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Cause
Clinicians are confronted frequently with
information about possible causal
relationships.
Example:
Relationship between the cigarette smoking
habits of obstetricians and vigor of babies
they delivered.
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Cause:anything producing an effect or a
result
 Cause is discussed under such heading as
“etiology “,” pathogenesis” ,”mechanism” ,
“risk factors”.
 Cause is important to practicing physicians
primarily because it guides their approach to
three clinical tasks: prevention , diagnosis
and treatment.
 Belief in a causal relationship underlies every
therapeutic intervention in clinical medicine.
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Koch set forth postulate for determining that
infectious agent is the cause of a disease . Basic
to his approach was the assumption that a
particular disease has one cause and a particular
cause results in one disease.
The organism must be present in every case of
the disease;
it must be isolated and grown in pure culture;
it must cause a specific disease when inoculated
into an animal;
it must then be recovered from the animal and
identified.
Usually many factors act together to cause
disease in what has been called the “web of
causation.”
 A causal web is well understood in conditions
such as coronary artery disease, but is also
true for infectious disease, where presence of
the organism is a necessary cause for disease
to occur but not necessarily a sufficient cause
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When biomedical scientists study cause , they
usually search for the underlying
pathogenetic mechanism or final common
pathway of disease.
 Disease is also determined by less specific
,more remote cause, or risk factors such as
peoples behavior or characteristic of their
environments.
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Even when the pathogenetic mechanism is
not clear, knowledge of strong risk factors
may still lead to effective treatment and
preventions.
 For many diseases , both pathogenetic
mechanism and nonspecific risk factors have
been important in the spread and control of
the disease.
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In fact, social and economic improvements
influencing host susceptibility ,such as less
crowded living space and better nutrition may
have played a more prominent role in the
decline in TB rates in developed countries
than treatments created through the
biomedical pathogenentic research model.
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When more than one cause acts together, the
resulting risk may be greater or less than
would be expected by simply combining the
effects of the effects of the separate causes .
this is called interaction.
Clinicians call this phenomenon synergism
when the joint effect is greater than the sum
of the effects of the individual causes and
antagonism when it is less. Sometimes, the
term biologic interaction is used to
distinguish it from statistical interaction.
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Interaction is often expressed as effect
modification when the strength of the
relationship between two variables is different
according to the level of some third variable ,
called an effect modifier.
It is only possible to increase ones conviction
of a cause-and-effect relationship by means
of empiric evidence to the point at which for
all intents and purposes , cause is established
 A postulated cause-and-effect relationship
should be examined in as many different
ways as possible
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Two factors _ the suspected cause and the
effect- obviously must be associated if they
are to be considered as causally related
however , not all association are causal.
 Selection and measurement biases and
chance can give rise to apparent associations
that do not exist in nature.
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When considering a possible causal
relationship , the strength of the research
design used to establish the relationship is an
important piece of evidence.
The best evidence for a cause-and –effect
relationship come from well conducted randomized
controlled trials , with adequate numbers of
patients , blinding of therapists ,patients and
researchers ;limited or no loss to follow_up ; and
carefully standardized methods of measurement
and analysis.
Clinicians ordinarily rely on RCTs to provide
evidence about causal relationship for
treatments and prevention.
 In general,the further one departs from
randomized trials,the less the research
design protects against possible biases and
the weaker the evidence is for a cause-andeffect relationship.
 Well-conducted cohort studies are the next
best design.
 Weakest of all studies are case series.
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Studies in which exposure to a risk factor is
characterized by the average exposure of the
group to which individuals belong are called
aggregate risk studies (ecological studies ).
Example:relationship between wine
consumption and cardiac mortality in
developed countries.
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Aggregate risk studies are rarely definitive in
and of themselves. The main problem is a
potential bias called the ecological fallacy,
which affected individuals in a generally
exposed group may not them selves have
been the ones exposed to the risk factor.
In a time-series study , the effect is measured
at various points in time before and after the
purported cause has been introduced
 If changes in the purported cause are
followed by changes in the purported
effect,the associations is less likely to be
spurious.
 An advantage of a time_series analysis is that
it can distinguish between changes occurring
over time from the effects of the intervention.
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In a time-series study, the suspected cause is
introduced into several different groups at
different times. Measurements are then made
among the groups to determine whether the
effect occurred in the same sequential
manner in which the suspected cause was
introduced.
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In 1965 the British statistician proposed a
set of features that should be sought when
deciding whether a relationship between
some environmental factor and a sickness is
causal or just an association.
Causes should obviously precede effects.
 Sometimes,however, the principal can be
overlooked when interpreting cross-sectional
and case-control studies,in which both the
purported cause and the effect are measured
at the same point in time.
 Although it is absolutely necessary for a
cause to precede an effect, an appropriate
temporal sequence alone is weak evidence for
cause.
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A strong association between a purported
cause ,as expressed by a large relative or
absolute risk , is better evidence for a causal
relationship than a weak association . Thus ,
the strength of association between smoking
and lung cancer is much greater than
smoking and renal cancer.
A dose-response relationship is present if
increasing the exposure to the purported
cause is followed by a larger and larger
effect.
 Although a dose-response curve is good
evidence for a causal relationship, especially
when coupled with a large relative or absolute
risk, its existence does not exclude
confounding factors.
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A factor is more likely to be a cause of
disease when ever its removal results in a
decreased risk of disease.
 Reversible associations are strong , but not
infallible , evidence of a causal relationship .
confounding could conceivably explain a
reversible association.
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When several studies,conducted at different
times indifferent settings and with diffirent
kinds of patients, all come to the same
conclusion , evidence for a causal relationship
is strengthened.
 It is often the case that different studies
produce different results. Lack of consistency
does not necessaily mean that the results of a
particular study are in valid . one good study
should outweigh several poor ones.
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The assertion that cause and effect is
consistent with our knowledge of the
mechanism of disease , as it is currently
understood , is often given considerable
weight when causation is being assessed.
 It is important to remember , however , that
what is considered biologically plausible
depends on the state of medical knowledge at
the time.
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It is more often found for acute infectious
disease(such as polio myelitis and tetanus)
and for genetic disease(such as FAP ,
ochronosis , and PKU).
 The presence of specificity is strong evidence
for cause,but the absence of specificity is
weak evidence for cause,but the absence of
specificity is weak evidence against a causeand-effect relationship.
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The argument for a cause-and-effect
relationship is strengthened when examples
exist of well-established causes that are
analogous to the one in question.
 In general ,analogy is weak evidence for
cause.
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When determining cause, one must consider
the evidence from all available studies.After
examining the research design and quality
and the elements for and against cause ,the
case for causality can be strengthened or
eroded . This calls for a good deal of
judgment , especially when the evidence from
different studies is conflicting.
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All relevant studies are reviewed according to
specific criteria. This systematic review is
then used to determine the strength of the
evidence for the causal relationship.