The IMPACT Model Project or How to help policy makers to make decisions

1. The IMPACT Model Project or How to help policy makers to make decisions Simon Capewell Martin O’Flaherty

2. Overview • The CHD problem • Epidemiological Models • The IMPACT Model Project • Past achievements • Current direction • Potential uses. • Aims and Challenges

3. The CHD Problem • CHD=Heart attack, angina, heart failure • Major cause of death and disability • Both in developed and developing countries • Causation reasonably well known • Risk factor paradigm: diet, smoking, BP, activity • Natural History well known • Long silent phase, [disease appears aged 60 or later] • Clinical phase = big reduction in life expectancy • Large evidence base for interventions • Strong evidence : treatments for clinical disease • Sparse evidence: interventions for reducing risk factors and incidence (Primary prevention)

4. CHD policy making problem • Enough evidence to act • Decision Making in Public health • Existing decision aids based on models

5. CHD policy making problem • Enough evidence to act • But need integration and synthesis • Some of the evidence needs processing before use at local level. • Decision Making in Public health • Chaotic • Not always based on existing evidence (politics and values play a significant role) • Need to address issues originated by national level policies (e.g: life expectancy targets, NSF) • Existing decision aids based on models…..

6. CHD policy making problem • Enough evidence to act • Decision Making in Public health • Existing decision aids based on models: • Health Economics modelling • Based on Health Economics Theory • Most are highly focused in the treatment aspect (limited scope) • Methods well developed and accepted as “standard” • “In House” Models • Developed for specific purposes • Examples: • London PHO Smoking and Life expectancy targets • ScHARR model (prediction of admissions) • Not much interest in “comprehensive” modelling of a disease or a group of diseases • Exception: WHO CHOICE program (http://www.who.int/choice/en/) • Mainstream Epidemiology still cautious about the modelling field

7. The CHD epidemiologic modelling world • A comprehensive view of the CHD problem has not been frequently addressed • 6 comprehensive models worldwide • Some have been used in real decision making • Prevent/RIVM CDM in use in the Dutch DH.

8. IMPACT 1: Explaining the trends in CHD mortality rates Decline in CHD mortality in the past 3 decades in the UK and most of the developed world • Main Question addressed by IMPACT: • To what extent changes in risk factors and treatments explain the observed trends in CHD mortality? • Additional questions: • What if? scenarios • Changing risk factors trends • Provision of secondary prevention treatments. • Cost-effectiveness analysis • Uncertainty • Analysis of the extremes method Unal et al. Circulation 2004; 109 (9): 1101.

9. Explaining the UK trends in CHD mortality rates Risk Factors worse: 13% Risk Factors better: 71% Treatments: 42% Unal et al. Circulation 2004; 109 (9): 1101.

10. IMPACT 1: Consistent results across countries and models NEJM 2007; 356:2388-2398

11. IMPACT 1 limitations • Static/Hybrid model • Trends: Compares world at just two time points • Methodological issues : Forecasting & “what if?” scenarios • Hard to implement and use • Limited by spreadsheet technology • NOT User-friendly- need a long period of training • Spreadsheet models considered “transparent”: • But could a model with 44,000+ cells be inspected easily?

12. MRC IMPACT PROJECT

13. IMPACT 2 CVD Policy Model Population Policies & Behaviours Biological Risk Factors Combined CVD Risk CVD Patient Groups Outcomes

14. IMPACT 2 Case fatality incidence Risk Function Risk Factor module CHD treatment module Death

15. Biological Risk Factors Combined CVD Risk CVD Patient Groups Population Policies & Behaviours Diabetes or IGT Physical Activity Unstable Angina Chronic Angina Combined CVD Risk Obesity Non CHD Death Diet Cholesterol LDL (& HDL) MI Early Heart Failure AMI 1st year From All States Smoking Blood Pressure Severe Heart Failure CHD Death MI Subsequent years Other CVD states STROKE etc Deprivation Additional CVD Risk Factors Populations: UK>E&W>Regions>PCT Outputs: Pop based incidence & prevalence/Deaths prevented/LYG/Life expectancy/Costs/CE ratios

16. Challenges • Build a versatile, flexible valid and credible quantitative model of cardiovascular disease epidemiology. • Methods • Data • Software • Transparency (data, structure and assumptions) • Web Interface • Develop a decision support system that uses the model to help make decisions • Take into account different • Decision levels • Scopes • Perspectives • User Skills • Innovative ways of presenting results • Take advantage of the growing availability of accessible data • Incorporate views of Policy makers early in model development

17. Reserve

18. IMPACT 1: How it works Uptake of Rx DPP • 84% • 696 Aspirin for Acute Myocardial Infarction: US, men 55-64 years X X X Decrease of 3.09 mmHg in systolic blood pressure, US population 1980-2000 number of deaths = (1 – e(coefficientxchange)) x deathsin 1980 = (1 – e(–0.035x3.09)) x 26,352 = 2701. NEJM 2007; 356:2388-2398