PLoS ONE (Jan 2013)

Quantifying policy options for reducing future coronary heart disease mortality in England: a modelling study.

  • Shaun Scholes,
  • Madhavi Bajekal,
  • Paul Norman,
  • Martin O'Flaherty,
  • Nathaniel Hawkins,
  • Mika Kivimäki,
  • Simon Capewell,
  • Rosalind Raine

DOI
https://doi.org/10.1371/journal.pone.0069935
Journal volume & issue
Vol. 8, no. 7
p. e69935

Abstract

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AIMS:To estimate the number of coronary heart disease (CHD) deaths potentially preventable in England in 2020 comparing four risk factor change scenarios. METHODS AND RESULTS:Using 2007 as baseline, the IMPACTSEC model was extended to estimate the potential number of CHD deaths preventable in England in 2020 by age, gender and Index of Multiple Deprivation 2007 quintiles given four risk factor change scenarios: (a) assuming recent trends will continue; (b) assuming optimal but feasible levels already achieved elsewhere; (c) an intermediate point, halfway between current and optimal levels; and (d) assuming plateauing or worsening levels, the worst case scenario. These four scenarios were compared to the baseline scenario with both risk factors and CHD mortality rates remaining at 2007 levels. This would result in approximately 97,000 CHD deaths in 2020. Assuming recent trends will continue would avert approximately 22,640 deaths (95% uncertainty interval: 20,390-24,980). There would be some 39,720 (37,120-41,900) fewer deaths in 2020 with optimal risk factor levels and 22,330 fewer (19,850-24,300) in the intermediate scenario. In the worst case scenario, 16,170 additional deaths (13,880-18,420) would occur. If optimal risk factor levels were achieved, the gap in CHD rates between the most and least deprived areas would halve with falls in systolic blood pressure, physical inactivity and total cholesterol providing the largest contributions to mortality gains. CONCLUSIONS:CHD mortality reductions of up to 45%, accompanied by significant reductions in area deprivation mortality disparities, would be possible by implementing optimal preventive policies.