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Abstract：
Modern medicine is overwhelmed by a plethora of both established risk factors and novel biomarkers for diseases. The majority of this information is expressed by probabilistic measures of association such as the odds ratio (OR) obtained by calculating differences in average \"risk\" between exposed and unexposed groups. However, recent research demonstrates that even ORs of considerable magnitude are insufficient for assessing the ability of risk factors or biomarkers to distinguish the individuals who will develop the disease from those who will not. In regards to coronary heart disease (CHD), we already know that novel biomarkers add very little to the discriminatory accuracy (DA) of traditional risk factors. However, the value added by traditional risk factors alongside simple demographic variables such as age and sex has been the subject of less discussion. Moreover, in public health, we use the OR to calculate the population attributable fraction (PAF), although this measure fails to consider the DA of the risk factor it represents. Therefore, focusing on CHD and applying measures of DA, we re-examine the role of individual demographic characteristics, risk factors, novel biomarkers and PAFs in public health and epidemiology. In so doing, we also raise a more general criticism of the traditional risk factors\' epidemiology. We investigated a cohort of 6103 men and women who participated in the baseline (1991-1996) of the Malmö Diet and Cancer study and were followed for 18 years. We found that neither traditional risk factors nor biomarkers substantially improved the DA obtained by models considering only age and sex. We concluded that the PAF measure provided insufficient information for the planning of preventive strategies in the population. We need a better understanding of the individual heterogeneity around the averages and, thereby, a fundamental change in the way we interpret risk factors in public health and epidemiology.
摘要：
现代医学被大量既定的风险因素和新的疾病生物标志物所淹没。这些信息的大部分通过关联的概率度量来表示，例如通过计算暴露组和未暴露组之间的平均“风险”差异获得的优势比（OR）。然而,最近的研究表明,即使是相当大的OR,也不足以评估危险因素或生物标志物区分将患该疾病的个体和不会患该疾病的个体的能力.关于冠心病（CHD），我们已经知道,新的生物标志物对传统风险因素的辨别准确性(DA)几乎没有增加.然而,传统风险因素以及年龄和性别等简单的人口统计学变量所增加的价值一直是较少讨论的主题。此外，在公共卫生方面，我们使用OR计算人口归因分数(PAF)，尽管这项措施没有考虑其所代表的风险因素的DA。因此,以冠心病为重点，应用DA措施，我们重新审视个体人口统计特征的作用，危险因素，公共卫生和流行病学中的新型生物标志物和PAFs。这样做,我们还对传统的流行病学风险因素提出了更普遍的批评。我们调查了6103名男性和女性，他们参加了马尔默饮食与癌症研究的基线（1991-1996），并随访了18年。我们发现，传统的危险因素和生物标志物都不能显着改善仅考虑年龄和性别的模型获得的DA。我们得出的结论是，PAF措施为规划人口预防策略提供了不足的信息。我们需要更好地了解平均值周围的个体异质性，因此，我们解释公共卫生和流行病学危险因素的方式发生了根本性变化.
