using microsimulation modeling to identify linguistic outreach and enrollment needs
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Daphna Gans, Ph.D. UCLA Center for Health Policy Research March 14 th , 2013 Eighth National Conference on Quality Health Care for Culturally Diverse Populations (March 11-14, 2013) Oakland Marriott City Center, Oakland, CA. - PowerPoint PPT PresentationTRANSCRIPT
Daphna Gans, Ph.D.UCLA Center for Health Policy Research
March 14th, 2013
Eighth National Conference on Quality Health Care for Culturally Diverse Populations (March 11-14, 2013) Oakland Marriott City Center, Oakland, CA
Using Microsimulation Modeling to Identify Linguistic Outreach and Enrollment Needs
Acknowledgment
Support for this analysis was provided by:The California Pan-Ethnic Health Network
Policy Brief:Gans D, Kinane CM, Watson G, Roby DH, Graham-Squire D, Needleman J, Jacobs K, Kominski GF, Dexter D., and Wu E. Achieving Equity by Building a Bridge from Eligible to Enrolled. Los Angeles, CA: UCLA Center for Health Policy Research and California Pan-Ethnic Health Network, 2012
http://www.healthpolicy.ucla.edu/pubs/files/enrolledpbfeb2012.pdf
Please note: Results may vary slightly when using newer version of the CalSIM model.
Goals of this Project Predict English proficiency and preferred language
among projected eligible Californians and enrollees in various insurance markets under the ACA
Focus on those eligible for subsidized coverage through the California Health Benefit Exchange (the subsidized Exchange)
Compare take-up rates with or without the Limited English Proficient (LEP) effect
Provide recommendations for policymakers to maximize enrollment of LEP individuals
Overview
Part I – The Need to Account for Limited English Proficiency
Part II – Limited English Proficiency Modeling
Part III – Limited English Proficiency Analyses Using California Simulation of Insurance Markets
(CalSIM) 1.8
Part IV – Recommendations
Overview
Part I – The Need to Account for Limited English Proficiency
Part II – Limited English Proficiency Modeling
Part III – Limited English Proficiency Analyses Using California Simulation of Insurance Markets
(CalSIM) 1.8
Part IV – Recommendations
Rationale for including English Language Proficiency
People of color represent roughly 60% of California’s population
Nearly 7 million Californians are considered Limited English Proficient (LEP)
LEP individuals are less informed of the ACA’s benefits
Language barriers currently likely impacting participation in public programs
English Language Proficiency
“Individuals who do not speak English as their primary language and who have a limited ability to read, write, speak, or understand English may be limited English proficient, or ‘LEP,’ and may be eligible to receive language assistance with respect to a particular type of service, benefit, or encounter.”
The Office of Civil Rights
Individuals who report speaking English “less than very well”
Agency for Healthcare Research and Quality (AHRQ) ; Shin & Kominski 2000
OverviewPart I – The Need to Account for Limited English
Proficiency
Part II – Limited English Proficiency Modeling
Part III – Limited English Proficiency Analyses Using California Simulation of Insurance Markets
(CalSIM) 1.8
Part IV – Recommendations
The core data set for CalSIM – Medical Expenditure Panel Survey, Household Component (MEPS-HC) – does not contain data on English proficiency, but contains reported comfort level with speaking English
Individuals reporting that they were uncomfortable speaking English in MEPS were classified as LEP
To determine LEP for the remainder of respondents who speak a language other than English at home (1%) the CalSIM model uses a probabilistic model fit to the 2009 California Health Interview Survey (CHIS)
Limited English Proficiency (LEP) Predictive Modeling
Logistic regression
Dependent variable: Binary LEP status
Predictors: 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)
Controls for gender, income, employment status, employer firm size, ability to understand primary care provider, and immigration status
Model statistics indicate goodness of fit
LEP Predictive Modeling Using CHIS 2009 Confidential Data
Integrating LEP Status into CalSIM Model Each adult is randomly assigned LEP status with
predicted probability estimated from model parameters
Include an adjustment to the predicted probabilities of individual insurance coverage take up for LEP individuals based on empirical analysis from Alegria et al. (2006) Reflects the degree of difference between insurance take-up and remaining
uninsured among Latino and Asian populations attributed to LEP
Marginal distributions of LEP from CHIS are included in CalSIM weighting process to control for larger LEP distribution in California
OverviewPart I – The Need to Account for Limited English
Proficiency
Part II – Limited English Proficiency Modeling
Part III – Limited English Proficiency Analyses Using California Simulation of Insurance Markets
(CalSIM) 1.8
Part IV – Recommendations
Eligibility for Medi-Cal (5.3 Million) and the Subsidized Exchange (2.7 Million), by
Race/Ethnicity, 2013
Source: CalSIM Version 1.8
52% 2.10 mil
47% 1.34 mil
New Medi-Cal/Healthy Families Eligible
Subsidized Exchange 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Native American/Alaskan Native/Multiple Race
Asian American/Pacific Islander
African American
Latino
White
Race
47%1.3 mil
55%2.9 mil
66% Minority
75% Minority
Eligibility for Medi-Cal (5.3 Million) and the Subsidized Exchange (2.7 Million) , by Language
Proficiency, 2013
Source: CalSIM Version 1.8
New Medi-Cal/Healthy Fami-lies
Subsidized Exchange 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Limited English Proficient (LEP)
Proficient
40% 1.1mil
40%2.1mil
Predicted Percentage of LEP Adults among Predicted Coverage Status Populations, 2019
Insurance Coverage, 2019 % LEPTotal Projected
Adult Non-Elderly Population
Employer Sponsored Insurance 16% 13,711,000
Medi-Cal/Healthy Families 41% 3,630,000Subsidized Exchange 32% 1,565,000Unsubsidized Exchange 15% 1,798,000
Uninsured 60% 3,442,000
Source: CalSIM Version 1.8 Base Scenario 2019
Language Other than English Spoken at Home among LEP Non Elderly Adults Enrollees, Medi-Cal (1.5 Million) and the Subsidized
Exchange (500,000), 2019
Source: Original analysis of 2009 California Health Interview Survey applied to CalSIM 1.8 projections, base scenario.
Comparing Take-up Rates with and without LEP effects
CalSIM without modeling LEP accounts for the base scenario assumptions
Individual enrollment decision is based on income, cost, chronic conditions
Assumes LEP is not a hindering factor to enrollment; or
Represents ideal conditions where all individuals regardless of English mastery can enroll
CalSIM with LEP modeling models accounts for the marginal effect of LEP on take-up holding other factors constant
Eligible and Enrolled LEP Adult Population in the Subsidized Exchange, with and without
Integrating LEP into CalSIM, 2019
Source: CalSIM 1.8
Enrollment Rate for the LEP Population: Potential Gap Between Eligible and Enrolled
Under ideal conditions, 56% of the eligible LEP individuals are expected to enroll
If Limited English Proficiency is a factor in enrollment behavior, only 46% of the eligible LEP individuals are expected to enroll
A potential gap affecting about 119,000 individuals
Overview
Part I – The Need to Account for Limited English Proficiency
Part II – Limited English Proficiency Modeling
Part III – Limited English Proficiency Analyses Using California Simulation of Insurance Markets
(CalSIM) 1.8
Part IV – Recommendations
Bridging the Gap from Eligible to Enrolled Planning for the Medicaid Expansion and Exchange is
very important
The Exchange is invested in properly planning for the population expected to enroll
Providers, counties and communities all need to get involved in planning
Proactive action is needed so we can to take advantage of federal Medicaid and Exchange subsidy dollars
Currently only 61% of individuals eligible actually enroll in Medicaid, and many of them do it due to a health episode
Bridging the Gap from Eligible to Enrolled Proactive measures:
Auto-enrollment Facilitate safe and confidential transition from the multiple current
public programs to Medi-Cal or the Exchange Improve capacity of physicians and other clinicians to deal with this
new demand for services Use CalSIM predicted number of eligible and projected enrolled LEP
individuals and languages spoken at home as guidance in planning outreach and other materials
Cultural and linguistic differences should be included in planning Target assistance resources to consumers with the highest needs Invest in culturally and linguistically appropriate marketing and
outreach Involve communities of color in decision-making processes
Daphna Gans, Ph.D.UCLA Center for Health Policy Research
Contact information:[email protected](310) 794-6196
Using Microsimulation Modeling to Identify Linguistic Outreach and Enrollment Needs.