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dc.contributor.authorPotter, Gail E.
dc.contributor.authorCarnegie, Nicole Bohme
dc.contributor.authorSugimoto, Jonathan D.
dc.contributor.authorDiallo, Aldiouma
dc.contributor.authorVictor, John C.
dc.contributor.authorNeuzil, Kathleen M.
dc.contributor.authorHalloran, M. Elizabeth
dc.date.accessioned2022-09-21T16:25:38Z
dc.date.available2022-09-21T16:25:38Z
dc.date.issued2021-09
dc.identifier.citationPotter, G. E., Carnegie, N. B., Sugimoto, J. D., Diallo, A., Victor, J. C., Neuzil, K. M., & Elizabeth Halloran, M. (2022). Using social contact data to improve the overall effect estimate of a cluster‐randomized influenza vaccination program in Senegal. Journal of the Royal Statistical Society: Series C (Applied Statistics).en_US
dc.identifier.issn0035-9254
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/17198
dc.descriptionThis is the peer reviewed version of the following article: [Using social contact data to improve the overall effect estimate of a cluster‐randomized influenza vaccination program in Senegal. Journal of the Royal Statistical Society: Series C (Applied Statistics) 71, 1 p70-90 (2021)], which has been published in final form at https://doi.org/10.1111/rssc.12522. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions: https://authorservices.wiley.com/author-resources/Journal-Authors/licensing/self-archiving.html#3.en_US
dc.description.abstracthis study estimates the overall effect of two influenza vaccination programs consecutively administered in a cluster-randomized trial in western Senegal over the course of two influenza seasons from 2009-2011. We apply cutting-edge methodology combining social contact data with infection data to reduce bias in estimation arising from contamination between clusters. Our time-varying estimates reveal a reduction in seasonal influenza from the intervention and a nonsignificant increase in H1N1 pandemic influenza. We estimate an additive change in overall cumulative incidence (which was 6.13% in the control arm) of -0.68 percentage points during Year 1 of the study (95% CI: -2.53, 1.18). When H1N1 pandemic infections were excluded from analysis, the estimated change was -1.45 percentage points and was significant (95% CI, -2.81, -0.08). Because cross cluster contamination was low (0-3% of contacts for most villages), an estimator assuming no contamination was only slightly attenuated (-0.65 percentage points). These findings are encouraging for studies carefully designed to minimize spillover. Further work is needed to estimate contamination – and its effect on estimation – in a variety of settings.en_US
dc.language.isoen_USen_US
dc.publisherWileyen_US
dc.rightscopyright Wiley 2021en_US
dc.rights.urihttps://web.archive.org/web/20200106202133/https://onlinelibrary.wiley.com/library-info/products/price-listsen_US
dc.rights.urihttp://web.archive.org/web/20190530141919/https://authorservices.wiley.com/author-resources/Journal-Authors/licensing/self-archiving.htmlen_US
dc.subjectadditive hazardsen_US
dc.subjectcluster randomizeden_US
dc.subjectcontaminationen_US
dc.subjectinterferenceen_US
dc.subjectoverall effecten_US
dc.subjectsocial networken_US
dc.subjectspilloveren_US
dc.titleUsing social contact data to improve the overall effect estimate of a cluster‐randomized influenza vaccination program in Senegalen_US
dc.typeArticleen_US
mus.citation.extentfirstpage1en_US
mus.citation.extentlastpage38en_US
mus.citation.issue1en_US
mus.citation.journaltitleJournal of the Royal Statistical Society: Series C (Applied Statistics)en_US
mus.citation.volume71en_US
mus.identifier.doi10.1111/rssc.12522en_US
mus.relation.collegeCollege of Letters & Scienceen_US
mus.relation.departmentMathematical Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US


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