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Interpreting health data and risk perceptionEdexcel International A Level Biology: Flashcards

What these 14 flashcards ask

  • What is morbidity?
  • What is mortality?
  • Define incidence.
  • Define prevalence.
  • Why compare rates, not raw numbers?
  • How is a mortality rate per 100,000 calculated?
  • What is a confounding variable?
  • Why does correlation not prove causation?
  • What strengthens evidence for causation?
  • Why is a larger sample better?
  • Define validity.
  • Define reliability.
  • Give two reasons why studies may give conflicting evidence.
  • Why might perceived risk differ from actual risk?

Exam questions on Interpreting health data and risk perception

  1. A health authority publishes mortality data for a disease. In 2010 there were 1,200 deaths from the disease in a population of 2,400,000 people. In 2020 there were 900 deaths in a population of 3,000,000 people.
    Calculate the percentage change in the mortality rate between 2010 and 2020.2 marks
  2. A study of the link between a type of fast food and heart disease recruited 60 volunteers, all of whom were students at one university. The researchers asked the volunteers to record what they ate for one week and then measured their blood pressure.
    Suggest two changes to the sample that would allow the conclusions to be applied more confidently to the general population.2 marks
  3. A study followed 100,000 adults for 12 years. Of the 20,000 adults who ate fewer than two portions of fruit and vegetables each day, 800 developed cardiovascular disease (CVD). Of the 20,000 adults who ate five or more portions each day, 500 developed CVD. The remaining adults ate between two and five portions each day. The diets were recorded by the adults themselves in questionnaires.
    Calculate the percentage reduction in the incidence of CVD in the group eating five or more portions each day compared with the group eating fewer than two portions.3 marks
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Written by the Exaim team, led by Shaun Daswani (Head of Upper Secondary, Improve ME Institute; MSc Financial Mathematics, Imperial College London; BSc, UCL) and Jason Daswani (operational lead, Improve ME Institute; LSE).