IMPACT: A Generic Tool for Modelling and Simulating Public Health Policy

J. D. Ainsworth; E. Carruthers; P. Couch; N. Green; M. O’Flaherty; M. Sperrin; R. Williams; Z. Asghar; S. Capewell; I. E. Buchan

doi:10.3414/ME11-02-0006

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2011; 50(05): 454-463
DOI: 10.3414/ME11-02-0006

Special Topic – Original Articles

Schattauer GmbH

IMPACT: A Generic Tool for Modelling and Simulating Public Health Policy

J. D. Ainsworth

¹North-west Institute for Bio-Health Informatics, School of Community Based Medicine, University of Manchester, UK

,

E. Carruthers

¹North-west Institute for Bio-Health Informatics, School of Community Based Medicine, University of Manchester, UK

,

P. Couch

¹North-west Institute for Bio-Health Informatics, School of Community Based Medicine, University of Manchester, UK

,

N. Green

¹North-west Institute for Bio-Health Informatics, School of Community Based Medicine, University of Manchester, UK

,

M. O’Flaherty

²Division of Public Health, University of Liverpool, UK

,

M. Sperrin

³Department of Mathematics and Statistics, Lancaster University, UK

,

R. Williams

¹North-west Institute for Bio-Health Informatics, School of Community Based Medicine, University of Manchester, UK

,

Z. Asghar

²Division of Public Health, University of Liverpool, UK

,

S. Capewell

²Division of Public Health, University of Liverpool, UK

,

I. E. Buchan

¹North-west Institute for Bio-Health Informatics, School of Community Based Medicine, University of Manchester, UK

› Author Affiliations

Abstract

Summary

Background: Populations are under-served by local health policies and management of resources. This partly reflects a lack of realistically complex models to enable appraisal of a wide range of potential options. Rising computing power coupled with advances in machine learning and healthcare information now enables such models to be constructed and executed. However, such models are not generally accessible to public health practitioners who often lack the requisite technical knowledge or skills.

Objectives: To design and develop a system for creating, executing and analysing the results of simulated public health and health-care policy interventions, in ways that are accessible and usable by modellers and policy-makers.

Methods: The system requirements were captured and analysed in parallel with the statistical method development for the simulation engine. From the resulting software requirement specification the system architecture was designed, implemented and tested. A model for Coronary Heart Disease (CHD) was created and validated against empirical data.

Results: The system was successfully used to create and validate the CHD model. The initial validation results show concordance between the simulation results and the empirical data.

Conclusions: We have demonstrated the ability to connect health policy-modellers and policy-makers in a unified system, thereby making population health models easier to share, maintain, reuse and deploy.

Keywords

Discrete event simulation - decision support - public health - policy modelling - computer simulation

Full Text

References

References
1 Morabia A. A history of epidemiologic methods and concepts. Birkhauser; 2004
2 Guo Z, Gill TM, Allore HG. Modeling repeated time-to-event health conditions with discontinuous risk intervals. An example of a longitudinal study of functional disability among older persons. Methods Inf Med 2008; 47 (02) 107-116.
3 Radespiel-Tröger M, Daugs A, Meyer M. A simulation model for small-area cancer incidence rates. Methods Inf Med 2004; 43 (05) 493-498.
4 Pfeifer B, Wurz M, Hanser F, Seger M, Netzer M, Osl M. et al. An epidemiological modeling and data integration framework. Methods Inf Med 2010; 49 (03) 290-296.
5 Capewell S, Allender S, Critchley J, Lloyd-Williams F, O’Flaherty M, Rayner M, Scarborough P. Modelling the UK burden of cardiovascular disease to 2020 A Research Report for the Cardio & Vascular Coalition and the British Heart Foundation. BHF 2008 http://www.bhf.org.uk/cvc/working_groups/modelling_burden_of_cvd.aspx Accessed 14/04/11
6 Response from the Faculty of Public Health to the Department of Health Consultation on Economic Modeling of a Policy of Vascular Checks http://www.fphm.org.uk/advocacy/consultations/downloads/2008/DH_vascular_checks_0808.pdf Accessed 14/04/11
7 Ford E, Ajani U, Croft J, Critchley J, Labarthe D, Kottke T. et al. Explaining the decrease in US deaths from coronary disease, 1980-2000. N Engl J Med 2007; 356 (23) 2388.
8 Moreau Y, Antal P, Fannes G, De Moor B. Probabilistic graphical models for computational biomedicine. Methods Inf Med 2003; 42 (02) 161-168.
9 Buchan I, Winn J, Bishop C. A unified modelling approach to data intensive healthcare. In: Hey T. et al. (eds). The Fourth Paradigm: Data Intensive Scientific Discovery. Microsoft Press; 2009
10 Taylor-Robinson D, Milton B, Lloyd-Williams F, O’Flaherty M, Capewell S. Policy-makers’ attitudes to decision support models for coronary heart disease: a qualitative study. J Health Serv Res Policy 2008; 13 (04) 209-214.
11 De Roure D, Goble C, Stevens R. The Design and Realisation of the myExperiment Virtual Research Environment for Social Sharing of Workflows. Future Generation Computer Systems 2008; 25: 561-567.
12 Wei L. The accelerated failure time model: A useful alternative to the Cox regression model in survival analysis. Statistics in Medicine 1992; 11: 1871-1879.
13 Banks J, Nelson B L, Nicol D M. Discrete-Event System Simulation. Prentice Hall, 2009
14 Prentice R, Kaldeisch J, Flournoy A, Farewell N and Breslow N. The analysis of failure times in the presence of competing risks. Biometrics 1978; 34 (04) 541-554.
15 Putter H, Fiocco M, and Geskus R. Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine 2007; 26: 2389-2430.
16 Lloyd-Jones D, Leip E, Larson M, D’Agostino R, Beiser A, Wilson P, Wolf P, Levy D. Prediction of life-time risk for cardiovascular disease by risk factor burden at 50 years of age. Circulation 2006; 113 (06) 791-798.
17 van Laarhoven P, and Aarts E. Simulated annealing: theory and applications. Kluwer; 1987
18 Lin J. Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory 1991; 37 (01) 145-151.
19 Jensen F, Nielsen T. Bayesian networks and decision graphs. Springer Verlag; 2007
20 Capewell S, Murphy NF, MacIntyre K, Frame S, Stewart S, Chalmers JWT. et al. Short-term and long-term outcomes in 133 429 emergency patients admitted with angina or myocardial infarction in Scotland, 1990-2000: population-based cohort study. British Medical Journal 2006; 92 (11) 1563.
21 Ford E, Ajani U, Croft J, Critchley J, Labarthe D, Kottke T, Giles W, Capewell S. Explaining the decrease in U. S. deaths from coronary disease, 1980-2000. N Engl J Med 2007; 356 (23) 2388-2398.
22 Capewell S, Pell J, Morrison C, McMurray J. Increasing the impact of cardiological treatments. Eur Heart J 1999; 20 (19) 1386-1392.
23 Wilkins J, Ning H, Berry J, Lloyd-Jones D. Lifetime Risk for Total Cardiovascular Disease by Sex and Risk Factor Burden: Cardiovascular Lifetime Risk Pooling Project. Circulation 2010; 122: A21180.
24 Prospective Studies Collaboration.. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 2002; 360: 1903-1913.
25 Prospective Studies Collaboration Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55000 vascular deaths. Lancet 2007; 370: 1829-1839.
26 McQueen M, Hawken S, Wang X, Ounpuu S, Sniderman A, Probstfield J, Steyn K, Sanderson J, Hasani M, Volkova E, Kazmi K, Yusuf S. Lipids, lipoproteins, and apolipoproteins as risk markers of myocardial infarction in 52 countries (the INTER-HEART study): a case-control study. Lancet 2008; 372 9634 224-233.
27 Ainsworth J, Buchan I. e-Labs and Work Objects: Towards Digital Health Economies. In Proceedings of The First International ICST Conference on Communications Infrastructure, Systems and Applications in Europe (EuropeComm 2009), LNICST, Springer, London, UK, August 2009
28 Bechhofer S, Ainsworth J, Bhagat J, Buchan I, Couch P, Cruickshank D, Delderfield M, Dunlop I, Gamble M, Goble C, Michaelides D, Missier P, Owen S, Newman D, De Roure D, Sufi S. Why Linked Data is Not Enough for Scientists. presented at the Sixth IEEE e-Science conference (e-Science 2010), Brisbane, Australia, 2010