National Institute for Health and Welfare, Helsinki, Finland.
Am J Epidemiol. 2010 Apr 1;171(7):837-47. doi: 10.1093/aje/kwp457. Epub 2010 Mar 2.
Quantification of the impact of exposure to modifiable risk factors on a particular outcome at the population level is a fundamental public health issue. In cohort studies, the population attributable fraction (PAF) is used to assess the proportion of the outcome that is attributable to exposure to certain risk factors in a given population during a certain time interval. This is done by combining information about the prevalence of the risk factor in the population with estimates of the strength of the association between the risk factor and the outcome. In case of mortality, the PAF demonstrates what proportion of mortality can be delayed during the given follow-up time. However, literature on carrying out model-based estimation of PAF and its variance in cohort studies while properly taking follow-up time into account is still scarce. In this article, the authors present formulas for estimation of PAF, its variance, and its confidence interval using the piecewise constant hazards model and apply a SAS macro created for the estimation of PAF (SAS Institute Inc., Cary, North Carolina) to estimate the mortality attributable to some common risk factors.
定量评估可改变的风险因素在人群水平上对特定结局的影响是一个基本的公共卫生问题。在队列研究中，人群归因分数（PAF）用于评估在特定时间间隔内，特定人群中暴露于某些风险因素导致的结局的比例。这是通过结合人群中风险因素的流行率信息和风险因素与结局之间关联强度的估计值来实现的。在死亡率的情况下，PAF 表明在给定的随访时间内可以延迟多少比例的死亡。然而，关于在队列研究中基于模型的 PAF 及其方差的估计并适当考虑随访时间的文献仍然很少。在本文中，作者提出了使用分段常数风险模型估计 PAF、其方差和置信区间的公式，并应用了一个为 PAF 估计创建的 SAS 宏（SAS Institute Inc.， Cary，North Carolina）来估计某些常见风险因素导致的死亡率。
