Databases for Estimating Health Insurance Coverage for Children A Workshop Summary (2010) / Chapter Skim
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6 Modeling Strategies for Improving Estimates
6 Modeling Strategies for Improving Estimates
Pages 47-55
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From page 47... ...
, the State Health Access Data Assistance Center (SHADAC) suggested that the "most desirable path would be to create estimates via statistical modeling that combine the strengths of the CPS-ACS and the fully implemented ACS.
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From page 48... ...
Bell summarized the strengths and weaknesses of the main data sources for SAIPE purposes. The CPS, the primary national survey mea suring population and poverty each year, provides the SAIPE program with national county-level child poverty estimates, through sampleweighted estimates of the numbers and the proportion of poor children among children aged 5-17 related to the primary householder (poor related school-age children)
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From page 49... ...
The regression variables in x i′ include a constant term and, for each state, a pseudo state child pov erty rate from tax return information, a tax "nonfiler rate," a Food Stamp Program participation rate, and a state poverty rate for children aged 5-17 estimated from the previous census, or residuals from regressing previous census estimates on other elements of x i′ for the census year. The actual model is estimated as follows: • Given s 2 and the vi , let ∑ = diag(s 2 + vi )
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From page 50... ...
. He also showed that the model estimates generally ignore nonsampling error in the direct survey estimates that provide the dependent variable in the model, which implicitly defines the target to be what the survey is estimating.
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From page 51... ...
The first live estimates were published in 2007, and the SAHIE program currently publishes state health insurance coverage estimates by age, sex, race, Hispanic origin (i.e., demographic characteristics) , and income categories (0-200 percent and 0-250 percent of the poverty threshold and the total poverty universe)
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From page 52... ...
Some planned areas of research include using ACS direct variance estimates. They can produce replicate-based estimates of the variances of ACS estimates, relaxing model assumptions by investigating alternatives to assumptions of independence and of constant variances, developing predictors for insurance coverage, such as those that now predict income status, testing the model to incorporate more income groups (e.g., 250-300 and 300-400 percent of poverty)
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From page 53... ...
In the NNHS, nursing homes are sampled and the nursing home staff provide information on the diagnosis of selected residents from medical records. For those aged 85 and over, 21 percent were in nursing homes, so improving coverage of this population required a combination based on design-based prevalence estimates from the two surveys for various chronic conditions.
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From page 54... ...
DISCUSSION In the discussion that followed these three presentations on modeling and combining data, the following points were made: • It is important, as in the SAHIE program, to refit the models every year, since the administrative data could change and affect the model coefficients between periods. This was important in 1997, when the welfare reform legislation changed the Food Stamp Pro gram and affected how the Food Stamp variable worked in the models.
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From page 55... ...
MoDELInG StrAtEGIES for IMProVInG EStIMAtES One such difference is state policies regarding how long children are maintained on the rolls even when no longer actively enrolled, due to 12-month continuous enrollment policies. • Combining data series, as discussed by Schenker, has other promis ing attributes.
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