Estimation of contribution of changes in classic risk factors to trends incoronary-event rates across the WHO MONICA Project populations

Citation
K. Kuulasmaa et al., Estimation of contribution of changes in classic risk factors to trends incoronary-event rates across the WHO MONICA Project populations, LANCET, 355(9205), 2000, pp. 675-687
Citations number
52
Categorie Soggetti
General & Internal Medicine","Medical Research General Topics
Journal title
LANCET
ISSN journal
01406736 → ACNP
Volume
355
Issue
9205
Year of publication
2000
Pages
675 - 687
Database
ISI
SICI code
0140-6736(20000226)355:9205<675:EOCOCI>2.0.ZU;2-R
Abstract
Background From the mid-1980s to mid-1990s, the WHO MONICA Project monitore d coronary events and classic risk factors for coronary heart disease (CHD) in 38 populations from 21 countries. We assessed the extent to which chang es in these risk factors explain the variation in the trends in coronary-ev ent rates across the populations. Methods In men and women aged 35-64 years, non-fatal myocardial infarction and coronary deaths were registered continuously to assess trends in rates of coronary events. We carried out population surveys to estimate trends in risk factors. Trends in event rates were regressed on trends in risk score and in individual risk factors. Findings Smoking rates decreased in most male populations but trends were m ixed in women; mean blood pressures and cholesterol concentrations decrease d, body-mass index increased, and overall risk scores and coronary-event ra tes decreased. The model of trends in 10-year coronary-event rates against risk scores and single risk factors showed a poor fit, but this was improve d with a 4-year time lag for coronary events. The explanatory power of the analyses was limited by imprecision of the estimates and homogeneity of tre nds in the study populations. Interpretation Changes in the classic risk factors seem to partly explain t he variation in population trends in CHD. Residual variance is attributable to difficulties in measurement and analysis, including time lag, and to fa ctors that were not included, such as medical interventions. The results su pport prevention policies based on the classic risk factors but suggest pot ential for prevention beyond these.