Menotti A, Keys A, Kromhout D, Nissinen A, Blackburn H, Fidanza F, Giampaoli S, Karvonen M, Pekkanen J, Punsar S
Laboratory of Epidemiology and Biostatistics, Istituto Superiore di Sanitá, Viale Regina Elena, Rome, Italy.
J Epidemiol Community Health. 1991 Jun;45(2):125-30. doi: 10.1136/jech.45.2.125.
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.
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).
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.
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.
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.
目标为(1)比较不同文化背景人群样本中的全因死亡率;(2)通过涉及心血管流行病学中通常测量的危险因素的风险函数交叉预测致命事件。
该研究为一项为期25年的前瞻性队列研究。使用多因素逻辑方程作为12个危险因素的函数来预测全因死亡率;使用相同因素通过多元线性回归来预测入组检查后存活的月数。人群样本:有五组年龄在40 - 59岁的男性,分别来自芬兰(两组,共1677名男性)、荷兰(一组,878名男性)和意大利(两组,共1712名男性)。
芬兰的队列来自地理上界定的农村地区,荷兰的队列来自荷兰中部的一个小镇,意大利的队列来自意大利北部和中部的乡村。
芬兰的全因死亡率最高(每1000人中有557人),荷兰(477人)和意大利(475人)较低。多因素逻辑函数的解显示某些因素具有显著且几乎普遍的预测作用,极少有例外情况。这些因素包括年龄、血压、吸烟和臂围(后者呈负相关)。在求解将入组检查后存活月数作为相同因素函数的多元线性回归方程时,也得到了类似结果。使用其他国家的风险函数预测每个国家的致命事件,产生的误差有限,最小误差为 -2%,最大误差为 +11%。当在所有队列中求解逻辑模型并添加用于识别国籍的虚拟变量时,还发现各国之间死亡率差异中只有一小部分无法用12个可用危险因素来解释。
一小部分危险因素似乎可以解释三种文化背景差异较大的人群样本中25年全因死亡率的队列间差异。