University of Massachusetts Medical School

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Presentation transcript:

University of Massachusetts Medical School Disparities in Treated Prevalence among Medicaid Beneficiaries with Mental Illnesses Presenter Mihail Samnaliev, PhD Funded by: RWJ Foundation Substance Abuse Policy Research Program Grant: “ Improving Treatment for Medicaid Beneficiaries with Co-occurring Disorders ” PI: Robin Clark, PhD Center for Health Policy & Research (chpr) University of Massachusetts Medical School

MI in Medicaid populations Background MI in Medicaid populations 43% of Medicaid beneficiaries have MI (Adelman 2003) Medicaid MI expenditures = $24 billion in 2001 (DHHS 2005) Minorities are overrepresented in Medicaid 25% of African Americans have Medicaid 22% of Hispanics have Medicaid 9% of Whites have Medicaid

Disparities documented in other populations Background Disparities documented in other populations A BCBS plan (Sheffler and Miller 1989) 3 private insurance plans (Diehr et al 1984) Separate Medicaid programs Very few studies of Medicaid populations Most include one state or a MCO within state Typically focus on pharmacological management

All beneficiaries with MI expected to have equal Tx Objectives All beneficiaries with MI expected to have equal Tx Same treatment within income and eligibility category No theoretical reasons why MI Tx should differ by ethnicity/race Does treated prevalence among Medicaid beneficiaries with MI differ by ethnicity? Are findings consistent across 6 diverse Medicaid populations? Study separately community-based, ED and hospital settings Significance of this study Greaten burden on individuals with MI and family members Implications for Medicaid policy making

6 states from each region of the U.S: Populations 6 states from each region of the U.S: Arkansas, Colorado, Georgia, Indiana, New Jersey and Washington Medicaid programs differ substantially Size, population mix, Eligibility criteria Behavioral health care arrangements States differ substantially Geographic location and demographics Local economies, policies, supply of medical resources.

Identify individuals with 12 month prevalence of MI Methods Identify individuals with 12 month prevalence of MI Logistic regression to model Pr [ MI treatment] Separate model by state and setting (18 models) Estimate effects of race/ethnicity

A total of 4 million beneficiaries Results A total of 4 million beneficiaries 55% White, 6% Hispanic, 30% African American 350,000 (9%) diagnosed with MI 176,000 (50%) diagnosed with comorbidities 5.2% had asthma,12% had COPD 14% had diabetes,23% had hypertension 12% had substance use disorders

Most had a disability (61% - 73%) Results Most had a disability (61% - 73%) Two thirds aged 21 to 44 ( vs 44 to 65) FFS ranged from 7% to 91% 68% to 78% had continuous M-aid coverage Treatment 32% to 72% were treated in community settings 6% to 15% in ED 2% to 14% in inpatient settings

Adjusted Odds Ratios Settings Community-based Inpatient Emergency department Hisp AfrAm AR .79a .89 .99 .94 1.23 1.55c CO .80c .66 c .58b 1.06 1.07 1.00 GA .67b .59 c .72 1.21b 0.65 1.22c IN .44 c .49 c 1.08 1.66c 1.74c NJ .88 c .64 c 1.29c 1.18c 0.81b 1.17b WA .75 c .63 c .72a 1.24a 0.94 1.02 a = p<0.1; b=p<0.05; c=p<0.01 compared to White. Adjusting for age, gender, continuity of eligibility, eligibility category, severity of mental illness, co-occurring substance use disorders, physical comorbidities, and local economy variables

Probability of community-based treatment

Lower rates of CB MI treatment among African Americans and Hispanics Conclusions Lower rates of CB MI treatment among African Americans and Hispanics Higher or similar rates of inpatient and ER Tx among African Americans Extends previous studies of single Medicaid programs Implications associated with greater burden of disease on ethnic minorities

Implications for Medicaid Medicaid coverage not sufficient by itself Future efforts for engagement in Tx should focus on interventions in the community Medicaid programs are well positioned to implement policies Waivers, new benefits, financial incentives to consumers and providers, culturally sensitive treatment