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Scientifica
Volume 2014 (2014), Article ID 748750, 21 pages
http://dx.doi.org/10.1155/2014/748750
Review Article

The Preventable Risk Integrated ModEl and Its Use to Estimate the Health Impact of Public Health Policy Scenarios

1British Heart Foundation Centre on Population Approaches to Non-Communicable Disease Prevention, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
2DCU School of Computing, Dublin City University, Dublin, Ireland

Received 27 April 2014; Accepted 8 September 2014; Published 25 September 2014

Academic Editor: Giuseppe Mastrangelo

Copyright © 2014 Peter Scarborough et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. IHME, Global Burden of Disease Visualisations, University of Washington, Washington, DC, USA, 2014, http://viz.healthmetricsandevaluation.org/gbd-compare/.
  2. M. Nichols, N. Townsend, P. Scarborough et al., European Cardiovascular Disease Statistics 2012 Edition, European Cardiovascular Disease Statistics, Brussels, Belgium, 2012.
  3. A. Keys, A. Menotti, M. J. Karvonen et al., “The diet and 15-year death rate in the seven countries study,” American Journal of Epidemiology, vol. 124, no. 6, pp. 903–915, 1986. View at Google Scholar · View at Scopus
  4. M. J. McQueen, S. Hawken, X. Wang et al., “Lipids, lipoproteins, and apolipoproteins as risk markers of myocardial infarction in 52 countries (the INTERHEART study): a case-control study,” The Lancet, vol. 372, no. 9634, pp. 224–233, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. WCRF/AICR, Food, Nutrition, Physical Activity, and the Prevention of Cancer: A Global Perspective, AICR, Washington, DC, USA, 2007.
  6. S. Lewington, R. Clarke, N. Qizilbash, R. Peto, and R. Collins, “Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies,” The Lancet, vol. 360, no. 9349, pp. 1903–1913, 2002. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Lewington, G. Whitlock, Prospective Studies Collaboration et al., “Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55,000 vascular deaths,” The Lancet, vol. 370, no. 9602, pp. 1829–1839, 2007. View at Publisher · View at Google Scholar
  8. G. Whitlock, S. Lewington, P. Sherliker et al., “Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies,” The Lancet, vol. 373, no. 9669, pp. 1083–1096, 2009. View at Google Scholar
  9. F. J. He, J. Li, and G. A. Macgregor, “Effect of longer-term modest salt reduction on blood pressure,” Cochrane Database of Systematic Reviews, no. 4, Article ID CD004937, 2013. View at Google Scholar · View at Scopus
  10. F. J. He, J. Li, and G. A. MacGregor, “Effect of longer term modest salt reduction on blood pressure: cochrane systematic review and meta-analysis of randomised trials,” British Medical Journal, vol. 346, no. 7903, Article ID f1325, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. D. M. Lloyd-Jones, P. W. F. Wilson, M. G. Larson et al., “Framingham risk score and prediction of lifetime risk for coronary heart disease,” The American Journal of Cardiology, vol. 94, no. 1, pp. 20–24, 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Hippisley-Cox, C. Coupland, Y. Vinogradova, J. Robson, M. May, and P. Brindle, “Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study,” The British Medical Journal, vol. 335, no. 7611, pp. 136–141, 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Nichols, P. Scarborough, S. Allender, and M. Rayner, “What is the optimal level of population alcohol consumption for chronic disease prevention in England? Modelling the impact of changes in average consumption levels,” BMJ Open, vol. 2, no. 3, Article ID e000957, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. J. M. Lightwood, P. G. Coxson, K. Bibbins-Domingo, L. W. Williams, and L. Goldman, “Coronary heart disease attributable to passive smoking: CHD Policy Model,” American Journal of Preventive Medicine, vol. 36, no. 1, pp. 13–20, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Carter, M. Moodie, A. Markwick et al., “Assessing Cost-Effectiveness in Obesity (ACE-Obesity): an overview of the ACE approach, economic methods and cost results,” BMC Public Health, vol. 9, article 419, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. D. A. Cadilhac, T. B. Cumming, L. Sheppard, D. C. Pearce, R. Carter, and A. Magnus, “The economic benefits of reducing physical inactivity: an Australian example,” The International Journal of Behavioral Nutrition and Physical Activity, vol. 8, article 99, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. B. Unal, J. Critchley, and S. Capewell, “Impact of smoking reduction on coronary heart disease mortality trends during 1981–2000 in England and Wales,” Tobacco Induced Diseases, vol. 1, no. 3, p. 185, 2003. View at Google Scholar
  18. S. K. Lhachimi, W. J. Nusselder, H. A. Smit et al., “Dynamo-HIA-a dynamic modeling tool for generic health impact assessments,” PLoS ONE, vol. 7, no. 5, Article ID e33317, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. C. Wang, K. McPherson, T. Marsh, S. L. Gortmaker, and M. Brown, “Health and economic burden of the projected obesity trends in the USA and the UK,” The Lancet, vol. 378, no. 9793, pp. 815–825, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. P. Scarborough, K. E. Nnoaham, D. Clarke, S. Capewell, and M. Rayner, “Modelling the impact of a healthy diet on cardiovascular disease and cancer mortality,” Journal of Epidemiology and Community Health, vol. 66, no. 5, pp. 420–426, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Holmes, Y. Meng, P. S. Meier et al., “Effects of minimum unit pricing for alcohol on different income and socioeconomic groups: a modelling study,” The Lancet, vol. 383, no. 9929, pp. 1655–1664, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. A. D. M. Briggs, A. Kehlbacher, R. Tiffin, T. Garnett, M. Rayner, and P. Scarborough, “Assessing the impact on chronic disease of incorporating the societal cost of greenhouse gases into the price of food: an econometric and comparative risk assessment modelling study,” BMJ Open, vol. 3, no. 10, Article ID e003543, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. S. S. Lim, T. Vos, A. D. Flaxman et al., “A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study,” The Lancet, vol. 380, no. 9859, pp. 2224–2260, 2010. View at Google Scholar
  24. C. J. L. Murray, M. A. Richards, J. N. Newton et al., “UK health performance: findings of the Global Burden of Disease Study 2010,” The Lancet, vol. 381, no. 9871, pp. 997–1020, 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. L. Dauchet, P. Amouyel, S. Hercberg, and J. Dallongeville, “Fruit and vegetable consumption and risk of coronary heart disease: a meta-analysis of cohort studies,” Journal of Nutrition, vol. 136, no. 10, pp. 2588–2593, 2006. View at Google Scholar · View at Scopus
  26. L. Dauchet, P. Amouyel, and J. Dallongeville, “Fruit and vegetable consumption and risk of stroke: a meta-analysis of cohort studies,” Neurology, vol. 65, no. 8, pp. 1193–1197, 2005. View at Publisher · View at Google Scholar · View at Scopus
  27. M. A. Pereira, E. O'Reilly, K. Augustsson et al., “Dietary fiber and risk of coronary heart disease: a pooled analysis of cohort studies,” Archives of Internal Medicine, vol. 164, no. 4, pp. 370–376, 2004. View at Publisher · View at Google Scholar · View at Scopus
  28. D. E. Threapleton, D. C. Greenwood, C. E. L. Evans et al., “Dietary fiber intake and risk of first stroke: a systematic review and meta-analysis,” Stroke, vol. 44, no. 5, pp. 1360–1368, 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. T. Norat, D. Chan, R. Lau, D. Aune, and R. Vieira, WCRF/AICR Systematic Literature Review Continuous Update Project Report: The Associations Between Food, AICR, Washington, DC, USA, 2010.
  30. P. E. Ronksley, S. E. Brien, B. J. Turner, K. J. Mukamal, and W. A. Ghali, “Association of alcohol consumption with selected cardiovascular disease outcomes: a systematic review and meta-analysis,” The British Medical Journal, vol. 342, article d671, 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. D. O. Baliunas, B. J. Taylor, H. Irving et al., “Alcohol as a risk factor for type 2 diabetes: a systematic review and meta-analysis,” Diabetes Care, vol. 32, no. 11, pp. 2123–2132, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. J. Rehm, B. Taylor, S. Mohapatra et al., “Alcohol as a risk factor for liver cirrhosis: a systematic review and meta-analysis,” Drug and Alcohol Review, vol. 29, no. 4, pp. 437–445, 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. M. J. Thun, L. F. Apicella, and S. J. Henley, “Smoking vs other risk factors as the cause of smoking-attributable deaths. Confounding in the courtroom,” Journal of the American Medical Association, vol. 284, no. 6, pp. 706–712, 2000. View at Publisher · View at Google Scholar · View at Scopus
  34. C. Willi, P. Bodenmann, W. A. Ghali, P. D. Faris, and J. Cornuz, “Active smoking and the risk of type 2 diabetes: a systematic review and meta-analysis,” Journal of the American Medical Association, vol. 298, no. 22, pp. 2654–2664, 2007. View at Publisher · View at Google Scholar · View at Scopus
  35. S. Gandini, E. Botteri, S. Iodice et al., “Tobacco smoking and cancer: a meta-analysis,” International Journal of Cancer, vol. 122, no. 1, pp. 155–164, 2008. View at Publisher · View at Google Scholar · View at Scopus
  36. B. Zhou, L. Yang, Q. Sun et al., “Cigarette smoking and the risk of endometrial cancer: a meta-analysis,” The American Journal of Medicine, vol. 121, no. 6, pp. 501.e3–508.e3, 2008. View at Publisher · View at Google Scholar · View at Scopus
  37. Y. C. A. Lee, C. Cohet, Y. C. Yang, L. Stayner, M. Hashibe, and K. Straif, “Meta-analysis of epidemiologic studies on cigarette smoking and liver cancer,” International Journal of Epidemiology, vol. 38, no. 6, Article ID dyp280, pp. 1497–1511, 2009. View at Publisher · View at Google Scholar · View at Scopus
  38. R. Clarke, C. Frost, R. Collins, P. Appleby, and R. Peto, “Dietary lipids and blood cholesterol: quantitative meta-analysis of metabolic ward studies,” British Medical Journal, vol. 314, no. 7074, pp. 112–117, 1997. View at Publisher · View at Google Scholar · View at Scopus
  39. E. Christiansen and L. Garby, “Prediction of body weight changes caused by changes in energy balance,” European Journal of Clinical Investigation, vol. 32, no. 11, pp. 826–830, 2002. View at Publisher · View at Google Scholar · View at Scopus
  40. A. D. M. Briggs, A. Mizdrak, and P. Scarborough Sr., “A statin a day keeps the doctor away: comparative proverb assessment modelling study,” The British Medical Journal, vol. 347, Article ID f7267, 2013. View at Publisher · View at Google Scholar · View at Scopus
  41. H. Becher, “The concept of residual confounding in regression models and some applications,” Statistics in Medicine, vol. 11, no. 13, pp. 1747–1758, 1992. View at Publisher · View at Google Scholar · View at Scopus
  42. M. Ezzati, A. D. Lopez, A. Rodgers, S. Vander Hoorn, and C. J. L. Murray, “Selected major risk factors and global and regional burden of disease,” The Lancet, vol. 360, no. 9343, pp. 1347–1360, 2002. View at Publisher · View at Google Scholar · View at Scopus
  43. A. D. Lopez, C. D. Mathers, M. Ezzati, D. T. Jamison, and C. J. Murray, “Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data,” The Lancet, vol. 367, no. 9524, pp. 1747–1757, 2006. View at Publisher · View at Google Scholar · View at Scopus
  44. C. Hennekens and J. Buring, “Epidemiology in medicine,” in Measures of Disease Frequency and Association, chapter 4, Little, Brown and Co., Boston, Mass, USA, 1987. View at Google Scholar
  45. J. Woodcock, O. H. Franco, N. Orsini, and I. Roberts, “Non-vigorous physical activity and all-cause mortality: Systematic review and meta-analysis of cohort studies,” International Journal of Epidemiology, vol. 40, no. 1, pp. 121–138, 2011. View at Publisher · View at Google Scholar · View at Scopus
  46. A. Mizdrak and P. Scarborough, The Sensitivity of an Obesity Model to Physical Activity Inputs, International Congress on Obesity, Kuala Lumpur, Malaysia, 2014.
  47. A. Ali, E. Becker, M. Chaudhury et al., Health Survey for England 2006. Cardiovascular Disease and Risk Factors in Adults, The Information Centre, Leeds, UK, 2008.
  48. T. E. Oliphant, “Python for scientific computing,” Computing in Science and Engineering, vol. 9, no. 3, article 10, Article ID 4160250, 2007. View at Publisher · View at Google Scholar · View at Scopus
  49. SQLAlchemy, “The python SQL toolkit and object relational mapper,” 2014, http://www.sqlalchemy.org/.
  50. M. Bélanger, M. Poirier, J. Jbilou, and P. Scarborough, “Modelling the impact of compliance with dietary recommendations on cancer and cardiovascular disease mortality in Canada,” Public Health, vol. 128, no. 3, pp. 222–230, 2014. View at Publisher · View at Google Scholar · View at Scopus
  51. A. D. M. Briggs, O. T. Mytton, A. Kehlbacher, R. Tiffin, M. Rayner, and P. Scarborough, “Overall and income specific effect on prevalence of overweight and obesity of 20% sugar sweetened drink tax in UK: econometric and comparative risk assessment modelling study,” BMJ, vol. 347, Article ID f6189, 2013. View at Publisher · View at Google Scholar · View at Scopus
  52. A. D. M. Briggs, O. T. Mytton, D. Madden, D. O'Shea, M. Rayner, and P. Scarborough, “The potential impact on obesity of a 10% tax on sugar-sweetened beverages in Ireland, an effect assessment modelling study,” BMC Public Health, vol. 13, no. 1, article 860, 2013. View at Publisher · View at Google Scholar · View at Scopus
  53. O. Mytton, A. Gray, M. Rayner, and H. Rutter, “Could targeted food taxes improve health?” Journal of Epidemiology & Community Health, vol. 61, no. 8, pp. 689–694, 2007. View at Publisher · View at Google Scholar · View at Scopus
  54. K. E. Nnoaham, G. Sacks, M. Rayner, O. Mytton, and A. Gray, “Modelling income group differences in the health and economic impacts of targeted food taxes and subsidies,” International Journal of Epidemiology, vol. 38, no. 5, pp. 1324–1333, 2009. View at Publisher · View at Google Scholar · View at Scopus
  55. P. Scarborough, S. Allender, D. Clarke, K. Wickramasinghe, and M. Rayner, “Modelling the health impact of environmentally sustainable dietary scenarios in the UK,” European Journal of Clinical Nutrition, vol. 66, no. 6, pp. 710–715, 2012. View at Publisher · View at Google Scholar · View at Scopus
  56. P. Scarborough, R. D. Morgan, P. Webster, and M. Rayner, “Differences in coronary heart disease, stroke and cancer mortality rates between England, Wales, Scotland and Northern Ireland: the role of diet and nutrition,” BMJ Open, vol. 1, no. 1, Article ID e000263, 2011. View at Publisher · View at Google Scholar
  57. CCC, The Fourth Carbon Budget. Reducing Emissions through the 2020s, Committee on Climate Change, London, UK, 2010.
  58. SACN. Scientific Advisory Committee on Nutrition, Salt and Health, The Stationery Office, London, UK, 2003.
  59. Health Canada, Eating Well with Canada's Food Guide, 2014, http://www.hc-sc.gc.ca/fn-an/food-guide-aliment/order-commander/eating_well_bien_manger-eng.php.
  60. T. Vos, R. Carter, J. Barendregt et al., “Assessing cost-effectiveness in prevention. ACE-Prevention,” Final Report, University of Queensland and Deakin University, Melbourne, Australia, 2010. View at Google Scholar
  61. Government U, Reducing Harmful Drinking, UK Government, London, UK, 2014, https://www.gov.uk/government/policies/reducing-harmful-drinking.
  62. E. Goddard, General Household Survey 2006. Smoking and Drinking among Adults, Office for National Statistics, Newport, UK, 2008.
  63. MAFF, National Food Survey: Household Foo Consumption and Expenditure 2000, Ministry of Agriculture, Fisheries and Food, London, UK, 2000.
  64. E. Audsley, M. Brander, J. Chatterton, D. Murphy-Bokern, C. Webster, and A. Williams, How Low Can We Go? An Assessment of Greenhouse Gas Emissions from the UK Food System and the Scope to Reduce Them by 2050, WWF-UK, London, UK, 2009.
  65. ONS, Family Food in 2008, The Stationery Office, London, UK, 2010.
  66. D. Moran, M. MacLeod, E. Wall et al., UK Marginal Abatement Cost Curves for the Agriculture and Land Use, Land-Use Change and Forestry Sectors out to 2022, with Qualitative Analysis of Options to 2050. Report to the Committee on Climate Change, Committee on Climate Change, London, UK, 2008.
  67. B. Mihaylova, J. Emberson, L. Blackwell et al., “The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials,” The Lancet, vol. 380, no. 9841, pp. 581–590, 2012. View at Publisher · View at Google Scholar · View at Scopus
  68. N. K. Stout, A. B. Knudsen, C. Y. Kong, P. M. McMahon, and G. S. Gazelle, “Calibration methods used in cancer simulation models and suggested reporting guidelines,” PharmacoEconomics, vol. 27, no. 7, pp. 533–545, 2009. View at Publisher · View at Google Scholar · View at Scopus
  69. P. G. Coxson, N. R. Cook, M. Joffres et al., “Mortality benefits from us population-wide reduction in sodium consumption: projections from 3 modeling approaches,” Hypertension, vol. 61, no. 3, pp. 564–570, 2013. View at Publisher · View at Google Scholar · View at Scopus
  70. G. C. Nelson, D. van der Mensbrugghe, H. Ahammad et al., “Agriculture and climate change in global scenarios: why don't the models agree,” Agricultural Economics, vol. 45, no. 1, pp. 85–101, 2014. View at Publisher · View at Google Scholar · View at Scopus
  71. N. Punyacharoensin, W. J. Edmunds, D. de Angelis, and R. G. White, “Mathematical models for the study of HIV spread and control amongst men who have sex with men,” European Journal of Epidemiology, vol. 26, no. 9, pp. 695–709, 2011. View at Publisher · View at Google Scholar · View at Scopus