Interpreting numbers – more tricky bits ScotPHO training course March 2011 Dr Gerry McCartney
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Interpreting numbers – more tricky bits ScotPHO training course March 2011 Dr Gerry McCartney Head of Public Health Observatory Division NHS Health Scotland [email protected]
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Interpreting numbers – more tricky bits ScotPHO training course March 2011 Dr Gerry McCartney Head of Public Health Observatory Division NHS Health Scotland [email protected]. Content. More on causality Attributable fractions Screening – pitfalls to watch out for. Does A cause B?. A. - PowerPoint PPT Presentation
Transcript of Interpreting numbers – more tricky bits ScotPHO training course March 2011 Dr Gerry McCartney
Interpreting numbers – more tricky bitsScotPHO training course
March 2011
Dr Gerry McCartney
Head of Public Health Observatory Division
NHS Health Scotland
Content
• More on causality• Attributable fractions• Screening – pitfalls to watch out for
Does A cause B?
A B
A B
A
B
A B
C
?
Factors which make causality more likely
Bradford-Hill criteria• Strength of association• Consistency• Specificity• Temporality• Biological gradient• Plausibility• Coherence• Experiment• Analogy
CoffeeIschaemic
heart disease
Does coffee cause ischaemic heart disease?
• Factors that do not lie on the causal pathway but which influence the magnitude of effect
Effect modifiers
SmokingIschaemic
heart disease
Male gender(effect modifier)
Asbestos exposure Asbestosis
Necessary or sufficient causes?
Smoking Lung cancer
Jumping from plane without parachute
Squished onto ground
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
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
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 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%
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
Screening
• Why do we screen for conditions? • When is screening appropriate?• Problems with evaluation of screening
programmes• Particular biases
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
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?
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
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
Death
• 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
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
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
Total 320 3030 3350
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
Total 320 3030 3350
Sensitivity = 300/320 = 94%
Specificity = 3000/3030 = 99%
PPV = 300/330 = 91%
NPV = 3000/3020 = 99%
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
