Coverage Matters Insurance and Health Care (2001) / Chapter Skim
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Appendix D: Multivariate Analysis
Appendix D: Multivariate Analysis
Pages 135-142
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From page 135... ...
They were derived by means of multivariate statistical analysis, using data from the 2000 Current Population Survey (CPS) in the form of a derived variable file made available to the Committee by Paul Fronstin and the Employee Benefit Research Instituted Four sets of analyses were performed to estimate and predict differences in uninsured rates by: ~ .
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From page 136... ...
Families earning <100% FPL 24.2a 1s.3a Families earning 100 - 149% FPL 19.6a 12.4a Families earning 150 - 199% FPL 1 5.8a 10.1 a Effect of Education Level on Uninsured rate (Reference Group: Primary Wage Earner with Postcollege Education) Primary wage earner has less than high school diploma 28.4a 16.2a Primary wage earner has high school diploma 12.oa 7.9a Primary wage earner has some college 8.2a 5.4a Primary wage earner has college degree 2.2a 1.4c Effect of Race and Ethnicity on Uninsured Rate (Reference Group: Non-Hispanic Whites)
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From page 137... ...
Models for poverty level and for education level include the following covariates: age, gender, nativity, race and ethnicity, whether urban or rural, family type, health status. Model for race and ethnicity includes the following covariates: primary wage earner's education level, primary wage earner's work status, primary wage earner's occupation, whether primary wage earner has full-time or part-time job, size of firm employing primary wage earner (indicator)
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From page 138... ...
Predicted Difference, if State's Covariates and Coefficients Were the Same as for National Averages (percentage points) Alabama -1.3 -1.2 Alaska 2.
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From page 139... ...
the predicted uninsured rate, expressed in terms of the difference with the uninsured rate for the reference group, as adjusted for the covariates Model for state includes the following covariates: primary wage earner's education level, primary wage earner's work status, primary wage earner's occupation, whether primary wage earner has fulltime or part-time job, size of firm employing primary wage earner (indicator) , family income, age, gender, nativity, race and ethnicity, family type, state (indicator)
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From page 140... ...
For example, the logistic regression model based on population characteristics in our CPS data set gives an estimated uninsured rate for Hispanics that is 22.2 percentage points higher than the estimated uninsured rate for non-Hispanic 3The first step of the adjustment process included state fixed effects to control for state policy and other differences that would generate intra-state cluster effects. 4An alternative approach would be to prepare a single logistic regression with covariates for the characteristic of race and ethnicity and all other characteristics, plus interaction terms to describe the relationships between the characteristic of race and ethnicity and all of the other characteristics.
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From page 141... ...
To estimate the predicted differences in estimated uninsured rates reported in column 2, logistic regression models were prepared for each racial and ethnic group in which the coefficients for the reference group were combined with covariate data for each comparison group.6 A preliminary analysis of the data suggested that stratifying the multivariate analysis by race and ethnicity would allow for the observation of important differences among populations of immigrants and naturalized citizens, especially useful for understanding uninsured rates within the Hispanic population. The analyses by poverty level and education level of primary wage earner and the analysis by state were conducted using an approach that differed only slightly from the analysis by race and ethnicity.
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From page 142... ...
and is not addressed by the models in this specific analysis. One would expect fairly large proportions of the differences in uninsured rates to remain unaccounted for by or associated with the specific characteristics evaluated, because there are many aspects of socioeconomic status, demographic characteristics, health status, and geography that are not measured in this analysis.
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