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Samenvatting Epidemiologie 3e bach BMW UHasselt
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Samenvatting van het vak Epidemiologie (HC's met opdrachten) zoals gegeven in het academiejaar 2017/2018
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ZSO1 CAUSALITYIntroduction
Epidemiology = the study of how disease is distributed in populations and the factors that influence or determine this
distribution.
Fundamental assumptions in epidemiology
1. Disease does not occur at random
2. Disease has causal and preventive factors
a. Disease is not randomly distributed throughout a population
b. Epidemiology uses systematic approach to study the differences in disease distribution in subgroups
c. Allows for study of causal and preventive factors
Milestones in epidemiology (origin)
First clinical trial (1747)
o James Lind treated 12 scorbutic ship passengers on a British navy ship with cider, vinegar, sea water,
oranges and lemon oranges and lemon patients recovered.
John Snow’s observations (1854)
o John Snow demonstrated the transmission of cholera from contaminated water via disease mapping.
o The majority of people who got ill, used the Broad Street Pump in London's Golden Square.
o He removed the pump handle from the polluted well and the spread of the disease stopped.
Lungcancer and smoking (1954)
o Prospective study: the mortality of doctors in relation to their smoking habits.
Mortalitiy and air pollution (1952)
o London Fog (5-9 december 1952)
Causal understanding
Epidemiology contributes to a causal understanding which is needed in prevention and treatment of disease.
Epidemiology can indicate causality even in the absence of a mechanistic understanding.
o Treatment of scurvy far ahead of the knowledge of vitamin C
o Association between dirty water and cholera, 44 years before Vibrio cholera was identified
,Hill criteria of causation
1. Strength of association (effect size): A small association does not mean that there is not a causal effect, but
the larger the association, the more likely that it is causal.
The stronger the effect the more plausible the causality.
2. Biological gradient (dose-response): Greater exposure should generally lead to greater incidence of the effect.
However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse
proportion is observed: greater exposure leads to lower incidence.
The higher the exposure, the higher the risk.
3. Consistency (reproducibility): Consistent findings observed by different persons in different places with
different samples strengthens the likelihood of an effect.
The same effect in other groups?
(Coherence): Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
However, Hill noted that "... lack of such (laboratory) evidence cannot nullify the epidemiological effect on
associations".
Different study types lead to the similar conclusions.
4. Specificity: Causation is likely if there is a very specific population at a specific site and disease with no other
likely explanation. The more specific an association between a factor and an effect is, the bigger the probability
of a causal relationship.
Exposure leads to specific risk.
5. Temporality: The effect has to occur after the cause (and if there is an expected delay between the cause and
expected effect, then the effect must occur after that delay).
The effect follows the cause and not vice versa.
6. Plausibility: A plausible mechanism between cause and effect is helpful.
7. Experimental evidence (intervention)
(Analogy): The effect of similar factors may be considered. A confounder is related to exposure and outcome,
but not through the causal pathway; there are different sources of bias, such as recall bias in a case-control
study.
Cause
A cause = an event, condition or characteristic that plays an essential role in producing an occurrence of the disease.
1. Sufficient cause = a factor or a combination of factors that will inevitably produce disease.
2. Component cause = a factor that contributes towards disease causation but is not sufficient to cause disease
on its own.
3. Necessary cause = any agent that is required for the development of a given disease.
Rates of head and neck cancer (per 100,000 Person-years)
NON-SMOKER SMOKER
NON-DRINKER 1 4
DRINKER 3 12
Among drinking smokers, what % of head and neck cancer is caused by smoking?
9 out of 12 cases, or 75% are attributable to smoking
Among drinking smokers, what % of head and neck cancer is caused by drinking?
8 out of 12 cases, or 65% are attributable to alcohol drinking
How can we attribute 75% of the cases to smoking and 67% to alcohol drinking among those who are exposed to both?
We can because some cases are counted more than once. Smoking and alcohol interact in some cases of head and
neck cancer, and these cases are attributable both to smoking and to alcohol drinking.
, Causal Pies
Proportion due to component cause B is 60% Proportion due to component cause I is 70%
Factor A is a necessary cause (present in all sufficient causes).
Insights from causal pies
1. Normally, an effect has more than one cause
2. Component causes interact to form a sufficient cause
3. If we sum the proportions of disease attributable to each component cause, there is no upper limit.
Induction time corresponds to the accumulation of component causes component causes will not act
simultaneously.
Catalyst factor ≠ causing factor Component cause ≠ sufficient cause
Types of cause
Predispose: age, sex, previous illness
Trigger: A factor that produces short-term physiological changes that may lead directly to onset of acute event
Interact: The effect of two or more causes acting together is often greater than would be expected on the
basis of summing the individual effects
o Smoking and exposure to asbestos ↑↑ risk of lung cancer
Chronic vs. acute effects
Acute exposures (hours to days) can trigger adverse events.
Medium term exposures (weeks to months) also can trigger adverse events.
Long-term exposures (years to decades) can contribute to a loss of physiologic reserve.
Early life exposures (perinatal and childhood) can define track for development of chronic conditions.
OPDRACHT
Pas de Hill criteria van causaliteit toe op de tekst van Bath et al. Lancet 2013.
We assessed whether mild iodine deficiency during early pregnancy was associated with an adverse effect on child
cognitive development.
1. Strength of association (effect size): odds ratio’s table 3 (p-values significant). Figure shows shape of
association.
2. Biological gradient (dose-response): our results suggest a worsening trend in cognitive outcome with
decreasing maternal iodine status (see figure).
3. Consistency (reproducibility): numbers in table 3 after adjustments don’t change that much not much
confounding. And difference between IQ and Reading don’t differ that much.
Coherence: there is a link with previous studies/literature (reference 13).
4. Specificity: IQ is influenced by environment and other genetic factors so link is not very specific.
5. Temporality: Validated, prospective cohort study (child is born after maternal period so the iron deficiency is
followed by the cognitive development). But iodine status was measured only once, not very reliable.
6. Plausibility: As a component of thyroid hormones, iodine is essential for foetal brain development, particularly
during gestation.
7. Experimental evidence (intervention): possible but not done. Ethics are ok, you can ask a certain group of
pregnant women to not take iodine supplements, but you can’t prohibit it.
Analogy: Iodine could be the direct cause or it is something that is strongly associated with iodine.
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