Abstract: To provide policy makers and advocates crucial information for defining, measuring, and addressing health insurance coverage issues, it is very important to have uninsured rates available for small areas. However, national surveys can typically support limited state or substate estimates. In order to develop an estimated uninsured rate at Senate/Assembly District level in California, a methodology using synthetic analyses was developed to derive uninsured estimations. The unit of analysis for insurance coverage was census tract, which was the basic unit for California Senate/Assembly District boundaries. We used a logistic regression model to predict insured rates for children and adults. Major factors, such as age, gender, race/ethnicity, and poverty were included in the logistic regression model. The data sources, such as, Year 2000 population estimates by age, gender, and ethnicity at census track in California from Claritas; 1989, 1990 and 1991 Current Population Survey (CPS); 1990 Census data; and 1998, 1999, 2000 CPS data were used in the process of the synthetic estimation. This session presents the key steps and major findings of the estimation. Hopefully, the methodology and analytical tools developed for this analysis can also be applied to other small area analyses in response to data requests by policy makers and community-based organizations. This work is funded by a grant from The California Endowment. See www.healthpolicy.ucla.edu
Learning Objectives: 1. Present the major findings of the estimation of uninsured children and adults for California Senate and Assembly Dsitricts; 2. Develop a methodology using synthetic analyses to derive uninsured rates for small areas.
Keywords: Health Insurance,
Presenting author's disclosure statement:
Organization/institution whose products or services will be discussed: None
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.