Empirical Bayes estimation of the prevalence of uninsured individuals by county in the state of Tennessee and analyses of predictive factors

Pui-wa Lei, Nicholas D. Warcholak, Hoi K. Suen, Bryan L. Williams, Melina S. Magsumbol

Research output: Contribution to journalArticle

Abstract

Lawmakers at the state level require good estimates of those without health insurance in the areas they serve to inform policy decisions. These estimates are often built on inadequate data from smaller geographic areas, such as counties. The Small Area Estimates Branch of the U.S. Census Bureau developed a method to generate stable estimates at the county level using data from the Annual Social and Economic Supplement to the Current Population Survey and several other sources. Using data collected in the state of Tennessee, this article presents a less complicated and arguably less expensive alternative to that method, while providing comparable results. Limitations of both methods and suggestions for future research are discussed.

Original languageEnglish (US)
Pages (from-to)47-63
Number of pages17
JournalEvaluation and the Health Professions
Volume30
Issue number1
DOIs
StatePublished - Mar 1 2007

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Statistical Factor Analysis
Censuses
Health Insurance
Economics
Population
Surveys and Questionnaires

All Science Journal Classification (ASJC) codes

  • Health Policy

Cite this

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Empirical Bayes estimation of the prevalence of uninsured individuals by county in the state of Tennessee and analyses of predictive factors. / Lei, Pui-wa; Warcholak, Nicholas D.; Suen, Hoi K.; Williams, Bryan L.; Magsumbol, Melina S.

In: Evaluation and the Health Professions, Vol. 30, No. 1, 01.03.2007, p. 47-63.

Research output: Contribution to journalArticle

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