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.

中文翻译：

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使用社会接触数据改进塞内加尔整群随机流感疫苗接种计划的总体效果估计

这项研究估计了在 2009 年至 2011 年的两个流感季节期间在塞内加尔西部进行的一项整群随机试验中连续实施的两项流感疫苗接种计划的总体效果。我们应用尖端方法将社会接触数据与感染数据相结合以减少偏差在集群之间的污染引起的估计。我们随时间变化的估计显示干预措施减少了季节性流感，而 H1N1 大流行性流感没有显着增加。我们估计在研究的第 1 年期间总体累积发生率（对照组为 6.13%）的附加变化为 -0.68 个百分点（95% CI：-2.53，1.18）。当 H1N1 大流行感染被排除在分析之外时，估计变化为 -1.45 个百分点并且显着（95% CI，-2.81，-0.08）。由于跨集群污染很低（大多数村庄的接触者为 0–3%），假设没有污染的估计量仅略微减弱（-0.65 个百分点）。这些发现对于精心设计以尽量减少溢出效应的研究来说是令人鼓舞的。需要进一步的工作来估计各种环境中的污染及其对估计的影响。