â€œEconomics of Mass Deworming Programsâ€ (co-authors Amrita Ahuja, Sarah Baird, Joan Hamory Hicks, Michael Kremer), Chapter 29 in Disease Control Priorities (third edition): Volume 8, Child and Adolescent Health and Development, (eds.) D. A. P. Bundy, N. de Silva, S. Horton, D. T. Jamison, and G. C. Patton. Washington, DC: World Bank, 2017.
Jade Benjamin-Chung, Benjamin F. Arnold, David Berger, Stephen P. Luby, Edward Miguel, John M. Colford Jr., Alan E. Hubbard. 2017. "Spillover effects in epidemiology: parameters, study designs and methodological considerations", International Journal of Epidemiology, 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.
Jade Benjamin-Chung, Jaynal Abedin, David Berger, Ashley Clark, Veronica Jimenez, Eugene Konagaya, Diana Tran, Benjamin F. Arnold, Alan E. Hubbard, Stephen P. Luby, Edward Miguel and John M. Colford Jr. 2017. "Spillover effects on health outcomes in low- and middle-income countries: a systematic review", International Journal of Epidemiology, doi: 10.1093/ije/dyx039.
Background: Many interventions delivered to improve health may benefit not only direct recipients but also people in close physical or social proximity. Our objective was to review all published literature about the spillover effects of interventions on health outcomes in low-middle income countries and to identify methods used in estimating these effects. Methods: We searched 19 electronic databases for articles published before 2014 and hand-searched titles from 2010 to 2013 in five relevant journals. We adapted the Cochrane Collaboration’s quality grading tool for spillover estimation and rated the quality of evidence. Results: A total of 54 studies met inclusion criteria. We found a wide range of terminology used to describe spillovers, a lack of standardization among spillover methods and poor reporting of spillovers in many studies. We identified three primary mechanisms of spillovers: reduced disease transmission, social proximity and substitution of resources within households. We found the strongest evidence for spillovers through reduced disease transmission, particularly vaccines and mass drug administration. In general, the proportion of a population receiving an intervention was associated with improved health. Most studies were of moderate or low quality. We found evidence of publication bias for certain spillover estimates but not for total or direct effects. To facilitate improved reporting and standardization in future studies, we developed a reporting checklist adapted from the CONSORT framework specific to reporting spillover effects. Conclusions: We found the strongest evidence for spillovers from vaccines and mass drug administration to control infectious disease. There was little high quality evidence of spillovers for other interventions.