THIS IS A SEMINAR PRESENTATION ON EPIDEMIOLOGY TOPICS WHICH ARE VERY IMPORTANT SPECIALLY DURING RESEARCH. USEFUL FOR PUBLIC HEALTH STUDENTS

1. CONFOUNDING
AND
INTERACTION

2. Presented by Chair person
Dr. Arundhati Dr. Annapurna K
MPH 2nd sem MPH faculty
JNMC JNMC
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3. CONTENTS
▪ Objectives
▪ Introduction
▪ Bias
▪ Confounding
▪ Interaction
▪ Summary
▪ References
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4. OBJECTIVES
To define confounding and to know about
controlling its effect on study designs
To define interaction and to present a framework
for detecting whether two factors interact to
influence the risk of a disease.
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5. INTRODUCTION
▪ EPIDEMIOLOGY- The study of the
distribution and determinates of health –
related states or events in specified populations
and the application of this study to control of
health problems
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6. HIERARCHY OF
STUDIES
The strongest study considered
is meta analyses and systematic
review
The weakest studies are case
reports, opinion papers etc..
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7. VARIABLE
A variable is a quantity which can vary from
one individual to another.
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8. TYPES OF VARIABLES
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VARIABLE
Continuous
independent
Dependent
Discrete
Controlled
Confounding
Intervening
Extraneous
organismic

9.  Measure of effect- It compares what would happen
to one population under two possible but distinct life
courses or conditions, of which at most only one can
occur.
 Measure of association- It compares what happens
in two distinct populations, although the two distinct
populations may correspond to one population in
different time periods.
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10.  Causal inference -Viewed as an exercise in
measurement of an effect rather than as a criterion-
guided process for deciding whether an effect is
present or not.
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11. BIAS
 Bias is defined as any systematic error in the design
conduct or analysis of study that results in a
mistaken estimate of an exposure’s effect on the risk
of disease.
 Term bias was first defined by Wynder and
coworkers.
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12. TYPES OF BIAS
1. Selection bias
2. Information bias
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13. CONFOUNDING
It is one of the most important problem in
observational epidemiologic studies.
It is important because in many studies we observe
a true association and are to derive a causal
interference when in fact the relationship may not
be causal.
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14. CONTINUED
Confounding can be described as confusion or
mixing of extraneous effects with the effect of
interest.
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15. CONTINUED
 In a study if whether factor A is a cause of disease
B then third factor X is confounder if
 Factor X is a know risk factor for diseases B.
 Factor X is associated with factor A but is not a
result of factor A
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16. CONTINUED
 Confounding occurs when the effects of two
associated exposures have not been separated,
resulting in the interpretation that the effect is due to
one variable rather than the other.
 The consequence of confounding is that the
estimated association is not the same as the true
effect.
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17. CONTINUED
 In epidemiologic studies, statistical analyses are
typically organized around three different sets of
variables: the exposure, the outcome, and the
confounder(s).
 The exposure and outcome are usually determined
by the causal question under investigation.
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18. CONTINUED
 The confounders, on the other hand, are not so
clearly defined; they must first be identified and then
appropriately adjusted for in the analysis.
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20. CONFOUNDERS
▪ These are the extraneous factors that are
responsible for difference in disease frequency
between the exposed and unexposed.
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21. FOR EXAMPLE
▪ The relationship between coffee and cancer of the
pancreas.
▪ Smoking is a confounder, because although we were
interested in a possible relationship between coffee
consumption (factor A) and pancreatic cancer
(disease B), the following are true of smoking (factor
X): 21

22. CONTINUED
1. Smoking is a known risk factor for pancreatic
cancer.
2. Smoking is associated with coffee drinking, but
is not a result of coffee drinking
 So if an association is observed between coffee
drinking and cancer of the pancreas, it may be
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23. CONTINUED
1. That coffee actually causes cancer of the pancreas,
or
2. That the observed association of coffee drinking and
cancer of the pancreas may be a result of
confounding by cigarette smoking.
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24. CRITERIA FOR A CONFOUNDING
FACTOR
1. The variable must be independently associated
with the outcome.
2. The variable must be associated with the
exposure under study in the source population.
3. It should not lie on the causal pathway between
exposure and disease.
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25. DIFFRENCE BETWEEN
SELECTION BIAS AND
CONFOUNDING
SELECTION BIAS
It arises from selection
affected by exposure
under study and is
beyond any practical
adjustment.
CONFOUNDING
It is the differential
selection which occurs
before exposure and
disease. And can be
controlled by adjustment
for factors responsible for
selection differences.
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26. OPTIONS TO CONTROL
CONFOUNDING
Various methods are used to help control
confounding in the study designs.
1. Randomization
2. Restriction
3. Matching
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27. CONTINUED
 RANDOMIZATION
▪ It is a method that allows one to limit confounding
by unmeasured factors probabilistically and to
account quantitatively for potential residual
confounding produced by these unmeasured
factors.
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28. CONTINUED
 RESTRICTION
 Other strategy used for avoiding confounding
is to restrict admission into the study to a group
of subjects who have the same levels of the
confounding factors.
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29. CONTINUED
 MATCHING
 Matching ensure that the study groups do not
differ with respect to possible confounders such
as age and gender by matching the two
comparison groups.
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30. INTERACTION
It is defined as when the incidence rate of
disease in the presence of two or more risk
factors differs from the incidence rate expected
to result from their individual effect.
The effect can be greater than what we would
expect i.e. positive interaction synergism or
less than what we would expect i.e. negative
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31. CONTINUED
Synergism.
The problem is to determine what we would
expect to result from the individual effects of the
exposure.
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32. CONTINUED
 Following is the algorithm for exploring the
possibility of interaction.
1. Is there an association?
2. If so, is it due to confounding?
3. Is there an association equally strong in strata
formed on basis of a third variable.
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33. Interaction Interaction
Present Absent
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NO Yes

34. CONTINUED
▪ If association is equally strong in all strata there is
no interaction.
▪ If association is of different strengths in different
strata formed on basis of age an interaction has been
observed between age and exposure in producing
the disease.
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35. FOR EXAMPLE
 The study of ( Berry and Linddle , 2004) to see
the effect of occupational exposure to asbestos
dust on lung cancer risk, which depends on
smoking status.
 The concept of interaction centers idea that the
effect of an exposure compared with a reference
unexposed condition may depend on the presence
of one or more other conditions. 35

36. SUMMERY
▪ Confounding and interaction describes the reality of
the interrelationships between certain factors and
outcomes.
▪ These play important roles in investigating genetic
roles and environmental factors in disease causation
and helps in assessing adverse health outcomes
from environmental exposures. 36

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38. REFERENCES
▪ Park K .Text Book of preventive and social
medicine. Banarsidas Bhanot. 23th Edition, 2015:
267
▪ G. Leon . Epidemiology. Fifth edition. Pg no.
266-276.
▪ K. Rothman. G. Sander, L.Timothy. Modern
epidemology. Third edition.58, 71,196.
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Thanks!
Any questions?
?