Empirical Bayes estimation of the prevalence of uninsured individuals by county in the state of Tennessee and analyses of predictive factors.
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.
Lei
Pui-Wa
PW
Department of Educational and School Psychology, Pennsylvania State University, Pennsylvania 16802, USA. PuiWa@psu.edu
Warcholak
Nicholas D
ND
Suen
Hoi K
HK
Williams
Bryan L
BL
Magsumbol
Melina S
MS
D000293:Adolescent; D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D004651:Employment; D005006:Ethnicity; D005260:Female; D006304:Health Status; D006801:Humans; D007182:Income; D008297:Male; D017057:Medically Uninsured / Q000706:statistics & numerical data*; D008875:Middle Aged; D015233:Models, Statistical; D012307:Risk Factors; D017062:Small-Area Analysis; D013714:Tennessee; D014505:Urban Population