SEC Work 
 What is OPOS?

 How does it differ from earlier co

model procedures?

 What are the reasons for using O How extensive is the problem? How is the mean excretion rate re

intake?

 How well does OPOS estimate the 

excretion rate?

 Problems with Implementation
 What is OPOS?

◦ “One Person, One Sample” (OPOS)

◦ “One Person, One Statistic” is a bet

description

◦ OPOS is the arithmetic average of a 

bioassay results in the time period

 Why use OPOS?

◦ Introduced by NIOSH to address pr

Data Dominance and Correlation

◦ SC&A examined extent of problem 

dominance and correlation at SRS a
 Assumes that bioassay results for the group 

lognormal distribution. 

 Sort ALL the data (FROM ALL WORKERS) for e

period from low to high results.

 Rank all the data and determine the 50th an

values for the ranked data.

 Log-transform the data, calculate the 

z-sc

transformed data point, and plot the 

z-sco

and the natural logarithms of their respect

axis.

 Use a line equation to calculate the 50th pe

and the 84th percentile value for each perio

 Determine intake rates by performing fits o

bioassay data sets (50th and 84th percentile 

associated with each radionuclide. For mointakes are assumed to be chronic.
 Assumes that bioassay results for a group of work

lognormal distribution. 

 For each worker, calculate the average of all bioas

YEAR (OPOS) theoretically using the face value of results.

 Sort ALL the OPOS results for EACH YEAR from low 

(each worker should have one OPOS result in the yanalyzed).

 Rank all the data and determine the 50th and 84th pe

the ranked data.

 Log-transform the data, calculate the 

z-score for ea

point, and plot the 

z-scores on the 

x-axis and the n

their respective data on the 

y-axis.

 Use a line equation to calculate the 50th percentile, t

percentile value for each period.

 Determine intake rates by performing fits of the two 

sets (50th and 84th percentile results) associated with 

For most data sets, intakes will be assumed to be ch
Urine samples results from all 

workers in a period of Time 

(for example 3 months Period)

Average o

results fro

in one ye

Rank th

workers in 

a lognorm

Find the 50th and 84th 

Percentiles of the 

Lognormal Distribution

Rank all urine results in a 

period of Time and build a 

lognormal distribution

Use IMBA to determine 50th and 

84th percentiles Intake Rates usin

Chronic Exposure Time Models
 OPOS was designed to address pr

◦ Data dominance: a large fraction of 

being submitted by small fraction of individuals.

◦ Correlated data: multiple samples su

individual can be correlated, which cuse of statistical tests.
 The use of OPOS on an annual (or other fixed-per

general matter does not appear to be scientifically 

of pooled individual bioassay data is recommende

known drawbacks. When there is clear evidence o

the samples related to a particular incident may b

provide a single composite data point to be insert

distribution of pooled data, resulting in a “mixed

 SC&A notes that even in this limited context, ther

of uncertainty in the estimated OPOS value due to collection times, the Regression through the Orighypothesis, the assumption that the variance of thin direct proportion to the magnitude of intake reand the use of weights inversely proportional to tmeasurement.
 How relevant is the problem of 

dominance?

 Will a large number of incident

samples from a few workers skdistributions used for coworke

 How frequently do we find data 

in DOE Facilities? 
Number of Bioassay

per Time Interval

Frequency

per quarter

Cumulative %

per quarter

Frequency 

per year

1 15,273 86.99% 7,402 2 1,644 96.35% 2,445 3 346 98.32% 867 4 148 99.16% 591 5 47 99.43% 219 6 41 99.66% 111 7 17 99.76% 65 8 14 99.84% 50 9 7 99.88% 31 

10 9 99.93% 19 

>10 12 100% 57 

In over 95% of the cases where OPOS would be applieworkers have no more than four Pu bioassays in the per
 ORAUT-OTIB-0078, Rev. 2, 2012

OPOS methodology: Inclusion of Codes 50 sa

study-large number of samples from several 

 ORAUT-OTIB-0078, Rev. 1, 2010

NO Codes 50 samples.

 Both versions of OTIB-0078 take into accoun

40 and 49, which are incident-related sampl

 The comparison of the 50th and 95th percent

derived in Rev. 1 (2010) and Rev. 2 (2012) of shown that in some years Rev. 1 produced hwhile in other years Rev. 2 produced higher rthat neither methodology has a systemic bias yield the higher intake rates.
 The fact that some workers have mor

than other workers in a given time peitself a basis to establish correlation.

 When workers are exposed in acciden

work assignments, it is not clear why problems end from one year to the ot

 At FEMP, the use of OPOS methodolog

resolved the dependence of monitorinfrom different and sequential intake pOTIB-0078 Rev. 2 (2012) explicitly exfor the 1994-2006 period, earlier intsignificantly biased later intake rates solubility Types of uranium compoun
 The computation of OPOS for the year 

activities collected from periods of no itogether with activities from periods wThe consequence is a strong dependenfrequency of monitoring, in addition to significant exposures.

 The annual individual OPOS result at Fe

calculated for workers involved in the swas shown to be influenced by the worfrequency of monitoring.

 For the same reason, when OPOS is app

compare two groups of workers, it is nthe monitoring protocols be the same fgroups in order to make a meaningful 
 The cornerstone of NIOSH’s def

OPOS is the proportional relatiomean excretion rate and intake when using weighted least squaregression to calculate the intakraises two technical questions:

1. When is the mean excretion rate 

to the intake?

2. How well does OPOS estimate a 

mean excretion rate?
 The least square regression result is only val

the intake when using the excretion results iconsequence of that intake. 

 NCRP 164, 2013: 

“This appendix provides a summary of the le

method formulas that can be used to derive starting from measurements of activity in bioThe formulas assume only one intake, no priabout the magnitude of the intake (i.e., unifoBayesian formulation of the intake derivation biokinetic model and its parameters are knowthat all measurements are independent, and normalized (e.g., all urine data represent excin 24 h).”
 From the IMBA User Manual:

Note: The least squares fitting method can be useonly in cases involving a single intake – with REA(explicit) error values on each data point, and a sibioassay quantity.

 When there are multiple intakes, the 

different.

 NIOSH's justification for the use of a 

value for each worker is based on a mapplies only to excretion results after takes place and not for excretions resmixed intakes, or for urine activities periods of no intakes lumped togetheactivities from periods with intakes.
 OPOS ignores the times when bio

collected during the year

 Weighted least squares method ig

ordering of the observations in ti

 Collection times are important fo

constructing time-weighted averaexcretion rates

The Actual Picture is More Com

0

100200300400

0 90 180 270 360 450 540 630 720 810 

Number of Days Between Pu Bioassays

Frequency

Frequency Distribution of Time Interval in 

Successive Plutonium Bioassays for Claimant
 If bioassays are collected at random t

the year, OPOS is interpreted as a Mointegral of the function f(t) with a smasamples
 Student-t distribution with N-1 d

freedom (DOF) is used to find conbounds on the mean excretion ra

 Width of a confidence interval for 

derived from the variance of the t distribution:
 If N is less than 4 (DOF<3), the v

the OPOS mean estimate is infin

 Over 90% of cases in the table o

have N less than 4 per year!
 When nondetects are present, OP

calculated using the Maximum Po(MPM) algorithm as described by 

 Step 1) Use the MDA or censoring level 

reported as <MDA.

 Step 2) If the analysis period includes a

data, use the mean result as a censoreperson.

 Step 3) If there are uncensored data du

analysis period, use the mean result as result for each person.
 Inconsistencies were found when 

applied with censored data.

 The face value of the CL should b

all data entries explicitly reported censored values and for all entrierecorded values that are below thAlthough numerical values below (usually zero and negative valuesreported in the data, these entriebe used to compute the MPM.