Preparing &
presenting
epidemiological
information: II
(Session 08)
SADC Course in Statistics
Learning Objectives
At the end of this session, you will be able to
• Explain why epidemiologists and other
scientists find causation complex, but key
• interpret and use concepts of types of
contribution to causal explanation
• criticise faulty claims of causal linkage
• appreciate the importance of controlled
comparative experimentation
• Recognise how observational data
contributes to scientific understanding of
causation
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Introduction to causation: 1
A big component of epidemiology is the
study of disease (or other event) causation
(“aetiology” = disease causation);
Of course ideas of cause and effect are also
important in many other areas!
Truly proving causation is very difficult in
many cases. First an easy example:
CAUSE ~ he was accidentally shot through
the heart with a high-calibre bullet; EFFECT
~ he died.
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Introduction to causation: 2
CAUSE? ~ he was knocked over by a cyclist;
EFFECT ~ he died. This seems less
inevitable! Maybe the cycle accident was an
immediate part of the causation, but the
victim was susceptible at the time because
he was 85 years old, had weak bones, and a
history of heart problems.
“The cause” is in several parts; one was
immediate; others were “pre-existing” “predisposing”; factors can also be “enabling”,
“precipitating” or “reinforcing”.
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Introduction to causation: 3
CAUSE? ~ he had been HIV positive for five
years; EFFECT ~ he died of tuberculosis.
Tuberculosis was perhaps immediate cause
of death. Untreated HIV predisposed him,
in the sense of lowering immune response,
but was not sufficient to cause disease or
death ~ he had to be infected, nor was it
necessary ~ people without HIV contract
TB, and can die if not effectively treated.
Causation is often a complicated web.
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Introduction to causation: 4
Causal description is often extended to
(1) Surrogate causes ~ the age of the cycle
accident victim is not a direct cause, but it
is a easy substitute description instead of
detailing age-related muscular wastage,
bone fragility etc
(2) More distant related factors ~ the TB
victim had untreated HIV because of social
taboos preventing acknowledgment of the
illness.
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Introduction to causation: 5
(2) More distant related factors can often be
explained in differing terms. Maybe (rather
than taboo) the victim was too poor to pay
doctors’ fees to get prescriptions, or to pay
for transport to the clinic.
The complex web of causation often spreads
indefinitely far back from the immediate
effect, but agreeing on, & seeking evidence
about, more distant determinants is often
impossible … e.g. murder mystery if that
shooting was perhaps not an accident.
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Critiquing causal argument
In ordinary life, and in non-expert interpretation of quantitative evidence, the most
common mistake is jumping to conclusions ~
logical gaps in reasoning:
(i) claiming to know a cause when evidence
does not prove cause/effect linkage beyond
reasonable doubt;
(ii) failure to see, and weigh up, other possible
explanations;
(iii) interpreting evidence as telling you about
things it does not actually state or cover.
Have you ever been wrongly accused..?
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Experiments & direct causation
If you can carry out a controlled experiment,
say on the efficacy of a drug treatment,
you may build up consistent, repeated,
long-term evidence that one treatment
works.
“Control” here implies that all other factors
you can think of have been accounted for
so they could not interfere in the overall
interpretation of the results.
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Experiments – clinical trial design
In a controlled, comparative clinical trial
(CCT) you might administer standard drugs
and treatment regime to a “control” group
of disease patients, while administering a
new experimental drug and the same
quality of care to an “experimental” group
with matched characteristics (average
disease duration and severity, ages, sex
mixture, other concurrent illnesses and
general medical history)
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Experiments – clinical trial data
Given your two parallel groups you look at
evidence of clinical outcomes e.g. more of
experimental group survive, they get well
sooner, they have fewer adverse side
effects and tolerate treatment with fewer
dropouts.
If all above favour the new treatment, is
there statistically significant evidence i.e.
more difference than might arise by chance
(see Higher modules) … and can it be
afforded?
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Cause-effect inference from CCTs
If after careful independent professional
review, CCT results are promising, it is
usual for repeat trials to be carried out in
several different settings e.g. different
countries, different disease severities, age
groups etc
This is to demonstrate that the promising
results are consistently reproducible, and
to define the range of cases for which the
treatment can be recommended.
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Observational study
CCT experimental approach is enormously
expensive, partly as huge professional
effort goes into ensuring the comparison is
fair, cases representative, other causal
factors excluded or balanced out.
This is often impossible and we use other
methods without the controls. Naturalistic
observation implies that we look at what
happens in real life, where various
unknown factors may be at play.
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Observation and association
Observation is generally not enough to
demonstrate causation. It is normal in
scientific work to claim only an association.
Variables are positively associated
statistically when they occur together more
frequently than would be expected by
chance.
Such association may or may not be causal;
for example two variables may be affected
by a common precursor, though not
themselves interacting at all.
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Example
There was a theory (promoted by cigarette
companies, but long discredited) that
smoking did not cause lung cancer. It was
claimed some indefinable physiological
“Factor X” had two effects, i.e. (i) causing
some people to get lung cancer, and
(ii) causing some people to want to smoke.
An experimental disproof of this would need
to force some non-smokers to start
smoking to see the outcome – ethically
impossible!
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The unethical experiment
Sample:- take a sample of 20 – 23 yr old adults
who smoke (S), and a similar who do not (D).
• Treatment:- (i) split Ss into two equivalent
groups forcing half to continue smoking (S-S),
half to stop (S-D), and (ii) forcibly split Ds
similarly as (D-D) and (D-S)... unethical!
• Result .. after 30 years:- If X is responsible
high prevalence amongst (S-S) and (S-D); if
smoking is responsible, high prevalence
amongst (S-S) and (D-S).
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Observation ~ causation links
Evans* offered a variety of challenges to the
assumption that a plausibly argued
mechanism is indeed a causal one.
His postulates are phrased in terms of
disease, but selections of the same broad
principles can profitably be applied in many
other contexts – not just epidemiological
ones
* Evans, A.S. (1976) Causation and disease. The
Henle-Koch postulates revisited. Yale Journal of
Biology and Medicine, 49, 175 – 195.
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Infer causation? Evans postulates: 1
1. The proportion of the individuals with the disease
should be significantly higher in these exposed to the
supposed cause than those who are not
2. Exposure to the supposed cause should be present
more commonly in those with than those without the
disease
3. The number of new cases of the disease should be
significantly higher in those exposed to the supposed
cause than in those not so exposed, as shown in
prospective studies
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Infer causation? Evans postulates: 2
4. Temporally, the disease should follow exposure to the
supposed cause with a distribution of incubation periods
on a unimodal, possibly skewed curve
5. A spectrum of host responses, from mild to severe,
should follow exposure to the supposed cause along a
logical biological gradient
6. A measurable host response (e.g. antibody, cancer cells)
should appear regularly following exposure to the
supposed cause in those lacking this response before
exposure, or should increase in magnitude if present
before exposure; this pattern should not occur in
individuals not so exposed
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Infer causation? Evans postulates: 3
7. Experimental reproduction of the disease should occur
with greater frequency in animals or man appropriately
exposed to the supposed cause than in those not so
exposed; this exposure may be deliberate in volunteers,
experimentally induced in laboratory, or demonstrated in
a controlled regulation of natural exposure
8. Elimination (e.g. removal of a specific infectious agent)
or modification (e.g. alteration of a deficient diet) of the
supposed cause should decrease the frequency of
occurrence of the disease
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Infer causation? Evans postulates: 4
9. Prevention or modification of the host’s response (e.g.
by immunisation or use of the specific lymphocyte
transfer factor in cancer) should decrease or eliminate
the disease that normally occurs on exposure to the
supposed cause
10. MOST IMPORTANT OF ALL:All relationships and associations should be biologically
and epidemiologically credible, preferably more so than
any other explanation, and consistent with the best
modern scientific understanding
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Further reading
1. As a practical supplement to the above,
find and read Epidemiology Supercourse
lecture 19091 “Measures of Association”
by Dr. Thomas Songer.
2. To reinforce and deepen understanding of
“Cause and effect: the epidemiological
approach”, find and read lecture 18481 by
Prof. Raj Bhopal.
These are well-structured, not hard to
follow, and very informative ~ enjoy!
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Practical work follows to
ensure learning objectives
are achieved…
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