Interpreting Data on the Cardiovascular System (AQA A Level Biology): Revision Note
Exam code: 7402
Interpreting data: cardiovascular disease & risk factors
Cardiovascular disease (CVD) is any disease of the heart and blood vessels, e.g.:
coronary heart disease (CHD): blockage of the coronary arteries that can lead to a heart attack
stroke: blood clots or bleeding that affects blood supply in the brain
congenital heart disease: birth defects that affect the structure of the heart
A risk factor for CVD is any factor that can increase the risk of developing CVD; examples include:
high blood pressure: this can damage the walls of blood vessels, leading to formation of fatty deposits and blood clots that can reduce blood flow
smoking: chemicals in cigarettes can narrow blood vessels and increase blood pressure
stress: high levels of stress can raise heart rate and increase blood pressure
diet: evidence suggests that a diet that is high in both cholesterol and saturated fat can increase the risk of fatty deposits forming in damaged blood vessels
genetic factors: individuals can have a genetic predisposition that increases their chance of developing CVD
age and biological sex: risk of CVD increases with age and is much more likely to affect men
Examiner Tips and Tricks
While you will not be asked to list risk factors or explain the development of CVD, you may be asked to interpret data relating to the connection between CVD and a particular risk factor.
Worked Example
A study was carried out into the relative risk of CVD in non-smoking adults exposed to a range of levels of cigarette smoke from a smoking partner. The study looked at 523 non-smoking partners of smokers.

(a) Describe what the data show about the link between smoke exposure and CVD risk.
[2]
(b) State what can be concluded about the link between smoke exposure and CVD risk.
[1]
(c) Comment on the validity of the data.
[3]
Answer:
(a) A description of this data could include the following:
Any two of the following:
As the number of cigarettes smoked per day increases, the relative risk of CVD increases; [1 mark]
Non-smokers exposed to 0 cigarettes per day have no increased risk of CVD / have a relative risk of 1.00; [1 mark]
Non-smokers exposed to 20 or more cigarettes per day have the highest risk of CVD AND they have a relative risk of 1.31; [1 mark]
(b) A conclusion from the data could be:
There is a correlation / association between increased exposure to cigarette smoke and an increased risk of CVD; [1 mark]
(c) A commentary on the validity of the data could include:
Any three of the following:
523 people is a fairly small sample size and may not represent an entire population; [1 mark]
More studies would need to be carried out to back up these results; [1 mark]
There is no information on how other risk factors might be interacting with smoking OR risk factors such as age / diet / biological sex / exercise levels may be affecting the results; [1 mark]
The data doesn't use any statistical tests so we cannot state the significance of the differences between the different levels of smoke exposure; [1 mark]
Examiner Tips and Tricks
Note that part (b) above draws attention to a common area of confusion; that of correlation vs causal relationship. It would not be correct here to say that increased smoke exposure causes an increased risk of CVD; instead we can only say for sure that there is a correlation, or association, between the two variables.
Conflicting evidence
Research may produce conflicting evidence on the role of risk factors in CVD
Conflicting evidence is that which shows a different pattern to the evidence gained elsewhere
When conflicting evidence arises, more research is needed to show which pattern is correct
When evaluating data from CVD studies that show conflicting evidence you could consider the following:
Factor | What to look out for in a study |
---|---|
Is the sample representative of the population? |
|
Has statistical analysis been carried out? |
|
Has an experimental control been included? |
|
Is there more data to back up a study's findings? |
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Does the study effectively control additional variables? |
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Is the study biased? |
|
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