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MSc Anaesthesia
Module: Basic Principles of Clinical Research 2004/05
Lecture 4
Causation in Epidemiological Research
TITLE SLIDE
In epidemiological research we often aim at showing causal relationships
between risk factors and health. An understanding of the causes for disease is
necessary for effective preventive, diagnosis and the application of correct treatment
methods. But when can we actually say that an association is causal?
SLIDE 2: LEARNING OBJECTIVES
•
Understand the definition of a causal factor for health and illness
•
Differentiate between causal factors and mechanisms for disease
•
Learn criteria how to practically determine causality
•
Be able to apply criteria for causality to an exampleSLIDE 3: WHY
BOTHER?
The aim of randomised controlled trials usually is to demonstrate that a
particular treatment “causes” health improvements. Establishing causation is not only
crucial for determining the best treatment it also proves the legitimacy for health
protection policies. For example, the recently implemented smoking ban in
workplaces was introduced on the grounds of epidemiological evidence that showed
causality between environmental tobacco exposure and health. Also environmental
standards such as prohibition of working with asbestos or special precautions when
working with noxious agents are usually underpinned by epidemiological research
evidence. Established causality can also guide disease prevention and health
education programmes. For example, if we know what predisposes for disease we can
screen for it in the population and can educate the general population about risk
factors for specific diseases. Established causality might also have implications for
legal action and litigation. Examples include law suits against tobacco companies in
the United States by patients with lung cancer and against the company that runs the
Sellafield nuclear power plant in the UK by patients with leukaemia.
SLIDE 4: A CAUSE
The most commonly used definition is provided by Rothman (1998) (see slide
4). One of the main components of the definition of a causal factor is that is must
precede the disease event. This is sometimes self-evident, however sometimes not
easy to establish because causation can go in both directions. For example, studies
have found that unemployed workers are more likely to be depressed than employed
workers. However, what came first? Did loosing the job precede the onset of
depression? Or did workers loose their job because of depression?
A naïve concept of causation states that there is just one causal agent, e.g. a
bacteria, that inevitably causes the onset of a disease. However diseases are usually
caused by a web of interacting causal factors, from which mechanisms of disease need
to be differentiated.
SLIDE 5: CAUSES OF CHOLERA
For example, the exposure to contaminated water is not the only causal factor
for the onset of cholera. Socio-economic conditions including poverty, malnutrition,
crowded housing and in addition genetic predispositions lead to increased
susceptibility for the cholera vibrio in infected water. In distinction to these causal
factors, the biological mechanisms for cholera to develop after the risk factors are
present include the ingestion of the cholera vibrio and its effects on the bowel wall
cells.
SLIDE 6: FACTORS IN CAUSATION
Four types of factors play a part in the causation of disease. Usually the term “risk
factor” is used for conditions that are associated with the risk of disease development.
One risk factor alone is rarely sufficient to cause a particular disease or state.

Predisposing factors such as age, gender and previous illness create a state of
heightened susceptibility to a disease agent

Enabling factors such as low income, poverty, poor nutrition, and poor medical
care favour the development of disease. Conversely, circumstances that assist in
recovery from disease or in the maintenance of good health can be called enabling
factors.

Precipitating factors such exposure to a specific noxious or disease agent may
lead to onset of the disease

Reinforcing factors such as repeated exposure to stress might aggravate an
established disease or state
SLIDE 7: THE WEB OF CAUSATION
A cause is termed sufficient when it inevitably produces or initiates a disease.
A sufficient cause is usually not a single factor but comprises different component
causes. A cause is necessary if a disease cannot develop in its absence. Necessary
causes usually include bacteria, viruses or noxious agents.
For example smoking causes lung cancer; however smoking is not a sufficient
cause in itself but a component of a sufficient cause. By itself it does not inevitably
lead to lung cancer. Many smokers smoke heavily over 50 years without developing
this disease, other components are required. However the cessation of smoking
reduces the risk for lung cancer even if the other components cases are not altered.
SLIDE 8: EXAMPLE
Imagine you are presented with results of a case-control study showing that
women hospitalised with breast cancer are more likely to have a family history of
breast cancer as compared to women hospitalised for other reasons breast cancer. Can
family history be considered as cause for breast cancer?
SLIDE 9: ASSESSING THE RELATIONSHIP BETWEEN A POSSIBLE
CAUSE AND A HEALTH OUTCOME
When we find an association between a risk factor and a disease how do we
know that the association is causal? Slide 8 shows an algorithm for examining
associations. The questions are:

Could this association be produced by selection or measurement bias?

If not, could it be produced by confounding?

If not, could it be a chance result?
Only if all the above alternative explanations are excluded we can consider causality
and apply specific criteria that establish causality (see next slides).
For our example:
First we have to take into consideration whether the association between family
history and breast cancer could have been spuriously produced by bias.

Selection bias could be present in this study because women with a family history
of breast cancer may be more likely to conduct breast self-examinations and to
seek medical attention as compared to women without such a family history. Thus
breast cancer might be detected earlier and to a higher proportion in women with
a family history. This bias would lead to an overestimation of the difference in
breast cancer prevalence between the cases and the controls because some of the
controls, although being ill, might not be diagnosed.

Measurement bias could be present because women with a family history might
be referred to elaborate diagnosis methods such as mammograms and biopsies by
their GPs compared to women without such a family history. Thus the diagnosis
of breast cancer would be more accurate and misclassification of symptoms as
benign less likely in women without a family history. This differential
measurement bias would also lead to an overestimation of the association
between family history and breast cancer.

The association could also be spuriously inflated by a confounder and be
explained by nutrition or healthy life style. Since healthy diets and lifestyles run
in families, these factors may be the “true” causes rather than family history in the
sense of a genetic predisposition.

Chance: The result of this one study could be just a chance finding. It needs to be
checked whether the found association was statistically significant (p-value) and
whether the estimate of the Odds Ratio was precise (confidence interval).
If all the above alternative explanations can be excluded we may consider the
association a causal one and can test the criteria for causation
SLIDES 10-11:
GUIDELINES FOR CAUSATION
A commonly used set of criteria was proposed by Hill (1965); it was an
expansion of a set of criteria offered previously in the landmark U.S. Surgeon
General’s Report on Smoking and Health that officially established causality between
smoking and lung cancer. These guidelines were subsequently slightly modified. The
list provided here was taken from Beaglehole, Bonita and Kjellström (1993).
Temporal relationship: Does the cause precede the effect? This is often self-evident,
although difficulties may arise in case-control studies because risky behaviour is often
altered if first pre-clinical symptoms show or upon onset of the disease. For example,
patients might stop smoking when they have breathing difficulties, eventually
resulting a high proportion of current non-smokers with lung cancer. Prospective
cohort studies with repeated measurements can strengthen the evidence.
Plausibility: Is the association plausible and underpinned by potential biological or
psychological known theory and mechanisms? For example, laboratory experiments
could have shown how exposure to the particular factor results in physiological
changes that may lead to a particular illness. However, lack of plausibility may simply
reflect lack of medical knowledge.
Consistency: Is the association consistent with associations found in other studies?
The more studies in different settings and with different populations show similar
results the less likely it is that all studies are making the same mistake. However lack
of consistency does not exclude a causal relationship since differences in exposure
levels and settings might have reduced produced differences in results.
Using systematic reviews and meta-analyses is a way of examining the consistency of
the evidence of studies addressing the same hypothesis. In meta-analysis, welldesigned studies with a strong study design are given the greatest weight.
Strength of the association: The magnitude of an association between a potential
cause and an effect is usually measured by a relative risk ratio or an odds ratio. A
strong association is more likely to be causal than a weak association, which could be
influenced by confounding or bias. However the fact that an association is weak does
not preclude it from being causal. Some causal factors are very difficult to measure.
For example information about diet or alcohol consumption are usually based on selfreports, which are often biased. In additions, these factors vary considerably over
time, which dilutes the association with a health outcome.
It is also important to review what is considered a strong association. Some
textbooks suggest that Relative Risk Ratios or Odds Ratios below 2 should be
dismissed. This is misleading because the magnitude of effect size needs to be judged
in the context of the unit of measurement. Even small ratios can be of high clinical
importance. For example, an Odds Ratio of 1.14 between age and hypertension means
one-year age increase multiplies the odds of hypertension by a factor of 1.14 or by
14%. This is substantial considering the small unit of analysis of one year. If we had
measured age in 10-year increments, the odds ratio would have been 3.6 indicating
that growing 10 years older multiplies the odds of hypertension by a factor of 3.6.
Dose-response relationship (biological gradient): A dose-response relationship
refers to the presence of a monotonic (unidirectional) dose-response curve indicating
a heightened disease risk with heightened exposure levels, or a heightened disease
risk with decreasing exposure levels. For example, more smoking means more
carcinogen exposure and more tissue damage, hence more opportunity for
carcinogenesis. Slide 12 shows an example of a biological gradient.
Reversibility: When the removal of the risk factors results in a reduced disease risk,
the likelihood of the association being causal is strengthened. For example, the
cessation of cigarette smoking is associated with a risk reduction of lung cancer
relative to people who continue smoking. If the cause leads to irreversible changes
that produce disease, then reversibility cannot be a condition for causality.
Study design: Is the evidence based on studies using strong study designs? Usually
experimental research is considered the strongest design for establishing causation.
However experimental designs are not always feasible. Slide 13 shows the ability
study designs to demonstrate causation.
SLIDE 11: JUDGING THE EVIDENCE
No empirically found association between a potential causal factors and an
effect will completely satisfy all criteria mentioned above. The evidence for a causal
relationship is heightened if many lines of evidence lead to the conclusion. Causal
inference is usually tentative and implies judgement of the scientific community. The
judgement must be made based on the available evidence, however uncertainty
always remains.
In judging the different aspects of causation referred to above, the correct
temporal relationship is essential, especially if reverse causation is a possible
alternative explanation. The importance of the other criteria depends on the risk factor
and the outcome under investigation. For example, a gradient between the risk factors
and the disease outcome (dose-response relationship) does not make sense for some
associations. In new areas of research, consistency of results might not be an issue due
to lack of publications addressing the same issue. Also the strength of associations
found in different studies need to be judged taking measurement difficulties for some
exposures and outcomes into account.
Initially Hill had included the criteria “specificity of association”, “coherence
with existing knowledge” and “analogy” into the guidelines. They were omitted here,
because they have proven less useful.
SLIDE 12: EXAMPLE FOR A DOSE-RESPONSE RELATIONSSHIP
SLIDE 13: ABILITY OF STUDY DESIGNS TO ESTABLISH CAUSATION
SLIDE 14: CAUSAL FACTOR, CAUSAL LINK OR MECHANISM?
In some research areas it is difficult to differentiate between a causal agent, a
causal link or a disease mechanism. Ultimately it depends on the theory and the
conceptual framework of the researchers.
For example we know that arteriosclerosis is a risk factor for heart disease. Is
arteriosclerosis a causal factor or a mechanism of disease? We could take a wider
perspective and postulate that a smoking is a causal factor leading to arteriosclerosis,
which is in turn the mechanism of disease leading to heart disease. We could even go
one step further and hypothesize that smoking is caused by other factors such as
stress. In the latter model stress is considered as the causal factor, whereas smoking
constitutes a causal link in the association between stress and arteriosclerosis. This
example shows that it is essential to clearly conceptualise all potentially contributing
factors and specify their potential relationship with the outcome.
Assignment questions:
1. Results obtained in a cohort study suggest that persons regularly smoking
marihuana are more likely to develop schizophrenia than persons who do not smoke
marihuana. Discuss how the results could have been affected by measurement bias,
selection bias and confounding.
2. Take an example of an association between a risk factor and a health outcome in
anaesthesiology and discuss causality using 3 of the Hill criteria for causality.