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. Poor living conditions
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!
Most of epidemiology concerns causality and several types of cause can be distinguished. It must be emphasised however that epidemiological evidence by itself is insufficient to establish causality, although it can provide powerful circumstantial evidence. A cause is termed necessary when it must always precede a disease
A cause is termed sufficient when it inevitably initiates or produces the 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
X is necessary and sufficient to cause Y. Both X and Y are always present together and nothing but X is needed to cause Y. For example the measles virus is all that is necessary to cause measles in an unimmunised individual or population.
X is necessary but not sufficient to cause Y. X must be present when Y is present but Y does not always follow X. Some additional factors are also required. For example mycobacterium tuberculosis is the necessary cause of tuberculosis but often is not sufficient cause without poverty, poor nutrition, overcrowding etc
X is not necessary but is sufficient to cause Y.Y is present when X is but X may or may not be present when Y is because Y has other causes and can occur without X. For example, an enlarged spleen can have many separate and unconnected causes. Lung cancer can be caused by each of radon, asbestos and smoking.
X is neither sufficient nor necessary to cause Y. Again X may or may not be present when Y is present. I X is present with Y, some additional factor must also be present. Here X is a contributory cause of Y in some causal sequence but not in others

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 P< 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……
Use
of Bifocals
Present
Absent
YES 17 83
100 NO 8 92 25
175
200
………………………………………………Odds Ratio 1.93

19. Dividing the subjects by age……..
Nocturnal eneuresis aged <60yrs
Nocturnal eneuresis aged >60yrs b i f o c a l s
Present
Absent
yes 1 19 20 16 64 80 no 4 76 5 95
100
Odds ratio = 1
…………………………………………….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. The organism occurs in every case of the disease
Koch’s 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
In a given study, if chance, bias and confounding are all determined to be unlikely alternative explanations of the findings, we can then conclude that a valid statistical association exists between the exposure and the disease in these data. It is then necessary to consider whether this relationship can be judged one of cause and effect, since the presence of a valid statistical association in no way implies causality. Such a judgement can only be made in the context of all the evidence available at that moment and as such must be reevaluated with each new finding. There are positive criteria that can aid in the judgement concerning causality, including strength of association, biological credibility of the hypothesis, consistency of the findings as well as other information concerning the temporal sequence and the presence of a dose-response relationship

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
Even if a theory passes all these criteria with flying colours, it does not necessarily prove causation beyond any shadow of doubt. However, the more criteria that are met the more likely it is that the causal hypothesis is right given the current state of out knowledge But any causal hypothesis is a hypothesis that accounts for what we know now but may be modified or overturned with advancing knowledge

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
Even if a theory passes all these criteria with flying colours, it does not necessarily prove causation beyond any shadow of doubt. However, the more criteria that are met the more likely it is that the causal hypothesis is right given the current state of out knowledge But any causal hypothesis is a hypothesis that accounts for what we know now but may be modified or overturned with advancing knowledge

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….
Even if a theory passes all these criteria with flying colours, it does not necessarily prove causation beyond any shadow of doubt. However, the more criteria that are met the more likely it is that the causal hypothesis is right given the current state of out knowledge But any causal hypothesis is a hypothesis that accounts for what we know now but may be modified or overturned with advancing knowledge

30. Gradient (dose response)
The presence of a clear dose/response relationship strengthens the evidence for a cause-effect relationship
Even if a theory passes all these criteria with flying colours, it does not necessarily prove causation beyond any shadow of doubt. However, the more criteria that are met the more likely it is that the causal hypothesis is right given the current state of out knowledge But any causal hypothesis is a hypothesis that accounts for what we know now but may be modified or overturned with advancing knowledge

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
Even if a theory passes all these criteria with flying colours, it does not necessarily prove causation beyond any shadow of doubt. However, the more criteria that are met the more likely it is that the causal hypothesis is right given the current state of out knowledge But any causal hypothesis is a hypothesis that accounts for what we know now but may be modified or overturned with advancing knowledge

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
Even if a theory passes all these criteria with flying colours, it does not necessarily prove causation beyond any shadow of doubt. However, the more criteria that are met the more likely it is that the causal hypothesis is right given the current state of out knowledge But any causal hypothesis is a hypothesis that accounts for what we know now but may be modified or overturned with advancing knowledge

33. Preventive Trials
If removal of the putative risk factor results in reduction of disease this is strong evidence to support cause and effect
Even if a theory passes all these criteria with flying colours, it does not necessarily prove causation beyond any shadow of doubt. However, the more criteria that are met the more likely it is that the causal hypothesis is right given the current state of out knowledge But any causal hypothesis is a hypothesis that accounts for what we know now but may be modified or overturned with advancing knowledge

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
Even if a theory passes all these criteria with flying colours, it does not necessarily prove causation beyond any shadow of doubt. However, the more criteria that are met the more likely it is that the causal hypothesis is right given the current state of out knowledge But any causal hypothesis is a hypothesis that accounts for what we know now but may be modified or overturned with advancing knowledge

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. But they do have to be REAL causes
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 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 Biological plausibility
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