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.
ACKNOWLEDGMENT
This work has been supported in part by grants from Wyeth Lederle Vaccines and USAID (HRN-A-00-96-9006). The opinions of the authors are not necessarily those of the Indian Health Service.
Notes
* Reference category: Units that received MnCC vaccine which on a given day had less than 25% of children enrolled in the study. The CMLEs are the log rate ratios comparing incidence in non-enrolled children in the given category to the reference category.