Use of Co worker Data

in Dose Reconstruction

James W. Neton, PhD, CHP

Associate Director for Science

Division of Compensation Analysis and Support

National Institute for Occupational Safety and Health

July 2013

Idaho Falls, ID
Reasons for Using Co worker D

 Worker was unmonitored and potentially

exposed

 Worker was monitored but:

• Data was lost or destroyed• Monitoring methods were not reliable

 Available data insufficient to complete a d

reconstruction
Sources of Co worker Data

 Covered facility databases

 ORAU Center for Epidemiologic Research

 Comprehensive epidemiologic data resour

(CEDR)

 Claimant data
General Approach to

Co worker Evaluation

 Evaluate available data for monitored popu

• Conduct data pedigree review

• Determine if measurement method is reliable

• Establish that the monitored population is

representative of the workforce

 Statistical approach described in ORAUT‐PR

0095
General Approach to

Co worker Evaluation—cont.

 Review data to determine if appropriate sta

distributions can be generated

• Fit data to lognormal distributions

• Group data as appropriate

• Generate summary statistics and evaluate fit

 External co worker models more straight for

than internal models
Example External Co worker Distr
Example External Co-worker Dat
Complications of

Internal Co-worker Modelin

 Often multiple bioassay results per monitor

period

• Possible correlation of data

 Raw data must be converted to intake and d Exposure pattern must be presumed

• Accute, Chronic, mixed?
Summary of Internal

Co-worker Model Calculatio
Example Bioassay Distribution for a Sin

GM 

84th percentile 
Example Fit of Bioassay Data to Ch

Intake Scenario Over Multiple Ye

Regression of median excretion rate on chronic Intake Retention F
Co-worker Application

 Based on potential for exposure an unmonitor

worker would receive either:

• The full intake distribution (i.e., the 50th percen

the Geometric Standard Deviation (GSD) as inp

parameters or;

• The 95th percentile of the distribution input as 

constant

 Each situation is evaluated on a site and case‐s

basis

 Approach to evaluation of data stratification

described in ORAUT‐RPRT‐0053

• Introduces concept of one person one sample (
One Person One Sample

 Minimizes issues related to correlated data

 Uses the Maximum Possible Mean (MPM) a

• Using MPM, censored data are taken to be a po

measurement, i.e., <0.05 dpm = 0.05 dpm

Example A: 10, 3, 5, 6Mean = 24/4 = 6 (report as 6)Example B: 10, <3, <5, 6Maximum Mean = 24/4 = 6 (report as 6)Example C: <10, <3, <5, <6Maximum Mean = 24/4 = 6 (report as <6)
Data Stratification

 Monitored population is really a conglome

of a number of subgroups

 Single distribution can be applied to

unmonitored workers if:

• Highest exposed workers were monitored or

• Representative sampling of the exposed worke

conducted

 If stratification suspected, can be statistica

evaluated

• Monte Carlo Permutation Test

• Peto‐Prentice Test

• Must consider the effect of multiple compariso
Monte Carlo Permutation Te

 Assumptions:

• Data can be described by a lognormal distribut

• Data is not heavily censored

 Stratify data using an a priori criterion

• Construction workers vs. non‐Construction wo• Area 100 workers vs. Area 200 workers

 For each strata calculate the Geometric me

(GM) and Geometric Standard Deviation (G
Monte Carlo Permutation Test

 Calculate the difference in the GM and GS

between strata

• These differences comprise one data point wit

coordinates

 Random Sample

• Combine all data and randomly pull samples w

replacement equal to the size of one strata

• Calculate GM and GSD of each random strata• Calculate and plot the difference in GM and GS

• Repeat 10,000 times
Monte Carlo Permutation Test

Not significantly different

Significantly diffe
Monte Carlo Permutation Test

 Benefits

 Can easily compare whether different size stra

significantly different

 Limitations

• Requires some a priori decision on distribution

• Doesn’t work if data set is heavily censored

‒ Too many random pulls of zero

‒ Peto‐Prentice Test is more appropriate
Peto‐Prentice Test

 Advantages

• Non‐parametric – i.e. no a priori distribution

assumption

• Can handle censored data sets

• Can compare whether different size strata are

significantly different (p‐value)

 For cases where both the Monte Carlo and 

Prentice are applicable, they typically lead 

same conclusion
Peto‐Prentice Test—cont.

Not significantly different

Significantly differ
Summary

 Co-worker models can be used to reconstr

doses to unmonitored workers if:

• Highest exposed workers were monitored or;

• Representative sampling of the exposed worke

conducted

 Data must be carefully reviewed for applic

• Data quality and representativeness

• Potential for stratification

 One person one sample approach is useful 

normalizing data

 Stratification can be evaluated using stand

statistical tests