Evaluating Data on Lung Disease (AQA AS Biology): Revision Note
Exam code: 7401
Interpreting data: risk factors
A risk factor is any factor that is linked to an increased probability of suffering from a particular condition or disease
Lung disease risk factors include:
smoking
air pollution
exposure to certain chemicals at work
genetics
asthma
Examiner Tips and Tricks
You are not expected to know the lung disease risk factors, but you need to be able to interpret data on their relationship to the incidence of lung disease.
Worked Example
A medical research group considered the risk factors involved in worsening cases of chronic obstructive pulmonary disorder (COPD). They studied a group of 230 patients and calculated the effect of each risk factor as an odds ratio (OR), a value that represents the probability of worsening COPD in that group of patients in comparison to a group without exposure to that factor.
OR = 1: no association with worsening of COPD
OR > 1: associated with increased probability of worsening COPD
OR < 1: associated with decreased probability of worsening COPD
Risk factor | OR |
|---|---|
Age 60+ years | 1.05 |
Biological male | 0.92 |
Current smoker | 0.64 |
Hospitalised due to COPD in past year | 3.26 |
Presence of other chronic conditions | 1.5 |
Analyse the data to determine what they show about the risk factors studied and worsening of COPD.
[3]
Answer:
Being over 60 AND the presence of other chronic conditions have a slight association with increased probability of worsening COPD; [1 mark]
Being hospitalised in the past year has the strongest association with increased probability of worsening COPD; [1 mark]
Being male AND being a current smoker are associated with a decreased probability of worsening; [1 mark]
Examiner Tips and Tricks
The exam question above contains data that suggests that being male and being a smoker are not associated with increased risk from COPD, a finding that is contradictory to other data sources. In an exam you might be asked to consider why this has happened in this study; you could suggest explanations such as:
a small sample size
a lack of statistical testing
a failure to control other variables, e.g. diet
Evaluating data: risk factors
Data from studies into risk factors in disease incidence are often used to inform government policy, e.g. the introduction of health warnings on cigarettes, and bans on smoking in public places
This means that it is important that such data are interpreted accurately
There are several considerations that should be taken into account when analysing data from studies on risk factors:
Control variables: it is difficult to eliminate the effects of other variables, e.g. diet, activity levels and genetics, on the outcome of a study on a specific lifestyle factor
Sample size: the number of individuals in a study needs to be large enough to represent the population as a whole
Sample demographic: the sample needs to contain individuals that represent the target demographic
E.g. a study that includes women aged 20-40 cannot be used to determine the effect of a risk factor on men, or on women aged 50-60
Correlation vs causation: risk factor data tends to rely on correlation and does not always indicate a causal relationship
E.g. it would be unethical to conduct a controlled experiment that involves asking one random group of people to smoke 10 cigarettes every day for 10 years, and another to avoid smoking for 10 years, so researchers rely on asking people about their chosen lifestyle factors and looking for patterns in the data
Statistical tests: statistical testing should be used to determine whether any associations between variables are significant or due to chance
Examiner Tips and Tricks
Exam questions may ask you to evaluate conclusions from a data set. In this situation you must remember to consider all of the factors listed above; think about the design of the study, who it is meant to represent, whether we can correctly assume causation, and whether statistical tests have been carried out.
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