interpreting Data on risk factors

correlation is where a change in one variable occurs at the same time as a change in another variable

causation is where the change in one variable causes the change in another variable

correlation ( a relationship between 2 variables) between a risk factor and a disease doesn’t mean that a casual relationship exists

  • scientists cannot assume that because there is a correlation between variables that one has caused the other

  • many other factors will influence the prevalence and likelihood of disease and these factors need to be taken into account when analysing and interpreting data

evaluating validity of data

  • larger sample sizes are more likely to give valid results as the sample is more likely to be representative of the population in question

    • results = valid if they are not influenced by external variables or poor experimental design, and have been analysed correctly

  • studies should be repeated or there should be many studies that show the same result, before conclusions can be drawn.

e.g.

  1. A description for this data could include the following

    • Non-smokers exposed to 0 cigarettes per day have the lowest relative risk of CVD, with a relative risk of 1.00

    • Non-smokers exposed to 20 or more cigarettes per day have the highest risk of CVD, with a relative risk of 1.31

    • As the number of cigarettes smoked per day increases, the relative risk of CVD increases from 1.00 at 0 cigarettes per day, to 1.23 at 1-19 cigarettes per day, and then to 1.31 at 20 or more cigarettes per day  

  2. A conclusion for the data could be

    • There is a correlation, or an association, between increased exposure to cigarette smoke and an increased risk of CVD

    • Note that while it could be concluded here that increased exposure to smoking causes an increase in the relative risk of CVD, it is best not to draw causal connections without more evidence

      • This is only a single study; there may be concerns over its validity, and other studies could show conflicting evidence

  3. A commentary on the validity of the data could include

    • The study included 523 people; this is a fairly small sample size and may not represent an entire population

    • This is only one study; more studies would need to be carried out to back up these results

      • Being able to replicate, or repeat, the results of a study shows that the results are reliable

    • There is no information on how other risk factors might be interacting with smoking to influence the risk of CVD

      • Risk factors such as age, diet, biological sex, or exercise levels may be playing a role, as these factors may be interacting with the smoking variable e.g.

        • Smokers are often older

        • More men may smoke than women

        • Smokers may be less likely to exercise

    • The data doesn't comment on the use of any statistical tests so we cannot state the significance of the differences between the different levels of smoke exposure

Recognising conflicting evidence

  • Evidence from one study is not enough to conclude that a risk factor is a risk to health or associated with a particular disease

  • Studies similar in design would need to be analysed together to make links

    • Such an analysis is referred to as a meta-analysis

  • Similar conclusions would need to be drawn from all studies in order to accept the findings

  • Conflicting evidence may be found that leads to a different conclusion

    • 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

    • Conflicting evidence is often a sign that other variables are involved

perception of risk

  • Risk is defined as the chance or probability that a harmful event will occur

  • The statistical chance of a harmful event occurring needs to be supported by scientific evidence gained from research 

  • An individual's perception of risk may be different to the actual risk of something occurring

    • Risk can be overestimated because of factors such as

      • Misleading information in the media

      • Overexposure to information

      • Personal experience of the associated risk

      • Unfamiliarity with the event

      • The event causing severe harm

    • Risk can be underestimated because of factors such as

      • Lack of information 

      • Misunderstanding of factors that increase the risk

      • A lack of personal experience of the associated risk

      • Unfamiliarity with the event

      • The harm being non-immediate