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Cause or merely association?

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Cause or merely association? ..explain what is meant by a cause-effect relationship in an epidemiological context ..recognise that associations may be present ... – PowerPoint PPT presentation

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Title: Cause or merely association?


1
Cause or merely association?
  • ..explain what is meant by a cause-effect
    relationship in an epidemiological context
  • ..recognise that associations may be present in
    the absence of a true cause-effect relationship
  • ..describe why it is important to distinguish
    causal from non-causal associations
  • ..evaluate the strength of evidence in favour of
    a cause-effect relationship

2
  • Causes of TB
  • Poor living conditions
  • Overcrowding
  • Poverty
  • Lowered immunity
  • Poor nutrition
  • Being debilitated in old age
  • HIV
  • Mycobacterium Tuberculosis
  • Causes of Measles
  • Measles virus

3
Causality
  • A cause is termed sufficient when it inevitably
    initiates or produces the disease.
  • A cause is termed necessary when it must always
    precede a disease
  • Any given cause may be necessary, sufficient
    neither or both!

4
Four conditions where X may cause Y
X is necessary X is sufficient Example
1 Measles and the Measles virus
2 - Tuberculosis and the Tubercle Bacillus
3 - Lung cancer and radon
4 - - Tuberculosis and poor living conditions
5
Exposures do not have to be necessary OR
sufficient causes of disease to be important
  • Alcohol/cirrhosis
  • Radiation/leukaemia
  • Smoking/heart disease
  • Traffic speed/pedestrian accidents

6
1. Explain what is meant by a cause-effect
relationship in an epidemiological context
  • Disease results from the interplay of factors
    from Host, Environment Agent.
  • In epidemiology a cause is an exposure/factor
    which increases the probability of disease.
  • Exposures do not have to be necessary OR
    sufficient to be important causes.
  • The aim is to use the knowledge to remove, avoid
    or protect against harmful factors.

7
2. Recognise associations may be present in the
absence of a true cause- effect relationship
8
Cohort Study
  • Start with Disease free individuals
  • (sometimes go back in time to do this)
  • Monitor exposures of interest
  • Measure frequency of occurrence of disease in
    exposed and non-exposed individuals
  • Incidence rate ratio
  • Is there an association between exposure and
    developing the disease?

9
Case Control Study
  • Start with cases of disease
  • Get controls (up to 5) for each case
  • Investigate exposures of interest in the past
  • Odds ratios
  • Is there an association between being a case and
    the exposure?

10
Epidemiological Reasoning-
  • 1. Hypothesis
  • Resulting from observations in clinical practice
    /lab research/surveillance/previous
    studies/theorising
  • 2. Analytical Study
  • To test the hypothesis
  • 3. Observed association
  • Test the validity of the observed association by
    excluding alternative explanations
    chance/bias/confounding

11
Chance
  • Any result could be due to chance
  • statisticians can estimate how big a role chance
    might have played
  • the results are stated and qualified according to
    how much might be due to chance
  • 95 confidence intervals
  • P value

12
95 confidence interval
  • With the data from this study, THIS observed
    value is the most likely estimate of the real
    underlying true odds ratio/incidence rate ratio
  • AND
  • We can be 95 sure that the real population value
    lies within THIS range
  • If the null value lies within this range (and the
    study was a reasonable size) then it is more
    likely that there is no true difference between
    the groups we have studied and the observed
    result was just due to chance

13
P value
  • The P value states how likely the results you
    have in your study would occur by chance if the
    null hypothesis were true
  • P 0.05 means that if there was no difference
    your results would occur completely by chance 5
    studies in 100
  • i.e. not that likely to be due to chance so
    there might well be a real difference
  • if Plt 0.05 it can be thought of as equivalent to
    the null value being outside the 95 confidence
    interval

14
Bias
  • Deviation of results or inferences from the truth
    or processes leading to such deviation
  • Any trend in the collection, analysis,
    interpretation, publication or review of data
    that can lead to conclusions that are
    systematically different from the truth

15
Bias can occur at any stage
  • Selection bias
  • Volunteers
  • Healthy worker effect
  • Controls from the same clinic in a hospital
  • Information bias
  • Cases who know the putative risk factor
  • Stigma attached to the true answer
  • Important to exclude bias at the design stage
    because you cannot do it later

16
Dealing with bias
  • Care with selection of controls
  • Care with questions used to ask about risk
    factors
  • Consider blinding investigators and subjects to
    the hypothesis
  • Check data collected with independent records
    made at the time

17
Confounding
  • The illusory association between 2 variables when
    in fact no association exists
  • It is caused by a third variable the confounder
    - which is associated with the first 2 variables
  • i.e. with both the exposure and the outcome

18
Are people who wet their bed at night more likely
to use bifocals?
Nocturnal eneuresis Nocturnal eneuresis Nocturnal eneuresis Nocturnal eneuresis Nocturnal eneuresis
Use of Bifocals Present Absent
Use of Bifocals YES 17 83 100
Use of Bifocals NO 8 92 100
Use of Bifocals 25 175 200
Odds Ratio 1.93 Odds Ratio 1.93 Odds Ratio 1.93 Odds Ratio 1.93 Odds Ratio 1.93
19
Dividing the subjects by age..
Nocturnal eneuresis aged lt60yrs Nocturnal eneuresis aged lt60yrs Nocturnal eneuresis aged lt60yrs Nocturnal eneuresis aged gt60yrs Nocturnal eneuresis aged gt60yrs Nocturnal eneuresis aged gt60yrs
b i f o c a l s Present Absent Present Absent
b i f o c a l s yes 1 19 20 16 64 80
b i f o c a l s no 4 76 80 4 16 20
b i f o c a l s 5 95 100 20 80 100
b i f o c a l s Odds ratio 1 Odds ratio 1 Odds ratio 1 Odds ratio 1
.no association .no association .no association .no association .no association .no association .no association .no association
20
  • Smoking confounds associations of social
    class/deprivation as a risk factor with diseases
  • Smoking strongly linked with lower social
    class/increasing deprivation (the exposure)
  • Smoking causes many diseases (the outcomes)
  • Solution is to stratify or correct using other
    statistical methods for known confounders
  • BUT there are probably many unknown and as yet
    unsuspected confounders.

21
  • An association is statistical dependence between
    2 or more events, characteristics or other
    variables
  • The presence of an association does not
    necessarily imply a causal relationship

22
Association between factor X and factor Y
  • Unknown confounder making it look as though X
    causes Y
  • i.e. not a true association
  • Causal association X does cause Y
  • Reverse causality Y causes X
  • can be a problem in case control studies
  • Factor A causes both X and Y
  • smoking causes chronic bronchitis and lung cancer
    but it might look as though chronic bronchitis
    causes lung cancer

23
2. Recognise that associations may be present in
the absence of a true cause-effect relationship
  • Hypothesis
  • Study to test the hypothesis
  • Validate any association found by excluding
    possible alternative explanations
  • Chance
  • Bias
  • Confounding
  • Could the statistical associations represent a
    cause-effect relationship between exposure and
    disease?

24
4. Evaluate the strength of evidence in favour
of a cause-effect relationship
  • How do epidemiologists attempt to establish
    causation decide whether factor A could
    possibly be the cause of disorder B?

25
Kochs Postulates (1877) to determine if an
infectious agent is the cause of a disease
  • The organism occurs in every case of the disease
  • It occurs in no other disease
  • On removal from the body and growing in pure
    culture it can induce the disease anew
  • very exacting

26
Bradford Hill proposed criteria
  • Strength of association
  • Time sequence
  • Consistency
  • Gradient
  • Specificity
  • Biological Plausibility
  • Experimental Models in Animals
  • Preventive Trials

27
Strength of association
  • Individuals who smoke heavily have a risk of
    mortality from laryngeal cancer that is 20 times
    that of non-smokers
  • this strong association increases the likelihood
    of it being cause and effect

28
Time sequence
  • The exposure of interest would HAVE to precede
    onset of disease for it to be a cause effect
    relationship, the existence of an appropriate
    time-sequence can be difficult to establish
  • Does low activity predispose to CHD OR do
    individuals with symptoms of CHD find it
    difficult to exercise?
  • Difficulty in case-control studies possible
    strength of cohort studies

29
Consistency
  • If a number of studies conducted by different
    investigators using alternative methodologies
    in different time frames and amongst different
    populations, all show similar results..
  • Cause-effect between smoking and risk of CHD
    many studies case-control and cohort millions
    of person-years of observation
  • All demonstrated increased risk
  • Artificial sweeteners and bladder cancer.
  • majority of studies no effect
  • those which have shown an effect have not been
    consistent in findings of who is at risk.

30
Gradient (dose response)
  • The presence of a clear dose/response
    relationship strengthens the evidence for a
    cause-effect relationship

31
Specificity
  • The exposure is specific to the disease (not
    always the case e.g. smoking)
  • Asbestos and mesothelioma
  • Malignant mesothelioma 3 cases per million for
    men 1.4 cases per million in women
  • Mesothelioma in asbestos workers is 100 to 200
    times higher
  • Specificity strengthens the case for causality
    but lack of it does not weaken the case

32
Biological Plausibility
  • Credible explanation of the mechanism by which
    the exposure could cause the disease
  • e.g. association between reduction of cardiac
    risk and moderate amounts of alcohol
    cause-effect relationship enhanced by knowledge
    that alcohol raises HDL cholesterol
  • Biological plausibility depends on current
    knowledge
  • Useful cause-effect relationship may be
    demonstrated before mechanisms are known
  • e.g. John Snow cholera Scurvy and vitamin C

33
Preventive Trials
  • If removal of the putative risk factor results in
    reduction of disease this is strong evidence to
    support cause and effect

34
Animal Models
  • Experimental exposure in animals to reproduce the
    disease
  • Exposure of an agent in animals CAN produce a
    disease similar to humans
  • BUT NOT ALWAYS
  • So can be helpful but failure does not mean much

35
Epidemiology is the study of the distribution
determinants of disease frequency in human
populations
  • 2 fundemental assumptions
  • That human disease does NOT occur at random
  • That human disease has causal and preventive
    factors that can be identified through systematic
    investigation

36
Epidemiological Reasoning-
  • 1.Hypothesis
  • Resulting from observations in clinical practice
    /research /surveillance/previous
    studies/theorising
  • 2. Analytical Study - To test the hypothesis
  • 3. Observed association
  • Test the validity of the observed association by
    excluding alternative explanations
    chance/bias/confounding
  • 4. Does the statistical association represent a
    cause-effect relationship
  • Judge whether the statistical association
    represents a cause-effect relationship requires
    inferences beyond the data from any single study
    and is done in the light of current knowledge

37
  • Disease results from the interplay of factors
    from Host, Environment Agent.
  • In epidemiology a cause is an exposure/factor
    which increases the probability of disease
  • Exposures do not have to be necessary OR
    sufficient to be important causes.
  • But they do have to be REAL causes
  • The aim is to use the knowledge to remove, avoid
    or protect against harmful factors and so reduce
    disease

38
Toxic shock syndrome
  • 1978 new disease in young women in North
    America
  • Fever, Rash Desquamation
  • Hypotension and multi-organ failure
  • In a very short time 50 cases and 3 deaths
    reported
  • Two questions urgently needed answers
  • Was this a new syndrome?
  • What was causing it?

39
Toxic shock syndrome
  • Disease often appeared during menstruation
  • Staph Aureus toxin implicated
  • Hypothesised using a new type of tampon caused
    many cases
  • Scientists from Centre for Disease Control
    studied the epidemic

40
Toxic shock syndrome
  • Case-control studies carried out
  • Odds ratio for tampon use 1.2
    all cases and 85 of controls used tampons
  • Odds ration for use of Rely brand was 8 Women
    using Rely brand were eight times more likely
    to develop TSS
  • Rely Tampons withdrawn from the market mid 1980
    and following this was a big reduction in case
    numbers

41
Toxic shock syndrome
  • Time sequence
  • tampon first marketed 3 years before big rise in
    cases
  • Biological plausibility
  • Characteristics of tampon predisposed to
    bacterial overgrowth
  • Preventive trial
  • Case numbers declined after withdrawal of product

42
Toxic shock syndrome reviewed in 1984/5
  • 2990 cases reported
  • 85 menstruating women
  • Estimated case-fatality 5.6
  • All cases evidence of Staph Aureus phage type
    52/29 with a particular exotoxin
  • Not a new disease or bug but a new susceptibility
    in young women using super-absorbant tampons
  • Epidemiological principles had been used to
    elucidate the causal pathway
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