From the detailed shipping information we calculated the average number of shipments per location (the total number of shipments divided by the total number of ship-to-sites

per state). Performing targeted queries, we also categorized shipments by type of provider, showing types of destinations for the distribution of vaccine. We also combined some of these categories in subgroupings to see which had a greater impact on these populations. For example, a targeted access group for categories serving specific populations; and a general access group, including categories available to all population sub-groups. Information was adequate to categorize more than 75% of the overall shipments. We constructed separate models for children (6 months to 17 years) and high-risk adults (25–64 year olds with a chronic condition) because we expected factors affecting coverage to differ across groups, and to differ from factors check details associated with vaccination rates in overall adults (18 and up, including those with high-risk conditions [12]). The primary technique used for modeling CP-673451 was multivariate linear regression (ordinary least squares). We used a logarithmic transformation of the vaccination

rate for children, to better approximate normality. We calculated simple descriptive statistics for all the analyzed outcomes and factors (means, standard deviations, and proportions). Outliers were not removed for the analysis. Data was linearly scaled to values in [0.1] before performing regressions.

We selected a number of potential initial predictors for each of the dependent variables based on their correlation with the outcomes. From these initial models we developed models by stepwise addition, elimination, or by interchange of factors. At each stage, we chose variables to include or remove based on their statistical significance and their potential to explain variability, while we examined correlations to avoid high collinearities in the model. Models were evaluated on adjusted R-square values and the F-statistic, with individual variables significant at p-value < 0.05. The regressions were performed with R statistical software package version 2.11.1 [32]. Some descriptive statistics were calculated in Microsoft Excel versions Rebamipide 11 and 12. A deeper explanation of the methodology can be found on Davila-Payan et al. [12], and in the Supplemental Methods Section. Nine independent variables were significantly associated with vaccination coverage in children and eight for high-risk adults (fifteen different independent variables in total, two of which are shared by both models). A list of these variables can be found in Table 1. The adjusted R-squared for the regression models is 0.82 for children (Table 2) and 0.78 for high-risk adults (Table 3), and both of their p-values are close to 0.