EHS 655
Lecture 7:
Exposure grouping strategies
Exposure analysis in a nutshell
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What we’ll cover today
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Attenuation bias
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Sampling issues and temporal trends
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Exposure groups
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Stata commands
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ATTENUATION BIAS
Heederik, Attfield, 2000
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Repeated measurements
Loomis, Kromhout, 2004
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Ways to reduce attenuation bias

Increase number of exposure measurements per
person
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Increase between-subject variability so interindividual exposure range is larger
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SAMPLING ISSUES

Several different sampling strategies
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Haphazard/convenience
Worst case
Representative
Random
Question: what are the
strengths and weaknesses of
each of these approaches?
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How exposures were collected may determine what
we can infer from them
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Also need to consider temporal trends
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Sampling issues - temporal trends
Heederik, Attfield, 2000
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Sampling issues – temporal trends
Davies, Teschke, Kennedy, Hodgson, Demers, 2008
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Individual-level assessment

Exposure varies
greatly over time and
space
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Measure individuals’
exposures
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Use repeated
measurements on
individual to calculate
individual average
(only their data)
Often considered gold
standard approach
Ignacio and Bullock, AIHA, 2006
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Question

If individual-level approach is gold standard, come up
with at least two reasons we might want to create
exposure groups?
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Reasons we might not want individuallevel assessments

Even with repeated measurements, measured average
level is at best approximation of true exposure

N of repeated measurements per individual usually small
(scarcity of data)

Higher within-person variability and/or smaller interperson variability = more attenuation of exposureresponse relationship

Logistical/financial challenges
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Lack of direct access to individuals
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EXPOSURE GROUPS

To address scarce individual data, we commonly pool
data across individuals

Create group estimates

Increased amount of data in groups reduces random
variability associated with estimate

Apply group estimates to individuals who likely have
similar exposures

May not be truly applicable to all individuals in group
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Group-level assessment

Create subgroups based on common features of
exposure

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Ideally, all workers within each group measured
Must consider between-group, within-group, within-individual
variability
Tielemans, Kupper, Kromhout, Heederik, Houba, 1998
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Advantages of grouping

Often more effective than individual-level assessment
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Especially when temporal variability large
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Logistically less demanding than individual approach
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Should result in almost unbiased estimated
coefficients of exposure-response relationship
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Attenuation very small with grouped exposure data
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Grouping and attenuation
Seixas and Sheppard, 1996
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Exposure groups
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Originally “Homogeneous exposure groups” (HEGs)
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Current trend is “Similar exposure groups” (SEGs)
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Groups frequently defined by common structures

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E.g., job title, work area, activity, behavior, agent, street,
etc
Individuals in different groups assumed to have different
exposures
Individuals within groups also have different exposures

Differences in activities, behaviors, protective equipment
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Exposure groups

Statistical measures of central tendency often applied
to groups
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Mean, median, mode
We treat every individual in the group as though they are
exposed at the this measure of central tendency
For categorical exposures, we assume groups are
exposed (1) or unexposed (0)
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Assumes zero variability in groups
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What do we need to think about when
grouping people?
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Specificity
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Summarizes variance within group
Large variance = highly specific
Precision

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Summarizes variance between groups
Large variance = highly precise
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Exposure grouping goals
Goal 1: create groups which
retain true individual
differences (specificity)

i.e., within-group
variability small compared
to between-group
variability
Goal 2: create groups that
are as large as possible
(precision)

Leads to exposure
estimates that are more
precise than
individual worker
means
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Tradeoff between specificity and
precision

Individual-based strategy

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Group-based strategy
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Precise (small standard error) but biased estimates of
exposure-response relationship
Unbiased (high validity) but imprecise (large standard error)
estimates of exposure-response relationship
Validity, not precision, most important
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Specificity vs precision (from the reading)
Precision
Specificity
Werner, Attfield, 2000
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Specificity vs precision
Within-group variation
Between-group variation
Heederik, Attfield, 2000
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Grouping strategies

A priori

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Grouping takes place before measurement is made
A posteriori
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Grouping takes place after measurement is made
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Ways to create exposure groups
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Group by
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Activity type
Process
Agent
Exposure pathway
Location
Time
Etc.
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Example: grouping by agent
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Note variation in group size, exposure range, repeated
measurements
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Example: grouping by job title
Lewne, Plato, Bellander, Alderling, Gustavsson, 2010
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Example - grouping outcomes
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Possibilities


Within group variability > between group variability
Between group variability > within group variability
Question: which
of these
grouping
approaches is
preferred?
Rappaport, Kromhout, Symanski, 1993
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Group size and attenuation bias

Reducing attenuation bias

Increasing number of subjects per group can be as effective
as increasing number of measurements per subject
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Question
70
80
90
100
110
What do you think of this as a grouping strategy, and why?
Carpenter
Electrician
Operator
Insulation
Cement Mason
Ironworker
Sheet Metal
Masonry
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Resources

EPA ExpoBox

https://www.epa.gov/expobox
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On to Stata

More basic data manipulation commands

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Create dummy variables for groups
example: tabulate varname, gen(newvarname)
Let’s make or identify some groups and compare them
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On to Stata
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Bivariate analysis examples
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tabulate varname1 varname2
tab2 varname1 varname2 varname3
tabstat varname, stat(mean sd count)
bysort varname1: tabstat varname2, stat(mean sd count)
table varname1, contents(mean varnamex sd varnamex)
by(varname2)
twoway scatter varname1 varname2
Graph matrix varname1 varname2 varname3, half
Graph box varname1, over(varname2)
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