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

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

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

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

5. Coffee Ischaemic heart disease Does coffee cause ischaemic heart disease?

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

7. Asbestos exposureAsbestosis Necessary or sufficient causes? SmokingLung 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 smokers166599 Non-smokers7422

10. Attributable fractions Lung cancer deaths per 1,000 population per year Coronary heart disease deaths per 1,000 per year Heavy smokers166599 Non-smokers7422 Excess risk of heavy smoking 166 – 7 = 159599 – 422 = 177

11. Attributable fractions Lung cancer deaths per 1,000 population per year Coronary heart disease deaths per 1,000 per year Heavy smokers166599 Non-smokers7422 Excess risk of heavy smoking 166 – 7 = 159599 – 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 Detected by screening Death

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 DiseaseTotal YesNo Screening test Positive30030130 Negative2030003020 Total32030303350

21. Measures for screening Sensitivity and Specificity Positive predictive value and Negative predictive value DiseaseTotal YesNo Screening test Positive30030130 Negative2030003020 Total32030303350 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 gmccartney@nhs.net