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Activity Number:
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520
- SPEED: Infectious Diseases, Spatial Modeling and Environmental Exposures, Speed 2
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Type:
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Contributed
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Date/Time:
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Wednesday, July 31, 2019 : 10:30 AM to 11:15 AM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #307906
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Title:
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Using Social Contact Data to Improve the Overall Effect Estimate of a Cluster-Randomized Influenza Vaccination Program in Senegal
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Author(s):
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Gail Potter* and Nicole Carnegie and Jonathan Sugimoto and Aldiouma Diallo and John C Victor and Kathleen Neuzil and M Elizabeth Halloran
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Companies:
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The Emmes Corporation and Montana State University and Fred Hutchinson Cancer Research Center and Institut de Recherche pour le Developpement and PATH and University of Maryland and University of Washington and Fred Hutchinson Cancer Research Center
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Keywords:
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interference;
cluster randomized trial;
influenza;
vaccine trial;
contamination;
social network
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Abstract:
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This study estimates the overall effect of a trivalent influenza vaccine program administered in a cluster-randomized trial in Senegal in 2009-2011. We apply cutting-edge methodology combining social contact data with infection data to reduce bias arising from contamination between clusters. Our time-varying additive effect estimate reveals that the vaccination program reduced influenza during the influenza season but increased it after pandemic H1N1 influenza appeared in the community. The estimated reduction in cumulative incidence due to the vaccination program was -0.68 percentage points in Year 1 of the study. While this suggests that the vaccine prevented 11% of infections (since control arm cumulative incidence was 6.13%), the estimate is not statistically significant. (A secondary analysis excluding A/H1N1pdm09 infections was significant.) The reduction in bias was small: a method assuming no contamination estimated a reduction of -0.65 percentage points. This is because contamination was low, ranging from 0-3% of contacts for most villages. More work is needed to estimate contamination – and its effect on estimation - for a variety of designs and settings.
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Authors who are presenting talks have a * after their name.
