title: 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.
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.
authors.affiliation: Institute of Public Health, Forvie Site, Robinson Way, University of Cambridge, Cambridge CB2 1SP, UK. h.gouda@uq.edu.au
authors.forename: Hebe N
authors.initials: HN
authors.lastname: Gouda
authors.forename: Julia
authors.initials: J
authors.lastname: Critchley
authors.forename: John
authors.initials: J
authors.lastname: Powles
authors.forename: Simon
authors.initials: S
authors.lastname: Capewell
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015982:Bias; D003327:Coronary Disease / Q000401:mortality*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011634:Public Health / Q000706:statistics & numerical data*; D014481:United States / Q000453:epidemiology