1. Data Needs for a Model Martin O’Flaherty Simon Capewell
Cardiovascular Epidemiology and Epidemiological Modelling
Data Needs for a Model
Martin O’Flaherty
Simon Capewell
Division of Public Health
University of Liverpool

2. Model data needs Depend on: Key issues Questions to be addressed
Choice of model logic
Desired outputs
Purpose:
Populate the model
Validate the model
Key issues
Availability
Format
A modeller’s dilemma: Built your model around your data OR
Build a model and then gather the data.

3. A generic model of a chronic disease
Healthy
Disease
Death

4. A generic model addressing a generic public health question
Healthy
Disease
Death
Primary Prevention
Secondary Prevention

5. A generic model of a chronic disease
What determines
INCIDENCE
Healthy
Disease
How large are the groups
What Determines prognosis
Death
Time

6. A summary of IMPACT data needs
Number of people in each group
In each risk factor
In each disease subgroup (eg: AMI, UA, CA, HF)
Number of deaths
What determines incidence
Risk factors effect measures (RR or b)
What determines prognosis:
Case fatality rates for each disease group (rates)
Interventions that reduce case fatality (RRR)
Uptake of those interventions (%)

7. A summary of IMPACT data needs
Take into account time
Trends of:
Number of people in each group
Levels of risk factors
Levels of uptake of treatments
Data to validate the model
Observed mortality

8. Some last (but not least) data need
Data to support assumptions:
Needed to fill gaps in knowledge
They are guesses, but the better the data supporting the guess the better the guess.
An example:
“English fatality rates: Scottish SLIDE data adjusted using England/Scotland SMR (see
Data will need:
Critical appraisal
Adaptation
Documented (appendices)

9. The data gathering task
This phase of a modelling project is critical:
Defines feasibility
Defines the quality of the final product (the principle of “garbage IN, garbage OUT”
It is time consuming: it will take a large amount of the available resources.
But as any activity in a modelling exercise, data gathering is an ITERATION:
We start simple, and we add layers of greater complexity.