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BMC Public Health. 2012; 12: 88.
Published online Jan 28, 2012. doi:  10.1186/1471-2458-12-88
PMCID: PMC3305465
Why choice of metric matters in public health analyses: a case study of the attribution of credit for the decline in coronary heart disease mortality in the US and other populations
Hebe N Gouda,corresponding author1,2 Julia Critchley,3,4 John Powles,1 and Simon Capewell5
1Institute of Public Health, Forvie Site, Robinson Way, University of Cambridge, Cambridge CB2 1SP, UK
2School of Population Health, University of Queensland, Brisbane, Australia
3Institute of Health and Society, Newcastle University, Leech Building, The Medical School, Newcastle-upon-Tyne NE2 4HH, UK
4Division of Population Health Sciences and Education, St George's University of London, Cranmer Terrace, London SW17 0RE, UK
5Division of Public Health, University of Liverpool, Whelan Building, The Quadrangle, Brownlow Hill, Liverpool L69 3 GB, UK
corresponding authorCorresponding author.
Hebe N Gouda: h.gouda/at/uq.edu.au; Julia Critchley: j.a.critchley/at/ncl.ac.uk; John Powles: jwp11/at/cam.ac.uk; Simon Capewell: capewell/at/liverpool.ac.uk
Received September 16, 2011; Accepted January 28, 2012.
Abstract
Background
Reasons for the widespread declines in coronary heart disease (CHD) mortality in high income countries are controversial. Here we explore how the type of metric chosen for the analyses of these declines affects the answer obtained.
Methods
The analyses we reviewed were performed using IMPACT, a large Excel based model of the determinants of temporal change in mortality from CHD. Assessments of the decline in CHD mortality in the USA between 1980 and 2000 served as the central case study.
Results
Analyses based in the metric of number of deaths prevented attributed about half the decline to treatments (including preventive medications) and half to favourable shifts in risk factors. However, when mortality change was expressed in the metric of life-years-gained, the share attributed to risk factor change rose to 65%. This happened because risk factor changes were modelled as slowing disease progression, such that the hypothetical deaths averted resulted in longer average remaining lifetimes gained than the deaths averted by better treatments. This result was robust to a range of plausible assumptions on the relative effect sizes of changes in treatments and risk factors.
Conclusions
Time-based metrics (such as life years) are generally preferable because they direct attention to the changes in the natural history of disease that are produced by changes in key health determinants. The life-years attached to each death averted will also weight deaths in a way that better reflects social preferences.
Keywords: Comparative Effectiveness Research, Policy analysis, Determinants of Mortality, Epidemiologic Methods, Coronary Heart Disease
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