Transcript Document

INTRODUCTION TO EPIDEMIOLO FOR POME 105.
Lesson 3:
R H THEKISO:SENIOR PAT TIME LECTURER
INE OF PRESENTATION
1.Epidemiologic measures of association
2.Study designs allowing for association
measurement
3.Confounding
4.Dealing with confounding
5.Group task for today
MEASURES OF ASSOCIATION
MEASURES OF ASSOCIATION ARE CLASSIFIED INTO
VARIOUS MEASURES SUCH AS THE FOLOOWING
1. Odds ratio (OR) (relative odds) used in case
control studies
2. Risk ratio or relative risk used in cohort
studies
3. Risk difference or attributable risk (AR)
4. Attributable fraction (AF)
Introduction to design of a case control
Were
exposed
Were not
exposed
Have the disease
Cases
Were
exposed
Were not
exposed
Do not have the
disease
Controls
Introduction to design of a case control
First select
Cases
(with
disease)
controls
(without
disease)
Then measure past exposure
Were exposed
Were not exposed
TOTALS
a
c
a+c
The odds of exposure in cases
The odds of exposure in controls
b
d
b+d
a
a+c
b
b+d
Introduction to design of a case control
A hypothetical example of a case control study of Coronary heart
disease(CHD) and cigarette smoking
First select
CHD cases
controls
(with disease) (without disease)
Then measure
Past exposure
Were exposed
112
176
Were not exposed
Total
88
200
224
400
Odds of exposure in cases
112
112+ 88
Odds of exposure in control
0.560
176
176 +224
0.440
Introduction to design of a case control
Explanation of the hypothetical example
1. We start with 200 people with CHD(cases) and
compare them to 400 people without CHD (control)
2. If there is a temporal relationship between smoking
and CHD,we would anticipate that a greater
proportion of the CHD cases than of the controls
would have been smokers(exposed)
3. Let us say we find that of the 200 CHD cases,112 were
smokers and 88 non smokers AND Of the 400
controls,176 were smokers and 224 were non
smokers
Odds ratio in case control
In case control studies, the odds ratio (OR) is the odds of
exposure in cases divided by the odds of exposure in control
i .e OR = a/a + c
b/b +d
i.e
OR = odds of exposure in cases
odds of exposure in control
i.e
OR = 0.560
0.440
i.e OR = 1.273
Interpretation of odds ratio:
1. If OR= 1 then the exposure is not related to disease
2. If OR greater than 1,then the exposure is positively related to
the disease
disease
exposure
3. If OR less than 1,then the exposure is negatively related to
the disease
disease
exposure
Introduction to design of a cohort study
Design of a cohort study beginning with exposed
and non exposed groups.
Exposed
Disease develops
Not exposed
Disease does
not develop
Disease
develops
Disease does
not develop
Explanation of the design of a cohort study’
1. In a cohort study investigator selects exposed individuals
and a group of non exposed individuals and follows the
groups to compare the incidence of disease in the two
groups
2. If a positive association exists between the exposure and
the disease, we would expect that the proportion of the
exposed group in whom the disease develops(incidence
in the exposed group) would be greater than that of the
non exposed group in whom the diseases
develops)incidence in the non exposed group)
Introduction to a table of the design of a cohort study
Then follow to see whether
Disease develops Disease does
Totals
develop
Incidence rates of
disease
Exposed
a
b
a+b
First
Select Not
exposed
a
a+b
c
d
c+d
c
c+d
Table of a design of cohort study
Introduction to a table of the design of a cohort study
Calculation of the design of a cohort study.
1. We begin with the exposed group and the non
exposed group
2. Of the (a +b) exposed person the disease
develops in a but not in b.
3. Thus the incidence of the disease among the
exposed is a/a +b
4. Similarly, in the (c+d) non exposed persons in
the study, the disease develops I c but not in d.
5. Thus the incidence of the disease among the
non exposed is c/c+d.
Hypothetical example of a cohort study of 3000 smokers and 5000 non smokers to
investigate the relation of smoking to the development of coronary heart
disease(CHD) over
Then follow to see whether
Disease Disease does
Totals
Incidence rates
develops not develop
of disease/1000
First
select
Exposed
84
not
exposed
87
2916
4913
3000
28.0
5000
17.4
Then calculate IR in exposed 84/3000=28.0 per 1000
And calculate IR in non exposed 87/5000=17.4 per 1000
Relative risk or risk ratio= 28.0/17,4=1.61
Interpretation of RR
1.
2.
3.
4.
5.
If RR =1,the numerator equals the denominator and the risk in
exposed persons equals the risk in non exposed person. Therefore
no evidence exists for any increased risk in exposed individuals or
for any association of disease with the exposure in question.
If RR>1,the numerator is greater than denominator and the risk in
the exposed persons is greater than the risk in non exposed
persons.This is evidence of positive association and may be
causal.
If RR<1 the numerator is less than the denominator and the risk in
exposed persons is less that the risk in non exposed .This is
evidence of a negative association and it may be indicativeof a
protective effect. Such finding can be observed in people who are
given an effective vaccine(“exposed” to vaccine”
Therefore the results obtained of RR>1 indicates that smoking
increases risk of development of CHD in the exposed.
The RR is important as a measure of the strength of association
and is a major consideration in deriving a causal inference.
Risk difference or attributable risk
We have seen how the RR is important as a measure of the
strength of association and a major consideration in deriving a
causal inference
However a further question may be asked
“How can we determine whether the excess risk is associated with
the exposure
Excess risk is determined by subtracting the risk in those who are
not exposed from the risk of those who are exposed e.g. from
cigarette smoking
Determination of excess risk
Incidence due to exposure
Incidence not due to exposure
in exposed
group
In non
exposed
group
Excess risk = Incidence in
- Incidence in
exposed group unexposed group
The total risk of disease in the exposed is indicated by height of the full bar in the left
The total risk of disease in the non exposed is indicated by height of the full bar in
the left.
The total risk of the disease is higher in the exposed group than in the non exposed
• Attributable fraction
• WE have seen how The RR is important as a measure of the
strength of association and a major consideration in deriving a
causal inference
• However a further question may be asked
• “How much of the disease that occurs can be attributable to a
certain exposure"?. This is answered by another measure of risk,
the attributable risk which is defined as the amount or proportion
of disease incidence that can be attributed to a specific exposure.
For example “how much of the lung cancer risk is experience by
smokers is attributed to smoking or put in another way “how much
of risk(incidence) can we hope to prevent if we are able to eliminate
exposure to the agent in question?
Determination of attributable fraction
The incidence of disease that is attributable to the exposure in the exposed
group is calculated as follows
We express the attributable risk as the proportion of the total incidence in the
exposed group that is attributable to exposure by dividing the formula by
incidence in the exposed
1.
2.
3.
4.
5.
Incidence in
- Incidence in
exposed group
unexposed group
Incidence in the exposed
Example :If IR in the exposed group is 28.0 per 1000 and IR in the non
exposed is 17.4 per 1000 then
Risk difference is 28.0 per 1000 -17.4 per 1000 =10.6 per 1000.
It means 10.6/1000 of the 28.0 /1000 in smokers are attributable to the fact
that these people smoke.
OR as a proportion: 28.0-17.4/28=10.6/28=0,379*100=37.9%
Thus 37.9% of the morbidity from CHD among smokers may be attributable
to smoking
Confounding in observational epidemiologic studies
Increased
coffee drinking
Increased
coffee drinking
smoking
Increased risk of
pancreatic cancer
Increased risk of
pancreatic cancer
Figure showing the association between increased coffee
drinking and increased risk of pancreatic cancer
In the figure shown
Smoking is known to be a risk factor pancreatic cancer
Smoking is associated with coffee drinking but is not a result of
coffee drinking .
Observed association
If an association is observed between coffee drinking and cancer
of the pancreas it may be that
Coffee actually causes cancer of the pancreas or
That the observed association of coffee drinking and cancer of
the pancreas may be as a result of confounding by cigarette a
third factor that is both a risk factor for the disease and is
associated with the exposure in question?
Dealing with confounding
 At the study design stage by:

Matching (for confounders) cases to controls

You must suspect (or know) what your confounders are in order
to do this
 At the data analysis stage by:
 Stratified analysis
 In the previous example, instead of using the crude death rate, they
should have stratified according to age groups
 Adjustment:
 Regression analysis
 Standardisation: Direct and indirect
 Restriction,
 randomisation
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8.
Read chapters 9,10,11,12 and chapter 15 and 5 of Leon Gordis 5th edition
Recruit volunteers and collect data for the study: Is there an association
between gender and headaches in students at SMUHS
The sample size should be 213 (based on what you have learned from this
lecture), but we will increase this to 240.
This means each student should collect data from 6 SMUHS STUDENTS
Please use the prepared data collection sheet, which has been prepared for the
collection of data from 6 participants.
After data collection, meet with your group members and summarise all your
data on the data summary sheet provided.
Bring all these data collection and data summary sheets back to class, and
present the data summary to the class
Using the data analysis sheet, analyse the summarised data for the whole class
(i.e. n = 240)