In order to address the possibility that the confounders in the preinfluenza period were different from the confounding factors in the influenza activity period, we also developed secondary multivariate models using a traditional analytic approach for confounder adjustment. In these secondary models, the covariate list (for both the pre-influenza-period-based and traditional adjustment) was based on evidence in the literature regarding associations with birth outcomes and availability of data in the Georgia PRAMS dataset. The covariate list included the following: gestational age at first antenatal visit, maternal age less than 19 y, maternal age more than 35 y, multiple births, maternal medical risk factors, labor/delivery complications, birth defects, maternal diabetes (gestational and/or non-gestational), hypertension (treated or untreated), mother insured, multivitamin use in pregnancy, history of smoking during pregnancy, history of alcohol use during pregnancy, black race, education less than 12 th grade, mother's marital status, and maternal weight before pregnancy. The ORs for association between the different covariates and receipt of influenza vaccine were computed using logistic regression.