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:dgans@ucla.edu(310) 794-6196

Using Microsimulation Modeling to Identify Linguistic Outreach and Enrollment Needs.