The goals of this project are to analyze differences in health outcomes among the insured, underinsured, and uninsured, to examine relationships between insurance and employment characteristics, and to test a model predicting rates of uninsurance in specific counties, both urban and rural. The specific objectives are: 1. To determine differences in use of medical care services among the insured, uninsured, and underinsured; 2. To specify reasons for differences in medical services; 3. To analyze specific differences in health outcomes across the following population groups: insured, uninsured, and underinsured; 4. To test prospectively a model that forecasts rates of uninsurance across urban and rural counties in the state; and 5. To test the relationship between insurance status and characteristics of employment. The project is designed to contribute to an understanding of the reasons for uninsurance and the consequences of being uninsured or underinsured. Individual level data, gathered in a random sample of adults in Nebraska, will be used to test hypotheses predicting relationships between individual insurance status and characteristics of the employment of the principal wage earner in the household. Data collected in each of 15 counties (12 rural and 3 urban) in Nebraska, using random sampling techniques to identify statistically significant samples of individuals in each county, will be used to test hypotheses predicting relationships between aggregate economic conditions and aggregated rates of uninsurance. Data will be collected through personal interviews in households that provide profiles of access to health care services and health outcomes for each of six groups: rural insured, rural uninsured, rural underinsured, urban insured, urban uninsured, and urban underinsured. Standard regression techniques will be used to test hypotheses positing relationships between economic conditions, both individual and aggregate, and rates of uninsurance. Previous work on this project suggests that state wide uninsurance rates have reached a higher base minimum during the past decade, a finding that will be tested in with an additional data point. Methods of categorical analysis, linear and log- linear models and logistic regression, will be used to test effects of locale of residence and the extent of insurance coverage on medical care utilization and health care outcome parameters. Transcriptions of personal interviews will be used to reveal the complete consequences of inadequate insurance coverage.
