title: Race, stability of health insurance coverage, and prescription medication use.
abstract: OBJECTIVE
  To determine the effects of health insurance and race on prescription medication use and expense.
  
  
  METHODS
  An observational, non-experimental design was used. Multivariable regression analyses were conducted to evaluate the independent effects of health insurance status and race on prescription medication use and expense while controlling for sociodemographic, geographic, and health status characteristics. The sample consisted of 19,035 participants in the 1996 through 2003 Medical Expenditure Panel Survey.
  
  
  RESULTS
  European Americans spent about $300 to $400 more and used three to four more prescriptions annually compared to other racial groups. Prescription medication expenses increased as time spent uninsured increased. Participants with part-year coverage filled four fewer prescriptions than those with full-year health insurance coverage. Participants with private coverage spent less on prescription medications compared to those with public and those with dual public and private coverage ($1,194 vs. $1,931 and $2,076, respectively; p < or = 0.001).
  
  
  CONCLUSIONS
  Significant racial and health insurance status disparities in prescription medication use and expenses exist after controlling for sociodemographic, geographic, and health status characteristics.
authors.affiliation: School of Nursing, University of Mississippi Medical Center, Jackson, MS 39216, USA. kwinters@son.umsmed.edu
authors.forename: Karen P
authors.initials: KP
authors.lastname: Winters
authors.forename: Sharon B
authors.initials: SB
authors.lastname: Wyatt
authors.forename: Todd G
authors.initials: TG
authors.lastname: Nick
authors.forename: Peggy O
authors.initials: PO
authors.lastname: Hewlett
authors.forename: John C
authors.initials: JC
authors.lastname: Hyde
authors.forename: Audwin B
authors.initials: AB
authors.lastname: Fletcher
mesh_terms: D000328:Adult; D001741:Black or African American / Q000706:statistics & numerical data*; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D005260:Female; D019538:Health Care Surveys; D006297:Health Services Accessibility; D054624:Health Status Disparities; D054625:Healthcare Disparities / Q000706:statistics & numerical data; D006630:Hispanic or Latino / Q000706:statistics & numerical data*; D006801:Humans; D019458:Insurance Coverage / Q000458:organization & administration*; D007348:Insurance, Health / Q000458:organization & administration*; D008297:Male; D017057:Medically Uninsured / Q000208:ethnology / Q000706:statistics & numerical data; D008875:Middle Aged; D015999:Multivariate Analysis; D012044:Regression Analysis; D012959:Socioeconomic Factors; D014481:United States; D044465:White People / Q000706:statistics & numerical data*