Modelling causal pathways in health services, part 2

15/04/2023

Modelling

• Representations of the world– Models of data and models of phenomena

• Make our assumptions clear and transparent

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

Bayesian Causal Networks

Outline

• Interested in the effect X->Y• Some information on • Lots of information on

X Z Yp q

Outline

• Interested in X->Y• But confounded by • Can still identify causal effect by making use of

X Z Y

u

Outline• Model describes relationships between variables• Can combine information on different data sources

InterventionUpstream endpoint

Patient outcomes

Example: Computerised Physician Order Entry

Example: Computerised Physician Order Entry

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

Nuckols et al.

Weekend mortality

Weekend admission

Errors Mortality

Health

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

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)

Weekend mortality• Front-door estimator

• RR: 1.03 [1.00, 1.06]

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

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.

Example: Weekend Consultants

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.

Conclusions