Division of Epidemiology, UC Berkeley School of Public Health, 101 Haviland Hall, Berkeley, CA 94720-7358, USA.
Division of Biostatistics, UC Berkeley School of Public Health, 101 Haviland Hall, Berkeley, CA 94720-7358, USA.
Int J Epidemiol. 2018 Feb 1;47(1):332-347. doi: 10.1093/ije/dyx201.
Many public health interventions provide benefits that extend beyond their direct recipients and impact people in close physical or social proximity who did not directly receive the intervention themselves. A classic example of this phenomenon is the herd protection provided by many vaccines. If these 'spillover effects' (i.e. 'herd effects') are present in the same direction as the effects on the intended recipients, studies that only estimate direct effects on recipients will likely underestimate the full public health benefits of the intervention. Causal inference assumptions for spillover parameters have been articulated in the vaccine literature, but many studies measuring spillovers of other types of public health interventions have not drawn upon that literature. In conjunction with a systematic review we conducted of spillovers of public health interventions delivered in low- and middle-income countries, we classified the most widely used spillover parameters reported in the empirical literature into a standard notation. General classes of spillover parameters include: cluster-level spillovers; spillovers conditional on treatment or outcome density, distance or the number of treated social network links; and vaccine efficacy parameters related to spillovers. We draw on high quality empirical examples to illustrate each of these parameters. We describe study designs to estimate spillovers and assumptions required to make causal inferences about spillovers. We aim to advance and encourage methods for spillover estimation and reporting by standardizing spillover parameter nomenclature and articulating the causal inference assumptions required to estimate spillovers.
许多公共卫生干预措施提供的效益不仅惠及直接接受干预的人，还会影响到与其身体或社交关系密切但并未直接接受干预的人。这种现象的一个经典例子是许多疫苗提供的群体保护。如果这些“溢出效应”（即“群体效应”）与对预期接受者的影响方向一致，那么仅估计对接受者的直接影响的研究可能会低估干预措施的全部公共卫生效益。疫苗文献中已经阐述了溢出参数的因果推断假设，但许多衡量其他类型公共卫生干预措施溢出效应的研究并未借鉴该文献。我们结合对中低收入国家实施的公共卫生干预措施溢出效应的系统评价，将实证文献中报告的最广泛使用的溢出参数分类为标准符号。溢出参数的一般类别包括：集群层面的溢出；基于治疗或结果密度、距离或接受治疗的社交网络联系数量的溢出；与溢出相关的疫苗效力参数。我们用高质量的实证例子来说明每一个参数。我们描述了用于估计溢出效应的研究设计以及进行因果推断所需的假设。我们旨在通过标准化溢出参数命名法并阐明进行溢出效应估计所需的因果推断假设，来推进和鼓励溢出效应的估计和报告方法。
