Health Benefits Mandates and Their Potential Impacts on Racial/Ethnic Group Disparities in Insurance Markets

J Immigr Minor Health. 2017 Aug;19(4):921-928. doi: 10.1007/s10903-016-0436-9.

Abstract

Addressing racial/ethnic group disparities in health insurance benefits through legislative mandates requires attention to the different proportions of racial/ethnic groups among insurance markets. This necessary baseline data, however, has proven difficult to measure. We applied racial/ethnic data from the 2009 California Health Interview Survey to the 2012 California Health Benefits Review Program Cost and Coverage Model to determine the racial/ethnic composition of ten health insurance market segments. We found disproportional representation of racial/ethnic groups by segment, thus affecting the health insurance impacts of benefit mandates. California's Medicaid program is disproportionately Latino (60 % in Medi-Cal, compared to 39 % for the entire population), and the individual insurance market is disproportionately non-Latino white. Gender differences also exist. Mandates could unintentionally increase insurance coverage racial/ethnic disparities. Policymakers should consider the distribution of existing racial/ethnic disparities as criteria for legislative action on benefit mandates across health insurance markets.

Keywords: Benefit mandates; Health insurance; Racial/ethnic group disparities; State health policy.

MeSH terms

  • Adult
  • California
  • Ethnicity / statistics & numerical data*
  • Female
  • Health Services Accessibility / legislation & jurisprudence
  • Health Services Accessibility / statistics & numerical data
  • Health Surveys
  • Humans
  • Insurance Coverage / legislation & jurisprudence
  • Insurance Coverage / statistics & numerical data*
  • Insurance, Health / legislation & jurisprudence
  • Insurance, Health / statistics & numerical data*
  • Male
  • Medicaid / legislation & jurisprudence
  • Medicaid / statistics & numerical data
  • Middle Aged
  • Racial Groups / statistics & numerical data*
  • Retrospective Studies
  • Sex Factors
  • United States