Am J Epidemiol. 2023 Feb 1;192(2):147-153. doi: 10.1093/aje/kwac191.
Here we discuss possible violations of the "no-multiple-versions-of-treatment" assumption in studies of outdoor fine particulate air pollution (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5)) owing to differences in particle composition, which in turn influence health. This assumption is part of the potential outcomes framework for causal inference, and it is needed for well-defined potential outcomes, as multiple versions of the same treatment could lead to different health risks for the same level of treatment. Since 2 locations can have the same outdoor PM2.5 mass concentration (i.e., treatment) but different chemical compositions (i.e., versions of treatment), violations of the "no-multiple-versions-of-treatment" assumption seem likely. Importantly, violations of this assumption will not bias health risk estimates for PM2.5 mass concentrations if there are no unmeasured confounders of the "version of treatment"-outcome relationship. However, confounding can occur if these factors are not identified and controlled for in the analysis. We describe situations in which this may occur and provide simulations to estimate the magnitude and direction of this possible bias. In general, violations of the "no-multiple-versions-of-treatment" assumption could be an underappreciated source of bias in studies of outdoor PM2.5. Analysis of the health impacts of outdoor PM2.5 mass concentrations across spatial domains with similar composition could help to address this issue.
在这里,我们讨论了由于颗粒组成的差异而导致户外细颗粒物空气污染(空气动力学直径小于或等于 2.5μm 的颗粒物(PM2.5))研究中可能违反“无多种治疗版本”假设的情况,而颗粒组成的差异反过来又会影响健康。这一假设是因果推理潜在结果框架的一部分,也是明确定义潜在结果所必需的,因为同一种治疗方法的多种版本可能会导致相同治疗水平的不同健康风险。由于 2 个地点可能具有相同的户外 PM2.5 质量浓度(即治疗),但具有不同的化学成分(即治疗版本),因此违反“无多种治疗版本”假设似乎是合理的。重要的是,如果不存在“治疗版本-结果关系”的未测量混杂因素,那么违反这一假设不会影响 PM2.5 质量浓度的健康风险估计。然而,如果在分析中没有识别和控制这些因素,就可能会出现混杂。我们描述了可能发生这种情况的情况,并提供了模拟来估计这种可能偏差的大小和方向。一般来说,违反“无多种治疗版本”假设可能是户外 PM2.5 研究中被低估的偏倚来源。分析具有相似组成的不同空间域的户外 PM2.5 质量浓度对健康的影响可能有助于解决这一问题。