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

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

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

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

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. 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

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 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

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 25 175 200 ………………………………………………Odds Ratio 1.93

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

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?

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…

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

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

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

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

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

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

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

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

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

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

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

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

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?

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 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

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