Analysing data on illness and mortalitySpec 1.15
In short
Illness and mortality data are compared using rates, such as deaths per 100 000 people per year, so that populations of different sizes can be compared fairly. A correlation between a factor and a disease does not prove that one causes the other, because another variable may be responsible. Different studies can give conflicting evidence.
Health data are usually given as rates so that populations of different sizes can be compared.
- Incidence: the number of new cases of an illness in a population in a stated time, often per 100 000 people per year.
- Prevalence: the total number of people with the illness at a given time, often per 100 000 people.
- Mortality rate: the number of deaths from a cause in a stated time, per 100 000 people (or per 1000).
Calculating a mortality rate
In one year 1500 people died from coronary heart disease in a town of 600 000 people. Calculate the mortality rate per 100 000 people.
- Rate per 100 000 = (number of deaths ÷ population) × 100 000
- = (1500 ÷ 600 000) × 100 000
Answer: 250 deaths per 100 000 people per year
To interpret data, describe the trend with figures from the table or graph, calculate percentage changes, and compare groups. Percentage change = (new value − original value) ÷ original value × 100.
Correlation and causation
A correlation is a relationship between two variables, often shown on a scatter diagram: as one rises the other rises (positive) or falls (negative). A correlation does not show that one variable causes the other. A third variable (a confounding variable) may be responsible for both, or the link may be due to chance.
Evidence for a causal relationship is stronger when there is a plausible biological mechanism, the effect increases with the dose of the factor, the link is found repeatedly in different studies, and intervention (removing the factor) reduces the disease.
Conflicting evidence
Studies on the same risk may reach different conclusions because they differ in sample size, the people studied, the length of the study, how data were collected and which other variables were controlled. When evidence conflicts, compare the quality of the studies and look at the overall weight of evidence.
Give rates with units (per 100 000 per year), and when calculating a percentage change always divide by the original value.
When asked why data do not prove causation, name a possible confounding variable (for example, other lifestyle differences between groups) rather than just writing 'correlation is not causation'.
Written and checked against the Edexcel International AS Level Biology (XBI11) specification · Updated October 2026