All cause mortality and its determinants in middle aged men in Finland, The Netherlands, and Italy in a 25 year follow up. Issue 2 (June 1991)

• Record Type: Journal Article
• Authors: Menotti, A; Keys, A; Kromhout, D; Nissinen, A; Blackburn, H; Fidanza, F; Giampaoli, S; Karvonen, M; Pekkanen, J; Punsar, S
• Is Part Of: Journal of epidemiology and community health. Volume 45:Issue 2(1991)
• Page Start: 125
• Page End: 130
• Publication Date: 1991-06
• Subjects: Public health -- Periodicals; Epidemiology -- Periodicals; 614.4
• DOI: 10.1136/jech.45.2.125
• Languages: English
• ISSNs: 0143-005X

Abstract

Abstract : STUDY OBJECTIVE--The aims were (1) to compare all cause mortality in population samples of different cultures; and (2) to cross predict fatal event by risk functions involving risk factors usually measured in cardiovascular epidemiology. DESIGN--The study was a 25 year prospective cohort study. The prediction of all cause mortality was made using the multiple logistic equation as a function of 12 risk factors; the prediction of months lived after entry examination was made by the multiple linear regression using the same factors. POPULATION SAMPLES--There were five cohorts of men aged 40-59 years, from Finland (two cohorts, 1677 men), from The Netherlands (one cohort, 878 men), and from Italy (two cohorts, 1712 men). SETTING--The Finnish cohorts came from geographically defined rural areas, the Dutch cohort from a small town in central Holland, and the Italian cohorts from rural villages in northern and central Italy. MEASUREMENTS AND MAIN RESULTS--All cause mortality was highest in Finland (557 per 1000), and lower in The Netherlands (477) and in Italy (475). The solutions of the multiple logistic function showed the significant and almost universal predictive role of certain factors, with rare exceptions. These were age, blood pressure, cigarette smoking, and arm circumference (the latter with a negative relationship). Similar results were obtained when solving a multiple linear regression equation predicting the number of months lived after entry examination as a function of the same factors. The prediction of fatal events in each country, using the risk functions of the others, produced limited errors, the smallest one being -2% and the largest +11%. When solving the logistic model in the pool of all the cohorts with the addition of dummy variables for the identification of nationality, it also appeared that only a small part of the mortality differences between countries is not explained by 12 available risk factors. CONCLUSIONS--A small set of risk factors seems to explain the intercohort differences of 25 year all cause mortality in population samples of three rather different cultures.