Using social contact data to improve the overall effect estimate of a cluster-randomized influenza vaccination program in Senega
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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 2009-2011. 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 -0.68 percentage points during Year 1 of the study (95 H1N1 pandemic infections were excluded from analysis, the estimated change was -1.45 percentage points and was significant (95 cross-cluster contamination was low (0-3 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.
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