Health Issues (AQA GCSE Biology): Revision Note
Exam code: 8461
Health & disease
Health is the state of physical and mental well-being; it is affected by multiple factors
Communicable and non-communicable diseases
Diseases, both communicable and non-communicable, are major causes of ill health
Ill-health affects not only the sufferer but also their family members and the contribution that the individual can make to society
Ill health can lead to a poorer quality of life or a reduced lifespan
Illness may generate financial costs
Individuals who are ill may not be able to work and may require expensive healthcare
Communicable diseases
Communicable diseases are caused by microorganisms called pathogens which can spread between individuals or individuals and animals
Examples of communicable diseases are:
chickenpox, a common childhood disease, is caused by a viral pathogen called the varicella-zoster virus
covid-19 is caused by the SARS-CoV-2 virus
Non-communicable diseases
Non-communicable diseases are not caused by pathogens and their effects on health tend to be longer-lasting
Examples of non-communicable diseases include:
asthma
coronary heart disease (CHD)
cancers
Factors affecting health
Lifestyle factors may have a profound effect on both physical and mental health, e.g.:
eating a balanced diet that provides the right amount of energy and nutrients helps maintain good health
a poor diet can lead to deficiencies, obesity, diabetes and poor mental health
constantly being under stress can lead to cardiovascular issues (such as high blood pressure, increasing the risk of CHD) and poor mental health
where a person lives and their income can have a profound impact on their health, affecting the standard of healthcare that is accessible to them and what housing and food they can afford
Interactions & disease
The effects of different types of disease may interact in ways that negatively affect the health of the individual, e.g.:
An individual with non-communicable immune deficiency may be more likely to suffer from communicable diseases
HIV, a viral infection, affects the immune system and may make someone more susceptible to bacterial infection
Viruses living in cells can be the trigger for cancers
E.g. HPV infection can increase the risk of cervical cancer
Immune reactions initially caused by a pathogen can trigger allergies,such as skin rashes and asthma
Severe physical ill health can lead to depression and other mental illness
Disease incidence: Representing & calculating
Scientists and doctors often record the incidence of disease – the number of new cases seen in a population over a set time period (e.g. number of measles cases per month)
Data on disease can take place in a variety of forms:
Graphical and numerical forms
Data can be presented as a table, which can be extracted into an appropriate graph; from there, numerical data can be extracted from the chart
Graphs must have clearly labelled axes and appropriate scales
Frequency tables, bar charts and histograms
Frequency tables present how many cases occur in each category
Bar charts are used for discrete data (e.g. cases per month)
Histograms are used for continuous data (e.g. ages of infected patients)
Correlations on scatter diagrams
Scatter diagrams plot two variables (e.g. pollution level vs. number of asthma cases)
Positive correlation: both variables increase together
Negative correlation: one variable increases as the other decreases
No correlation: no clear pattern
Correlation does not prove causation – the link between variables may be due to another factor
Sampling
Epidemiology is the study of the spread of disease
When investigating the spread of disease, it is often impractical to collect data from every individual in a population; scientists therefore take a sample
This involves collecting data from a smaller number of individuals and using the results to represent a larger population
Importance of sampling
Sampling saves time and resources while still allowing meaningful data collection
Large, well-chosen samples produce more reliable and representative results
Take several samples, find the mean, then scale up to the whole population or volume (e.g. if each sample is 0.02 cm³, multiply the mean count by 50 to estimate the number per 1 cm³). Scaling up from a single sample is not reliable
Sampling in epidemiology
Sampling enables scientists to identify patterns of disease spread, risk factors, and vulnerable groups
Findings are used to guide public health strategies, such as vaccination campaigns or screening programmes
Worked Example
The table below shows measles cases over a period of six months:
Month | Number of cases |
|---|---|
January | 8 |
February | 15 |
March | 22 |
April | 25 |
May | 18 |
June | 10 |
(a) Plot this data as graph.
(b) Describe the incidence of measles between January and June.
Answer (a)

Features of the graph should include:
Data plotted as a bar graph with separate bars
Months on the x axis and number of cases on the y axis
Axes labelled clearly
Graph takes up more than half of the available space
Bars drawn correctly with a ruler and pencil
Answer (b)
The number of cases rises between January and April from 8 to 25
The highest incidence is in April
Incidence falls again in May and June down to 10 cases
There are more cases at the end of the time period than at the start
The largest increases occur between January and February, and between February and March (an increase of 7 cases each month)
Examiner Tips and Tricks
Remember that descriptions should state what can be seen in the data, including numbers where relevant.
Avoid giving explanations or drawing conclusions in describe questions.
Worked Example
Scientists plotted the average level of air pollution (PM2.5) against the number of asthma cases in five towns.

(a) State the relationship between air pollution and asthma cases
(b) State what can be concluded about the relationship in (a)
Answer (a)
There is a positive correlation
As air pollution increases, asthma cases also increase
Answer (b)
There is a link / relationship / association between air pollution and asthma
Examiner Tips and Tricks
Do not assume that correlation = causation. Data may show a relationship, but that doesn't always mean that one variable has caused a change in the other; there may be other factors involved.
You should be able to:
translate disease incidence information between graphical and numerical forms
construct and interpret frequency tables and diagrams, bar charts and histograms
use a scatter diagram to identify a correlation between two variables
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