Presentation given by Dr Yen-Fu and Dr Sam Watson at the CLAHRC WM Programme Steering Committee meeting on 15th April 2015

1. Health economics and causal
modelling in health services research
23/04/2015
Yen-Fu Chen & Sam Watson
Warwick Centre for Applied Health Research & Delivery (W-CAHRD)
CLAHRC WM Programme Steering Committee Meeting, 15 April 2015

2. 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

3. 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 data
o Elicitation of expert belief
3.Estimate intervention effectiveness using Bayesian approach

4. 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)
HTA
Explanatory (independent) variables
Dependent
variables
Intervening variables

5. Consultant presence at weekendsStructure
Intervening
variables /
mechanisms
Higher 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

6. Consultant presence (at weekends)Structure
Intervening
variables /
mechanisms
Higher 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

7. Causal modelling
• Three key steps:
- Build qualitative causal model
- Populate the model
o Systematic review of quantitative data
Quality, quantity, relevance, heterogeneity
o Elicitation of expert belief
- Estimate intervention effectiveness using Bayesian
approach
• Over to Sam

8. 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%