Association and Causation PPT

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2.  Introduction
 Terminologies in causal philosophy
 Historical perspectives in causation
 What is association?
 Types of association
 What is cause ?
 Types of causal relationship
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3.  Criteria for a causal relationship
 Guidelines for Judging whether the association is causal evidence for a causal
relationship
 Modified guidelines for evaluating the evidence of a causal relationship
 Other models of causation
 Conclusion
 References
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4. In epidemiological studies, ascertainment of cause-effect relationships is one of the
central and most difficult tasks of all scientific activities.
Epidemiological principles stand on two basic assumptions:
 Human disease does not occur at random.
 The disease and its cause as well as preventive factors can be identified by a
thorough investigation of population.
Hence, identification of causal relationship between a disease and suspected risk
factors forms part of epidemiological research.
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5.  Conceptually, a two-step process is followed in carrying out studies and evaluating
evidence:
 1. Determine whether there is an association between an exposure or characteristic
and the risk of a disease. To do so, we use:
 a. Studies of group characteristics: ecologic studies
 b. Studies of individual characteristics: case-control and cohort studies
 2. If an association is demonstrated, we determine whether the observed association
is likely to be a causal one or not.
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6.  An ecological study is an observational study in which at least one variable is
measured at the group level. An ecological study is especially appropriate for initial
investigation of causal hypothesis.
 The first approach in determining whether an association exists might be to conduct
studies of group characteristics called ecologic studies.
 Ecological interference fallacy
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8.  Recognizing the limitations discussed above of ecologic studies that use only group
data, we turn next to studies of individual characteristics: case-control and cohort
studies.
 In case-control or cohort studies, for each subject we have information on both
exposure (whether or not and, often, how much exposure occurred) and disease
outcome (whether or not the person developed the disease in question).
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9.  Inductivism : In this philosophy, scientific reasoning is said to depend on making
generalizations or inductions from observation to general laws of nature.
E.g. : cholera epidemic, Edward Jenner small pox
 Refutationism
 Bayesianism
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10. Ancient views
 Punitive theory : one’s attitude towards deity caused sickness
 Humoral theory: Phlem, Yellow bile, Black bile and Blood
 Miasmatic theory: air arising from certain kind of swamps caused the disease
 Contagion theory:
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11. Modern views
 Germ theory
 Epidemiological triad
 Epidemiological tetrad
 Multifactorial theory
 BEINGS theory
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12.  Defined as occurrence of two variables more often than would be expected by
chance.
 Literally an association means connection
 If two attributes say A and B are found to co-exit more often than an ordinary
chance.
 It is useful to consider the concept of correlation.
 Correlation indicates the degree of association between two variables
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13.  Spurious associations
 Indirect association or non causal association
 Causal association or direct association
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14.  Spurious means false or not genuine
 Observed association between a disease and suspected factor may not be real. This
is due to selection bias
 Eg : Neonatal mortality in hospitals and home
 We say that a relationship between two variables is spurious when it is actually due
to changes in a third variable
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16.  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.
 This becomes third variable effect in association
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18.  The association between the two attributes is not through the third attributes. When
the disease is present, the factor must also be present.
 Direct (Causal) association:
 1. One –to- one causal association
 2. Multifactorial causation
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One to one causal association
 The variables are stated to be casual related (AB) if a change in A is followed by a
change in B.
 When the disease is present, the factor must also be present.
 This concept is sometimes complicated by the fact that single factor lead to multiple
outcome

20.  Multiple factor leads to the disease.
 Common in non-communicable diseases
 Causal factors can be acted independently or cumulatively
 Eg : Lung cancer can occur due to multiple outcome
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21.  Factor 1
 Factor 2
 Factor 3
 Example
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Reaction at cellular level Disease

22.  Factor 1
+
 Factor 2
+
 Factor 3
 Example
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Reaction at cellular level Disease

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24.  It is something that produces an effect.
At fundamental level we can define cause as
 an event or events that provide the generative force that is the origin of something or
 a series of actions advancing a principle or tending towards a particular end
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25.  The most widely applied models are: –
 The epidemiological triad (triangle)
 The web of causation
 The wheel
 The sufficient cause and component causes models (Rothman’s component causes
model)
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29.  NECESSARY cause - causal factor whose presence is required for the occurrence of
the effect.
 SUFFICIENT cause: Rothman defined a sufficient cause as "...a complete causal
mechanism" that "inevitably produces disease." Consequently, a "sufficient cause" is
not a single factor, but a minimum set of factors and circumstances that, if present in
a given individual
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30.  Rothman's model has emphasized that the causes of disease comprise a collection of
factors.
 These factors represent pieces of a pie, the whole pie (combinations of factors) are
the sufficient causes for a disease.
 It shows that a disease may have more that one sufficient cause, with each sufficient
cause being composed of several factors
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32. 4 types of causal relationship can be established
1) Necessary And Sufficient
(2) Necessary, But Not Sufficient
(3) Sufficient, But Not Necessary
(4) Neither Sufficient Nor Necessary
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33.  A factor is both necessary and sufficient for producing the disease.
 Without that factor, the disease never develops and in the presence of that factor, the
disease always develops
 Each factor is both necessary and sufficient
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34.  Each factor is necessary, but not, in itself, sufficient to cause the disease .
 Thus, multiple factors are required, often in a specific temporal sequence.
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35.  Factors independently can produce the disease
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36.  A factor by itself, is neither sufficient nor necessary to produce disease
 This is a more complex model, which probably most accurately represents the causal
relationships that operate in most chronic diseases or non communicable diseases
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37.  In 1840 ‘s Henle postulated for causation was expanded by Koch in 1880’s
 Koch Postulates
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39.  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
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40. 1.Temporal relationship
 exposure must precede the outcome
2.Strength of association
 The larger the relative effect, the more likely the causal role of the exposure. eg
Smoking > 20 cigarettes/day –RR=20 of developing laryngeal cancer
 Not all strong associations are causal eg Downs syndrome and birth rank
 Weak associations do not rule out causality eg passive smoking and lung cancer (RR
1.4)
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41. 3.Dose-response relationship
 If the risk increases with increasing dose of the exposure, the more likely the causal
role of the exposure.
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42. 4.Replication of findings
 If similar associations are found in different studies in different populations, the
more likely the causal role of the factor. Eg Smoking and lung cancer
 > 100 studies over last 30 years demonstrate increased risk
 Lack of consistency does not rule out causality eg: blood transfusion not always a
risk for HIV: virus must be present
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43. 5.Association makes biological sense/plausibility
 E.g.: Histopathological effects of smoking on epithelium and
 Depends on current knowledge
 e.g. John Snow and cholera epidemic in London (Vibrio cholerae was not yet
discovered)
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44. 6.Consideration of alternate explanations
 Takes into account the extent to which the researchers has considered alternative
explanations for the outcome e.g. confounding
7.Reversibility
 Reduction or removal of the risk factor must reduce the risk of the outcome
 Quitting smoking reduces risk
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45. 8.Specificity of the association
 Specific exposure is only related to one disease
 Cigarette manufacturers use this to support their views
 Not necessarily true where cigarette smoking is associated with lung cancer, heart
disease, COPD, bladder cancer, etc
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46. 9.Consistency with other knowledge
 Associations between the exposure and the outcome must be consistent with existing
knowledge.
 Variation in smoking between sexes Difference in lung cancer incidence by sex
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47.  It was proposed by US Public Health Service in 1989.
1. Major criteria
 Temporal relationship: An intervention can be considered evidence of a reduction in
risk of disease or abnormality only if the intervention was applied before the time the
disease or abnormality would have developed.
 Biological plausibility: A biologically plausible mechanism should be able to explain
why such a relationship would be expected to occur.
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48.  Consistency: Single studies are rarely definitive. Study findings that are replicated
in different populations and by different investigators carry more weight than those
that are not. If the findings of studies are inconsistent, the inconsistency must be
explained.
 Alternative explanations (confounding): The extent to which alternative
explanations have been explored is an important criterion in judging causality
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49. 2. Other considerations
 Dose-response relationship: If a factor is the cause of a disease, usually the greater
the exposure to the factor, the greater the risk of the disease.
 Strength of the association: Usually measured by the extent to which the relative
risk or odds depart from unity.
 Cessation effects: If an intervention has a beneficial effect, then the benefit should
cease when it is removed from a population.
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50. Potential outcome model ( counterfactual model)
 When we are interested to measure effect of a particular cause, we measure effect in
a population who are exposed.
 • We calculate risk ratios & risk differences based on this model
 • The difference of the two effect measures is the effect due the cause we are
interested in.
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51. Causal Diagrams
 Diagrams consisting of variables connected by arrows or lines are widely used in
epidemiology depicted as Directed Acyclic Graph (DAG)
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53. Causation and the means for achieving causally valid conclusions in research is the last
of the three legs on which the validity of research rests. Thus researches emphasize that
understandings of causal relationships are always partial. Researchers must always
wonder whether they have omitted some relevant variables from their controls or
whether their experimental results would differ if the experiment were conducted in
another setting or at another time in history.
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54.  Park K. Park's textbook of preventive and social medicine,23rd edi: M/s Banarsidas
Bhanot publishers; 2015.pp.87-92.
 L Gordis. Epidemiology,5th ed: Elsevier Saunders;2014.pp243-250.
 Bhopal R. Cause and effect: the epidemiological approach. Concepts of Epidemiology.
Integrating the Ideas, Theories, Principles and Methods of Epidemiology’ 2nd ed: Oxford;
2002.
 Chattopadhyay A. Oral health epidemiology: principles and practice,1st ed: Jones &
Bartlett Learning; 2011.
 Rothman KJ, Greenland S, Lash TL. Modern epidemiology,3rd ed.Philadelphia:
Lippincott Williams &Wilkins ; 2008.
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