Cox Louis Anthony Tony, Sanders Edward
Cox Associates, 503 Franklin St., Denver, CO 80218, USA.
Risk Anal. 2006 Aug;26(4):881-92. doi: 10.1111/j.1539-6924.2006.00785.x.
Epidemiology textbooks often interpret population attributable fractions based on 2 x 2 tables or logistic regression models of exposure-response associations as preventable fractions, i.e., as fractions of illnesses in a population that would be prevented if exposure were removed. In general, this causal interpretation is not correct, since statistical association need not indicate causation; moreover, it does not identify how much risk would be prevented by removing specific constituents of complex exposures. This article introduces and illustrates an approach to calculating useful bounds on preventable fractions, having valid causal interpretations, from the types of partial but useful molecular epidemiological and biological information often available in practice. The method applies probabilistic risk assessment concepts from systems reliability analysis, together with bounding constraints for the relationship between event probabilities and causation (such as that the probability that exposure X causes response Y cannot exceed the probability that exposure X precedes response Y, or the probability that both X and Y occur) to bound the contribution to causation from specific causal pathways. We illustrate the approach by estimating an upper bound on the contribution to lung cancer risk made by a specific, much-discussed causal pathway that links smoking to a polycyclic aromatic hydrocarbon (PAH) (specifically, benzo(a)pyrene diol epoxide-DNA) adducts at hot spot codons at p53 in lung cells. The result is a surprisingly small preventable fraction (of perhaps 7% or less) for this pathway, suggesting that it will be important to consider other mechanisms and non-PAH constituents of tobacco smoke in designing less risky tobacco-based products.
流行病学教科书常常将基于2×2表格或暴露-反应关联的逻辑回归模型得出的人群归因分数解释为可预防分数，即如果去除暴露因素，人群中可预防的疾病比例。一般来说，这种因果解释并不正确，因为统计关联并不一定意味着因果关系；此外，它没有明确去除复杂暴露中的特定成分可以预防多少风险。本文介绍并举例说明了一种从实际中常见的部分但有用的分子流行病学和生物学信息类型计算可预防分数有效因果解释的有用界限的方法。该方法应用了系统可靠性分析中的概率风险评估概念，以及事件概率与因果关系之间的边界约束（例如暴露X导致反应Y的概率不能超过暴露X先于反应Y的概率，或者X和Y同时发生的概率）来界定特定因果途径对因果关系的贡献。我们通过估计一条将吸烟与多环芳烃（PAH）（具体为苯并(a)芘二醇环氧化物-DNA）加合物在肺细胞p53热点密码子处联系起来的特定且备受讨论的因果途径对肺癌风险的贡献上限来说明该方法。结果表明，这条途径的可预防分数出奇地小（可能为7%或更低），这表明在设计风险较低的烟草制品时，考虑烟草烟雾的其他机制和非PAH成分将非常重要。
