The estimated insured rate from the modeling is the posterior mean insured rate conditioned on the CPS ASEC data. The effect of this is similar to that of the empirical Bayes method used in the SAIPE program's estimates. The final estimates for counties where there is no sample is the same as the regression estimate, while the estimates for counties with lots of sample or very high insured rates and, thus, low variance, tend to be closer to the direct estimates.
The estimated number of insured in a county is the estimated insured rate times an estimate of the CPS universe. We create an estimate of the CPS universe by adjusting estimates of the total resident population to the CPS universe by subtracting unpublished demographic estimates of the group quarters population by age and the appropriate type of group quarters from the estimate of the total resident population. The number of uninsured, then, is that estimated CPS universe minus the estimated number of insured. The reported confidence intervals are based on the posterior standard deviation of the insured rate, conditioned on the CPS ASEC data.
The last steps in the production process are controlling the county estimates to the national CPS ASEC estimates and forming the state-level estimates. The number of uninsured from the model are aggregated to the state and national levels, and the ratio of the national CPS ASEC direct estimate to the aggregated national model-based estimate is formed; this ratio is the raking factor. The raking factor is multiplied with all of the county- and state-level uninsured to get the controlled numbers of uninsured. This is subtracted from the state and county CPS ASEC universe estimates, yielding the estimated numbers of insured. Finally, everything is rounded to an integer.