Using microsimulation model to predict language barriers and access to health care under the Affordable Care Act

Background: As we draw closer to 2014 and the full implementation of the Affordable Care Act (ACA), it is important to develop a better understanding of the demographic characteristsics of newly eligible Californians for insurance coverage through the Medi-Cal expansion or through the subsidized exchange. Objectives: This project aims to estimate the effects of language barriers on enrollment and present policy recommendations to support culturally and linguistically sensitive outreach. Methods: Analyses are conducted using the California Simulation of Insurance Markets (CalSIM), a micro-simulation model that estimates the effects of the ACA on the enrollment of individuals in insurance coverage in California, developed by the UC Berkeley Center for Labor Research and Education and the UCLA Center for Health Policy Research. The core data set supplying individual-level data for CalSIM contains information that indicates comfort level with speaking English. Individuals reporting that they were uncomfortable speaking English were classified as LEP. To determine LEP for the remainder of respondents, the CalSIM uses a probabilistic model fit to the 2009 California Health Interview Survey (CHIS). The model includes a variety of factors associated with LEP populations as predictors and controls, including, language spoken at home, survey interview language, race/ethnicity, level of education, and age at which the individual moved to the United States (if not U.S. born), gender, income, employment status, employer company size, ability to understand primary care provider, and immigration status. Results: CalSIM projections indicate that of the 1.42 million nonelderly adults newly eligible to Medi-Cal, over 35% (or 500,000 individuals) will speak English less than very well. Even with broad outreach efforts, only 55% (780,000 people) of the newly eligible are expected to enroll in 2014. Forty-three percent (or 130,000 people) of the 300,000 adult Californians who are expected not to enroll in Medi-Cal despite being eligible, are individuals with limited English proficiency. Specific analyses on the subsidized exchange further indicate that eligible LEP Californians are projected to enroll at lower rates than their non-LEP counterparts. Without target outreach, about 119,000 individuals who speak English less than very well will not likely enroll in the subsidized despite being eligible. Conclusions: Proactive action is required to enroll eligible individuals with language difficulties. Enrollment process must be simplified, including a “no wrong door” process, and information must be given in multiple languages and with targeted culturally sensitive outreach to communities of color.