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Modelling causal pathways in health services, part 2
15/04/2023
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Modelling
• Representations of the world– Models of data and models of phenomena
• Make our assumptions clear and transparent
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Why?• For policy we need a causal effect• Usually ATE or ATET
– E.g.
• Barriers:– Observational data– Can’t measure endpoints
• But data, even observational data, tell us something
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Bayesian Causal Networks
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Outline
• Interested in the effect X->Y• Some information on • Lots of information on
X Z Yp q
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Outline
• Interested in X->Y• But confounded by • Can still identify causal effect by making use of
X Z Y
u
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Outline• Model describes relationships between variables• Can combine information on different data sources
InterventionUpstream endpoint
Patient outcomes
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Example: Computerised Physician Order Entry
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Example: Computerised Physician Order Entry
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CPOE ME ADE
𝑅𝑅=𝑃 (𝐴𝐷𝐸∨𝐶𝑃𝑂𝐸=1)𝑃 (𝐴𝐷𝐸∨𝐶𝑃𝑂𝐸=0)
=𝑃 (𝐴𝐷𝐸∨𝑀𝐸 )𝑃 (𝑀𝐸∨𝐶𝑃𝑂𝐸=1)𝑃 (𝐴𝐷𝐸∨𝑀𝐸)𝑃 (𝑀𝐸∨𝐶𝑃𝑂𝐸=0)
=𝑃 (𝑀𝐸∨𝐶𝑃𝑂𝐸=1)𝑃 (𝑀𝐸∨𝐶𝑃𝑂𝐸=0)
Using only studies with ADE endpoint Using studies with ADE and ME endpoint
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Nuckols et al.
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Weekend mortality
Weekend admission
Errors Mortality
Health
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Weekend mortality• Many studies have examined the effect of weekend admission on
risk of mortality (at least 105).• In the UK the estimated relative risk 1.1-1.2 (Meacock, Doran, and
Sutton, 2015, Freemantle et al., 2012)• Confounded by patient health
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Weekend mortality• Examine data that measure day of admission, mortality, and errors• SPI2 data
– Patients aged >65 with acute respiratory illness
• Crude mortality relative risk: 1.17 [0.79, 1.60]• Adjusted (age, sex, number of comorbidities) RR: 1.19 [0.79, 1.75]
• Similar point estimates. Under powered (n=670)
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Weekend mortality• Front-door estimator
• RR: 1.03 [1.00, 1.06]
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Weekend mortality
• Assumption of no relationship between errors and health may be too strong:– Sicker patients more exposed to risk of error– Sicker patients more likely to die, less exposed to risk of error
Weekend admission
Errors Mortality
Health
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Weekend mortality• Examine performance of estimators under different assumptions
using simulated data– Two types of individual: sick v healthy. Sick 4x more likely to die.
• Only when there is no unobserved confounding due to health is the ‘standard’ estimator preferred, even with fairly large relationship between errors and health.
• No evidence of a difference in errors by weekend or by health in SPI2 data.
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Example: Weekend Consultants
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Expert Elicitation• What happens when there are no data?
• Can use expert elicitation.
Figure: Example group subjective prior, from Yao et al. (2012) BMJ Qual Saf. See also Lilford et al. (2014) BMC Health Serv Res.
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Conclusions