1. Interpreting numbers – more tricky bits
ScotPHO training course
March 2011
Dr Gerry McCartney
Head of Public Health Observatory Division
NHS Health Scotland

2. Content More on causality Attributable fractions
Screening – pitfalls to watch out for

3. Does A cause B? A B A B A C B A ? B

4. Factors which make causality more likely
Bradford-Hill criteria
Strength of association
Consistency
Specificity
Temporality
Biological gradient
Plausibility
Coherence
Experiment
Analogy

5. Does coffee cause ischaemic heart disease?

6. Effect modifiers
Factors that do not lie on the causal pathway but which influence the magnitude of effect
Male gender
(effect modifier)
Smoking
Ischaemic
heart disease

7. Necessary or sufficient causes?
Asbestos exposure
Asbestosis
Smoking
Lung cancer
Jumping from plane
without parachute
Squished onto
ground

8. Attributable fractions/risk
“What fraction of disease incidence in the exposed group is attributable to the risk factor?”
Calculated by taking the relative risk in an unexposed group from the relative risk in an exposed group

9. Attributable fractions
Lung cancer deaths per 1,000 population per year
Coronary heart disease deaths per 1,000 per year
Heavy smokers
166
599
Non-smokers 7
422

10. Attributable fractions
Lung cancer deaths per 1,000 population per year
Coronary heart disease deaths per 1,000 per year
Heavy smokers
166
599
Non-smokers 7
422
Excess risk of heavy smoking
166 – 7 = 159
599 – 422 = 177

11. Attributable fractions
Lung cancer deaths per 1,000 population per year
Coronary heart disease deaths per 1,000 per year
Heavy smokers
166
599
Non-smokers 7
422
Excess risk of heavy smoking
166 – 7 = 159
599 – 422 = 177
Attributable risk of heavy smoking
159 / 166 = 95.8%
177 / 599 = 29.5%

12. Attributable fractions/risk
Attributable fractions can also be applied to the whole population using the formula:
= (risk in total population – risk in unexposed population) / risk in total population

13. Screening Why do we screen for conditions?
When is screening appropriate?
Problems with evaluation of screening programmes
Particular biases

14. Why screen for conditions?
To improve outcomes for individuals
Keep Well health checks
Breast mammography
To improve outcomes for populations
Port health checks
Employment checks

15. When should you screen? Based on the Wilson – Junger criteria:
Is there an effective intervention?
Does earlier intervention improve outcomes?
Is there a screening test which recognises disease earlier than usual?
Is the test available and acceptable to the target population?
Is the disease a priority?
Do the benefits outweigh the costs?

16. Screening – why is it different?
Individuals may not benefit
Involves people who are well subjecting themselves to testing – medicalisation
Creation of a pre-disease state
False positive tests
False negative tests
Initiated by health professionals not individuals
Cost-benefit depends on prevalence within a population
Inequalities implications

17. Particular biases Lead time bias
Given that screening picks up disease at an earlier stage – the time between diagnosis and death increases without any actual increase in survival
Symptoms
Death
Death
Detected by screening

18. Length time bias
Screening is more likely to detect less aggressive disease and therefore can give impression of improved survival X X X X X X X

19. Measures used in screening
Sensitivity is the likelihood that those with disease will be picked up by the screening test
Specificity is the likelihood that those with a negative screening test will not have the disease
Positive predictive value is the likelihood that those with a positive test will have the disease
Negative predictive value is the likelihood that those with a negative test will not have the disease

20. Measures for screening
Sensitivity and Specificity
Positive predictive value and Negative predictive value
Disease
Total
Yes No
Screening test
Positive
300 30
130
Negative 20
3000
3020
320
3030
3350

21. Measures for screening
Sensitivity and Specificity
Positive predictive value and Negative predictive value
Disease
Total
Yes No
Screening test
Positive
300 30
130
Negative 20
3000
3020
320
3030
3350
Sensitivity = 300/320 = 94%
Specificity = 3000/3030 = 99%
PPV = 300/330 = 91%
NPV = 3000/3020 = 99%

23. Summary
Bradford-Hill criteria can be used to judge whether an association is likely to be causal
Attributable fractions can help identify the discrete contribution of particular risks to an outcome
Screening is different to other medical interventions and can cause harm
Screening evaluations have their own potential biases – lead time and length time bias

24. Questions