Abstract
When a sufficiently high proportion of a population is immunized with a vaccine, reduction in secondary transmission of disease can confer significant protection to unimmunized population members. We propose a straightforward method to estimate the degree of this indirect effect of vaccination in the context of a community-randomized vaccine trial. A conditional logistic regression model that accounts for within-randomization unit correlation over time is described, which models risk of disease as a function of community-level covariates. The approach is applied to an example data set from a pneumococcal conjugate vaccine study, with study arm and immunization levels forming the covariates of interest for the investigation of indirect effects.
Publication types
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Comparative Study
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Randomized Controlled Trial
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Research Support, Non-U.S. Gov't
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Research Support, U.S. Gov't, Non-P.H.S.
MeSH terms
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Child, Preschool
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Heptavalent Pneumococcal Conjugate Vaccine
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Humans
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Infant
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Logistic Models
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Meningococcal Vaccines / immunology
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Meningococcal Vaccines / therapeutic use
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Pneumococcal Infections / epidemiology*
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Pneumococcal Infections / immunology
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Pneumococcal Infections / prevention & control*
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Pneumococcal Vaccines / immunology
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Pneumococcal Vaccines / therapeutic use*
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United States
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United States Indian Health Service
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Vaccines, Conjugate / immunology
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Vaccines, Conjugate / therapeutic use
Substances
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Heptavalent Pneumococcal Conjugate Vaccine
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Meningococcal Vaccines
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Pneumococcal Vaccines
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Vaccines, Conjugate