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

Gouda, Hebe N., Critchley, Julia, Powles, John and Capewell, Simon (2012) 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. Bmc Public Health, 12 1: 88-1-88-8. doi:10.1186/1471-2458-12-88


Author Gouda, Hebe N.
Critchley, Julia
Powles, John
Capewell, Simon
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
Journal name Bmc Public Health   Check publisher's open access policy
ISSN 1471-2458
Publication date 2012-01-01
Sub-type Article (original research)
DOI 10.1186/1471-2458-12-88
Open Access Status DOI
Volume 12
Issue 1
Start page 88-1
End page 88-8
Total pages 8
Place of publication London, United Kingdom
Publisher BioMed Central
Collection year 2013
Language eng
Abstract Published: 28 January 2012.
Formatted 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.
Keyword Comparative Effectiveness Research
Policy analysis
Determinants of Mortality
Epidemiologic Methods
Coronary Heart Disease
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
School of Public Health Publications
 
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