Logistic Regression will be used to estimate Vaccine Effectiveness (VE) and 95% confidence intervals (CIs) from the adjusted odds ratios (aORs) by using the formula VE = (1-aOR) X 100. Cases and controls will be matched on age, enrollment date, and geographic region. Cases will be compared to matched controls in order to evaluate the effectiveness of full (2 doses) and partial (1 dose) vaccination. Initially, univariate conditional logistic regression analyses will be performed to determine which covariates should be included in the multivariable analysis. The explanatory variable of interest is vaccination status. If sample size permits, vaccine effectiveness will be calculated by serotype. Other possible covariates to be included are: date of birth, age at onset, season, insurance status (public/none versus private) and point of care (hospital or ED). Variables with p-values ≤ 0.20 will selected as candidates for multivariable analysis. Multicollinearity among the covariates will be examined by evaluating variance inflation factors. If collinearity is present, only one of the correlated variables will be entered into the model at a time. Analysis will be rerun choosing alternative correlated variables and the models will be compared. The model with the lowest Akaike information criterion (AIC) will be chosen as the final model. Model selection will include stepwise, forward selection, and backwards elimination. The Hosmer-Lemeshow test will be used to determine goodness of fit.