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A history of the population attributable fraction and related measures

机译：人口归因分数的历史和相关度量

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Purpose: Since Doll published the first PAF in 1951, it has been a mainstay. Confusion in terminology abounds with regard to these measures. The ability to estimate all of them in case-control studies as well as in cohort studies is not widely appreciated. Methods: This article reviews and comments on the historical development of the population attributable fraction (PAF), the exposed attributable fraction (EAF), the rate difference (ID), the population rate (or incidence) difference (PID), and the caseload difference (CD). Results: The desire for PAFs to sum to no more than 100% and the interpretation of the complement of a PAF as the proportion of a rate that can be attributed to other causes are shown to stem from the same problem: a failure to recognize the pervasiveness of shared etiologic responsibility among causes. A lack of appreciation that "expected" numbers of cases and deaths are not actually the numbers to be expected when an exposure or intervention appreciably affects person-time denominators for rates, as in the case of smoking and nonnormal body mass, makes many CD estimates inflated. A movement may be gaining momentum to shift away from assuming, often unrealistically, the complete elimination of harmful exposures and toward estimating the effects of realistic interventions. This movement could culminate in a merger of the academic concept of transportability with the applied discipline of risk assessment. Conclusions: A suggestion is offered to pay more attention to absolute measures such as the rate difference, the population rate difference, and the CD, when the latter can be validly estimated and less attention to proportional measures such as the EAF and PAF.
机译：目的：自从Doll在1951年发布第一个PAF以来，它一直是主流。关于这些措施，术语上充斥着混乱。在病例对照研究和队列研究中估计所有这些因素的能力并未得到广泛认可。方法：本文回顾并评论人口归因分数（PAF），暴露归因分数（EAF），比率差异（ID），人口比率（或发病率）差异（PID）和病例数的历史发展差异（CD）。结果：PAF总计不超过100％的愿望以及将PAF补语解释为可归因于其他原因的比率所显示的原因是同一问题：无法识别原因之间普遍存在病因共担责任。缺乏理解，即“预期的”病例和死亡人数实际上不是预期的数字，因为暴露或干预会显着影响人-时间分母，例如吸烟和体重异常，因此许多CD估计值膨胀。一个运动可能正在获得动力，从通常不切实际地假设完全消除有害暴露而转向估计现实干预的效果。这一运动最终可能将可运输性的学术概念与风险评估的应用学科相结合。结论：建议可以更有效地注意诸如速率差异，人口速率差异和CD的绝对量度，而后者可以被有效地估计，而对于诸如EAF和PAF等比例量度的关注则要少一些。

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