Evidence-informed policy on infections requires estimates of their effects on health. However, pathogenic variation, whereby occurrence of adverse outcomes depends on the infecting strain, might complicate the study of many infectious agents. Here, we consider the interpretation of epidemiologic studies on effects of infections on health when there is heterogeneity in strain-specific effects and information on strain composition is unavailable. We use potential outcomes and causal inference theory for analyses in the presence of multiple versions of treatment to argue that oft-reported quantities in these studies have a causal interpretation that depends on population frequencies of infecting strains. Moreover, as in other contexts where the treatment-variation-irrelevance assumption might be violated, transportability requires additional considerations, beyond those needed for non-compound exposures. This discussion, that considers potential heterogeneity in strain-specific effects, will facilitate interpretation of these studies, and for the reasons mentioned above, also highlights the value of pathogen subtype data.

翻译：感染相关政策的循证制定需要对其健康影响的估计。然而，病原变异——即不良结局的发生取决于感染菌株的特性——可能使许多传染源的研究复杂化。本文探讨当病原株特异性效应存在异质性且菌株构成信息缺失时，流行病学研究对感染健康效应的解释问题。我们运用潜在结果与因果推断理论，结合多重处理版本的分析框架，论证此类研究中常报告的量值具有依赖于感染菌株群体频率的因果解释。此外，与治疗变异无关性假设可能被违背的其他情境类似，此类研究结果的跨群体迁移需要比非复合暴露更复杂的考量。本文通过探讨病原株特异性效应中潜在的异质性，将促进对这些研究的解读，并基于上述原因强调了病原亚型数据的价值。