TY - JOUR
T1 - Empirical Bayes estimation of the prevalence of uninsured individuals by county in the state of Tennessee and analyses of predictive factors
AU - Lei, Pui Wa
AU - Warcholak, Nicholas D.
AU - Suen, Hoi K.
AU - Williams, Bryan L.
AU - Magsumbol, Melina S.
PY - 2007/3
Y1 - 2007/3
N2 - 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.
AB - 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.
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U2 - 10.1177/0163278706297335
DO - 10.1177/0163278706297335
M3 - Article
C2 - 17293608
AN - SCOPUS:33846656316
SN - 0163-2787
VL - 30
SP - 47
EP - 63
JO - Evaluation and the Health Professions
JF - Evaluation and the Health Professions
IS - 1
ER -