Week 4 From Association to Causation

Question 1

How can causation be inferred?

Answer 1

1) Through demonstration of statistical association between a factor and a disease
2) By examining all available evidence to ascertain the meaning of the association - i.e. Is the factor causal?

Question 2

What is the definition of causality?

Answer 2

Any event, condition, or characteristic that precedes disease, without which the disease would not have occurred, or would not have occurred till much later

Question 3

What are some approaches to observing aetiology in humans?

Answer 3

Studies of group characteristics: Aggregate risk studies

Studies of individual characteristics: Case-control studies, cohort studies, experimental studies

Systematic review, meta analyses, randomised control trials

Question 4

What is an Aggregate Risk Study?

What are the uses and shortcomings?

Answer 4

Looking at a population’s average exposure, and then the incidence of the disease

Shortcomings:

1) there is no way to infer whether or not the individuals with higher exposures are the ones with the disease or not
2) Not useful for causation

Uses:
1) can be useful for highlighting areas for future studies

Question 5

What are the various types of associations?

Answer 5

Artefactual

Interrelated but Not Causal

Uncontrolled Confounding

Effect Modification

True Association

Question 6

Define Artifactual Association

Answer 6

Associations that occur due to statistical artefact

I.e. due to poor study design: selection biases, biases in obtaining information, etc.

Question 7

What are Interrelated but not Causal Associations?

Answer 7

Factors that may occur with disease, but that are not causal of disease.

I.e. Low socioeconomical status often occurs with lung cancer, but is not causal of lung cancer. To infer that it was, would be a false association

Question 8

What are Uncontrolled Confounding Associations?

Answer 8

Confounding = confusion variables, produce mixing of effects

Occurs when the effect of the true exposure (the true causal agent) is mixed together with another variable which does not actually cause the disease - this leads to bias

E.g. smoking and drinking alcohol often occur together, but only smoking causes alcohol. If we inferred that alcohol caused lung cancer, that would be a confounding bias.

Question 9

How can we differentiate between confounding and true causal associations?

Answer 9

If the relationship is truly causal, then incidence of disease should decline with removal of the causal agent

If the relationship is confounding, then the occurrence of disease will be unaffected by the removal of the confounder

**This can still be difficult if the confounder and causal agent are very closely related/intwined

Question 10

What is an ‘Effect Modification’ association?

Answer 10

Effect modification occurs when the strength of the relationship between two variables is altered by the presence of a third variable.

E.g. smoking is causal of lung cancer. However, smokers who also work in coal mines have even higher rates of lung cancer.

Thus, coal mining works as an effect modifier in that is acts in synergy with smoking and magnifies the occurrence of the disease.

Question 11

Define ‘necessary’ and ‘sufficient’ exposures

Answer 11

Necessary exposures = the exposure is necessary for the disease to occur. It always precedes disease

Sufficient exposure = the exposure is sufficient to cause disease - i.e. makes disease inevitable

This gives rise to 4 types of causal relationships:

Necessary but not sufficient
Unnecessary but sufficient
Necessary and sufficient
Neither necessary or sufficient

Question 12

Necessary but not sufficient

Answer 12

The exposure is necessary to cause the disease (the disease will not occur without it) but it does not cause the disease by itself

Question 13

Necessary and Sufficient

Answer 13

The exposure alone will inevitably cause the disease. The disease will always follow the exposure, and will not occur without the exposure

Question 14

Sufficient but not necessary

Answer 14

The exposure will always cause the disease, but it is not the only cause of the disease - other things can cause the disease

Question 15

Neither sufficient or necessary

Answer 15

The exposure is not needed for disease, and it cannot cause the disease by itself.

E.g. MI has a multifactorial aetiology. There are many potential causes: none of them alone are sufficient to cause MI, and not all of them have to be present for MI to occur

Question 16

Define Causal Complements

Answer 16

The effect of an exposure on a population will depend on the prevalence of ‘causal complements’ - i.e. factors that ‘enable’ the exposure to cause the disease.

E.g. the effect of mycobacterium on a fully immunised and this immune population may be nil
However, the effect of mycobacterium on a non-immunised, susceptible population may be as high as 100%.

In this case, absence of immunisation is the causal complement

Question 17

How do we assess causality?

Answer 17

A postulated cause-and -effect relationship should be examined in as many ways as possible.

Should attempt to isolate the proposed cause as much as possible - make every effort to eliminate confounding variables and biases from the study design

Causality is inferred based on all available evidence

Question 18

What is the Bradford Hill criteria?

Answer 18

Used to determine is an association is likely causal or not…

1) Temporality
2) Strength
3) Dose-relationship
4) Reversibility
5) Consistency
6) Biological Plausibility
7) Specificity (not always so important given prevalence of multi-factoral aetiologies for most diseases)
8) Analogy

Question 19

Temporality

Answer 19

The exposure must precede the disease

Question 20

Strength

Answer 20

There must be a large calculated relative risk (for cohort studies), or large Odds ratio for case control*

Question 21

Dose-relationship

Answer 21

The incidence of disease must be greater with higher dosages of exposure

Question 22

Reversibility

Answer 22

The incidence of disease must reduce with reduction in exposure (e.g. reduction in risk of developing LC in ex-smokers)

Question 23

Consistency

Answer 23

The relationship must be demonstrated in several studies, using different study designs, in different populations, different context, etc

Question 24

Biological Plausibility

Answer 24

The causality must be logical in light of biological understandings of the time

Question 25

Specificity

Answer 25

One cause leads to one effect - not so relevant in health as so many diseases are multifactorial in aetiology

Question 26

Analogy

Answer 26

Examples exist of well established causes to the disease that are analogous to the one in question

E.g. that smoking causes LC is more plausible in light of longstanding evidence that LC is also caused by other inhaled toxins such as asbestos etc.

Question 27

Things to consider when inferring causality

Answer 27

Consider all available evidence

Examine the quality of the study

Consider evidence both for and against the causality in question