an audit of the program but rather an evaluation to determine if 

quantitative fit testing was performed and whether it is likely a respirator was worn 

during the times that credit is given for the protection. This evaluation may rely on any 

source of information, including a comparison of airborne results to bioassay, interviews, 

or written documentation. It should not rely solely on a written administrative 

requirement unless there is some evidence of the enforcement or normal compliance with 

that requirement. 

5.3 Source Term Evaluation

Without bioassay or air sample data, the last resort is to attempt a determination of the 

airborne concentrations using source term evaluations. Besides the factors previously 

mentioned, the key ingredients of this evaluation are the dispersible quantity of material 

available, and the fraction of this quantity that actually produces airborne contamination 

in the individual’s breathing zone. The distinction between dispersible and nondispersible material is important. For example, it would not be realistic to assume the 

entire mass of a large piece of uranium metal produces airborne contamination. It would 

however, be realistic to assume the bare piece of metal might corrode and produce some 

oxides that could create airborne contamination. 

If only limited information is known about the material, published values for 

resuspension factors can be used, however, an effort must be made to ensure the most 

appropriate factor is chosen for the given situation. If no information is available about 
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the material type, the entire quantity can be considered to be dispersible. If this 

assumption creates an airborne concentration estimate that is unreasonable or inconsistent 

with other information (anecdotal, photographic, etc.) then the assumption should not be 

used.

6.0 PRELIMINARY DOSE ESTIMATES

A complicated dose reconstruction could require days, weeks, or even months to 

complete, even after all the data is available. While accuracy is an important parameter 

for dose reconstruction, the dose reconstruction analyst must keep in mind that the 

ultimate purpose is to determine whether the covered exposure to radiation is “at least as 

likely as not” to have caused a particular cancer. This implies that the dose 

reconstruction only be sufficiently refined to ensure that the decision for compensibility 

is correct. This allows the use of some very conservative assumptions (either high or 

low) for initial estimates which require further refinement only if the likelihood of 

compensibility is not clear. For example, if the upper limit of a possible exposure 

scenario is unrealistically high but still results in a low probability of causation, no 

refinement is necessary. Likewise, if only the recorded bioassay results (once validated 

and without accounting for missed dose) are sufficiently large to result in the individual 

having a high probability of causation, no refinement to the dose estimate is necessary.

The dose reconstruction efficiency process is described in 42 CFR part 82 paragraph 

82.10 (k). This process allows for the degree of research and analysis to be limited to 

that which is necessary to determine if the radiation dose will reach a compensable level 

(i.e. a dose producing a probability of causation of 50% or greater at the 99% credibility 

limit). The first step in the efficiency process is to determine whether the radiation dose 

is clearly high or low when compared to this criterion. This criterion is not simply one 

number for a dose of radiation or even one number based on the type of cancer. The 

exposure dates, the age of the individual, the types of radiation as well as other factors 

affect this decision. For the purposes of an initial estimate, the bioassay data, the type of 

cancer and the age of the individual should be sufficient to determine the appropriate 

starting point. If the covered cancer is not in a metabolic organ for the particular 

radionuclide (e.g. spleen cancer for a plutonium intake), the internal radiation dose to that 

organ is likely low. Also, if the analyses do not indicate any detectable results, the 

radiation dose is likely (but not necessarily) low. This evaluation should be performed 

for each radionuclide to which the individual is potentially exposed. If the organ of 

interest is a metabolic organ for any of the alpha emitting radionuclides in which there 

was detectable bioassay result, assume the radiation dose is high.

Once this decision is made, the dose reconstructionist can follow the general steps 

outlined below to perform a preliminary internal dose estimate. The steps below are 

described to evaluate inhalations of low solubility compounds of actinides with bioassay 

data available for the radionuclide through the majority of the individual’s employment. 

While this may appear to be a very limited situation, it should be the largest category of 

the many possible categories that will be encountered. Also, the dose reconstruction for 

many of the remaining categories may be based largely on this process with only minor 
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modifications. The remaining situations should follow a similar philosophy in arriving at 

a preliminary dose estimate.

6.1 Preliminary Dose Estimate – Low Dose Potential

If the analyst has determined that the radiation dose to the organ of concern is likely low, 

the next step is to perform a preliminary dose estimate that reasonably and realistically 

maximizes the dose to the organ. This preliminary estimate is performed as follows:

1. Choose a radionuclide from the radionuclides for which the individual was 

monitored.

2. Recalculate the bioassay values using the higher of the MDA or the actual 

result plus two standard deviations.

3. Choose a solubility class from the credible classes given the radionuclide and 

the individual’s work area.

4. Assume an acute inhalation on the first day of employment and determine the 

highest intake that will produce a predicted bioassay value that will equal at 

least one of the recalculated bioassay values from step 2 above.

5. Assume a constant chronic exposure throughout the individual’s entire 

employment and determine the highest intake that will produce a predicted 

bioassay value that will equal at least one of the recalculated bioassay values 

from step 2 above.

6. Repeat steps 4 and 5 for all potential solubility classes.

7. Determine the scenario that produces the highest 50 year committed dose to 

that organ. (If the time between exposure and diagnosis is <10 years, use the 

first and last year doses instead)

8. Using the scenario that produces the highest dose, determine the annual doses 

to the organ of concern from this scenario. This will be used in a preliminary 

probability of causation (PC) analysis later.

9. Choose the next radionuclide for which the individual was monitored and 

repeat steps 2 through 8.

10. Once all radionuclides have been accounted for, group them by major 

radiation type emitted (i.e. alpha, beta, gamma, etc.) 

11. Sum the annual doses by radiation type from each isotope.

12. Use the total annual dose input along with the external annual dose input to 

run the NIOSH-IREP program. Use the “constant” distribution since the dose 

determined is the upper bound.

13. If the PC is below 50%, no further refinements to the internal dose estimate is 

required. However, if the external dose estimate is preliminary, the entire PC 

calculation must be performed again once the external values are finalized.

14. If the PC is greater than or equal to 50%, refinements to the estimate are 

required. If the PC is much higher than 50%, perform a preliminary estimate 

assuming a high dose potential (see section 6.2). Sources of refinement that 

should be explored include:
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A. Running the PC calculation for internal and external separately. If one 

mode produces a much higher PC, concentrate efforts on refining that 

mode.

B. Adjusting the intake scenario to a more reasonable representation of the 

intakes. Dates, chronic vs. acute, and amounts can all be adjusted 

provided all but one of the predicted bioassay values are above the values 

calculated in step 2. The potential of several scenarios capable of giving 

the same results must be explored. If more than one is found, the scenario 

that produces the highest dose must still be used.

C. If one sample is driving the analysis and it appears to be anomalous, 

remove the sample from the data set and recalculate the dose and the 

subsequent PC. If that greatly changes the PC, evaluate the potential of 

permanently removing that sample as anomalous.

D. Evaluate the interrelationship of the various samples to determine if the 

individual results used are possibly real. For example, if gross alpha 

analysis is used for one radionuclide and a specific chemical extraction is 

used for a different alpha emitting radionuclide, activity from the second 

radionuclide will be accounted for in both analyses. The amount of the 

gross alpha analysis attributed to the second radionuclide should be 

subtracted from the alpha analysis thus lowering the highest potential 

concentration of the first radionuclide.

15. At some point it becomes counterproductive to continue refining a preliminary 

estimate and a detailed dose reconstruction must be undertaken. Before 

reaching that decision, both a high and low estimate of the individual’s 

internal dose should have been performed.

6.2 Preliminary Dose Estimate – High Dose Potential

1. If sample data exists for the individual for more than one radionuclide, use 

professional judgment to choose a radionuclide to start. This judgment can be 

based on the radionuclide that will deliver the most dose per unit intake or the 

one with the highest bioassay results. 

2. If there is a clear increase in activity at some date, use that date for an 

inhalation.

3. Choose a solubility class from the credible classes given the radionuclide and 

the individual’s work area.

4. Assume an acute inhalation on the first day of employment and determine the 

highest intake that will not exceed any of the measured bioassay values. Do 

not use the MDA values or subtract the uncertainty from the measured values.

5. If the acute scenario does not produce a realistic curve, attempt to find a 

chronic scenario that more reasonably depicts the measured data. Insure the 

predicted bioassay results do not exceed any measured values.

6. Repeat steps 4 and 5 for all potential solubility classes.
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7. Determine the scenario that produces the lowest 50 year committed dose to 

that organ. (If the time between exposure and diagnosis is <10 years, use the 

first and last year doses instead)

8. Using the scenario that produces the lowest dose, determine the annual doses 

to the organ of concern from this scenario. 

9. Use the annual dose input along with the completed external annual dose input 

(if available) to run the NIOSH-IREP program. Use the “constant” 

distribution since the dose determined is a lower bound.

10. If the PC is >50%, no further refinements to the internal dose estimate are 

required. It is also not necessary to perform an external dose estimate. 

Additional dose from any source will only cause a higher PC.

11. If the PC is <50%, use professional judgment to refine the estimate. If the PC 

is greatly below 50%, perform a preliminary dose estimate assuming a low 

dose potential. Sources of refinement that should be explored include:

A. Use the most credible solubility class instead of the one that produces the 

lowest dose.

B. Use a more credible exposure scenario rather than the one that produces 

the lowest dose.

C. Repeat the process for other radionuclides for which the individual was 

monitored and add this dose to that already calculated.

D. If bioassay results that are below MDA are driving the intakes scenario 

down a great deal, use the lower of the MDA value or the result plus 2 

standard deviations and reevaluate the scenario.

E. If one bioassay sample is driving the analysis and appears to be 

anomalous, remove it from the data set. If that greatly changes the PC, 

evaluate the potential of permanently removing that sample as anomalous.

F. If not already done, add the external dose to the probability of causation 

analysis.

G. If refinements fail to result in a PC >50%, run the PC calculation 

separately for internal and external exposure. If one is clearly higher, 

attempt to refine that estimate first.

12. At some point it becomes counterproductive to continue refining a preliminary 

estimate and a detailed dose reconstruction must be undertaken. Before 

reaching that decision, both a high and low estimate of the individual’s 

internal dose should have been performed.

6.3 Modifications to the Preliminary Dose Estimate Process

As stated in the beginning of this section, the process outlined above applies to only a 

limited number of cases. However, most cases can be evaluated using this approach with 

only minor modifications. Consider the situation where the individual was not monitored 

for all the potential radionuclides to which they were exposed. If monitored 

radionuclides cause the PC calculation to exceed 50% then the estimation process works. 

On the other hand, if the monitored radionuclides cause a very low PC the estimation 
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process needs to be modified. In this situation, the process can be followed for all the 

radionuclides for which the individual was monitored. Then an upper bound must be 

determined for the unmonitored radionuclide. This may be done using as a fraction of the 

monitored radionuclides or by other means available to the analyst. The important point 

to remember is that if the potential of this exposure is known, the information used to 

establish that potential can normally be used to determine some bounds for the exposure.

Uncertainty can be added to a preliminary estimate that produces a PC that is barely 

under or over the PC of 50%. For example, assume an individual has a relatively high 

dose received from intakes of radionuclides. The preliminary dose estimate based on the 

high dose potential produces a PC of 48% after several refinements. Since this is a lower 

bound, it may be possible to perform two new estimates to determine an upper bound and 

a most likely dose. The upper bound relying on the results plus 2 standard deviations and 

the most likely based on only half the samples being above the predicted results instead 

of all the samples. These three points can then be used to establish a triangular 

distribution. This causes the original estimate to be the lower bound of a distribution 

instead of a point estimate and should raise the PC (possibly beyond 50%) by more 

accurately describing the individual’s potential dose distribution.

Ideally, internal dosimetry program data will exist that encompasses the individual’s 

entire exposure history. However, it is very likely that gaps in the information will be 

encountered. When this occurs, there are several options. The first option is to 

interpolate between existing bioassay data. For this option to be effective, the period of 

missing data must be “bounded” by periods of valid data that are representative of the 

missing period. For example, interpolation would be most appropriate for a period of 

missing data in which data exists before and after the period and all three time frames 

represent the same type of work, with the same type of material, in the same location. 

This interpolation applies to bioassay, air sampling, and any other type of data that is 

used. Options for interpolating data points has been previously published (CrawfordBrown et. al., 1989)

Another method for filling in data gaps is the use of co-worker data. If the individual had 

co-workers in the same area, any data from these co-workers could theoretically be used 

to estimate the individual’s dose. Since this information is not actually from the 

particular individual, it must be judged for applicability. When possible, it is best to use 

data from several co-workers. If data from several co-workers are available but of 

varying applicability, appropriate weighting factors can be assigned to each data point so 

that the most relevant data is weighted more heavily. For example, if a co-worker was 

performing the same job in an area with airborne concentrations twice as high as the 

individual in question, a weighting factor of 0.5 could be assigned to the co-worker’s 

dose. This allows for several doses to be used to determine a more representative value, 

even when few co-workers closely matched the individual’s exposure. If weighting 

factors are used, the basis for these factors must be documented. 

Many other modifications are possible in order to estimate the internal dose for a 

particular case. The process used, even if the preliminary process is followed, must be 
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documented in the dose reconstruction report. Unless a detailed dose reconstruction is 

performed, the dose utilized must be clearly high (for PC<50%) or low (for PC>50%).

6.4 The Probability of Causation Calculation

The primary product of any dose reconstruction is the input for the probability of 

causation calculation. This calculation is performed by the computer program NIOSHIREP which utilizes Monte Carlo techniques to perform its calculations. It is important 

to realize that the values produced by this program may vary slightly due to the nature of 

a Monte Carlo calculation. Varying the numbers of trials or the input “seed” value can 

cause the calculated PC to change by several percentage points. Therefore, while the 

process outlined above utilizes a PC of 50% as a decision level, care must be utilized 

when performing these calculations. A PC between 40% and 60% should be re-evaluated 

with various numbers of trials and seed values before any decisions are made.

6.5 Refining Preliminary Estimates

The process of performing a preliminary dose estimate includes steps for refining the 

estimate. The process steps list a number of potential refinements. When a refinement is 

necessary, a more rigorous approach to the dose reconstruction must be adopted. Since 

initial estimates often rely on very conservative assumptions, the refinement process 

attempts to find more valid values for these parameters. This may require a search for 

additional data. Like the initial estimates, the refinement need not attempt to be 100% 

accurate, only more accurate than the original estimate. As previously stated, the degree 

of accuracy required is that sufficient to render an accurate decision for compensation. 

A useful technique for refining dose reconstructions is to compare estimates from 

different methods. For example, gaps in an individual’s bioassay data could be estimated 

by interpolation, by co-worker data or by air sample data. By evaluating each dataset, 

there may be only a small band of possible answers that fits all three methods. This 

evaluation could then lead to a calculation of the average of the results with an 

uncertainty distribution. Since uncertainty is an input into the NIOSH-IREP program, 

this will be reflected in the probability of causation outcome. This method may also help 

to recognize anomalies. If there is no answer that fits all three methods, at least one of 

the methods must be in error. Finding the erroneous assumption or sample could change 

the assumptions elsewhere in the dose reconstruction and eventually produce a more 

accurate result.

Comparing estimates from different methods does not have to be limited to periods when 

gaps exist in the data. In vivo counts or air sample measurements could be used to place 

an upper or lower limit on an intake indicated by urinalysis. As discussed earlier, 

multiple urinalyses could be used to evaluate the elimination of an acute uptake resulting 

in more accurate solubility parameters. Also, numerous air sample data in a particular 

area could be used to determine a pattern of airborne activity. This pattern could indicate 

an overestimate or underestimate of the intake calculated by other means.
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Other refinements include obtaining studies or data that more accurately define some of 

the parameters used, such as solubility class or particle size. This is especially possible 

when some default parameters are used. Particle size studies, solubility studies, as well 

as ventilation tests, are all sources of potential refinements. 

7.0 DETAILED DOSE ESTIMATES

The preliminary dose estimates described above should minimize the analysis and 

research necessary to complete many dose reconstructions in accordance with 42 CFR 

part 82. This allows for a more efficient process that will finalize dose reconstruc tions in 

a more timely manner. However, this efficiency process only works in cases where the 

decision for compensation can be shown to be clear. Some cases will likely be too close 

to determine a clear decision for compensation without a detailed dose estimate.

The dose reconstructionist will use professional judgment in determining the point at 

which preliminary dose estimates are counterproductive and a detailed dose 

reconstruction must be undertaken. Information obtained and calculations performed 

during the preliminary estimates may be used to the fullest practical extent during the 

detailed dose reconstruction process. However, the desire for efficiency should not 

interfere with the need for accuracy of the detailed dose reconstruction.

In performing a detailed dose reconstruction, the dose reconstructionist is attempting to 

find the best estimate of the individual’s dose rather than find the upper or lower bound 

of the dose. Because of this, the detailed dose reconstruction requires the uncertainty in 

the analysis to be quantified. It is still important, however, to keep in mind the purpose 

of the dose reconstruction. Efforts should still be directed to the parameters that make the 

largest difference in the individual’s dose. Worst-case (claimant favorable) assumptions 

can still be used for parameters that produce little change in the estimated dose.

Although individual cases vary too much for an all inclusive step-by-step instruction to 

be developed, some additional considerations when performing a detailed dose 

reconstruction are included below.

7.1 Estimate of Intake Date

The time of intake is an important parameter in assessing bioassay data. Based on one 

positive sample, the intake could have occurred anytime since the last non-detectable 

sample. The difference in a calculated intake, based on assuming the intake occurred at 

either the beginning or the end of this period, can vary by orders of magnitude. Without 

any additional information, the standard approach should be to assume the exposure 

occurred midway between the two sample dates. The rationale and logic behind this 

assumption is discussed below.

Since bioassay samples are correlated, a large intake detected in a bioassay sample will 

likely continue to be detected in the next several samples. Eventually, the pattern of 

subsequent sample results provides a means of estimating the intake date, and thus the 
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quantity. Also, multiple small intakes (such as a chronic exposure) will eventually reach 

a level where subsequent samples are detectable. Once this level is reached, the total 

intake from the multiple intakes can be estimated fairly accurately. The assumed intake 

date of each individual intake may be in error but the overall intake estimate will be 

accurate. This implies the midpoint of sampling dates will only be used in the case of a 

few small intakes. In that case, the overall dose is likely to be small. The exception to 

this are radionuclides that quickly clear from the body. In these cases, the residence time 

in the body is so short that even a large intake does not produce a significant dose. This 

implies the largest errors occur with the smallest doses and therefore the midpoint 

estimate between two dates should not significantly affect the decision for compensation.

Even without the correlation of multiple samples, the midpoint estimate will likely yield 

reasonable results. While it is possible that an individual received an intake just after 

leaving one routine sample, it is somewhat unlikely. The possibility that such an event 

occurs sequentially multiple times is even more unlikely. In fact, if a more rigorous 

Monte-Carlo calculation is performed, assuming an equal chance of an intake on each 

day between samples, the mean value is the midpoint. Using Monte-Carlo calculation in 

this approach it is a valuable tool that can be used to determine the midpoint and to 

estimate the uncertainty associated with the intake. 

It is also important to keep in mind that the midpoint is only used in situations when there 

is no other information. Incident reports or air sample results, as well as other sources of 

information, can be very useful in determining the date the intake occurred. 

7.2 Uncertainty

The uncertainty of the internal dose calculations has a number of components that can be 

difficult to quantify. However, the largest uncertainty associated with internal dose 

calculations will predominately be associated with determining the intake. This implies 

that the method used to assess the uncertainty depends on the method used to assess the 

intake. For non-correlated techniques (air sample measurements, injections of known 

quantities, etc.) the uncertainty of a single sample is usually understood and readily 

calculated. Combinations of results from these methods (such as averaging air sample 

results) are readily dealt with using standard propagation of errors techniques. The 

standard equation for this is:

Where σ is the standard deviation of the function (f) or the independent variable α. The 

summation (Σ) must be performed for all independent variables. It is important to note 

that this equation is only applicable if all the variables are independent. When variables 

are not independent, correlation coefficients must be applied.

Bioassay samples are correlated by their very nature. The correlation coefficients depend 

on the length of time between the individual intakes as well as a number of other factors. 

( )

2

2 2

s ∂a s a

∂

= Σ

f

f
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Since the date of the intake can be somewhat arbitrary, this coefficient can be very 

difficult to calculate.

However, the correlation among samples does provide a more accurate result for total 

intake. Consider the following example. An individual is working with Pu-239 with a 

solubility class of S. He unknowingly receives an intake of 1 μCi of Pu-239 on January 

25th. He then submits a routine urine sample on January 31 which contains 0.37 pCi of 

Pu-239. With no other information, the analyst assumes the midpoint of January 15th for 

the date of intake (based on the date of the last sample) and thus calculates an 

overestimate of the intake of 2.02 μCi. On February 24th the individual is involved in an 

incident in which he inhaled Pu-239. A bioassay sample submitted on February 28th

contains 0.42 pCi. Still relying on the midpoint estimate, the analyst would calculate that 

0.34 pCi of that sample is attributed to the first intake. The remaining 0.08 pCi is 

attributed to the new intake on February 24th which predicts a 0.135 μCi intake on 

February 24th. However, in reality, the first intake was 1.0 μCi on January 25th so only 

0.171 pCi of Pu would be left in the urine on February 28th. Thus, 0.249 pCi (0.42 pCi –

0.171 pCi) of the February 28th sample is due to the February 24th intake. Therefore, the 

February 24th intake was actually 0.42 μCi. 

In this scenario, the real intake was 1.42 μCi (1.0 μCi + 0.42 μCi) while the estimated 

intake was 2.155 μCi (2.02 μCi + 0.135 μCi). The initial intake was overestimated by 

1.02 μCi (102%) but after the 2nd intake, the total overestimate dropped to 0.735 μCi 

(52%). As subsequent intakes are evaluated, the estimate of the total intake becomes 

increasingly accurate. Since the most accurate estimate in this analysis will be the total 

intake, the best uncertainty value to use is the relative error associated with the total 

intake.

To calculate the relative error it is first necessary to determine the error associated with 

each intake. This can be done by applying the relative error of the bioassay sample on 

which the intake is based. Next, propagate the errors of all the intakes to determine the 

absolute error associated with the total intake. Finally, divide this error by the total 

intake to obtain the relative error. This relative error will be the error applied to the 

calculated doses.

It should be noted that this procedure does not accurately reflect the uncertainty of the 

initial intake. This is because it does not account for the accuracy of the date chosen or 

the correlation between samples. However, it can be considered an accurate 

representation of that component of the total intake. Just as the dates can be arbitrary, the 

size of this component can be considered arbitrary. The important number is the 

uncertainty of the total intake. This number is found by propagating the error of the 

individual components of the total intake.

Therefore, if an individual receives only one intake, the error associated with the total 

intake will be equal to the error associated with that one intake. Conversely, if an 

individual receives multiple intakes, the relative error associated with the total intake will 

be less than any one of the individual relative errors. 
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This approach has the advantage of easily allowing the incorporation of other methods of

intake assessment into a single uncertainty analysis. For example, if an individual 

received several intakes that were evaluated using either bioassay or air monitoring data, 

the error of each intake can first be determined separately, then all the intakes can be 

summed and the errors propagated as discussed above. 

It is important to note that, while the uncertainty of an internal dose estimate can be 

dominated by the uncertainty in determining the intake, this is not always the case. The 

intakes for individuals that submit many detectable bioassay samples may have their total 

intake calculated fairly accurately. However, this intake is based on a particular 

biokinetic model. Any inaccuracies or biases produced by this model must be 

considered.

Uncertainties associated with the biokinetic models are difficult to assess. While some 

attempts have been made to evaluate the uncertainty of the overall models, (NCRP, 1998; 

Till et. al, 2000), it is important to tailor the uncertainty assessment to the specific 

situation at hand.

For example, an uncertainty assessment for PuO2 inhalation was performed by Radiation 

Assessment Corporation (Till et al, 2000). This assessment listed values for specific 

organs and particle sizes. The report listed the uncertainties as lognormal distributions 

with geometric standard deviations (GSD) that varied between 1.9 and 4.5 depending on 

the organ and the particle size. However, it appears that the dominant factor in the 

uncertainty was the solubility class. If the solubility for a particular compound is well 

known, the uncertainty associated with this compound must be lower than that described 

by the report. Also, this assessment was based on the inhalation of a known (or 

calculated) amount of material. If the intake is determined from bioassay data, a very 

different result is obtained, especially for non-metabolic organs. 

An acceptable approach, when feasible, is to determine the lowest possible, most likely, 

and highest possible doses given the data set used for the particular individual. Once 

these values are determined, a triangular distribution can be assumed using these three 

points as the parameters of the distribution. This approach gives credit for the parameters 

that are known while accounting for the parameters that are not well known. When 

properly performed, this method also inherently accounts for correlated parameters. 

Figure 5 shows a typical triangular distribution with a minimum value of zero, a 

maximum value of three and a most likely value of one.
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Figure 5. Example of a Triangular Probability Distribution

7.3 Missed Dose

Missed dose is the quantity of dose that could have been received with all measurements 

remaining below the detection limit of the sampling method employed. It may appear 

that assigning all non-detectable samples a value equal to the detection limit would be 

appropriate. However, this approach will, in most instances, significantly overestimate 

the missed dose. 

The first problem with assigning the detection limit to the samples it that this process 

assumes that a person received an intake that resulted in a bioassay sample that was just 

under the detection limit. While this is possible, it is extremely unlikely to occur each 

and every time a sample is taken.

Missed dose from airborne activity samples are usually small since these samples are 

normally counted long enough to detect activity at very low concentrations. However, if 

this does become a problem, it can be easily overcome if the actual results were recorded 

as something other than “<MDA” (< minimum detectable activity). Since the individual 

samples will be used collectively (i.e. averaged) the detection limit of the batch (the 

average) is the important quantity, not the individual detection limit. By propagating the 

errors of the individual samples, the standard deviation of the overall average will 

decrease. Because of this, the detection limit of the overall average is lower than the 

detection limit for an individual samples. Once the actual results are averaged, that 

average can be compared with the detection limit associated with the batch average. 

If the results were recorded as “<MDA” then other means must be used to estimate the 

actual airborne activity. If there are a number of samples with detectable results, one 

option is to evaluate the distribution of the samples. From that distribution, an estimate 

of the central tendency along with its associated uncertainty could be determined. This 

distribution can then be used to describe the sample results that are not detectable.

The second problem in determining missed dose is the correlation between bioassay 

samples. For bioassay samples, if a person receives an intake of an insoluble compound 

that produces a bioassay sample just under the detection limit, some portion of this intake 

will continue to be present in the next sample. This makes it unlikely for a person to 

receive this maximum uptake in two successive sample periods without producing a 

.000

.006

.011

.017

.022

0.00 0.75 1.50 2.25 3.00
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detectable bioassay result. After several sample periods the likelihood becomes 

increasingly small. This effect is most pronounced when inhaling fairly insoluble 

compounds (class S solubility). For very soluble compounds, the correlation among 

samples is less important but the missed dose associated with such intakes is also 

relatively low.

The most appropriate method for determining the missed dose associated with a bioassay 

sample will vary depending on the bioassay program itself, as well as other factors 

surrounding the individual exposure scenario. Missed dose could be assigned based on 

factors such as co-worker data or interpolation. For example, if the individual tours a 

facility for approximately one hour each day and had no detectable bioassay samples, it 

could be helpful to evalua te the exposure of others working in the facility all day. If they 

also had no detectable bioassay samples, it would not be appropriate to assign the same 

missed dose to both individuals. The missed dose of the individual with limited exposure 

time (from a tour) should be proportional to the other individual who was in the work 

area for the whole time. This prorating could apply even if the worker submitted 

detectable bioassay samples. Provided the exposure potentials are equal (other than 

exposure time) the touring individual’s missed dose should be based on the other 

individual’s detectable dose.

When there is no other information on which to base a decision, the missed dose should 

be determined using the following protocol:

1. Determine the standard deviation of the bioassay method at the detection 

limit. If this cannot be readily determined, assume that it is 0.3 times the 

value of the detection limit. This factor is derived from the fact that the 

sample analysis probably consists of a gross sample result minus a blank 

result. It assumes that the standard deviation of the gross result is at least as 

high as the blank result and therefore propagating the error dictates that the 

standard deviation of the detection limit must be at least √2 times the standard 

deviation of the blank. The factor also assumes some variation of the standard 

Currie equation (LD = 2.71 + 4.65 σb) (Currie, 1968) was used to determine 

the detection limit. If the 2.71 is ignored, σb (the standard deviation of the 

blank) becomes LD/4.65 (where LD is the detection limit) and the standard 

deviation of the detection limit then becomes √2*LD/4.65 or 0.3*LD. Even 

though this is not an exact value, this should provide a reasonable 

approximation in the situations when no other information is available.

2. Subtract 1.645 times the standard deviation from the detection limit to achieve 

a new target value. This provides a target value that 95% of the samples at 

this level will not exceed the detection limit.

3. Assume a constant chronic exposure over the entire period in question and 

determine that intake based on the highest bioassay sample equaling this 

target value from number 2 above.

4. The uncertainty of this estimate will be considered to be normally distributed 

with a relative error equal to the standard deviation determined in step number 

1 divided by the detection limit times 100%.
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It must be noted that this procedure should be used only when there is a large deficiency 

of information. It should also be noted that this is likely a high estimate. This is because 

a number of bioassay samples that all indicate less than the detection limit are more likely 

closer to zero than to the detection limit itself. This method produces a target value that 

is 1⁄2 the detection limit (LD-1.645*0.3*LD = 0.5*LD). If the standard deviation is 

known, the target value could be higher or lower but it would be based on more 

information. 

7.4 Radon

Radon 222 and its progeny is an exception to much of the discussion provided in this 

document. It is a naturally occurring source of radiation exposure as well as an 

occupational source under certain conditions. Radon and its short-lived progeny (decay 

products) are continuously produced from the decay of Ra-226, a member of the naturally 

occurring U-238 series. As an inert gas, radon emanates from soils containing natural 

levels of U-238. Chemical separation of the uranium from its ore will strip out the 

majority of the Ra-226 as well as other progeny. While the decay process will again 

produce Ra-226, this process takes thousands of years for the Ra-226 to re-achieve 

equilibrium. Therefore, not all uranium will be a significant source of radon exposure. 

In fact, many DOE facilities only handled uranium after it had undergone a chemical 

separation. The Ra-226 removed during the process was at times used in other processes 

or experiments. At other times, it was stored as waste. Any of these processing or 

storage locations are potential sources of occupational radon exposure. 

Since radon is a naturally occurring radionuclide, it can be difficult to distinguish 

environmental radon exposure from occupational sources of radon. For the purposes of 

dose reconstruction under EEOICPA, occupational radon exposure is considered to be 

radon emanating from sources other than those naturally occurring in the area. 

The natural (background) level of radon in the area must be assessed and subtracted from 

any measured values whenever estimating occupational levels of radon. Since by 

definition, natural radon is not a result of DOE activities, current background levels of 

radon can be assessed and applied to prior years. The background however, must be 

measured without interference from the current or past DOE activity. 

The dose from radon will not be determined directly because the probability of causation 

risk coefficients are based on working level months (WLM) of exposure which is a direct 

input into the NIOSH-IREP program. A working level month is the exposure to one 

working level of Rn-222 progeny for 170 hours (40 hours per week for one month). A 

working level is defined as follows:

“Any combination of short-lived radon daughters in one liter of air that result in 

the emission of 1.3x105 Mev of potential alpha energy.” (NCRP 78, 1984)
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Therefore, one working level has an equilibrium equivalent concentration of 100 pCi of 

Rn-222 in one liter of air.

Since radon-222 and its progeny are very short-lived, air sampling is the only type of 

monitoring that is feasible on a routine basis. Radon was typically not considered an 

occupational concern or at least a very low concern compared to other sources of 

radiation exposure. For this reason, air sample data may be limited, especially in the 

earlier years (1945-1980s). However, there may be information that can be used to relate 

interference to other air monitors. 

Respiratory protection factors afforded by HEPA filtered respirators can be taken into 

account in accordance with section 9 of this guide. The efficiency of HEPA type 

filtration is nearly 100% for radon progeny due to its electrostatic nature. However, since 

radon is an inert gas, no protection is provided by HEPA filters for the Rn-222 gas itself. 

Because it is chemically inert, the majority of the gas is exhaled without depositing in the 

lungs. Some of the gas will however diffuse into the body tissues. With the majority of 

the gas being exhaled, the dose from the gas is small in comparison to the progeny. In 

these cases, when HEPA filtered respirators are used, an assumption that the gas 

saturated the soft tissues in the body is usually a high estimate of the dose that produces 

very little exposure. Since the risk factors in NIOSH-IREP are based on WLMs, and a 

WLM is defined only for progeny, any exposure to Rn-222 gas (without its 

accompanying progeny) will have to be calculated as a dose and input into NIOSH-IREP 

as a dose instead of a WLM.

8.0 EXAMPLE DOSE ESTIMATES

This example internal dose reconstruction is provided to demonstrate the general 

approach to dose reconstruction, as well as many of the other concepts discussed in this 

document. It is a fictitious example designed to help illustrate several points. The 

example is based on the following scenario.

8.1 Scenario

The individual was born in 1931 and worked from November 7, 1969 to August 9, 1973 

in an area where he had the potential to inhale plutonium. The plutonium was pure Pu239 with about half in the form of insoluble oxides and the other half being unspecified 

compounds. The only dosimetry data that exist are bioassay (urine) samples. The 

analytical technique that was used to measure the samples was capable of detecting 

approximately 0.07 pCi/L or about 0.1 pCi/day at a nominal daily excretion rate of 1400 

ml of urine per day. The bioassay data available is normalized to daily excretion. 

Results indicating less than the detection limit were recorded “as read”. The worker’s 

records indicate he was involved in an incident on 1/20/73 that resulted in the inhalation 

of Pu-239 by several people. The individual was diagnosed with liver cancer in late 

December 1998.
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As indicated in Table 3, the bioassay data consists of 51 samples with 38 of them 

indicating greater than the detection limit.

TABLE 3. Bioassay Data for Example Dose Reconstruction

Date pCi/Day (Pu-239) Date pCi/Day (Pu-239)

11/7/1969 0.0 11/26/1971 0.07

12/6/1969 0.36 1/5/1972 0.19

1/23/1970 0.075 1/28/1972 0.18

2/20/1970 0.0825 2/25/1972 0.2919

3/28/1970 0.09 3/31/1972 0.2071

5/1/1970 0.1593 5/26/1972 0

5/29/1970 0.1083 6/23/1972 0.37

6/26/1970 0.02 9/1/1972 0.66

7/24/1970 0.087 11/25/1972 0.3431

8/15/1970 0.03 12/1/1972 0.09

8/21/1970 0.0741 1/12/1973 0.4156

9/11/1970 0.252 1/22/1973 0.3926

10/17/1970 0.02 2/1/1973 0.85

10/23/1970 0.2575 2/8/1973 1.38

12/4/1970 0.1201 2/15/1973 1.36

1/8/1971 0.2794 2/22/1973 1.22

2/5/1971 0.3475 3/1/1973 1.2635

3/5/1971 0.38 3/8/1973 1.42

4/13/1971 0.3 3/22/1973 1.42

5/16/1971 0.2 4/12/1973 1.4

6/11/1971 0.26 4/26/1973 1.39

7/16/1971 0.4278 5/3/1973 1.34

8/15/1971 0.2401 6/7/1973 1.26

9/10/1971 0.08 7/12/1973 1.33

10/24/1971 0.12 8/9/1973 1.25

11/7/1969 0.1704

8.2 Case Evaluation

No information is available on particle size so the ICRP 66 default of 5 micron will be 

used. From the information available about the type of compounds used, the solubility of 

the material will be considered to be 1⁄2 class M and 1⁄2 class S. 

With so many samples, it may be best to start with an overall impression of the data by 

creating a graph as seen in Figure 6. 
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Pu-239 sample results

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

7/16/1969 2/1/1970 8/20/1970 3/8/1971 9/24/1971 4/11/1972 10/28/1972 5/16/1973 12/2/1973

 Figure 6. Graph of Bioassay Samples for Example

From Figure 6, it is clear that the individual had an intake in January of 1973. This 

corresponds well with the report of an incident on 1/20/1973. That information alone is 

considered sufficient to verify that the elevated results after January of 1973 are valid 

results, not an analytical problem or anomalies. In this case the individual had several 

high sample results, which would discount the possibility of a contaminated sample. 

Since they are all relatively consistent, the possibility of cross contamination of the 

sample is remote. Also a number of the results indicate greater than the detection limit 

prior to the 1/20/73 incident. Again, with so many samples elevated and the results being 

consistent, they can be considered to be true values and not an analytical anomaly. 

Therefore, the overall impression of this data is that the individual received several 

intakes prior to a larger intake on 1/20/73.

The liver tends to concentrate plutonium. Combining this with the fact that the liver is 

fairly radiosensitive, and the fact that the individual is believed to have received several 

real intakes, it would be prudent to perform a preliminary dose estimate using the data at 

hand to create an underestimate of dose to the liver. This underestimate could be done 

quickly to determine if the individual will easily fall into a high probability category. 

8.3 High Dose Potential Preliminary Estimate

An underestimate can be accomplished by estimating the dose received from the large 

intake during the 1/20/73 incident alone. In determining the amount of the acute intake 

on 1/20/73, it is indicated that the concentration in the urine should drop off much faster 

than the data show. While the cause of this is unclear, all that matters is that there are 

credible reasons for the observations, such as the incident contaminated the work area 

enough to deliver a chronic intake each day for some time. With that assumption stated, 
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the underestimate was performed by modeling the intake as two back-to-back chronic 

exposures. The first of 2000 pCi per day from 2/7/73 to 4/17/73 and the second of 1000 

pCi per day from 4/18/73 to 8/7/73. Figure 7 shows the predicted bioassay results of this 

exposure scenario compared to the actual bioassay samples. It can be seen clearly from 

the graph that the predicted values are lower than the reported values, so this is a 

conservative underestimate of the exposure. This underestimated exposure resulted in the 

minimum organ doses presented in Table 4. In this case, the probability of causation 

determination for this underestimated dose to the liver produced a probability of 

causation of 83%. Since a detailed reconstruction would only increase the dose, there is 

no reason to perform a detailed dose reconstruction.

Had the probability been below the compensation level, the estimate could be refined 

easily by adding another chronic exposure to account for the time frame when he 

received several detectable intakes. This would increase the preliminary dose estimate 

while still being an underestimate. 

Pu Bioassay data

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

7/20/1972 10/28/1972 2/5/1973 5/16/1973 8/24/1973 12/2/1973

Date

Urine Pu (pCi)

Bioassay

samples

Predicted

Figure 7. Example of Underestimated Intakes from Bioassay Samples

8.4 Low Dose Potential Preliminary Estimate

In the interest of furthering this example, assume that the cancer developed by this 

individual is bladder cancer. The bladder does not concentrate plutonium so the dose to 

the bladder is likely small. In this situation, the analyst would want to perform an 

overestimate of the dose to determine if there is any need to continue. One very simple 

and very conservative technique to overestimate the dose would be to assume the 

individual received an acute intake on his first day of employment that was so large, all 
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of the sample results fall at or below the predicted urine concentration. This scenario 

would result in an intake estimate of 6x105 pCi of class M Pu-239 and 6x105 pCi of class 

S Pu-239. A graph of the predicted bioassay samples compared to the actual samples is 

provided Figure 8. This figure clearly shows that this estimate is a gross over-estimate of 

the intake. As indicated in Table 4, the annual dose to the bladder was usually below one 

rem per year. The probability of causation associated with his bladder dose would be 

31%. Since this is a worst-case over-estimate, there would be no need to perform a 

detailed dose reconstruction. 

Pu Bioassay data

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

12/28/1968 10/24/1969 8/20/1970 6/16/1971 4/11/1972 2/5/1973 12/2/1973

Date

Urine Pu (pCi)

Bioassay

samples

Predicted

Figure 8. Example of Overestimated Intakes from Bioassay Samples

If this analysis resulted in a value close to 50% probability of causation, the overestimate 

could be refined slightly by assuming a chronic exposure that would predict results 

higher than all the actual results. This would still overestimate the intake, but to a lesser 

extent. 
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Table 4. Annual Doses Calculated from Preliminary Estimates

Maximum Dose (Rem) Minimum Dose (Rem)

YEAR U. BLADD LIVER LUNG U. BLADDLIVER LUNG

1969 0.096 4.9109 58.7 0.0000 0.0000 0.0000

1970 1.188 89.13 249.4 0.0000 0.0000 0.0000

1971 1.287 116.59 136.38 0.0000 0.0000 0.0000

1972 1.163 123.7 94.44 0.0000 0.0000 0.0000

1973 1.044 125.23 69.06 0.0191 1.3246 20.2700

1974 0.958 126.4 52.278 0.0293 2.5114 2.4270

1975 0.895 127.3 40.603 0.0264 2.6848 1.5187

1976 0.851 128 32.557 0.0236 2.7501 1.0953

1977 0.815 127.5 26.522 0.0214 2.7685 0.8175

1978 0.791 127 22.299 0.0197 2.7728 0.6295

1979 0.773 126.3 19.082 0.0186 2.7837 0.4973

1980 0.764 125.6 16.773 0.0179 2.7828 0.4055

1981 0.756 123.7 14.765 0.0172 2.7587 0.3369

1982 0.751 121.9 13.26 0.0168 2.7342 0.2865

1983 0.749 121 12.058 0.0165 2.7091 0.2492

1984 0.752 119.1 11.06 0.0164 2.6928 0.2213

1985 0.751 117.1 10.079 0.0162 2.6547 0.1974

1986 0.753 115.1 9.3 0.0162 2.6166 0.1791

1987 0.755 113.7 8.602 0.0162 2.5772 0.1636

1988 0.760 112.3 8.006 0.0162 2.5488 0.1509

1989 0.762 110.1 7.427 0.0162 2.4988 0.1389

1990 0.766 108.4 6.92 0.0163 2.4588 0.1290

1991 0.769 106.6 6.453 0.0163 2.4258 0.1200

1992 0.775 105.1 6.048 0.0164 2.3908 0.1123

1993 0.777 103.1 5.639 0.0165 2.3392 0.1047

1994 0.781 101.4 5.292 0.0165 2.3018 0.0982

1995 0.784 99.8 4.955 0.0166 2.2634 0.0922

1996 0.790 98.4 4.67 0.0167 2.2291 0.0868

1997 0.792 96.5 4.381 0.0168 2.1882 0.0815

1998 0.795 94.9 4.123 0.0169 2.1516 0.0769

8.5 Detailed Dose Reconstruction

In the event that a determination can not be made from preliminary estimates, a detailed 

reconstruction is necessary. The following is a detailed reconstruction for the example 

case.

Since every intake will result in an increase in Pu-239 concentration in the urine for some 

time to come, it is necessary to work in chronological order when evaluating bioassay 

data.
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Intake #1

On 12/6/1969 the individual submitted a positive bioassay sample. His previous sample 

on 11/7/69 was non-detectable. The 12/6/1969 sample indicated 0.36 pCi Pu-239 in the 

urine. Two subsequent samples on 1/23/70 and 2/20/70 indicated less than the detection 

limit, however, both indicated high in that range. Subsequently, the two samples were 

averaged and assigned a date of 2/6/70. Using the 50% Class M and 50% class S 

assumption, the intake date and amount was adjusted to achieve a good fit with the two 

points (12/6/69 & 2/6/70). The best fit to this data indicated an acute intake of 18200 pCi 

of Pu-239 on 12/1/69.

Intake #2

On 3/28/70 the individual submitted another positive bioassay sample followed by one 

additional positive and four additional negative samples. Two of the negative samples 

were essential zero while the other two were 87% and 75% of the MDA. The two that 

were nearly zero could not be reconciled with any possible scenario associated with 

intake #1. This led to the decision to disregard these near zero samples and concentrate 

on the remaining samples. When the residual concentrations from intake #1 were 

considered, the remaining four data points provided a good fit to an acute intake scenario 

of 9000 pCi Pu-239 on 3/20/70.

Intake #3

On 8/21/70 the individual again submitted a positive sample (0.252 pCi). This sample 

was followed up by a sample on 9/11/70 that indicated 0.02 pCi (<MDA). Due to the fast 

clearance, intake #3 was assumed to be acute and to have occurred near the sample date 

of 8/21/70. The intake amount and date could not be adjusted to align the two points 

(8/81 & 9/11) but it was adjusted so that the second point was less than the detection 

limit. This resulted in intake #3 being an acute intake on 8/20/70 of 1500 pCi of Pu-239.

Intake #4

On 10/17/70 the individual submitted a sample indicating 0.257 pCi Pu-239. On 

10/23/70 the results were substantially lower but not below the detection limit. An acute 

intake was assumed and the date and amount adjusted to line up the two points. The 

results were that intake #4 occurred on 10/13/70 with an intake of 7000 pCi Pu-239. 

Intake #5 through Intake #13

The next three samples each increased above the previous sample. While the shape of the 

curve appeared to fit a chronic exposure, no scenario could be developed that did not 

result in numerous later samples being much lower than predicted. Therefore, a series of 

acute exposures was assumed. With no second point to help determine the timing of the 

intakes, the midpoint between samples was assumed as a starting point. With this date 

the amount of the intake could not be adjusted to keep samples on 8/15/71 & 11/26/71 

below the detection limit. The date and amount were then adjusted to achieve this goal. 

The analysis then proceeded with intake #6 in the same manner, each intake adding to the 

predicted values of the samples on 8/15/71 & 11/26/71. The process eventually required 

intakes #5 through #13 to all be considered simultaneously to reach the best fit with the 
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data. The final results have the predicted value for the samples on 8/15/71 & 11/26/71 

indicating 0.123 pCi and 0.108 pCi respectively. While these values are above the 

detection limit, they are close and could easily have produced a sample result below the 

detection limit.

The results for intakes #5 through #13 are as follows

#5 1500 pCi 12/3/70

#6 2100 pCi 1/7/71

#7 7000 pCi 2/2/71

#8 1500 pCi 3/4/71

#9 650 pCi 4/12/71

#10 1200 pCi 5/15/71

#11 2700 pCi 6/10/71

#12 1000 pCi 7/15/71

#13 500 pCi 10/23/71

Intake #14

Intake #14 was assumed to be an acute intake resulting in positive bioassay samples on 

1/5/72 and 1/28/72. The date and amount of the intake were adjusted to line up these two 

points. The results were later changed as discussed in intake #15 and #16. The final 

results showed an acute intake of 17000 pCi on 12/16/71.

Intake #15

Intake #15 was assumed to be an acute intake resulting in positive bioassay samples on 

2/25/72 & 3/31/72. The date and amount of the intake were adjusted to line up these two 

points. At this point it was noted that the predicted value of the sample on 5/26/72 & 

12/1/72 were considerably higher than the actual result. The date and quantity of intake 

#14 and #15 were adjusted to minimize these points but no acceptable combination could 

be found. These intakes were then modeled as chronic exposures using various intake 

dates and quantities. Eventually a suitable combination was found that allowed the 

predicted result for 12/1/72 to drop below the detection limit, however, the sample on 

5/26/72 was still above. Since the results of the sample on 5/26/72 was 0.0 pCi, it was 

believed that the sample could be flawed and the results were rejected. The results of 

intake #14 and #15 were again changed as a result of evaluating the next intake. The 

details are discussed under intake #16. The final result of intake #15 was an acute 

exposure of 10500 pCi on 2/18/72

Intake #16, #17, and #18

Intake #16 and #17 were then modeled using various combinations of acute and chronic 

exposures but all failed to reconcile with the sample on 12/1/72. An additional exposure 

was then added but it too failed to reconcile the difference. At that point the sample on 

12/1/72 was rejected. Since this sample led to several decisions pertaining to intake #14 

and #15, the analysis was redone starting with intake #14 but without the sample results 

for 12/1/72. This led to intakes #14 and #15 being reevaluated as acute exposures with 

the final values listed above.
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Intakes #16 and #17 were then evaluated simultaneously. Since the 12/1/72 sample was 

rejected, an attempt was made to model these intakes so that the predicted value matched 

the actual value on 11/25/72. The final results indicated an acute exposure of 43000 pCi 

and 23000 pCi on 5/11/72 and 8/26/72 for intakes #16 and #17 respectively. This then 

required an additional intake (#18) to match the data on 1/12/73 and 1/22/73. This was 

modeled as an acute intake on 12/30/72 in order for the predicted line to match both 

samples. The intake quantity was 17000 pCi.

Since the samples on 1/12/73 & 1/22/73 were only slightly higher than the sample on 

11/25/72, it was realized that these three samples could actually represent statistical 

uncertainty of one predicted line. This scenario was explored by eliminating intake #18 

and adjusting the date and quantity of intakes #16 and #17 in order to minimize the 

residuals of these three samples. This scenario resulted in no change to intake #16 while 

intake #17 was 40000 pCi on 8/24/72. Notice that this exactly matches the total of the 

original intake #17 and #18 (23000 + 17000). 

In an attempt to further reduce the residuals by “flattening out” the predicted line through 

the three samples (11/25/72, 1/12/73, & 1/22/73), intake #16 was moved back as far as 

possible to the day of the previous sample (3/31/72 since 5/26/72 was rejected). This 

resulted in intake #16 being a 53000 pCi intake. When only intake #17 was added the 

residuals were minimized when intake #17 occurred on 8/24/72 with 35000 pCi. The last 

scenario to explore was to reconsider the two intakes (#17 & #18) with intake #16 

consisting of 53000 pci on 3/31/72. This resulted in intake #17 being 23000 pCi on 

8/26/72 with intake #18 being 13000 pCi on 12/30/72. This yielded a total of 36000 pCi 

(23000 + 13000) compared to 35000 pCi for the one intake scenario. This indicated that 

while it was not clear which scenario was correct, the final outcome was comparable. 

Also note that the total intake from all four scenarios yielded results of intakes of 89000, 

88000, 83000, and 83000 pCi. Even moving intake #16 back 41 days changed the total 

intake by <10%. 

This information implies that the total intake is not very sensitive to the actual 

intake date. The primary objective is to match data as closely as possible by some 

non-subjective means (such as residuals) regardless of the chosen scenario. 

INTAKE #19 through #30

With the conclusions of the above sensitivity analysis in mind, the remaining intakes 

were modeled using the midpoint between samples for an intake date and adjusting the 

quantity to match the data. This resulted in intakes #19 through #30 as shown below.
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#19 24000 pCi 1/27/73

#20 31500 pCi 2/4/73

#21 23700 pCi 2/11/73

#22 14700 pCi 2/18/73

#23 15000 pCi 2/25/73

#24 18500 pCi 3/4/73

#25 35800 pCi 3/15/73

#26 47000 pCi 4/1/73

#27 10500 pCi 4/19/73

#28 2000 pCi 4/29/73

#29 21000 pCi 5/23/73

#30 35700 pCi 6/24/73

If additional information were available, the estimated intakes would have been evaluated 

based on that information. For example, area or personal air sample data could have 

helped determine the date of intake or whether the intake was acute or chronic. 

Below is the graph of the bioassay data presented earlier with the predicted 

concentrations from the dose reconstruction overlaid.

Pu Bioassay data

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

12/28/1968 10/24/1969 8/20/1970 6/16/1971 4/11/1972 2/5/1973 12/2/1973

Date

Urine Pu (pCi)

Bioassay

samples

Predicted

Figure 9. Predicted Bioassay Overlaid with Actual Bioassay

The annual doses to the liver and to the bladder were calculated for these intakes. The 

results of this calculation are shown in Table 5. The IREP input for liver cancer is 

included in Appendix A. Since alpha particles deliver the majority of the doses from Pu239, the entire dose was considered to be from alpha radiation. The probability of 

causation calculation produces a value of 98.8% for the individual’s liver cancer using 
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the constant probability distribution. If bladder cancer is assumed for this example, the 

probability of causation calculation produces a value 12.5%.

Table 5. Annual Doses from Detailed Dose Estimate

Calendar Year Liver Dose (rem) Bladder Dose (rem)

1969 0.033 0.001

1970 1.774 0.025

1971 4.163 0.051

1972 8.845 0.107

1973 27.873 0.351

1974 42.577 0.464

1975 45.365 0.426

1976 46.498 0.386

1977 46.879 0.353

1978 47.130 0.330

1979 47.188 0.313

1980 47.193 0.302

1981 46.837 0.292

1982 46.497 0.286

1983 46.070 0.282

1984 45.712 0.280

1985 45.051 0.278

1986 44.487 0.277

1987 43.877 0.277

1988 43.369 0.279

1989 42.617 0.279

1990 41.962 0.279

1991 41.305 0.281

1992 40.755 0.283

1993 39.994 0.283

1994 39.340 0.285

1995 38.684 0.286

1996 38.154 0.288

1997 37.424 0.289

1998 36.796 0.290

8.6 Missed Dose

In the preceding example, the intakes were determined for the periods in which many 

detectable bioassay samples were submitted over a period that covered the entire 

employment period. However, it is instructive to discuss how this would be done if the 

employment period were for a longer period of time.

Consider the situation where the individual actually started working at the facility two 

years prior to his first detectable bioassay sample. Assume he routine ly left samples on a 

monthly basis that all indicated no detectable Pu-239. He left a total of 24 negative 

samples in this period. He was performing the same type of work and the samples were 

analyzed in the same manner with the same detection limit. No more information is 

known about how the value for the detection limit was derived.
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In keeping with section 8.2 of this guide, the uncertainty of the bioassay samples is 

assumed to be normally distributed with a standard deviation equal to 0.3 times the 

detection limit. Therefore the standard deviation is assumed to be 0.03 pCi/day. Now the 

missed dose is estimated by assuming a chronic dose for the entire period that will predict 

a maximum urine concentration of 0.04 pCi/day. This process results in estimating a 

chronic intake of 23 pCi/day for a 702 day (approx. 2 year) period. 

If this intake is then inserted into the detailed model, it is discovered that the predicted 

line is raised above the actual results for many of the samples. This is due to the fact that 

this chronic exposure alone will produce some amount of Pu-239 concentration in the 

urine for years to come. As a result, the entire detailed intake assessment must be reevaluated. Previously it was mentioned that it is best to work with bioassay samples in 

chronological order. This example demonstrates the necessity of including missed dose 

into that approach. Figure 10 graphs the data for this extended employment period 

example.

Pu Bioassay data

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1/28/1967 6/11/1968 10/24/1969 3/8/1971 7/20/1972 12/2/1973

Date

Urine Pu (pCi)

Bioassay

samples

Predicted

Figure 10. Affect of Missed Dose on Predicted Bioassay Values

This also demonstrates the concept that the most accurate quantity is the total intake. The 

two years of very small chronic exposure added a total 16146 pCi of intake. However, 

since the estimated intakes previously determined will all have to be lowered, the overall 

increase in the total activity may likely be zero.
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8.7 Uncertainty

Another parameter that must also be determined is the uncertainty of the intake 

assessment. It is important to remember at this point that if the preliminary overestimate 

or underestimate is conclusive, no uncertainty analysis is required since the estimate is 

already a bounding case.

In cases when a detailed dose estimate is required, the uncertainty of the intake must be 

addressed. Section 8.1 of this guide describes how this process should work. In the 

example case, the uncertainty of the individual bioassay samples is unknown. However, 

the detection limit is known. Therefore, in keeping with section 8.2, the uncertainty of 

the samples at the detection limit can be estimated by multiplying by 0.3. For our 

example, this indicates a standard deviation of 0.03 pCi for a sample of 0.1 pCi which 

yields a relative error of 30%. This relative error can then be assumed to be constant for 

all of these bioassay samples. As noted before, this is not an exact estimate of the 

uncertainty but only a reasonable approximation in a situation with many unknowns. 

This 30% standard deviation is then applied to each intake to obtain an absolute standard 

deviation for each intake. For example, for the first intake of 18200 pCi, the standard 

deviation can be estimated as 5460 pCi (18200 x 0.3). This process is repeated for each 

of the 30 intakes. Then the total uncertainty is propagated by taking the square root of 

the sum of all the standard deviations squared. This gives an absolute uncertainty of the 

total intake of 32715 pCi. The total intake itself is just the sum of all the intakes or 

450250 pCi. The overall relative error can then be calculated as the standard deviation of 

the overall intake divided by the overall intake itself or 32715/450250 = 7.3%. This 

relative error is applied to the dose calculated for each year for the organ of interest.

The error is relatively small as can be expected when a large number of detectable 

samples are submitted. This relative error is applicable to the intake amount only; it 

assumes the biokinetic model is accurate. With an intake error this low, it is necessary to 

assess the uncertainty of the biokinetic model in order to develop a realistic uncertainty 

for this individual’s dose.

Once again, the overall purpose of EEOICPA must be kept in mind. In this case, if the 

best estimate of the individual’s dose with only the intake uncertainty applied produces a 

probability of causation above the 50% requirement, no uncertainty of the bioassay 

model need be applied. 

In the case of probability of causation values less than 50%, a more detailed analysis will 

be required. It may be necessary to reproduce the biokinetic model in a Monte Carlo 

calculation with known values given as constants. The results of this calculation can then 

be used to describe a detailed probability distribution of the organ dose.
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9.0 REFERENCES

1. The Energy Employees Occupational Illness Compensation Program Act, Public 

Law 106-398, 114 Stat. 1654, 1654A-1231 (October 2000), enacted as Title 

XXXVI of the Floyd D. Spence National Defense Authorization Act for Fiscal 

Year 2001.

2. Executive Order 13179, Providing Compensation to America’s Nuc lear Weapons 

Workers (65 FR 77487), December 7, 2000.

3. Probability of Causation Guidelines Under the Energy Employees Occupational 

Illness Compensation Program Act of 2000, Final Rule at 42 CFR 81 (67 FR 

22296), May 2, 2002.

4. Methods for Radiation Dose Reconstruction Under the Energy Employees 

Occupational Illness Compensation Program Act of 2000, Final Rule at 42 CFR 

82 (see 67 FR 22314), May 2, 2002.

5. Crawford-Brown et. al. (1989) Procedures for Assessing Occupational Radiation 

Monitoring Data for use in Epidemiologic Studies, ORAU 89/A-127, Oak Ridge 

Associated Universities, Oak Ridge TN.

6. Currie, L.A. (1968) Limits for Qualitative Detection and Quantitative 

Determination. Application to Radiochemistry, Anal. Chem. 40, 586

7. Department of Health and Human Services (1991) The International 

Classification of Diseases Clinical Modification (9th Revision) Vol. I&II, 

Department of Health and Human Services Publication No. (PHS) 91-1260, U.S. 

Government Printing Office, Washington D.C.

8. ICRP, (1959) International Commission on Radiological Protection. Permissible 

Dose for Internal Radiation, ICRP Publication 2, Pergamon Press, Oxford, 

England.

9. ICRP, (1979) International Commission on Radiological Protection. Limits for 

Intakes of Radionuclides by Workers, Part 1, ICRP Publication 30, Annals of the 

ICRP Vol. 2 (3/4), Pergamon Press, Oxford, England.

10. ICRP, (1989) International Commission on Radiological Protection. Agedependent Doses to Members of the Public from Intake of Radionuclides, Part 1, 

ICRP Publication 56, Annals of the ICRP Vol. 20 (2), Pergamon Press, Oxford, 

England.

11. ICRP, (1994) International Commission on Radiological Protection. Human 

Respiratory Tract Model for Radiological Protection, ICRP Publication 66, 

Annals of the ICRP Vol. 24 (1-3), Pergamon Press, Oxford, England.
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12. ICRP, (1994) International Commission on Radiological Protection. Agedependent Doses to Members of the Public from Intake of Radionuclides, Part 2, 

ICRP Publication 67, Annals of the ICRP Vol. 23 (3/4), Pergamon Press, Oxford, 

England.

13. ICRP, (1994) International Commission on Radiological Protection. Dose 

Coefficients for intakes of Radionuclides by Workers, ICRP Publication 68, 

Annals of the ICRP Vol. 24 (4), Pergamon Press, Oxford, England.

14. ICRP, (1995) International Commission on Radiological Protection. Agedependent Doses to Members of the Public from Intake of Radionuclides, Part 3, 

ICRP Publication 69, Annals of the ICRP Vol. 25 (1), Pergamon Press, Oxford, 

England.

15. NCRP, (1984) National Council on Radiation Protection and Measurements, 

Evaluation of Occupational and Environmental Exposures to Radon and Radon 

Daughters in the United Sates, NCRP Report No. 78, National Council on 

Radiation Protection and Measurements, Bethesda, Maryland.

16. NCRP, (1998) National Council on Radiation Protection and Measurements, 

Evaluating the Reliability of Biokinetic and Dosimetric Models and Parameters 

Used to Assess Individual Doses for Risk Assessment Purposes., NCRP 

Commentary No. 15, National Council on Radiation Protection and 

Measurements, Bethesda, Maryland.

17. NRC, (1993) Office of Nuclear Regulatory Research, Air Sampling in the 

Workplace, NUREG-1400, US Nuclear Regulatory Commission, Washington 

D.C.

18. NRC, (1992) Office of Nuclear Regulatory Research, Air Sampling in the 

Workplace, Regulatory Guide 8.25, US Nuclear Regulatory Commission, 

Washington D.C.

19. Ritter, P.D., Huntsman, B.L., Novick, V. J. Alvarez, J.L., Rich, B.L., (1984) 

EG&G Idaho, Inc. The Role of Personal Air Sampling in Radiation Safety 

Programs and Results of a Laboratory Evaluation of Personal Air Sampling 

Equipment, NUREG/CR-4033, EG&G Idaho, Inc., Idaho Falls, Idaho

20. Till, J. E., Grogan, H. A., Sinclair, W. K. Voilleque, P. G., (2000) Radiological 

Assessment Corp., Assessing Risks of Exposure to Plutonium, Rev. 2, 

Radiological Assessments Corp., Neeses, SC.
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APPENDIX A – IREP-EXCEL INPUT FORMAT

PERSONAL INFORMATION

Claimant 

Name Claim # Claimant SSN

DOL Claim 

Center Gender Birth Year Year of Diagnosis

Cancer 

Model

Should alt 

model be 

run?

John Doe 000000 000-00-0000 CL Male 1931 1998 Liver No

CLAIMANT CANCER DIAGNOSES

Primary 

Cancer #1

Primary 

Cancer #2

Primary 

Cancer #3

Secondary 

Cancer #1

Secondary 

Cancer #2

Secondary 

Cancer #3

Cancer Type Liver N/A N/A N/A N/A N/A

Date of 

Diagnosis 12/30/98 N/A N/A N/A N/A N/A

EXPOSURE INFORMATION

Number of 

exposures

Dose 

Intensity

Dose 

Uncertainty

30

Exposure # Exposure Year Exposure 

Rate Radiation Type Dose Distribution Type

Parameter 

1

Parameter 

2

Parameter 

3

1 1969 chronic Alpha Constant 0.032 0.000 0.000

2 1970 chronic Alpha Constant 1.770 0.000 0.000

3 1971 chronic Alpha Constant 4.160 0.000 0.000

4 1972 chronic Alpha Constant 8.840 0.000 0.000

5 1973 chronic Alpha Constant 27.800 0.000 0.000

6 1974 chronic Alpha Constant 42.500 0.000 0.000

7 1975 chronic Alpha Constant 45.300 0.000 0.000

8 1976 chronic Alpha Constant 46.400 0.000 0.000

9 1977 chronic Alpha Constant 46.800 0.000 0.000

10 1978 chronic Alpha Constant 47.100 0.000 0.000

11 1979 chronic Alpha Constant 47.100 0.000 0.000

12 1980 chronic Alpha Constant 47.100 0.000 0.000

13 1981 chronic Alpha Constant 46.800 0.000 0.000

14 1982 chronic Alpha Constant 46.400 0.000 0.000

15 1983 chronic Alpha Constant 46.000 0.000 0.000

16 1984 chronic Alpha Constant 45.700 0.000 0.000

17 1985 chronic Alpha Constant 45.000 0.000 0.000

18 1986 chronic Alpha Constant 44.400 0.000 0.000

19 1987 chronic Alpha Constant 43.800 0.000 0.000

20 1988 chronic Alpha Constant 43.300 0.000 0.000

21 1989 chronic Alpha Constant 42.600 0.000 0.000

22 1990 chronic Alpha Constant 41.900 0.000 0.000

23 1991 chronic Alpha Constant 41.300 0.000 0.000

24 1992 chronic Alpha Constant 40.700 0.000 0.000

25 1993 chronic Alpha Constant 39.900 0.000 0.000

26 1994 chronic Alpha Constant 39.300 0.000 0.000

27 1995 chronic Alpha Constant 38.600 0.000 0.000

28 1996 chronic Alpha Constant 38.100 0.000 0.000

29 1997 chronic Alpha Constant 37.400 0.000 0.000

30 1998 chronic Alpha Constant 36.700 0.000 0.000