The document discusses the complex nature of causal relationships in epidemiology, differentiating between associations, correlations, and various types of causal associations. It outlines key concepts such as confounding factors and establishes guidelines for assessing causation based on criteria from Henle-Koch and Hill. Additionally, it emphasizes the importance of evidence evaluation in determining causality, including considerations like temporal relationships, strength, and biological plausibility.

1. Dr. Aparna Sen Chaudhary
15-05-2018
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2.  Association
 Association to Causation
 Cause and its strength
 Factors for disease causation
 Causal Relationship
 Evidence of causal relationship
 Modifications
 References
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3. Environmental
Exposure or Host
Characteristics
Disease or Other Health
Outcome
Disease or Other Health
Outcome
Environmental
Exposure or Host
Characteristics
Is an
Association
Observed
An
Association
is Observed
Is the Observed
Association
Causal?
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Clinical Observations
Available Data
Case-control Studies
Cohort Studies
Randomized Trials

5.  Descriptive studies help in identification of disease problem in the
community, it endeavours an aetiological hypothesis.
 Analytical and experimental studies test the hypothesis derived.
 When the disease is multifactorial numerous factors become implicated
in the web of causation.
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6.  “ASSOCIATION” and “RELATIONSHIP” are often used interchangeably.
 Defined as –
 Statistical dependence between two variables, that is, the degree to which
the rate of disease in person with a specific exposure is either higher or
lower than the rate of disease among those without that exposure.
 It does not imply a causal relationship
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7.  “CORRELATION” indicates the degree of association between two
characteristics.
 Correlation coefficient range from -1.0 to +1.0.
 Cannot be used to invoke causation since temporal association cannot be
established.
 Does not measures risk
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8.  Association can be grouped under
a) Spurious (artefactual)association
b) Non-causal association
c) Causal association
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9. Spurious Association
• Association between a disease and suspected factor may not be real.
• Example -
oMore perinatal deaths in hospital delivery than home delivery.
oMore number of people dying from disease in places with more number
of doctors.
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10. Non-causal Association
• It is a statistical association between a
characteristic (or variable) of interest and a
disease due to the presence of another factor,
known or unknown, that is common to both
the characteristic and the disease.
• The third factor is also known as
“CONFOUNDING” variable.
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11. Confounding
• Mixing of the effect of the exposure under study on the disease with
that of a third factor.
• This factor must be associated with exposure and, independent of
that exposure, be a risk factor for the disease.
• Lead to overestimate or underestimate of true association
• Can change the direction of observed effect.
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 Direct (causal) Association
• One-to-one causal relationship
•Can be direct or indirect

14.  The following conditions have been met:
• Adequate Sample Size
• Free of Bias
• Adjusted for possible confounders
 There is an association between exposure of interest and the disease
outcome
 Is the association causal?
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Statistical Association
Yes No
Likely Unlikely
Yes No
Cause
Bias in selection
or measurement
Chance
Confounding
Cause

16.  Cause of a specific disease event can be defined as
“an antecedent event, condition or characteristics that was necessary for the
occurrence of the disease at the moment it occurred, given that the other
conditions are fixed.”
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17.  The strength of a factor’s effect is usually measured by the change in
disease frequency produced.
 Can be measured in Absolute or Relative terms
 Strength depends on the Time-specific distribution of its causal
complements in the population.
 Over a period of time, the strength of the effect of a given factor may
change, because the prevalence of the causal complements may also
change. 15-05-2018 17

18. Sufficient Factors:
One which inevitably produces disease i.e., presence always result in
disease.
Ex- Rabies virus for rabies
Necessary Factors:
Without which disease does not occur, but by itself, not sufficient to
cause disease.
Ex- Mycobacterium TB for TB
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19.  A Causal Relationship can be defined as -
“ change in the Frequency or Quality of exposure or characteristics results in
a corresponding change in the Frequency of the disease or outcome of
interest.
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20.  Four types are possible:
I. Necessary and Sufficient One-to-one relationship
II. Necessary, but not Sufficient
III. Sufficient, but not Necessary
IV. Neither Sufficient nor Necessary
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Multifactorial relationship

21. Necessary & Sufficient
• Without that factor, the disease never develops (Necessary)
• In presence of that factor, the disease always develops (Sufficient)
• Rare situation
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22. Necessary, But Not Sufficient
• Each factor is necessary, but not, in itself
sufficient to cause disease.
• Often required in a specific sequence.
• Example - Carcinogenesis, Tuberculosis
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23. Sufficient, but not necessary
• Factors independently can produce the
disease.
• Examples- Radiation or Benzene exposure
can produce leukaemia.
• Although both factors are not needed,
other cofactors are probably needed.
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24. Neither Sufficient nor Necessary
• More complex model
• Most accurately represent the causal
relationship operating in CHRONIC
DISEASE.
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25. Henle-Koch’s Postulates (1884 and 1890)
Hill’s Criteria (1965)
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26. Henle-Koch’s Postulates (1884 and 1890)
• Organism is always found with the disease.
• Organism must be isolated from a diseased animal and grown in pure
culture.
• Cultured organism should cause disease when introduced into a
healthy animal.
• Organism must be re-isolated from the experimentally infected
animal.
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27. Guidelines for Assessing Causation (Hill’s Criteria, 1965)
1. Temporal Relationship
2. Strength of Association
3. Dose-Response Relationship
4. Replication of the findings
5. Biological Plausibility
6. Consideration of Alternate Explanations
7. Cessation of Exposure
8. Consistency with Other Knowledge
9. Specificity of the Association
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28. 1. Temporal Relationship
• Most important criteria
• Exposure precedes disease development with
adequate elapsed time
oLatency period
oIncubation period
• Often easier to establish by Prospective cohort
study.
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29. 2. Strength of the Association
• Measured by the relative risk (or odds ratio)
• Stronger the association, the more likely it is that the relation is
causal.
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30. 3. Dose-Response Relationship
• As the dose of the exposure
increases, the risk of disease also
increases.
• It is a strong evidence for a causal
relationship
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31. 4. Replication of Findings
• It is supportive if the same finding can be replicated in subgroups or
different populations and/or by using various study designs.
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32. 5. Biological Plausibility
• Refers to knowledge of biological (or social) model or mechanism that
explains the cause-effect association.
• Epidemiologic studies often identify cause-effect relationships before
a biological mechanism is identified.
• Examples-
i. Teratogenic viruses and Rubella and congenital cataracts;
ii. Thalidomide and limb defects 15-05-2018 32

33. 6. Consideration of Alternate Explanations.
• Take into account the extent to which the researchers has considered
alternative explanations for the outcome.
• Example - Confounding
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34. 7. Cessation of Exposure
• The risk of disease to decline when
exposure to the factor is reduced or
eliminated.
• Ex- Reduction in Eosinophilia-myalgia
syndrome epidemic after withdrawal of L-
tryptophan in 1989.
• Cessation data if available provide helpful
supporting evidence for a causal
association.
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35. 8. Consistency with Other Knowledge
• If a relationship is causal, the findings will
be consistent with the other data.
• Ex- Lung Cancer and Cigarette smoking in
men and women
• Absence of consistency would not
completely rule out the hypothesis.
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36. 9. Specificity of the Association
• Association is specific when a certain exposure is associated with only
one disease.
• Weakest of all the guidelines
• When found, it provides additional support for a causal inference.
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37. 1. TEMPORAL RELATIONSHIP
• H.pylori linked to chronic gastritis.
• In a study, 11% of the patients with duodenal
ulcers were positive for H.pylori.
2. STRENGTH OF ASSOCIATION
• H.pylori is found in at least 90% of patients with
duodenal ulcers.
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3. DOSE-RESPONSE RELATIONSHIP
• Density of H.pylori per sq. mm of gastric mucosa
is higher in patients with duodenal ulcer.
4. REPLICATION OF THE FINDINGS
• Observations have been replicated.

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5. BIOLOGICAL PLAUSIBILITY
• H.pylori has binding sites on antral cells
• Induces mediators of inflammation
• Infected mucosa – susceptible to damaging
effects of acid.

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6. CONSIDERATION OF ALTERNATE
EXPLANATIONS
• Smoking can increase the risk of duodenal ulcer
in infected patients.
7. CESSATION OF EXPOSURE
• Eradication of H.pylori heals ulcers
• Long-term ulcer recurrence rates were zero after
triple-antimicrobial therapy.

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8. CONSISTENCY WITH OTHER KNOWLEDGE
• Prevalence is same in men and women
• Prevalence peaked in the latter part of the 19th
century.
9. SPECIFICITY OF THE ASSOCIATION
• Prevalence of H.pylori in patients with duodenal
ulcers is 90-100%.

42. Process for using evidence includes
1. Categorization on the basis of quality of sources
2. Evaluation of the evidence of a causal relationship using
standardized guideline.
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43. Quality of sources
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Trials
Cohort or case-control
Time-series studies
Case-series studies

44. Evidence of causal relationship
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Major Criteria
• Temporal Relationship
• Biological Plausibility
• Consistency
• Alternative explanation
Other considerations
• Dose-response
relationship
• Strength of the
association
• Cessation effects

45.  Gordis L. Epidemiology. 5th edition. Philadelphia. Elsevier. 2015.
 Park K. Park’s Textbook of Preventive and Social Medicine. 24th edition. Jabalpur, India.
Banarsidas Bhanot. 2017.
 Hennekens CH, Buring JE. Epidemiology in Medicine. Philadelphia. Lippincott Williams &
Wilkins. 1987.
 Kanchanaraksa S. Causal Association. Johns Hopkins Bloomberg School of Public Health. 2008.
 Rothman KJ, Greenland S. Causation and Causal Inference in Epidemiology. American Journal
of Public Health.2005;95:S144-50.
 Wakeford R. Association and causation in epidemiology- half a century since the publication
of Bradford Hill’s interpretational guidance. Journal of the Royal Society of
Medicine.2015;108(1):4-6.
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