Potter, Gail E.Carnegie, Nicole BohmeSugimoto, Jonathan D.Diallo, AldioumaVictor, John C.Neuzil, Kathleen M.Halloran, M. Elizabeth2022-09-212022-09-212021-09Potter, 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).0035-9254https://scholarworks.montana.edu/handle/1/17198This 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.his 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-UScopyright Wiley 2021https://web.archive.org/web/20200106202133/https://onlinelibrary.wiley.com/library-info/products/price-listshttp://web.archive.org/web/20190530141919/https://authorservices.wiley.com/author-resources/Journal-Authors/licensing/self-archiving.htmladditive hazardscluster randomizedcontaminationinterferenceoverall effectsocial networkspilloverArticle