Spillover effects in epidemiology: parameters, study designs and methodological considerations

• Published in: International journal of epidemiology 2018-02, Vol.47 (1), p.332-347
• Main Authors: Benjamin-Chung, Jade, Arnold, Benjamin F, Berger, David, Luby, Stephen P, Miguel, Edward, Colford, Jr, John M, Hubbard, Alan E
• Format: Article
• Language: English
• Subjects: Disease Transmission, Infectious - prevention & control; Education Corner; Epidemiologic Methods; Health Promotion - methods; Humans; Immunity, Herd; Public Health; Research Design; Systematic Reviews as Topic; Vaccination
• ISSN: 0300-5771; 1464-3685
• 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.