Negative binomial regression models. As both deviance and Pearson goodness-of-fit statistics suggested over-dispersion of pneumococcal case counts, we evaluated the relationship between IPD rates, influenza, and environmental exposures using a series of negative binomial regression models [13] . Models incorporated population data derived from the 2001 and 2006 censes as offsets, with linear interpolation and extrapolation used to generate population denominators for noncensus years [14] . Model construction evaluating the impact of influenza A and B (combined), influenza A, or influenza B on IPD risk was performed by including viral infection incidence and environmental exposures considered a priori to be likely to influence pneumococcal disease epidemiology. Candidate exposures were based on prior observations by our group and previously published research by others [5, [15] [16] [17] . Environmental exposures included UV index, temperature, and relative humidity and both virological and environmental exposures were considered at lags of up to 4 wk prior to pneumococcal case occurrence, which we considered to be a biologically plausible window for effects. Models also incorporated seasonal and multi-year trend terms.