Weed Douglas L
Office of Preventive Oncology, Division of Cancer Prevention, National Cancer Institute, Rockville, MD 20852, USA.
Int J Occup Med Environ Health. 2004;17(1):77-81.
Applying the Precautionary Principle to public health requires a re-evaluation of the methods of inference currently used to make claims about disease causation from epidemiologic and other forms of scientific evidence. In current thinking, a well-established, near-certain causal relationship implies highly consistent statistically significant results across many different studies, large relative risk estimates, extensive understanding of biological mechanisms and dose-response relationships, positive prevention trial results, a clear temporal relationship between cause and effect, and other conditions spelled out in terms of the widely-used causal criteria. The Precautionary Principle, however, states that preventive measures are to be taken when cause and effect relationships are not fully established scientifically. What evidentiary conditions, as reflected in the causal criteria, will be certain enough to warrant precautionary preventive action? This paper argues that minimum evidentiary requirements for causation need to be articulated if the Precautionary Principle is to be successfully incorporated into public health practice. Two precautionary changes to criteria-based methods of causal inference are examined: reducing the number of criteria and weakening the rules of inference accompanying the criteria. Such changes point in the direction of identifying minimum evidentiary conditions, but would be premature without better understanding how well current methods of causal inference work.
将预防原则应用于公共卫生需要重新评估当前用于从流行病学和其他形式的科学证据中推断疾病因果关系的方法。在当前的思维中,一个既定的、几乎确定的因果关系意味着在许多不同的研究中具有高度一致的统计学显著结果、较大的相对风险估计值、对生物学机制和剂量反应关系的广泛理解、阳性预防试验结果、因果之间明确的时间关系,以及根据广泛使用的因果标准阐述的其他条件。然而,预防原则指出,当因果关系尚未在科学上完全确立时,应采取预防措施。因果标准所反映的哪些证据条件足以保证采取预防性预防行动?本文认为,如果要将预防原则成功纳入公共卫生实践,就需要阐明因果关系的最低证据要求。本文研究了基于标准的因果推断方法的两个预防性变化:减少标准数量和弱化与标准相关的推断规则。这些变化指向了确定最低证据条件的方向,但在没有更好地理解当前因果推断方法的效果之前,这样做还为时过早。