Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
decision sciences
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: Applied Statistics, Volume 71, No. 1, Year 2022
Notification
URL copied to clipboard!
Description
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
Authors & Co-Authors
Potter, Gail E.
United States, Bethesda
National Institutes of Health Nih
Sugimoto, Jonathan D.
United States, Seattle
University of Washington
United States, Seattle
Va Puget Sound Health Care System
United States, Seattle
Fred Hutchinson Cancer Center
Diallo, Aldiouma M.
France, Marseille
Ird Institut de Recherche Pour le Developpement
Victor, John C.
United States, Seattle
Path Seattle
Neuzil, Kathleen Maletic
United States, Baltimore
University of Maryland School of Medicine
Elizabeth Halloran, M.
United States, Seattle
Fred Hutchinson Cancer Center
Statistics
Authors: 6
Affiliations: 8
Identifiers
Doi:
10.1111/rssc.12522
ISSN:
00359254
Study Design
Randomised Control Trial
Cohort Study
Study Locations
Senegal