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APHA Scientific Session and Event Listing

Using a multilevel approach to capture the complexities of health insurance status

Nancy Cheak, MA, Health Policy and Management, Saint Louis University, 3545 Lafayette Ave. Suite 300, St. Louis, MO 63104, 314-977-8128, cheaknc@slu.edu and Jenine K. Harris, MA, MAT, Center for Tobacco Policy Research, Saint Louis University, 3545 Lafayette Ave., Suite 300, St. Louis, MO 63104.

In 2004, the number of uninsured in the United States topped 45.8 million, or 15.7% of the population. Many of the uninsured have social, economic, and health related disadvantages that are insurmountable obstacles to obtaining health insurance. For these individuals, the absence of insurance affects quality of life, utilization of medical services, and the ability to be productive members of society. Understanding the complex issues that contribute to lack of insurance has been a challenge for researchers, health practitioners, and policymakers. To date, models predicting insurance status have primarily relied on associating demographic factors with risk of being uninsured. While successful in identifying many factors, these models do not include important multilevel characteristics of key variables and are unable to differentiate how supply and demand side variables affect insurance status. To address these issues, Pollack and Kronbusch (2004) propose a new multilevel model to predict insurance status. The multilevel modeling (MLM) approach can take complex relationships into consideration by extending traditional regression models to include multiple levels of analysis. For example, health status affects income and employability, which affect whether a person is eligible for insurance. Using MLM, health status could be included as a predictor of income and employability as well as of overall insurance status. Using nationally representative data from the 2004 Medical Expenditure Panel Survey, we take a multilevel approach to predict health insurance status. This model will assist researchers, health practitioners, and policymakers in understanding and addressing the growing problem of the uninsured.

Learning Objectives:

  • At the conclusion of the presentation, the participant should be able to

    Keywords: Health Insurance, Statistics

    Presenting author's disclosure statement:

    Not Answered

    Handout (.pdf format, 2362.5 kb)

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