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e-Labs and the Stock of Health Method for Simulating Health Policies

e-Labs and the Stock of Health Method for Simulating Health Policies. Philip Couch, Martin O’Flaherty, Matthew Sperrin, Benjamin Green, Panagiotis Balatsoukas, Stephen Lloyd, James McGrath, Claudia Soiland-Reyes, John Ainsworth, Simon Capewell, Iain Buchan.

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e-Labs and the Stock of Health Method for Simulating Health Policies

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  1. e-Labs and the Stock of Health Method for Simulating Health Policies Philip Couch, Martin O’Flaherty, Matthew Sperrin, Benjamin Green, Panagiotis Balatsoukas, Stephen Lloyd, James McGrath, Claudia Soiland-Reyes, John Ainsworth, Simon Capewell, Iain Buchan Philip Couch, Medinfo 2013

  2. Objectives • Develop realistic models of disease that can be used to appraise health policy options • Develop an approach that allows disease models to be used with regional data • Use emerging technology to allow rapid and collaborative development of models

  3. Stock of Health Artery Atheroma Thrombosis

  4. SoH model The Stock of Health lost in year t ( ) is modelled by

  5. Interventions • When a risk factor shift occurs, an instant change in the SoH is allowed at the time of the shift Risk factor shift SoH Calendar time

  6. Coronary Heart Disease Models • Cardiovascular Disease is a public health priority in many parts of the world • 30% of all global deaths in 20081 • Significantly contributes to health inequalities (3 fold higher in most deprived groups)2 • 40% of CVD deaths attributed to coronary heart disease1 • Stock of Health models for Coronary Heart Disease in England and Wales, UK • Mortality • Incidence • 5 risk factors: SBP, Cholesterol, BMI, smoking, diabetes • Global status report on non-communicable diseases 2010. Geneva, World Health Organization, 2011 • Global atlas on cardiovascular disease prevention and control. Geneva, World Health Organization, 2011

  7. Model fitting • Risk factor coefficients • Data from Cardiovascular Lifetime Risk Pooling Project • Parameters determined from an accelerated failure time model fitted to the cohort data • SoH jump • Data from the Prospective Studies Collaboration • Parameters determined by matching simulated hazard or odds ratios for risk factor shifts • Nelder-Mead optimisation

  8. England and Wales, UK • Baseline rate of decline and random element optimised for the UK England and Wales population • Ischemic heart disease mortality data from the UK Office of National Statistics • Risk factor distributions from Health Survey for England • Minimised the distance between: • The observed and simulated: total and age group specific number of CHD deaths (1985 – 2010) • The mean and variance of the age at CHD death (1993 – 2004)

  9. Calibration E&W

  10. GPU Acceleration Host Compute device Compute unit Compute unit Processing elements Processing elements Birth cohort

  11. Information Design

  12. Knowledge Management Work/MethodObject Work protocol Statistical analysis scripts Data-sources Analysis-logs & notes Find Share Reuse Data-preparation scripts Figures/Graphics Working datasets Reports References Slides D2RQ

  13. Conclusion • We have presented: • A flexible modelling methodology (SoH) that enables health policy to be appraised using local health data • A software engineering approach using GPU hardware to accelerate simulations • An information architecture that makes it simpler to develop, share and re-use digital artefacts across social networks of health professionals

  14. Acknowledgements • Funding • UK National Institute for Health Research (NIHR) as part of the Greater Manchester CLAHRC • UK Medical Research Council • European Union and EC FP7 • UK Higher Education Funding Council • Resources • Collaborative Computational Facility at the University of Manchester

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