Interpreting Data on Risk Factors (Edexcel International A Level Biology)

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Naomi H


Naomi H



Quantitative Data on Illness

Analysing data on illness and mortality

  • Analysing data on illness and mortality is an essential part of the study of risk factors
  • It is important to remember that correlation between a risk factor and a diseases does not always mean that a causal relationship exists
    • Scientists cannot assume that because there is 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

Describing data

  • This means identifying trends and stating what the results show e.g. the data show that the oldest age group has the highest relative risk of heart disease
    • When describing data, it is always good to use numbers from the data to back up your descriptions, e.g. the data show that the oldest age group of 80+ has the highest relative risk of heart disease of 2.4

Drawing conclusions from data

  • This means working out what the data show about the relationships between variables, e.g. the data show that there is an association, or correlation, between age and the relative risk of heart disease 
    • Conclusions should always be limited to what the data show
      • Causal relationships cannot be concluded from one data set, e.g. it cannot be concluded from one study that older age causes an increase in the relative risk of heart disease
      • Conclusions cannot be extrapolated beyond the setting of a study e.g. a study carried out in 40-50 year old adults cannot be applied to people over 70, and a study carried out in mice cannot be directly applied to humans

Evaluating the 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 are considered to be valid if they measure what they set out to measure, i.e. they are not influenced by external variables or poor experimental design, and have been analysed correctly
  • Statistical analysis should be used to check that any differences between results are statistically significant
  • Some studies need to have a control with which to compare the results
    • E.g. when testing a drug to treat heart disease, a control group that is not given the drug should be included in the study to ensure that any effect shown is due to the drug and not any other factor
  • Studies should be repeated, or there should be many studies that show the same result, before conclusions can be drawn
  • The study should be designed to control any variable that is not being tested
  • Researchers should not be biased, i.e. looking for a particular outcome
    • This could be a problem if someone is being paid to come up with a particular result

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. 

  1. Describe the data
  2. Draw a conclusion from the data
  3. Comment on the validity of the dataanalysing-data-on-heart-disease
  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

Exam Tip

Beware of mixing up correlation and causation.

  • 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.

Just because two factors correlate does not mean one causes the other.  Be mindful with the words you use when answering questions on data. 

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Naomi H

Author: Naomi H

Naomi graduated from the University of Oxford with a degree in Biological Sciences. She has 8 years of classroom experience teaching Key Stage 3 up to A-Level biology, and is currently a tutor and A-Level examiner. Naomi especially enjoys creating resources that enable students to build a solid understanding of subject content, while also connecting their knowledge with biology’s exciting, real-world applications.