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

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

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!

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

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

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.

2. Recognise associations may be present in the absence of a true cause- effect relationship

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?

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?

Epidemiological Reasoning:-

1. HypothesisResulting from observations in clinical practice /lab research/surveillance/previous studies/theorising

2. Analytical StudyTo test the hypothesis

3. Observed associationTest the validity of the observed association by excluding alternative explanations: chance/bias/confounding

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

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

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 100i.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

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

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

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

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

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 92 100

25 175 200

………………………………………………Odds Ratio 1.93

Dividing the subjects by age……..

Nocturnal eneuresis aged <60yrs

Nocturnal eneuresis aged >60yrs

bifocals

Present

Absent Present

Absent

yes

1 19 20 16 64 80

no 4 76 80 4 16 20

5 95 100

20 80 100

Odds ratio = 1

Odds ratio = 1

…………………………………………….no association

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….

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

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

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?

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?

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…

Bradford Hill proposed criteria

• Strength of association• Time sequence• Consistency• Gradient• Specificity• Biological Plausibility• Experimental Models in Animals• Preventive Trials

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

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

ConsistencyIf 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 effectthose which have shown an effect have not been consistent in findings of who is at risk….

Gradient (dose response)

The presence of a clear dose/response relationship strengthens the evidence for a cause-effect relationship

Specificity

• The exposure is specific to the disease (not always the case e.g. smoking)

• Asbestos and mesotheliomaMalignant 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

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

Preventive Trials

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

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

Epidemiology is the study of the distribution & determinants of disease frequency in human populations

2 fundemental assumptions1. That human disease does NOT

occur at random2. That human disease has causal

and preventive factors that can be identified through systematic investigation

Epidemiological Reasoning:-1.Hypothesis

Resulting from observations in clinical practice /research /surveillance/previous studies/theorising

2. Analytical Study - To test the hypothesis

3. Observed associationTest the validity of the observed association by excluding

alternative explanations: chance/bias/confounding

4. Does the statistical association represent a cause-effect relationshipJudge 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

• 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

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 reportedTwo questions urgently needed answers:

Was this a new syndrome? What was causing it?

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

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

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

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