Lesson 1.3.1

1.3.1 Interpreting illness and mortality data Quiz: Pearson Edexcel Biology A (Salters-Nuffield), Unit 1

20 questions

Written by Revision Ninja · Last reviewed 11 October 2026

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Lesson 1.3.1, Interpreting illness and mortality data: 20 multiple choice questions for the Pearson Edexcel Biology A (Salters-Nuffield) (9BI0), Unit 1: Lifestyle, Health and Risk, written with Revision Ninja.

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The 20 questions

  1. What is the mortality rate per 100 000 people if 250 deaths occur in a population of 500 000 people over one year?

    • 500 deaths per 100 000 people
    • 50 deaths per 100 000 people
    • 250 deaths per 100 000 people
    • 5 deaths per 100 000 people

  2. Which measure gives the proportion of a population that has a disease at a given time?

    • Incidence
    • Relative risk
    • Mortality rate
    • Prevalence

  3. Which measure counts the number of new cases of a disease arising in a population over a set period?

    • Incidence
    • Prevalence
    • Survival rate
    • Odds ratio

  4. Why are age-standardised death rates used when comparing two populations?

    • They show the exact number of people who died, which is more accurate than any rate
    • They remove the need for a control group, because all ages are treated identically
    • They correct for differences in age structure, so the populations can be compared fairly
    • They prove that age causes death, so populations of different ages cannot be compared

  5. A town has 20 000 people, and 60 people die of CVD in a year. What is the CVD mortality rate per 100 000 people?

    • 300 per 100 000
    • 60 per 100 000
    • 30 per 100 000
    • 3000 per 100 000

  6. A study finds that ice cream sales and drowning deaths are both higher in summer. What is the most appropriate interpretation?

    • Drowning causes people to buy more ice cream, because they need the energy after swimming
    • The two events are unrelated, because a correlation can never exist between two variables
    • The two are correlated because a third factor, hot weather, increases both, so they are not causally linked
    • Ice cream causes drowning, because people who eat ice cream are more likely to go swimming

  7. What does a positive correlation between two variables indicate?

    • As one variable increases, the other tends to increase as well
    • As one variable increases, the other tends to decrease
    • The two variables are causally linked in every case
    • The two variables have no relationship with one another

  8. A scatter diagram shows a strong negative correlation between daily fruit intake and blood pressure. Which statement is correct?

    • Blood pressure has no relationship to fruit intake because the correlation is negative
    • Fruit intake and blood pressure are both caused by a third factor that always gives a zero correlation
    • Higher fruit intake tends to go with lower blood pressure, but a causal link must be tested further
    • Higher fruit intake causes higher blood pressure in every individual in the study

  9. Which of the following is a reason that two studies on the same health risk may give conflicting results?

    • Conflicting results always indicate that one of the studies has been fabricated by the researchers
    • Results from two studies can never differ, because the same data are always analysed in the same way
    • The laws of biology change between countries, so the same risk factor always has different effects
    • Different sample sizes, populations or study designs can produce different results for the same question

  10. A cohort of 10 000 people is followed for ten years. Of the 2000 people who smoke, 300 develop lung disease, compared with 200 of the 8000 non-smokers. What is the approximate relative risk for smokers?

    • About 10
    • About 1.5
    • About 6
    • About 0.2

  11. Which term describes a variable that is associated with both the risk factor and the disease, and so can give a false impression of a causal link?

    • Control variable
    • Dependent variable
    • Confounding variable
    • Independent variable

  12. Why is it important to distinguish correlation from causation when interpreting mortality data?

    • A correlation always proves causation when the data come from a large population sample
    • Mortality data can never show correlations, so correlation and causation are always identical
    • Causation can be identified only from correlation and never from any other form of evidence
    • A correlation may reflect a third factor, so treating it as cause could lead to wrong public health decisions

  13. Which of these would most strengthen the claim that a dietary factor causes an illness?

    • Evidence that the illness rate falls when the factor is removed in a controlled intervention
    • A survey in which people report that they dislike the food and feel unwell afterwards
    • A single case report describing one patient who ate the food and then became ill
    • A newspaper article that describes the factor as harmful without citing any data

  14. The number of deaths from a disease in a country falls from 800 to 600 between two years, while the population rises from 5 million to 6 million. What is the change in the crude death rate per 100 000 people?

    • It falls from 800 to 600 per 100 000
    • It rises from 16 to 20 per 100 000
    • It falls from 16 to 10 per 100 000
    • It falls from 16 to 6 per 100 000

  15. Why might a rise in reported mortality from a disease not reflect a true rise in its incidence?

    • Mortality always rises in direct proportion to incidence, so the two measures are the same
    • Incidence is always measured in deaths, so a rise in mortality must mean a rise in incidence
    • Reported mortality cannot change between years because the population is constant over time
    • Improved diagnosis or reporting may detect more cases that were previously missed or recorded differently

  16. Which statement about relative risk is correct?

    • A relative risk is calculated by subtracting the risk in the comparison group from the exposed group
    • A relative risk greater than 1 means the exposed group has a higher risk than the comparison group
    • A relative risk of exactly 1 means that the disease is always caused by the risk factor
    • A relative risk less than 1 means the exposed group has a higher risk than the comparison group

  17. A table shows CVD deaths per 100 000 for two countries but does not report the age structure of either. Which conclusion is most appropriate?

    • The rates may be affected by different age profiles, so the comparison needs caution
    • The countries have the same risk of CVD, because the rates are given per 100 000 people
    • Country A has more CVD deaths because its population is older, which is directly proven by the table
    • Age structure has no effect on mortality rates, so the table is a fair comparison of the two countries

  18. A study reports that 15% of people with high LDL cholesterol had a heart attack, compared with 5% of people with normal LDL. What is the absolute difference in risk?

    • 15 percentage points
    • 10 percentage points
    • 300 percentage points
    • 3 percentage points

  19. Which of these is the best reason for a researcher to report confidence intervals alongside a risk estimate?

    • They replace the need for a control group, because the range of values accounts for all variables
    • They prove that the risk estimate is exactly correct and cannot change in future studies
    • They show the range within which the true value is likely to lie, indicating the precision of the estimate
    • They show the proportion of people in the study who were smokers, which is needed to calculate risk

  20. A researcher wants to show that a risk factor causes disease. Which evidence is the weakest for causation?

    • A correlation found in a single cross-sectional survey with no information on timing or exposure
    • A dose-response relationship in which higher exposure is associated with greater disease risk
    • A reduction in disease rate after the exposure is removed in a controlled intervention study
    • A consistent association replicated across several independent studies in different populations

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