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
Content
• More on causality
• Attributable fractions
• Screening – pitfalls to watch out for
Does A cause B?
A
B
A
B
A
C
B
A
?
B
Factors which make causality more likely
Bradford-Hill criteria
• Strength of association
• Consistency
• Specificity
• Temporality
• Biological gradient
• Plausibility
• Coherence
• Experiment
• Analogy
Does coffee cause ischaemic heart
disease?
Coffee
Ischaemic
heart disease
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
Necessary or sufficient causes?
Asbestos exposure
Asbestosis
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
Coronary heart disease
1,000 population per year deaths per 1,000 per year
Heavy smokers
Non-smokers
166
599
7
422
Attributable fractions
Lung cancer deaths per
Coronary heart disease
1,000 population per year deaths per 1,000 per year
Heavy smokers
Non-smokers
Excess risk of
heavy smoking
166
599
7
422
166 – 7 = 159
599 – 422 = 177
Attributable fractions
Lung cancer deaths per
Coronary heart disease
1,000 population per year deaths per 1,000 per year
Heavy smokers
166
599
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%
Non-smokers
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
Death
Death
Detected by
screening
• 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
Yes
Screening Positive
test
Negative
300
Total
Total
No
30
130
20 3000
3020
320 3030
3350
Measures for screening
• Sensitivity and Specificity
• Positive predictive value and Negative predictive value
Disease
Yes
Screening Positive
test
Negative
Total
300
Total
Sensitivity = 300/320 = 94%
No
30
130
20 3000
3020
320 3030
3350
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
Questions
gmccartney@nhs.net