Health economics and causal modelling in health services

research

15/04/2023

Yen-Fu Chen & Sam Watson

Warwick Centre for Applied Health Research & Delivery (W-CAHRD)

CLAHRC WM Programme Steering Committee Meeting, 15 April 2015

Challenges in economic evaluation of health services research

• Clinical outcomes: too rare to measure reliably, e.g. transfusion of incompatible blood

e.g. a £10 million computer system for a hospital with 50,000 admissions/year only needs to save 2 lives per 1000 patients

• Process outcomes: too diffuse to measure• Experimental evidence may be scarce

• Cost effectiveness = Costs / Effects • Difficulties in measuring intervention effects

Lilford et al. BMJ 2010; 341:c4413

Use of causal modelling

• Overcomes difficulties in measuring specific processes or outcomes• Allows the integration of all available evidence• Three key steps:

1.Build qualitative causal model

2.Populate the model o Systematic review of quantitative datao Elicitation of expert belief

3.Estimate intervention effectiveness using Bayesian approach

Service delivery causal chain

Process

Generic service

intervention

Targeted service

intervention

Policy intervention

Clinical intervention

Structure

Generic process

Outcome

Targeted process

Clinical process

Context (moderating

variables)

HTAExplanatory (independent) variables

Dependent variables

Intervening variables

Consultant presence at weekendsStructure

Intervening variables /

mechanismsHigher level of clinical competence

Stronger leadership in case management

Process

Outcome

More accurate diagnosis

Earlier intervention

Higher throughput (shorter waiting time & procedural delay, quicker discharge & shorter length of stay)

Better Monitoring

Better administration of intervention

Better patient satisfaction

Reduced errors & adverse events

Enhanced learning for junior doctors

Faster decision on palliative cases

Reduced mortality

Consultant presence (at weekends)Structure

Intervening variables /

mechanismsHigher level of clinical competence

Process

Outcome

Prompt investigation & more accurate diagnosis

Earlier intervention

Higher throughput (waiting time; procedural delay; length of stay)

Better Monitoring

Better administration of intervention

Better patient satisfaction

Reduced errors & adverse events

Enhanced learning for junior doctors

Faster decision on palliative cases

Reduced mortality

Stronger leadership in case management

Causal modelling

• Three key steps: - Build qualitative causal model - Populate the model

o Systematic review of quantitative dataQuality, quantity, relevance, heterogeneity

o Elicitation of expert belief - Estimate intervention effectiveness using Bayesian

approach • Over to Sam

Fielding et al. 2013 (Clin Med 13;344-8)

• Consultant delivered care (n=260) vs. standard care (n=150)• 16 weeks• Length of stay (median): 4 days vs 7 days• 30-day readmission: 17% vs 14%• In-hospital mortality: 3% vs 6%