This document discusses causation and causal inference in epidemiology. It outlines concepts of single and multiple causes of disease. The Bradford Hill criteria are presented as guidelines for making causal inferences, including strengths like temporal relationship, plausibility, consistency, and dose-response relationship. Finally, it emphasizes that no single criterion proves causation but evaluating the totality of evidence using these guidelines can help strengthen causal judgments.

1. CAUSATION AND CAUSAL
INFERENCES IN EPIDEMIOLOGY
DR BAYU BEGASHAW (PHD
PREVENTIVE MEDICINE AND
PUBLIC HEALTH)
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2. OUTLINE
• Concepts of causes and types
• Single and Multiple Causes
models
• Bradford Hill criteria as a guide
in causal inferences
• Conclusion
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3. BEFORE ANY JUDGMENT IN
CAUSATION IN EPIDEMIOLOGY
• As you read their responses, consider the following questions:
1. What distinction do they make between "risk factors" and
"cause“?
2. One of the tobacco industry witnesses suggests that lung
cancer is multi-factorial. Is this a reasonable possibility?
3. And, if smoking is multifactorial in etiology, does this mean
that tobacco is not a cause?
4. Can anything be proven to have caused a given case of lung
cancer?
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4. CAUSAL INFERENCE
• Understanding of the causes of disease is important
in the health field not only for prevention but also in
diagnosis and the application of treatment
• an exercise in measurement of an effect rather than
as a criterion-guided process for deciding whether
an effect is present or not
• A cause of a disease is an event, condition,
characteristic, or combination of these factors which
plays an important role in producing the disease
• A cause could be sufficient or necessary
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5. CAUSE VS CAUSAL INFERENCE
CAUSE
• is something that
produces or occasions
an effect
CAUSAL INFERENCE
• is the thought process
that tests whether a
relationship of cause to
effect exists
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6. SUFFICIENT CAUSE
• A cause is termed sufficient when it inevitably/certainly
produces or initiates a disease. •
• It is not usually a single factor, but often comprises
several components.
• e.g. cigarette smoking is one component of the sufficient cause in
lung cancer
• But it is not necessary to identify all the components of a
sufficient cause before effective prevention can take
place
• B/c the removal of one component may interfere with the
action of the others and thus prevent the disease
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7. FEATURES OF THE SUFFICIENT-COMPONENT
CAUSE MODEL
1. A cause is not a single component, but a minimal set of conditions or events that inevitably produces
the outcome.
2. Each component in a sufficient cause is called a component cause
3. There may be a number of sufficient causes for a given disease or outcome
4. A component cause that must be present in every sufficient cause of a given outcome is referred to as
a necessary cause. E.g., HIV exposure is necessary for AIDS to occur, and TB exposure is necessary
for TB infection to occur.
5. The completion of a sufficient cause is synonymous with the biologic occurrence of the outcome, e.g.,
the transition to a malignant cancer within a single cell marks the biologic onset of the cancer.
6. The components of a sufficient cause do not need to act simultaneously; they can act at different
times. E.g., , a mutation in a proto-oncogene in a prostate cell may promote cell replication at one
point in time, and it may be some time later when another mutation diminishes the function of an
anti-oncogene in the same cell. Thus, each component cause may have a different induction period
(the interval between the exposure's presence and disease onset)
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8. NECESSARY
CAUSE
• A cause is termed necessary if a
disease cannot develop in its
absence
• Each sufficient cause has a
necessary cause as a
component.
• E.g., M.tuberculosis
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9. LIMITATIONS
1.Determining causation are inadequate
2.The causative organism may disappear when the
disease develops
3.Certain micro-organisms cannot (currently) be
grown in pure culture
4. Not all organisms exposed to an infectious agent
will acquire the infection.
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10. TYPES OF FACTORS IN
CAUSATION
• Four types of factors play a part the causation of disease
• All may be necessary but will rarely be sufficient to cause a
disease
1.PREDISPOSING FACTORS: create a state of susceptibility to a
disease agent
• e.g., age, sex, previous illness
• These may have no direct bearing on the cause of the disease,
but they aid other risk factors e.g., salivary gland diseases for
caries development.
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11. TYPES OF FACTORS…
2.ENABLING FACTORS
Environmental conditions which favor the development of
disease
E.g. low income, poor housing, poor nutrition, inadequate
medical facility
3.PRECIPITATING FACTORS
Specific or noxious agent, exposure to which can be
associated with the onset of a disease
E.g. pollens in asthmatic attack
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12. TYPES OF FACTORS…
4.REINFORCING FACTORS
Factors which aggravates an already established
disease or state e.g. repeated exposure and unduly
hard work
The term Risk factors are those factors that have a
direct link to the cause of the disease but are not
sufficient to cause the disease i.e. they heighten the
chance of contacting a disease condition but
themselves not enough
e.g. Refined sugar, salt, saturated fat, prolonged time,
bacteria for caries
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13. INTERACTION
• The effect of two or more causes acting together is
often greater than would be expected on the basis of
individual effects
• Two or more causes acting together to amplify (greater
than additive) the intensity of the effect produced
• • E.g. risk of cancer in smokers exposed to asbestos is
greater than the summation of effect of each of the
factor
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14. ESTABLISHIN
G THE
CAUSE OF A
DISEASE
Causal inference
Is the term used for the process of
determining whether observed
associations are likely to be causal
The use of guidelines and the making
of judgments are involved
Before an association is assessed for
the possibility that it is causal, other
,explanations such as chance, bias
and confounding have to be excluded
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15. STEPS ???
• OBSERVED ASSOCIATION COULD IT BE DUE TO
SELECTION OR MEASUREMENT BIAS?
• COULD IT BE DUE TO CONFOUNDING ?
• COULD IT BE A RESULT OF CHANCE? COULD IT
BE CAUSAL?
• APPLY GUIDELINES AND MAKE JUDGEMENTS
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16. EVALUATION OF EVIDENCE OF
CAUSALITY…
Evaluation of Evidence of causality…
• While assessing the causal association, at least, the
following processes should be taken in to
consideration.
Develop Hypothesis
Testing Hypothesis , assess presence of
association
Use criteria to establish association (Bradford hills
criteria)
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17. EVALUATION OF EVIDENCE OF
CAUSALITY….
 Hypothesis is a testable proposition (Ho Vs HA)
 Hypothesis can be established from descriptive studies.
 Then those hypotheses tested or assessed the presence of association using the different
analytical studies.
 Steps in hypothesis testing
State the hypothesis to be tested…Select a sample and collect data….Calculate the
test statistics…Evaluate the evidence against Ho
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18. EVALUATION OF EVIDENCE
OF CAUSALITY….
• Evaluation of the presence of a valid statistical association
• Findings of epidemiological study may reflect true effect, or
alternative explanation like chance, bias, confounding or
reverse causality
The Role of Chance
o A test of “statistical significance” is performed to assess the
degree to which the data are compatible with the null
hypothesis of no association in which the “p-value” is
determined
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19. EVALUATION OF EVIDENCE OF
CAUSALITY….
The Role of Confounding
• Confounding is a third variable that distorts the observed relationship
between the exposure & outcome
Criteria for a confounding factor:
• Must be associated with the outcome of interest.
• Must be associated with the exposure of interest
• Must not be an intermediate step in the causal pathway between the
exposure and outcome
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20. EVALUATION OF EVIDENCE OF
CAUSALITY…HYPOTHESIS TESTING
• By convention, if p < 0.05 = “statistically
significant” =
not due to chance alone. Consider CI too.
 The Role of Bias
Bias is a systematic error resulting in a
mistaken estimate of an exposure/disease
relationship
Bias can be selection bias, information
bias,…
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21. GUIDELINE
S FOR
CAUSATIO
N
Bradford Hill (1965) suggested that
the following aspects of an association
be considered in attempting to
distinguish causal from non-causal
associations
1.Temporal relation
2.Plausibility
3.Consistency
4.Strength
5.Dose response relationship
6.Reversibility
7.Judging the evidence
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22. EVALUATION OF EVIDENCE
BRADFORD HILL CRITERIA
1. Strength of the association,
2. Consistency of the finding,
3. Specificity,
4. Temporal relation ship,
5. Biologic gradient/credibility of the hypothesis or,
6. Plausibility,
7. Coherence,
8. Experimental evidence and
9. Analogy
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23. EVALUATION OF EVIDENCE OF CAUSALITY….
Strength of the association
• The stronger the association is the greater the magnitude risk
observed and the less likely due to confounding
• A weak association does not necessarily indicate absence of
association and stronger association is not always causal
• E.g. strong association of down syndrome incidence with
birth rank can confounded maternal age
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24. CONSISTENCY OF FINDINGS
• Refers to the repeated observation of an association in
different populations under different circumstances
obtained from different studies
• Lack of consistency, however, does not rule out a causal
association, because different exposure levels and other
conditions may reduce the impact of the causal factor in
other causes
• E.g. Blood transfusion can cause blood born
infections…but not always
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25. SPECIFICITY
• The criterion of specificity has two variants.
• One is that a cause leads to a single effect, not multiple
effects.
• The other is that an effect has one cause, not multiple
causes.
• Specificity can be used to distinguish some causal
hypotheses from non causal hypotheses, when the
causal hypothesis predicts a relation with one outcome but no
relation with another outcome
• E.g., A study of screening using mammography, which was associated in
case-control studies with a 50% to 70% reduction in mortality from breast
tumors, within the reach of the mammography, but no reduction in
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26. TEMPORAL RELATIONSHIP
• Very essential before other criteria are considered
(plausibility, consistency and dose response relationship)
• The likelihood of a causal association is heightened when
many different types of evidence lead to the same conclusion
• The necessity that the cause precedes the effect in time.
• Cohort and interventional (best) studies are type of
observational study is best for documenting time sequence
• It is problem with case control and cross sectional studies
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27. BIOLOGIC GRADIENT
• A dose-response relationship occurs when
changes in the level of a possible cause are
associated with changes in the prevalence or
incidence of the effect
• E.g. smoking and lung cancer
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28. PLAUSIBILITY
• Refers to the scientific credibility of an
association
• Plausibility of epidemiologic association if any...
Was that causal/no-causal?
• Frequently, they are not, but causal inference must
be done nevertheless, with inputs from toxicology,
pharmacology, basic biology, and other sciences
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29. COHERENCE
• That a cause- and-effect interpretation of
an association does not conflict with
current knowledge on the natural history
and biology of disease
• It tells how well do all the observations fit
with the hypothesized model to form a
coherent picture
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30. EXPERIMENTAL EVIDENCE
• Two different observers, experimental evidence can
refer to clinical trials, to laboratory experiments with
rodents or other nonhuman organisms, or to both.
• According to Hill, however, experimental evidence
meant something else:
• “The experimental, or semi-experimental evidence
obtained from reducing or eliminating a putatively
harmful exposure and seeing if the frequency of
disease subsequently declines.
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31. ANALOGY
It analogy provides a source of more elaborate
hypotheses about the associations under study;
absence of such analogies reflects only lack of
imagination or experience, not falsity of the hypothesis
• It relates with whether there have been similar
situations in the past
• Hill reasoning by analogy from the thalidomide and
rubella tragedies made it more likely to him that other
medicines and infections might cause other birth
defects
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32. JUDGING THE EVIDENCE
• No completely reliable criteria
• Except for temporality, no criterion is
absolute
• Once temporality has been established the
greatest weight may be given to plausibility,
consistency and the dose-response
relationship
• But each criterion that is met strengthens our
assurance in reaching a judgment of
causality
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33. PROXIMAL & DISTAL
DETERMINANTS
Proximate determinants
Factors that directly influence the
risk of disease and the outcomes
of disease processes in
individuals
■ Distal (underlying) determinants
Social, economic, and cultural factors
that influence the health status of
a population by operating through
one or more of the proximate
causes
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34. CONCLUSION
• The knowledge of causation is an
integral part of epidemiology
• It enables us to make the proper
diagnosis
• Formulate the correct treatment plan &
• Take necessary measures in the
prevention of a certain disease
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