EHS 655
Lecture 1:
Introduction and overview
What we’ll cover today
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Syllabus and expectations

Course overview
2
My goals for this class

Give you tools to analyze exposures
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Help you understand strengths and limitations of
exposure data
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Help you understand possible approaches to data
analysis
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Give you ability to conduct analyses of exposure data,
ranging from simple to sophisticated
3
Motivation for developing this class

Historical focus of exposure science has been exposure
measurement

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Current focus still stresses this

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In other words, collection of exposure data
Exposome, molecular epidemiology
Less focus/training on how to make sense of data
collected
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Example of why we need this class (at least
in my experience)

Datasets used in biostatistics and
epidemiology classes


Clean, well organized,
well documented
Real-life exposure datasets
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Usually none of the above
5
Syllabus and expectations

Syllabus on Canvas
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Any changes will be announced via Canvas
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Readings posted on Canvas
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Assignments posted on Canvas
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Pretty much everything…on Canvas
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Components of class

Lectures will
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Computer labs will

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Describe common data analysis techniques
Familiarize you with different approaches
Give us common environment to explore shared dataset and
apply analytical techniques
Readings will
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Provide examples of application of approaches
7
Evaluation
Weekly
analyses
18%
Participation/
attendance
20%
Article
review
12%
Midterm report
20%
Final report
30%
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Evaluation

Class participation and attendance

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Weekly analysis questions
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Poster on 1 peer-reviewed exposure analysis paper
Midterm report
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Short answers posed about newly available data and readings
Article review

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Pretty straightforward – come and participate!
Presentation of preliminary analysis results
Final report

Presentation of final analysis results and recommendations
9
My expectations of you

Come to class (on time)

Don’t be shy – participate!
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Ask questions
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Let me know if aspects of class aren’t working
10
Topic schedule
Dates
Topics
Jan 5, 12
Jan 17, 19
Jan 24, 26
Jan 31, Feb 2
Introduction, overview, NO CLASS Jan 10
Data types, descriptive stats
Variability and measurement error
Grouping strategies
Feb 7, 9
Feb 14, 16
Feb 21
Comparing imperfect measures
Inferential statistics, repeated measures
Exposure modeling, NO CLASS Feb 23
Feb 28, Mar 2
SPRING BREAK
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Topic schedule
Dates
Topics
Mar 9
Mar 14, 16
Mar 21, 23
Mar 28, 30
Exposure modeling, NO CLASS Mar 7
Exposure modeling, regression
Random and mixed effects, protective behaviors
Exposure-response modeling
Apr 4, 6, 11
Apr 13, 18
Risk management, presentation of data
Article reviews
12
Assignment
schedule
13
Overview of exposure analysis

Information  data when organized
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Data  understanding when analyzed
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Analysis lets us tell a story with data
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How to test success of analysis

Ask “Can I now explain these data?”
14
Data
Raw data
Under-analyzed
One summary statistic
Over-analyzed
15
Definitions
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Exposure: level or quantity of contact with an agent
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Different from vector carrier (e.g., air, water)
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Agent: chemical, biological, physical hazard or set of
conditions capable of causing biological response
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Dose: level or quantity of agent actually deposited
within body
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Response: biological response to agent
16
The ugly truth about exposure
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True exposure (T) can never be perfectly measured
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We need to define aspect(s) of exposure (E) and dose
(D) that can be measured
17
Exposures can be…
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Simple (e.g., one vaccination)
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Complex (e.g., smoking exposure
over a lifetime)
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Single, short-duration, known
exposure, well-documented
Long-duration, complicated
exposure, poorly-documented
Really complex (e.g., exposure
associated with a disaster)
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Varying duration, poorly-documented
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Examples of types of agents

May cause acute physiological effects (e.g., food)
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May cause or protect from disease or injury
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Influence disease diagnosis or outcome (e.g.,
screening procedures, treatment)
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May confound association between another agent and
outcome
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May modify effects of another agent on outcome
White E, Armstrong BK, Saracci R. 2008
19
Effect modification vs. confounding

What’s the difference?
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Confounding

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Observed association is not correct due to effects of different
(lurking!) variable associated with both potential risk factor
and outcome, but not a causal factor itself
Effect modification
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Effect is real but magnitude of effect is different for
different groups of individuals
20
Confounding
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Will not be a primary focus of this class, but…
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Potential confounders do need to be considered in exposureresponse analyses
21
What is exposure analysis?
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Process that quantifies human contact with a hazard
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Quantification of exposure


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Across individuals
Across time
Across space
22
Where does exposure analysis lie in an
exposure-response model?
Exposure analysis
23
Exposure analysis
Data collection vs. exposure prediction vs.
exposure estimation
24
Why do we need exposure analysis?

Without it we can’t relate
exposure to response

Exposure measurements
increasingly yield vast
amounts of data


Real-time sensing,
crowdsourced data
collection, longitudinal
monitoring
Need increasingly
sophisticated and robust
analytical approaches
25
Goals of exposure analysis

Create most accurate estimates of exposure possible
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Evaluate exposure controls

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Need for
Effectiveness of
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Characterize exposure-response relationship
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Understand compliance with regulatory and
recommended exposure limits
26
Historical context for exposure analysis
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Early exposure-response analyses


Substantial exposures
Substantial health risks
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Simple approaches were sufficient
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Current exposures, health risks lower,
more variable

More complex exposures require
increasingly sophisticated analyses
27
Difficulties in exposure analysis

May be no single cause for outcome of interest (e.g.,
multitude of possible agents)
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Any single component may contribute little to outcome risk
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Interval from onset of exposure to outcome often long
(e.g., weeks, months, years, decades)
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Agent may leave no measurable indicator of past
exposure

Complex exposures (e.g., diet, psychosocial)
White E, Armstrong BK, Saracci R. 2008
28
What happens if we do it poorly?
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Inaccurate exposure estimates are source of bias in
epidemiological studies
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Magnitude of bias is under-appreciated

Small errors in assigned exposures can reduce epidemiological
risk ratios by 50% or more!
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Greatly reduces our ability to accurately characterize
exposure-response relationship

Could this at least partly explain discrepancies
between lab and human studies?
29
Information needed to understand exposure
and dose
Frequency
Protective
behaviors
Duration
Dose
Personal
risk factors
Intensity
Variability
Question: how could you get exposure
information for the following situations…

Silica exposure to communities near fracking sites?
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Exposure to cosmic radiation in flight crews?
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Exposures to norovirus among daycare facilities?
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Micronutrient exposures through diet?
31
Exposure analysis issues
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What kinds of data are available?
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Personal vs. potential (environmental or area)
exposure measurement
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Subjective vs. objective data
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Present vs. past exposure
32
Levels of exposure analysis
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Ideally, we would have actual, individual-level
measurements over entire exposure period
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Alternatively, can do group-level analysis
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Individual-level analysis
Just compute average/cumulative exposure
Sound to good to be true? It is. Almost never possible
A priori
A posteriori
Or hybrid analysis combining individual and group
33
How do we describe exposures?
Question: when might we be interested in these specific measures?
34
Common exposure measures
Kriebel, Checkoway, Pearce, 2007
35
Cumulative dose
10
10
8
8
Exposure
Exposure
Question: are all these doses equal?
6
4
2
0
6
4
2
0
Time
10
10
8
8
Exposure
Exposure
Time
6
4
2
0
6
4
2
0
Time
Time
We can represent exposure or dose in
different ways
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Duration of employment
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Duration of exposure
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Exposure rate (avg exp/yrs exp)
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Intensity for job with highest exposure
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Estimated highest peak exposure
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Cumulative exposure
= Often highly correlated
= Often poorly correlated
37
Variability in exposure
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Quantitative and qualitative
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Results from heterogeneity across
people, places, time
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Types of variability
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Affects precision of exposure estimates and generalizability
Spatial: variation due to location
Temporal: variation due to time
Inter-individual: e.g., age, sex, race, activity patterns
Intra-individual: variation in physiology or behavior
Cannot be reduced, only better characterized
38
Uncertainty in exposure

Lack of knowledge about factors affecting exposure or
risk of health outcome
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Results in inaccurate or biased exposure estimates
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Types of uncertainty

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
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Scenario (information)
Parameter (mathematical)
Model (scientific knowledge)
Unlike variability, can be reduced
EPA, 2011
39
Variability vs uncertainty

Difference between variability and uncertainty may
not always be clear

In general…

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Data have variability
Statistics have uncertainty
40
Measurement error

Difference between measured value of quantity and
true value

Reason we can never know true exposure
41
Comparing imperfect measures
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Agreement between multiple estimates of exposure
Kaupinen, 1991
42
Methods for exposure analysis
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Low complexity

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Moderate complexity

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Univariate statistics
Bivariate statistics
ANOVA, non-parametric equivalents
High complexity

Modeling
Statistics in Medicine, 3rd ed
43
Exposure modeling
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Deterministic (regression)
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Linear
Logistic
Poisson
Survival
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Probabilistic
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Bayesian
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Hybrid
Will focus on these
Will not address these
Will touch lightly on these
44
Accounting for protective behavior

Must consider anytime exposed individuals could
receive protection from exposure, e.g.

Use of personal protective equipment

Use of protective agents

Change in behavior
Exposure-response relationships

Will not be a primary focus of class

Will explore a bit to give you complete experience analysis
experience
Image courtesy of Andrew Maynard, University of Michigan
46
Exposure data and risk management

Risk analysis synthesizes data from multiple sources to
produce interpretable risk estimates


E.g., “worst-case” or “typical” exposure
Made difficult by incomplete, conflicting, or poor quality data
Tielemans, Marquart, De Cock, Groenewold, Van Hemmen, 2002
47
Presentation of data and results
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Best practices for presenting data


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Numerical
precision
Data
visualization
Table format
48
Presentation of data and results

Best practices for presenting results
Altman, 1980
49
Conclusions and next session
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Next session we’ll

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Talk about the dataset we’ll be using, and the study it
stemmed from
Start to get acquainted with Stata
50
Resouces

EPA Exposure Factors
Handbookhttp://www.epa.gov/ncea/efh/pdfs/efhcomplete.pdf

Human Exposure Assessment: Environmental Health
Criteria 214
http://www.inchem.org/documents/ehc/ehc/ehc214.
htm
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