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The Geographic Support System Initiative will integrate improved address coverage, spatial feature updates, and enhanced quality assessment and measurement.
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We estimate the number of people with health insurance coverage by state within demographic groups and income categories. The number insured in a group is the product of the number in the group and the proportion in that group who are insured. Correspondingly, our model has two main parts: one for estimating the numbers of people in state demographic and income groups, and one for estimating the proportions with health insurance in these groups. Each part is a hierarchical two-level regression model. We use Bayesian methods to estimate the model. We estimate the number without insurance as the difference between the number of people in a category and the number with insurance. The demographic groups and income categories are described in the Model Details section.
The dependent variables in the regression models are:
The CPS ASEC estimates of the number of people in a state demographic and income group, and of the proportion insured, are assumed to be unbiased. The other dependent variables are related to and indicative of these numbers or proportions but are not assumed to be unbiased estimates for them.
The universe for these health insurance estimates is the CPS poverty universe. Therefore, we use demographic estimates of the population adjusted to the CPS poverty universe.
For further information on the dependent variables and population estimates, see information about data inputs.
We control the estimates for states so the following conditions are met:
The CPS ASEC estimates for different states have different reliability because of the size of samples in each state. Our estimates consider this factor. Estimates from states with larger samples tend to be closer to the direct estimates.
We provide a confidence interval for each estimate that represents uncertainty from both sampling and modeling. These confidence intervals are Bayesian credible regions calculated using posterior standard deviations and a normal approximation.