Attributable Risk
HSS4303B
March 25, 2010
Erin Russell
MSc. Candidate, Epidemiology
What we’ll cover today
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What is attributable risk?
How does attributable risk differ from
relative risk?
Attributable risk (AR) for the exposed group
Attributable risk percentage (AR%)
Population attributable risk (PAR)
Example calculations
A sample literature review
Design your own final exam tutorial
What is Attributable Risk?
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AKA - Measure of impact
The amount or proportion of disease
incidence (or disease risk) that can be
attributed to a specific exposure.
For example, “How much of the lung cancer
risk experienced by smokers can be
attributed to smoking?”
In practice, “How much of the risk
(incidence) of disease can we hope to
prevent if we eliminate exposure to the
agent in question?”
Relative vs. Attributable Risk
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Relative risk is important as a measure of
the strength of the association
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Important in establishing etiological
relationships
Attributable risk is more about: “How much
of the disease that occurs can be attributed
to a certain exposure?”
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Important in clinical practice and public
health
Cautions to bear in mind
1.
If the exposure is not causally linked to the
outcome, then measures of impact are
meaningless as there would be no change
in the outcome frequency even if the
exposure were to be completely
eliminated.
Cautions to bear in mind
2.
Even if there is a causal link, it does not
follow that removing the exposure would
lead to a reduction in risk in the exposed
person or that such a reduction would be
prompt.
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Smoking and cancer: may have already
produced DNA damage and a carcinoma in
situ
Smoking and heart disease: risk returns to
lower levels over several years
Measures of Impact/AR
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There are many measures of ‘impact’
Lack of consensus about which measures
are important and different interests of
different groups
Not to scare you, but different authors may
even use the same abbreviation (ie. AR) to
mean different things
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Kahn and Sempos use this abbreviation to
mean at least 4 diffferent things within 5
pages of text in one book!
Attributable Risk (AR) Exposed
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This is simply the risk difference between
the Cumulative Incidences for the two
groups:
AR = CIExposed – CIUnexposed
Attributable Risk Percentage
(AR%)
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This is also called the Attributable Risk
Fraction, the Etiologic Fraction Exposed,
and the Attributable Fraction Exposed.
Whatever you call it, this number refers to
the proportion of the risk among the
exposed population which could be
attributed to the exposure:
AR% = CI1 – CI0
CI1
Population Attributable Risk
Percentage (AR(P))
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This is also called the Attributable Risk
(Population), the Etiologic Fraction
Population, and the Population Attributable
Fraction Proportion.
It refers to the entire population (including
both exposed and unexposed people)
It gives the proportion of the outcome risk in
the entire population which could be
attributed to the people who were exposed.
Population Attributable Risk
Percentage (AR(P))
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The value of the AR(P) depends on:
a)
b)
What proportion of the population
has been exposed.
The extent to which exposure
increases risk.
AR(P) = CItotal – CIunexposed
CItotal
Population Attributable Risk
Percentage (AR(P))
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A situation where an exposure increases
risk by 100-fold might be expected to have
a large impact among exposed people, but
if only 0.1% of the population is exposed,
the actual population-wide impact would be
low.
I was taught that this is the most useful
and common impact measure.
Population Attributable Risk
Percentage (AR(P))
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Similar to the AR%, except that it also
employs the risk of the entire population as
a referent.
Even if you know the risk in exposed and
unexposed people, you cannot compute
AR(P) unless you know the proportion of
the population who are exposed to the
factor under consideration.
Example - Question
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Estimate the impact of HRT exposure on:
i) users and
ii) the entire community (where feasible)
Exposure group
Number of cases Person-years of
of breast cancer follow-up
None
923
344,942
Any estrogen or
progestin use
393
120,356
Total
1,316
465,298
What is the question asking for?
Example – Step one
AR = CI1 – CI0
= 393/120356 - 923/344,942
= 3.265/1000 PYs – 2.676/1000 PYs
= 58.9 cases/100,000 person-years
Exposure group
Number of cases Person-years of
of breast cancer follow-up
None
923
344,942
Any estrogen or
progestin use
393
120,356
Total
1,316
465,298
Example – Step two
AR% = CI1 – CI0
CI1
= 3.265 / 1000 PYs – 2.676 / 1000 PYs
3.265 / 1000 PYs
= 18.0%
Exposure group
Number of cases Person-years of
of breast cancer follow-up
None
923
344,942
Any estrogen or
progestin use
393
120,356
Total
1,316
465,298
Example - Conclusions
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So what can we say about the impact of
HRT exposure on the user?
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As an individual on HRT, 18% of your risk of
breast cancer is attributed to your exposure
to hormones
And at the population level?
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We do not have information regarding the
proportion of the population who are
exposed to the factor under consideration.
Your posters are due in 16 days,
do you know what you’re doing?
If not, how can I help?
Question and protocol
development
Knowledge syntheses use rigorous
scientific methods to identify, assess
and synthesize the available evidence
(worldwide)
 Consult widely to ensure that the
review is relevant and addresses the
needs of different potential
stakeholder audiences
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Why have a protocol?
Literature reviews are scientific
research
 Plan methods
 Reduce bias
 Avoid duplication of effort
 Rest of review follows
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Focus for thinking about review
 Planning and allocating tasks
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Developing a protocol
1.
2.
3.
4.
5.
6.
Title/review authors
Background
Objectives
Selection criteria
Search Strategy
Methods
Review authors
Technical expertise
 Content expertise
 Organizational expertise
 Knowledge users
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Background
Description of the condition and its
significance
 Description of the intervention and it’s
role in practice
 How the intervention might work
 Why it is important to do the review
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Objectives
Follow naturally from the background
 What are the questions?
 Questions should be clearly framed
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Selection criteria
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Help you design the search
Selecting studies
Start thinking about the analysis
One way to minimize bias
Follow naturally from the objectives
 P – population
 I – Intervention
 C – comparison
 O – outcomes
 S – setting
 C - context
Diverse synthesis questions
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What are the benefits, harms, and costs of
treatment x?
What are the benefits, harms, and costs of
a new delivery service configuration?
What is the accuracy of diagnostic test y?
Does use of diagnostic test y lead to better
outcomes?
What is the prevalence of condition a?
Diverse synthesis questions
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Is risk factor a associated with disease b?
How strong is the association between gene
a and disease b?
What are the beliefs of patients about
disease a?
What are the experiences of patients
undergoing treatment z?
What is the accuracy of reoutine coding
following hospital discharge?
Search strategy
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Search strategy should follow from review
question and selection criteria
Highly technical
Development of search strategy involves
trade-offs between sensitivity and specificity
Be aware of assumptions as they can
dramatically impact on workload (if
searches have poor specificity) and value of
review (if searches have poor sensitivity)
Key issue is transparency
Why is a rigorous approach
to searching important?
To achieve up-to-date, relevant,
unbiased reviews
 Searching is often a matter of trying
terms and seeing what results you get
 Protocol should state planned
searches (post-hoc modifications can
be made if appropriate)
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Methods
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How searches will be screened
How full text articles will be retrieved and
screened
How data abstraction will be undertaken
What data will be abstracted (cross
reference back to the selection criteria)
How the risk of bias/quality of included
studies will be assessed
Planned comparisons, subgroup analyses
Would you like an example?
I have two to offer
1.
2.
Dairy consumption as a risk factor
for breast cancer.
A review of the current economic
literature regarding the use of
antiplatelet agents in the secondary
prevention of vascular events in
adults undergoing percutaneous
coronary intervention.
Design your own final exam
tutorial
Who wants to come?
When should it be?
What material should we cover?
When should it be?
Sun
Mon
Tues
Wed
Thurs Fri
Sat
1
11:30 Tutorial
2
3
4
5
Last class
6
7
8
11:30 Tutorial
9
10
Research
Day
11
12
13
14
15
11:30 Tutorial
16
17
Erin in Halifax
18
Erin in Halifax
19
Erin in Halifax
20
Erin in Halifax
21
22
11:30 Final
Tutorial
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27
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29
Exam
Day
30
What to cover?
Wk. #
Dates
Topics
1
Jan 7
Intro to epi
2
Jan. 11 & 14
Interpretation of epi lit,
Measurements of M&M
3
Jan. 18 & 21
Mortality et al
Natural history of disease
4
Jan. 25 & 28
Standardization,
K-M survival curves
5
Feb 1 & 4
Life tables
Screening tests
6
Feb. 8 & 11
Agreement
Bias
7
Feb 15 & 18
READING WEEK
What to cover?
Wk. #
Dates
Topics
8
Feb. 22 & 25
Midterm review,
Midterm
9
March 1 & 4
Study design,
Attack rates
10
March 15 &
18
Randomization,
Molecular and genetic epidemiology
11
March 22 &
25
Risk estimation,
Attributable risk
12
March 29 &
Apr. 1
Bias and confounding,
Causal relationships
13
April 5
Genetic and environmental factors in
disease causation,
Evaluation of health services,
Evaluation of screening programs