Causation in epidemiology

CAUSATION IN
EPIDEMIOLOGY
SOYEBO O.A.

OUTLINE
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INTRODUCTION.
CONCEPT OF CAUSE
SINGLE AND MULTIPLE CAUSES.
FACTORS IN CAUSATION.
INTERACTION.
GUIDELINES FOR CAUSATION.
CONCLUSION.

INTRODUCTION

• A major goal of epidemiology is to assist in the prevention
and control of disease and in the promotion of health by
discovering the causes of disease and the ways in which
they can be modified.

CONCEPT OF CAUSE
• An 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.
• 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

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.
• In general, it is not necessary to identify all the components of
a sufficient cause before effective prevention can take
place, since the removal of one component may interfere with
the action of the others and thus prevent the disease.

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.

SINGLE AND MULTIPLE CAUSES
• Pasteur’s work on microorganisms led to the formulation, first by Henle and then by
Koch, of the following rules for determining whether a specific living organism causes
a particular disease:
 it must be present in every disease case.
 Must be able to be Isolated and grown in pure culture.
 Cause specific disease when inoculated in susceptible animal.
 it must be recovered from the animal and identified.

• A given disease can be caused by more than one causal
mechanism, and every causal mechanism involves the
joint action of a multitude of component causes.

LIMITATION
• Anthrax was the first disease demonstrated to meet these rules which
have proved useful with some other infectious disease but for most
disease (both infectious and non-infectious) , Koch’s rules for determining
causation are inadequate.
 The causative organism may disappear when the disease develops.
 Certain micro-organisms cannot (at the present time) be grown in pure
culture.
 Not all organisms exposed to an infectious agent will acquire the infection.

FACTORS IN CAUSATION
• Four types of factor play a part the causation of disease.
All may be necessary but will rarely be sufficient to cause
a disease.
• 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.

• ENABLING FACTORS: environmental conditions which
favor the development of disease. E.g. low income, poor
housing, poor nutrition, inadequate medical facility.
• PRECIPITATING FACTORS: specific or noxious
agent, exposure to which can be associated with the onset
of a disease. E.g. pollens in asthmatic attack.

• 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, time, bacteria for caries

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 factors.

ESTABLISHING 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.

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

GUIDELINES FOR CAUSATION
• Bradford Hill (1965) suggested that the following aspects of an
association be considered in attempting to distinguish causal
from non-causal associations:
 Temporal relation
 Plausibility
 Consistency

 Strength
 Dose response relationship
 Reversibility
 Judging the evidence

TEMPORAL RELATIONSHIP
• refers to the necessity for a cause to precede an effect in time.
• This is usually self-evident, although difficulties may arise in
case-control and cross sectional studies when measurements
of the possible cause and effect are made at the same time
and the effect may in fact alter the exposure.

• E.g. use of seat belt

PLAUSIBILITY
• An association is plausible and more likely causal if
consistent with other knowledge.
• Problem with plausibility: it is too often not based on logic
or data, but only on prior beliefs. Lack of which may be a
simple reflection of medical knowledge.

CONSISTENCY
•

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.

STRENGTH
• Hill’s argument is that strong association between
possible cause and effect are more likely to be causal than
weak associations .
•

The fact that an association is weak does not rule out a
causal connection. example would be passive smoking
and lung cancer.

DOSE-RESPONSE RELATIONSHIP

• 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

REVERSIBILITY
• when the removal of a possible cause results in a reduced
disease risk, the likelihood of the association being causal
is strengthened.
• Cessation of smoking reduces the risk of developing lung
cancer

JUDGING THE EVIDENCE
• There’s no completely reliable means of establishing a causal
relationship and sometimes evidence can be conflicting. To
make a causal inference, all available evidence must be
considered.
• Correct Temporal relationship is very essential before other
criteria are considered (plausibility, consistency and doseresponse relationship). The likelihood of a causal association
is heightened when many different types of evidence lead to
the same conclusion

CONCLUSION

• The knowledge of causation is an integral part of
epidemiology as it enables us to make the proper
diagnosis, formulate the correct treatment plan and take
necessary measures in the prevention of a certain disease.

Causation in epidemiology

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Causation in epidemiology