Stanley H Hsia1, Monica L Desnoyers, Martin L Lee. 1. Division of Endocrinology, Metabolism & Molecular Medicine, Department of Internal Medicine, Charles R. Drew University of Medicine and Science, 1731 East 120th Street, Los Angeles, CA 90059, USA. Electronic address: stanleyhsia@cdrewu.edu.
Abstract
BACKGROUND: Across the United States, hyperlipidemia remains inadequately controlled and may vary across states according to differences in health insurance coverage and/or race/ethnicity. OBJECTIVE: To examine relationships between states' health insurance and race/ethnicity characteristics with measures of hyperlipidemia management across the 50 U.S. states and the District of Columbia. METHODS: Cross-validated, multiple linear regression modeling was used to analyze associations between states' health insurance patterns or proportions of racial minorities (from the 2010 U.S. Census data) and states' aggregate frequency of checking cholesterol within the previous 5 years or prescriptions written for lipid-lowering medications (from national survey and population-adjusted retail prescription data, respectively), with adjustments for age, sex, body mass index, race/ethnicity, and poverty. RESULTS: In states with proportionately more uninsured, cholesterol levels are checked less often, but in states with proportionately more private, Medicare, or Medicaid coverage, providers are not necessarily more likely to check cholesterol or to write more prescriptions. In states with proportionately more African-Americans and/or Hispanics, cholesterol is more likely to be checked, but in states with more African-Americans, more prescriptions were written, whereas in states with more Hispanics, fewer statin prescriptions were written. CONCLUSION: Variations across states in insurance and racial/ethnicity mix are associated with variations in hyperlipidemia management; less-insured states may be less effective whereas states with more private, Medicare, or Medicaid coverage may not be more effective. In states with proportionately more African-Americans vs. Hispanics, lipid medications may be prescribed differently. Our findings warrant further investigations.
BACKGROUND: Across the United States, hyperlipidemia remains inadequately controlled and may vary across states according to differences in health insurance coverage and/or race/ethnicity. OBJECTIVE: To examine relationships between states' health insurance and race/ethnicity characteristics with measures of hyperlipidemia management across the 50 U.S. states and the District of Columbia. METHODS: Cross-validated, multiple linear regression modeling was used to analyze associations between states' health insurance patterns or proportions of racial minorities (from the 2010 U.S. Census data) and states' aggregate frequency of checking cholesterol within the previous 5 years or prescriptions written for lipid-lowering medications (from national survey and population-adjusted retail prescription data, respectively), with adjustments for age, sex, body mass index, race/ethnicity, and poverty. RESULTS: In states with proportionately more uninsured, cholesterol levels are checked less often, but in states with proportionately more private, Medicare, or Medicaid coverage, providers are not necessarily more likely to check cholesterol or to write more prescriptions. In states with proportionately more African-Americans and/or Hispanics, cholesterol is more likely to be checked, but in states with more African-Americans, more prescriptions were written, whereas in states with more Hispanics, fewer statin prescriptions were written. CONCLUSION: Variations across states in insurance and racial/ethnicity mix are associated with variations in hyperlipidemia management; less-insured states may be less effective whereas states with more private, Medicare, or Medicaid coverage may not be more effective. In states with proportionately more African-Americans vs. Hispanics, lipid medications may be prescribed differently. Our findings warrant further investigations.
Authors: Lakshmi Venkitachalam; Kaijun Wang; Avi Porath; Ramon Corbalan; Alan T Hirsch; David J Cohen; Sidney C Smith; E Magnus Ohman; Ph Gabriel Steg; Deepak L Bhatt; Elizabeth A Magnuson Journal: Circulation Date: 2012-04-09 Impact factor: 29.690
Authors: Michael H Davidson; Kevin C Maki; Thomas A Pearson; Richard C Pasternak; Prakash C Deedwania; James M McKenney; Gregg C Fonarow; David J Maron; Benjamin J Ansell; Luther T Clark; Christie M Ballantyne Journal: Am J Cardiol Date: 2005-08-15 Impact factor: 2.778
Authors: David D Waters; Carlos Brotons; Cheng-Wen Chiang; Jean Ferrières; JoAnne Foody; J Wouter Jukema; Raul D Santos; Juan Verdejo; Michael Messig; Ruth McPherson; Ki-Bae Seung; Lisa Tarasenko Journal: Circulation Date: 2009-06-22 Impact factor: 29.690
Authors: Joshua Choi; Amir M Khan; Michael Jarmin; Naila Goldenberg; Charles J Glueck; Ping Wang Journal: Lipids Health Dis Date: 2017-07-24 Impact factor: 3.876
Authors: Parth Shah; Charles J Glueck; Naila Goldenberg; Sarah Min; Chris Mahida; Ilana Schlam; Matan Rothschild; Ali Huda; Ping Wang Journal: Lipids Health Dis Date: 2017-01-23 Impact factor: 3.876