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

TL;DR

This study uses social contact data to refine the effect estimates of influenza vaccination in Senegal, demonstrating that accounting for contamination improves accuracy and reveals a significant reduction in seasonal influenza incidence.

Contribution

It introduces a novel methodology combining social contact data with infection data to reduce bias from contamination in cluster-randomized trials.

Findings

Vaccination reduced seasonal influenza incidence by approximately 0.68 percentage points.

Excluding H1N1 pandemic infections, the reduction was about 1.45 percentage points and statistically significant.

Cross-cluster contamination was low, minimally affecting the estimates.

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 2009-2011. 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.