Using social contact data to improve the overall effect estimate of a cluster-randomized influenza vaccination program in Senegal

Abstract

This 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 to 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 non-significant 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. © 2021 Royal Statistical Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA