This document discusses the concepts of association and causation in epidemiology. It defines association as the occurrence of two variables more often than expected by chance. Association can be spurious, indirect, or direct (causal). Additional criteria for judging causality include temporal relationship, strength of association, dose-response relationship, replication of findings, biological plausibility, and consideration of alternative explanations. Establishing causality requires evaluating these criteria to determine if changes in the suspected cause are consistently linked to changes in the effect.

1. ASSOCIATION AND CAUSATION
Presented by : Dr. Vini Mehta
MDS 1st
Year
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2. Contents
• Introduction
• Defining Association
• Types of Association
• Additional Criteria for Judging Causality
• Establishing a Casual Inference
• Problems in Establishing Causality
• Conclusion
• References
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3. Introduction
Epidemiological principles stand on two basic assumptions
 Human disease does not occur at random
 The disease and its causal as well as preventive factors
can be identified by a thorough investigation of
population
• Identification of causal relationship between a disease
and suspected risk factors forms part of epidemiological
research.
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4. Association
(Correlation, Covariation, Statistical dependence, Relationship)
• Defined as occurrence of two variables more often than
would be expected by chance
• If two attributes say A and B are found to co-exit more often
than an ordinary chance
• Correlation indicates the degree of association between two
variables
• Causal association: when cause and effect relation is seen
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5. • Association does not necessarily imply a causal
relationship.
• Association can be broadly grouped under three
headings:
• a. Spurious association
• b. Indirect association
• c. Direct (causal) association
• one to one causal association
• multifactorial causation
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6. a. Spurious Association
• (Spurious= not real, artificial, false, non-causal
associations due to chance, bias or confounding)
• Observed association between a disease and suspected
factor may not be real
• This is due to selection bias
Ex: Neonatal mortality was observed to be more in the
newborns born in a hospital than those born at home.
This is likely to lead to a conclusion that home delivery is
better for the health of newborn.
However, this conclusion was not drawn in the study
because the proportion of “high risk” deliveries was
found to be higher in the hospital than in home 6

7. a. Indirect association:
• It is a statistical association between a characteristic
of interest and a disease due to the presence of
another factor i.e. common factor (confounding
variable).
• So the association is due to the presence of another
factor which is common to both, known as
CONFOUNDING factor.
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8. Example of an indirect association is
1) Altitude and endemic goiter
• Endemic goiter is generally found in high
altitudes, showing thereby an association
between altitude and endemic goiter.
• Current knowledge- endemic goiter is not due
to altitude but due to environmental
deficiency of iodine.
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9. a. Direct (causal) association:
One to one causal relationship:
• The association between the two attributes is not
through the third attributes.
• When the disease is present, the factor must also
be present.
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10. • Direct (Causal) association:
1.One –to- one causal association
2.Multifactorial causation
Sufficient & necessary cause
Web of causation (Interaction)
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11. Koch’s postulates-
The germ theory of disease insisted that the
cause must be
a. necessary and
b. sufficient for the occurrence of the
disease.
.
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12. One –to- one causal association
• The variables are stated to be causal related if a change
in A is followed by a change in B.
• When the disease is present, the factor must also be
present.
• A single factor (cause) may lead to more than one
outcome.
• Hemolytic Streptococci
Streptococcal
tonsillitis
Scarlet fever
Erysipelas
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13. Multifactorial causation:
• Multiple factor leads to the disease.
• Common in non-communicable diseases
• Alternative causal factors each acting
independently.
Ex: In lung cancer more than one factor (e.g. air
pollution, smoking, heredity) can produce the
disease independently.
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14. Model of multifactorial causation
• Factor 1
• Factor 2
• Factor 3
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REACTION AT
CELLULAR
LEVEL
Disease

15. • Model of multifactorial causation showing
synergism
• In this model , the causal factors act cumulatively to
produce disease. This is probably the correct model
for many diseases. It is possible that each of the
several factors act independently , but when an
individual is exposed to 2 or more factors, there
may be a synergistic effect.
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Factor 1
Factor 2
Factor 3
REACTION AT
CELLULAR
LEVEL
Disease
+
+

16. ADDITIONAL CRITERIA FOR JUDGING CAUSALITY
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1. Temporal relationship
2. Strength of the association
3. Dose-response relationship
4. Replication of the findings
5. Biologic plausibility
6. Consideration of alternate explanations
7. Cessation of exposure
8. Consistency with other knowledge
9. Specificity of the association

17. Temporal association
• The causal attribute must precede the disease or
unfavorable outcome.
• Exposure to the factor must have occurred before
the disease developed.
• Length of interval between exposure and disease
very important

18. Strength Of The Association
• Relationship between cause and outcome could be strong or
weak.
• With increasing level of exposure to the risk factor an
increase in incidence of the disease is found.
• There are statistical methods to quantify the strength of
association ( calculation of relative risk, attributable risk )

19. Dose-Response Relationship
• As the dose of exposure increases, the risk of disease also
increases
• If a dose-response relationship is present, it is strong
evidence for a causal relationship.
• However, the absence of a dose-response relationship does
not necessarily rule out a causal relationship.
• In some cases in which a threshold may exist, no disease may
develop up to a certain level of exposure (a threshold); above
this level, disease may develop

20. Replication of the Findings
• If the relationship is causal, we would expect to find it
consistently in different studies and in different populations
• Replication of findings is particularly important in
epidemiology.
• If an association is observed, we would also expect it to be
seen consistently within subgroups of the population and in
different populations, unless there is a clear reason to expect
different results.

21. Biologic Plausibility Of The Association
• The association must be consistent with the other knowledge
( mechanism of action, evidence from animal experiments
etc).
• Sometimes the lack of plausibility may simply be due to the
lack of sufficient knowledge about the pathogenesis of a
disease

22. Consideration of Alternate Explanations
• We have discussed the problem in interpreting an observed
association in regard to whether a relationship is causal or is
the result of confounding
• In judging whether a reported association is causal, the extent
to which the investigators have taken other possible
explanations into account and the extent to which they have
ruled out such explanations are important considerations.

23. Cessation of Exposure
• If a factor is a cause of a disease, we would expect the risk of
the disease to decline when exposure to the factor is reduced
or eliminated

24. Consistency Of The Association
• Consistency is the occurrence of the association at some
other time and place repeatedly.
• If a relationship is causal, the findings should be consistent
with other data.
• If lung cancer incidence increased as cigarette use was on the
decline, we would have to be able to explain how this was
consistent with a causal relationship.
• If there is no consistency it will weaken a causal
interpretation.
• The causal association between smoking and lung cancer due
to its consistency.

25. Specificity Of The Association
• The weakest of the criteria
• Specific exposure is associated with only one disease.
• This is used by tobacco companies to argue that smoking is
not causal in lung cancer.
 Smoking is associated with many diseases.
• Specificity implies a one to one relationship between the
cause and effect.

26. Establishing a Causal Inference
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27. Problems in Establishing Causality
• The existence of correlation/ association does not
necessarily imply causation.
• Concept of single cause  concept of multiple
causation
• Koch’s postulates cannot be used for non-infectious
diseases.
• The period between exposure to a factor and
appearance of clinical diseases is long in non-
infectious diseases.
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28. Conclusion
• Results from epidemiological studies are often used
as inputs for policy and judicial decisions.
• It is thus important for public health and policy
makers to understand the fundamentals of causal
inference.
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29. References
1. Health Research Methodology- A guide for training in
Research methods – Second edition-World Health
Organization, Pg 125-140
2. Park’s Textbook of Preventive and Social Medicine-20th
edition,Pg 83-87
3. Epidemiology-Leon Gordis, W.B.Saunders Company1996,
Pg 167-182
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