Spillover effects in epidemiology: parameters, study designs and methodological considerations
Spillover effects in epidemiology: parameters, study designs and methodological considerations
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DOI：
10.1093/ije/dyx201
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发表时间：
2018-02-01
影响因子：
7.7
通讯作者：
Hubbard, Alan E.
中科院分区：
文献类型：
--
作者：
Benjamin-Chung, Jade;Arnold, Benjamin F.;Hubbard, Alan E.
许多公共卫生干预措施所提供的惠益超出了其直接受益者的范围，并影响到身体或社会关系密切的人，而这些人本身并没有直接接受干预措施。这种现象的一个典型例子是许多疫苗提供的群体保护。如果这些“溢出效应”（即“羊群效应”）与对预期接受者的影响方向相同，则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。在疫苗文献中已经阐明了溢出参数的因果推断假设，但许多测量其他类型公共卫生干预措施溢出的研究并没有借鉴该文献。结合我们对低收入和中等收入国家公共卫生干预措施的溢出效应进行的系统性综述，我们将经验文献中报告的最广泛使用的溢出参数归类为标准符号。一般类别的溢出参数包括：集群一级的溢出;溢出条件的治疗或结果密度，距离或治疗的社会网络链接的数量;和疫苗的有效性参数有关的溢出。我们利用高质量的实证例子来说明这些参数。我们描述了研究设计，估计溢出和假设所需的因果推断溢出。我们的目标是通过标准化溢出参数命名法和阐明估计溢出所需的因果推断假设，来推进和鼓励溢出估计和报告方法。
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.