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

Author

Listed:
  • Gail E. Potter
  • Nicole Bohme Carnegie
  • Jonathan D. Sugimoto
  • Aldiouma Diallo
  • John C. Victor
  • Kathleen M. Neuzil
  • M. Elizabeth Halloran

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.

Suggested Citation

  • Gail E. Potter & Nicole Bohme Carnegie & Jonathan D. Sugimoto & Aldiouma Diallo & John C. Victor & Kathleen M. Neuzil & M. Elizabeth Halloran, 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, Royal Statistical Society, vol. 71(1), pages 70-90, January.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:1:p:70-90
    DOI: 10.1111/rssc.12522
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