Confounding due to differences between the vaccinated and unvaccinated individuals is a recognized issue in observational studies of influenza vaccination-particularly studies evaluating vaccine effectiveness for reducing all-cause mortality in the elderly [12] . One approach to account for confounding is to choose a period when the vaccine was available but the influenza virus was not circulating as the ''control'' pre-influenza period [11, 12] . In the pre-influenza period, there should be no vaccine effect, and observed effects during this period are assumed to be due to confounding. Therefore, as our primary strategy for confounder adjustment, we identified a group of covariates that would move the odds ratios (ORs) of association between maternal influenza immunization and birth outcomes during the pre-influenza period to 1.0 (i.e., no effect), hence arriving at a set of covariates that could effectively control for confounding due to the differences between the vaccinated and the unvaccinated women in analyses of all influenza activity periods. We selected covariates for the separate multivariate models for each of the birth outcomes (i.e., prematurity and SGA) using a modified version of the approach described by Jackson et al. [11] and Nelson et al. [12] . Briefly, variables for each birth outcome were evaluated for potential confounding by selecting the covariates that, in bivariate models, modified the association between maternal influenza vaccine and the birth outcome and moved the OR towards 1. From this initial group of variables, we arrived at a parsimonious model by sequentially dropping each covariate and observing a change in OR of the association between maternal immunization and the birth outcome. We excluded the variable whose removal moved the OR the most towards 1. If dropping a covariate resulted in moving ORs away from 1 and a change in magnitude of less than 1%, we removed the covariate that caused the least change and then repeated the process. If the change was more than 1%, we considered the current set of covariates as the smallest group of variables required to account for confounding.