Nine "viewpoints" for causality were set out by Bradford Hill  .
Strong associations are more likely to be causal than weak ones. In common source outbreaks, we look for one (or two) vehicles of infection, so we would expect the risk ratio for the true source to be very high. They often are. In outbreaks of food borne disease, association between illness and exposure to a common source may lead to odds ratios exceeding 50   . On the other hand, not all strong associations are causal and may be due to confounding. This can be illustrated by the association observed between multiple births and Down syndrome, an association that is actually confounded by age of the mother . The use of the strength of the association as a causal criterion can be misleading where confounding factors are not known.
Weak associations do not rule out causality and may still have public health importance. This can apply when an exposure is common in a population. e.g. passive smoking and lung cancer (Risk Ratio = 1.3) .
Repeated observations of an association in different populations under different circumstances showing the same or similar results suggest that the results of a single study are not due to chance. Over 100 studies into the association between smoking and lung cancer demonstrate increased risk of the disease. Note that the weighted results of similar studies, if statistically homogeneous, can be combined together in a meta- analysis.
However, consistency can also be misleading. Five studies compared the risk of dying from meningococcal disease after administration of oral antibiotics before admission to hospital versus no administration. All 5 studies showed lower mortality in those given antibiotics . The results were consistent and statistically homogeneous, thus in favour of a protective role for oral antibiotics before hospitalisation. However, doctors are more likely to prescribe oral antibiotics if they do not suspect meningococcal disease i.e. in patients with milder illness. We can infer that the association observed is confounded by disease severity. Consistency of results in observational studies may simply be due to the presence of the same confounding factors.
Epidemiologists should be cautious not to mistake statistical significance for consistency. Different studies may show similar effect measures but with different levels of significance (including significant and not significant) and still be consistent.
The criterion of specificity requires that a cause leads to a single effect, not multiple effects.
It is not very helpful in establishing causality. The fact that one agent contributes to multiple diseases should not be evidence against its role in any one disease. Smoking, for example, can lead to many ill effects in the smoker. The specificity criterion has repeatedly been used by those against ‘smoking as a cause of lung cancer’ as their main argument.
Exposure must precede the disease.
This is the only criterion that is fundamental to postulating a cause and effect relationship and fits our intuitive understanding of causality.
However the example of Mumps Measles Rubella (MMR) vaccine and autism illustrates that our self-taught causal inference can be erroneous. Some parents observe the beginning of autism shortly after an injection of MMR. Quite naturally, they attribute this illness to the vaccine. But autism often begins at the age when MMR is given. Studies have shown that autism is just as likely to occur before as after an MMR injection, and that children who have not been given MMR are just as likely to get autism as those who have .
It is sometimes difficult to document sequence, especially if there is a long lag between the exposure and the disease, subclinical disease, exposure (e.g., a treatment) brought on by an early manifestation of the disease.
A biological gradient exists when the risk of disease/outcome increases with increasing exposure to the suspected risk factor.
A linear relationship between dose and response supports causality. For example, the higher the number of cigarettes smoked, the greater the risk of lung cancer.
On the other hand, a lack of dose response does not exclude a causal link. Causal associations showing a single jump (threshold, saturation effects) rather than a monotonic trend have been described.
Sometimes associated non-causal factors may also increase in a similar way to the causal factor. As discussed earlier, the risk of Downs syndrome seems to increase with birth rank, while birth rank increases with age of the mother. Although a gradient is observed, the cause of the increased risk of Down's syndrome is linked to age of the mother not birth rank .
This refers to the biological plausibility of the hypothesis i.e. its consistency with current biological knowledge about the disease (for example, oral contraceptivs and *** cancer). Being largely based on prior beliefs, it remains a subjective judgement.
During an outbreak of psittacosis in Australia, reported in the Lancet ,16 cases had spent 17.5 hrs in the garden compared with controls who had only spent 5.2 hrs . Cases were more likely to mow the lawn than their controls (OR 8.8, 95%CI 1.2 – 389). It was quite plausible that this was a causal relationship (and still is). However, the authors in their study, had not taken account of the gender of controls. Controls were evenly distributed between males and females yet nearly all the cases were male. Stratification by gender reduced the strength of association, as measured by Mantel Haenszel Weighted Odds Ratio, to 5.5 and its lower limit of the confidence interval to 0.6 (p=0.19). Although the OR was still raised increased after stratification, it was misleading to present the results without taking gender into account.
In this way, plausibility may sometimes mislead in drawing conclusions.
We speak of coherence when the interpretation of cause-effect relationship does not conflict with what is known of the natural history and biology of disease. This is similar to plausibility. All observations are expected to fit with a hypothesized model to form a coherent picture.
Absence of coherence cannot be taken as evidence against causality, and vice versa. Many studies have shown that prevalence of meningococcal carriage in teenagers rises with age. This has been explained biologically as the result of changes in mucosal characteristics that occur with age. However, when prevalence was adjusted for social factors such as going to pubs and clubs, kissing and smoking, the increasing trend disappeared .
Ideally experimental evidence should be obtained if at all possible. Robert Koch proposed four postulates that establish an micro-organism as a cause of a disease.
Summary of Koch's postulates
(i) the micro-organism must be consistently present in the diseased and not in the healthy individual
(ii) the micro-organism must be isolated and grown
(iii) pure culture of the micro-organsim should induce disease
(iv) the micro-organism must be re-isolated and shown to be the same as in (i)
These four postulates are regarded as sufficient but not necessary to establish causation. Certain types of study designs may provide more convincing evidence than others. In current systems for classification of evidence (eg. the Scottish Intercollegiate Guidelines Network), randomised controlled trials are considered to provide strong evidence of cause and effect  . The highest level is provided by systematic reviews of randomised controlled trials.
Since it is sometimes unethical and/or impractical to conduct controlled trials, a possible alternative is to remove the exposure and see if the disease decreases, unless the causal process is regarded as irreversible. This is observed in “natural experiments” when intervention leads to change in one direction, and removal of that intervention reverses direction in outcome. The introduction of pertussis vaccination in the UK for instance, led to a fall in the incidence of whooping cough. Unsubstantiated concerns about adverse effects later led to a fall in its uptake. This was followed by a rise in incidence of the disease. Uptake then rose again and incidence fell correspondingly .
The existence of other cause-effect relationships analogous to the one under study supports a causal interpretation. This is a weak criterion for causality, but can be useful for speculating how a risk factor may operate in a different context.
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