Medicare, Medicaid, and Managed Care Analysis MMMCA Project Presented by Alexander Cowell, PhD, RTI

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Medicare, Medicaid, and Managed Care Analysis
(MMMCA) ProjectPresented byAlexander Cowell,
PhD, RTI InternationalKay Miller, Thomson
MedstatPresented atJoint Conference on Mental
Health Block Grant and National Conference on
Mental Health StatisticsWashington, DCMay 30,
2006
This work is being conducted under Substance
Abuse and Mental Health Services Administration
(SAMHSA) Task Order 280-2003-00026-0001 by RTI
International, Medstat, New England Research
Institutes, and Brandeis University for the
Center for Mental Health Services (CMHS).
RTI International is a trade name of Research
Triangle Institute
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MMMCA Project Team

• Nainan Thomas, PhD (CMHS)
• Alexander Cowell, PhD Todd Grabill, BA
• Ling Lew, BA Eric Kotecki, BA
• Kay Miller, BA
• Eva Witt, BA
• Mary Jo Larson, PhD
• Christopher Tompkins, PhD
• Jennifer Perloff, PhD

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Project Overview

• Title
• Medicare, Medicaid, and Managed Care Analysis
  (MMMCA) Project
• Overall goal
• Analyze public and private sector health care
  utilization and cost data on claimants with
  mental health (MH) and substance abuse (SA)
  disorders
• Contract dates
• Work ongoing since 1995
• Option period of current contract exercised
• Until September 2007

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Databases

• Three data sources
• Two acquired from Centers for Medicare Medicaid
  Services (CMS)
• Medicare Standard Analytic Files (1995?2002)
• Medicaid Analytic Files (1994?1999)
• Pre-1999 State Medicaid Research Files (SMRF)
• Post-1999 Medicaid Analytic eXtract (MAX) files
• MarketScan data acquired from Thomson Medstat
  (1994?1998, 2001)
• Augmented pharmacy data with Red Book starting in
  1999
• Use both claims and eligibility information

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Types of Analyses

• Tables
• Prevalence
• Service utilization
• Service payments
• Reports
• Policy issues
• Specific diseases
• Specific comorbidities between conditions

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Deliverables

• Analytic file construction
• Analytic tables
• Analytic reports
• Project Web site
• Technical assistance (TA)

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Features of the Project Web Site

• Emulates Decision Support 2000
• Easy to navigate
• Links to tables, reports, and documentation
• Link to fully Web-enabled TA
• Search function
• Whats New and Contact Us links

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Other Reference Material Available on Project
Web Site

• Defining MH/SA claimants in Medicaid
• Sample methodology
• Frequently asked questions
• Links to other relevant Web sites

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Accessing the Project Web Site

• Go to www.ds2kplus.org
• Click Links on the left-hand side
• Select Medicare, Medicaid, and Private Sector
  Analyses
• Direct link www.mhsapayments.org

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Online Technical Assistance Training
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Introduction

• Need for TA
• Role of Medicaid data in MH/SA research
• Potential uses of data
• How we meet that need overview of TA
• Audience for TA
• Overview of sessions
• Next steps

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Why the Need for Training

• Medicaid funds are an increasingly large
  percentage of state agency revenues
• Medicaid accounted for 38 of state MH agency
  controlled revenues in 2002 (Source NASMHPD
  Research Institute, October 2004)
• Medicaid is a rich data source
• Provides data not available within MH/SA agency
  data systems
• Examples pharmacy costs, detailed information on
  outpatient service use, cost of fee-for-service
  (FFS) care
• MH/SA agencies either do not have access to or do
  not fully utilize their states Medicaid data
• Our experience can help them use these data

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Potential Uses of Data

• Monitoring performance
• Identifying prevalence of conditions in the
  Medicaid population
• Tracking utilization and expenditures
• Conducting special analyses
• Reporting
• Integrating with MH/SA agency data

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Overview of TA Training

• Method of administration Web-based
• Includes PowerPoint slides and narration
• Options available
• Audiovisual presentation
• Audiovisual presentation with text
• Download slides
• Download text
• Site now available as a BETA test site

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Audience

• Primary audience MH/SA agency personnel who want
  to work with their own states Medicaid data
• Secondary audience SAMHSA staff or other
  researchers wishing to do similar analysis
• Level of expertise required Assumes basic
  knowledge of Medicaid and research methods but
  will give some background to put topics in
  context

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Overview of Sessions

• Session 1 Introduction and Overview
• Session 2 Defining the Research Question and
  Developing an Analysis Plan
• Session 3 Acquiring Data and Assessing
  Quality and Completeness
• Session 4 Defining Samples and Designing the
  Analytic Database
• Session 5 Building Analytic Files and Tables
  (Part A, Part B)

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Steps to Policy Research
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Session 1 Introduction and Overview

• Purpose
• Importance of MH/SA research
• Role of Medicaid data in MH/SA research
• Potential uses of data
• Overview of project database
• Description of data
• Hardware and software used
• Types of analyses performed with data
• Overview of research steps

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Example Slide Session 1Potential Uses of Data

• Monitoring performance
• Identifying prevalence of conditions in the
  Medicaid population
• Tracking utilization and expenditures
• Special analyses
• Reporting
• Integrating with MH/SA agency data

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Session 2 Defining the Research Question and
Developing an Analysis Plan

• Issues faced by state MH agencies
• How Medicaid can help address issues
• Examples include
• Cost containment
• Requests from legislative staff
• Demonstration on how to
• Define the research question
• Develop an analysis plan

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Example Slide Session 2Step 1A Define the
Research Question
Identify the Issue
List the Research Questions
Choose the Primary Research Questions
Organize the Primary Research Questions
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Example Slide Session 2Step 1B Develop an
Analysis Plan
Draft an Outline
Sketch the Background
Determine Which Data to Use
Determine Which Statistical Methods to Use
Complete the Analysis Plan
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Session 3 Acquiring Data and Assessing Quality
and Completeness

• Data acquisition (four steps)
• Understand available Medicaid files
• Create data use agreements
• Request documentation
• Determine which data are needed
• Assessment of data quality and completeness
  (three steps)
• Conduct initial data runs
• Review critical areas
• Perform a final assessment

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Example Slide Session 3Data Acquisition Step
1 Understand Available Medicaid Files

• Eligibility files
• Claim/encounter-level files
• Fee-for-service (FFS) ? Claim-level files
• Managed care ? Encounter-level files
• (sometimes stored in a separate system)
• Provider-level files

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Example Slide Session 3Data AssessmentStep 2
Review Critical Areas

• Review basic frequencies to uncover data
  shortcomings
• Examine data element flags that serve as quality
  indicators
• Check variables critical for analyses (e.g.,
  diagnosis codes, procedure codes)
• Analyze verification tables that display
  information (e.g., number of people, number of
  claims, total expenditures) by
• Type of service (e.g., inpatient)
• Population (e.g., MH/SA claimants)
• Time period (e.g., monthly)
• Compare with initial statistics or benchmarks

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Session 4 Defining Samples and Designing the
Analytic Database

• Keys to success
• Partnership between analysts and programmers
• Planning
• Defining samples
• Types of samples
• Sample selection criteria
• Key considerations for sample selection
• Sample exclusion criteria
• Defining the analytic database
• Develop statistical analysis and table
  specifications
• Organize the data
• Retain key elements from original data
• Create additional variables

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Example Slide Session 4Keys to Success

• Partnership between analyst and programmer
• Analyst responsibilities
• Prepare a thorough analysis plan
• Communicate detailed specifications
• Programmer responsibilities
• Identify and communicate problems early
• Prepare documentation of the process,
  particularly decisions made

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Example Slide Session 4Sample Selection
Criteria MH/SA Examples

• Diagnosis code
• Major depressive disorder
• Alcoholic psychoses
• Procedure code
• Detoxification
• Psychotherapy
• Service/provider type code
• Inpatient psychiatric facility
• Mental health clinic
• Psychiatrist/psychologist

• Revenue/cost center code
• Psychiatric or detoxification room
• Drug/alcohol rehabilitation
• Prescription drug code
• Antidepressant

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Example Slide Session 4Organize the Data
Person Summary File

• Combines claim and enrollment information for
  each person
• Demographic characteristics
• Annual and monthly eligibility
• Summary of claim data by type of service
• Sample markers and summary variables (discussed
  later)
• Minimizes need for repeated processing of large
  claim-level files

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Session 5 Building Analytic Files and Tables

• Most detailed session split into two parts
• --------------------------------------------------
  ---------------------Part A
• Building analytic files
• Identify disease-specific samples
• Select final samples
• Build analytic claims-level file
• --------------------------------------------------
  ---------------------Part B
• Build person-summary files
• Key considerations for building analytic files
• Assuring confidentiality
• Preparing data documentation
• Building analytic tables

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Example Slide Session 5Building Analytic Files
Data Processing Flowchart
Medicaid Claims
Medicaid Enrollment
Step 1 Identify Disease-Specific Samples
Step 2 Select Final Samples
Step 3 Build Analytic Claim-Level Files
Step 4 Build Person Summary Files (PSFs)
Claimant ID File
Analytic Claim-Level File
Final ID File
Annual PSF
Historical PSF
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Example Slide Session 5Select Final Samples
Exclude Records as Needed

• Groups not fully represented or not applicable
• Designated in analysis plan
• May include
• Managed care enrollees
• Elderly
• Enrollees with poor data
• Denied claims
• Records outside of time period

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Example Slide Session 5Create Claim-Level
Markers Examples

• MH/SA diagnostic categories
• Primary vs. all diagnosis codes
• Summary vs. detailed categories
• Other disease categories (e.g., asthma, diabetes)
• Service categories

• Ambulatory facility
• Lab/x-ray
• Pharmacy
• Premium payments

• Inpatient
• Inpatient psychiatric
• Other long-term care
• Physician

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Example Slide Session 5Keys to Saving Time and
Resources

• Close collaboration between analysts and
  programmers
• Well-designed final analytic files
• Carefully planned naming conventions
• Improve efficiency in using data files
• Allow analysts to use computer output and avoid
  need to generate tables until results are final
• Well-designed Person Summary Files
• Support most analysis

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Discussion Next Steps

• Obtain feedback on current sessions
• Revise/update as needed
• Create new modules
• One possibility analysis of data
• How to produce simple descriptive statistics
• Showing some universally applicable examples
• Other suggestions?
• Market to state agencies how to get the word
  out?
• Questions/comments?

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Analytic Tables
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Analytic Tables (I)(see online examples)

• Overview tables
• Numbers of claimants
• By demographic and broad diagnostic categories
• Data source-specific tables
• Modality of care
• Diagnosis
• Enrollment status
• Longitudinal
• Chapter in Mental Health, United States, 2004

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Analytic Tables (II) (see online examples)

• Type of service
• MH/SA utilization broken into
• Mental health
• Substance abuse
• Indistinguishable
• Detoxification and rehabilitation
• Detailed psychotropic prescription drugs
• Outpatient emergency room
• Enrollment status
• Managed care enrollment by demographic status

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Table Findings Prescription Drugs (I)

• In Medicaid, prescription drug costs have been
  rising
• Evidence suggests that these costs are higher for
  those with MH/SA conditions

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Table Findings Prescription Drugs (II)
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Table Findings Prescription Drugs (III)
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Table Findings Prescription Drugs (IV)
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Table Findings Prescription Drugs (V)

• Those using antidepressant drugs are more
  expensive than those who are not
• Reasons are complex and numerous
• Each of the following explain the increase in
  part, but none fully explain it
• Cost of prescription drugs
• Demographic differences
• Case mix
• Underlying, time-variant characteristics
• Use of other MH/SA services

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Analytic Reports
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Analytic Reports Topics (I)

• General interest mental health and substance
  abuse
• Dual eligible Medicaid and Medicare beneficiaries
• HIV/AIDS
• Posttraumatic stress disorder
• Substance abuse and major depression
• Childrens mental health services

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Analytic Reports Topics (II)

• Diabetes and depression
• Managed care penetration
• Antidepressants
• Block grants
• Cost offset
• Medicare comorbidities
• MarketScan plans

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Analytic Reports Examples (I)

• General interest mental health and substance
  abuse (MH/SA)
• Cowell, A.J., T.C. Grabill, E.G. Foley, K.
  Miller, M.J. Larson, C. Tompkins, J. Perloff, and
  R. Manderscheid. 2005. Trends in Number of and
  Payments for Persons with Mental Health and
  Substance Abuse Disorders in Public and Private
  Sector Health Plans. Draft submitted to appear
  in Mental Health, United States, 2004.
• Presents trends for 1995 to 1998 on the number of
  people with MH/SA disorders, as well as the
  utilization and costs associated with treatment.
  Three data sources are used that represent the
  three largest payers of treatment for MH/SA
  disorders Medicare, Medicaid, and the private
  sector.

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Analytic Reports Examples (II)

• Dual eligible Medicare and Medicaid beneficiaries
• Larson, M.J., A.J. Cowell, M. Urato, and L.Y.
  Lew. 2005. Prescription Drug Expenditures for
  Medicaid/Medicare Dual Eligible Beneficiaries
  with Mental Health/Substance Abuse Conditions.
  Draft report to SAMHSA.
• Example to be discussed in detail.
• HIV/AIDS
• Larson, M.J., K. Miller, and J.W. Bray. 2004.
  HIV/AIDS ClaimsDiagnosis in Four States Among
  Medicaid Enrollees with Mental Health and
  Substance Abuse Disorders. Report to SAMHSA.
• Concludes that the presence of a co-occurring
  substance use diagnosis places those with MH
  disorders at particularly high risk for HIV/AIDS
  and that MH disorders alone are not associated
  with substantial increased risk.

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Analytic Reports Examples (III)

• Posttraumatic Stress Disorder
• Cummings, J., J.W. Bray, W. Schlenger, and R.
  Manderscheid. 2004. Diagnosed Prevalence and
  Associated Health Care Payments of PTSD in the
  Public Sector. Report to SAMHSA.
• This study provides baseline estimates of the
  diagnosed prevalence and the associated financial
  implications of PTSD using 1997 fee-for-service
  (FFS) Medicare and Michigan Medicaid claims data.
• Cowell, A.J., J.W. Bray, T.C. Grabill, and R.
  Manderscheid. 2004. Health Care Costs Associated
  with Substance Abuse for Public and Private
  Sector Claimants With and Without Major
  Depression. Report to SAMHSA.
• To date, very little is known about the impact of
  SA and major depression on actual payments using
  data from more than one payment source. This
  study addresses this gap by using a claims
  database with samples of three unique populations
  that span the U.S. health care system Medicare,
  Medicaid, and the private sector.

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Analytic Reports Examples (IV)

• Substance Abuse and Major Depression
• Cowell, A.J., J.W. Bray, T.C. Grabill, and R.
  Manderscheid. 2004. Health Care Costs Associated
  with Substance Abuse for Public and Private
  Sector Claimants With and Without Major
  Depression. Report to SAMHSA.
• To date, very little is known about the impact of
  SA and major depression on actual payments using
  data from more than one payment source. This
  study addresses this gap by using a claims
  database with samples of three unique populations
  that span the U.S. health care system Medicare,
  Medicaid, and the private sector.

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Analytic Reports Examples (V)

• Childrens Mental Health Services
• Larson, M.J., S. Sharma, K. Miller, and R.
  Manderscheid. 2004. Childrens Mental Health
  Services in Medicaid. Health Care Financing
  Review 26(1)5-22.
• This study analyzed annual service use and
  payment data for children in racial/ ethnic
  subgroups in Medicaid programs of four states and
  compared service use of youth treated with MH/SA
  conditions to youth without such conditions.

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Analytic Reports Examples (VI)

• Diabetes and Depression
• Finkelstein, E.A., J.W. Bray, H. Chen, M.J.
  Larson, K. Miller, C. Tompkins, A. Keme, and R.
  Manderscheid. 2003. Prevalence and Costs of
  Major Depression among Elderly Claimants with
  Diabetes. Diabetes Care 26(2)415-420.
• This analysis compares the odds of major
  depression among Medicare claimants with and
  without diabetes and tests whether annual medical
  payments are greater for those with both diabetes
  and major depression or for those with diabetes
  alone.

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Analytic Reports Examples (VII)

• Managed Care Penetration
• Tompkins, C., and J. Perloff. 2005. The Impact
  of Managed Care on Medicaid Fee-For-Service
  Expenditures. Draft report to SAMHSA.
• Example to be discussed in detail.
• Antidepressants
• Cowell, A.J., J. Cummings, J.W. Bray, and R.
  Manderscheid. 2004. Medicaid Costs Associated
  with Classes of Antidepressants. Report to
  SAMHSA.
• Using 1997 Medicaid data for Michigan, New
  Jersey, Pennsylvania, and Washington, this study
  estimated health care payments associated with
  antidepressants. The results provide baseline
  estimates of the association between four classes
  of antidepressants and annual health care
  payments against which more recent data may be
  compared.

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Analytic Reports Examples (VIII)

• Block Grants
• Cowell, A.J., and J.W. Bray. 2004. The
  Association Between Federal Block Grants and
  Individual Mental Health and Substance Abuse
  Expenditures. Report to SAMHSA.
• Despite block grants being a primary source of
  funding to address MH/SA needs, the impact of
  such public expenditures and policies on
  individual behavior has received little attention
  in the literature. To help address this gap in
  knowledge, this report uses data from several
  sources to assess the impact of the MH and SA
  block grants on individual MH and SA
  expenditures.

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Analytic Reports Examples (IX)

• Cost Offset
• Bray, J.W., and T.C. Grabill. 2004. The Cost
  Offset of MH/SA Treatment in Four Medicaid
  States. Report to SAMHSA.
• This study analyzes the claims history of
  Medicaid recipients with identified MH/SA
  disorders and estimates a cost offset of MH/SA
  treatment.

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Analytic Reports Examples (X)

• Medicare Comorbidities
• Finkelstein, E.A., J.W. Bray, H. Chen, M.J.
  Larson, K. Miller, and C. Tompkins. 2002.
  Medicare Cost Implications Associated with
  Substance Abuse for Claimants With and Without
  Comorbid Mental Health Conditions. Report to
  SAMHSA.
• This study presents evidence on whether the
  increase in costs (to Medicare) associated with
  treating a substance abuse condition is less if
  the individual is already being treated for a
  mental illness. The study also tests whether
  general medical costs for claimants with an SA
  condition are greater than for those without an
  SA condition, and whether the cost implications
  are even greater for dual diagnosis claimants.

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Analytic Reports Examples (XI)

• MarketScan Plans
• Tompkins, C., M. Glavin, K. Miller, and T.
  Winger. 2001. Does Managed Care Differentially
  Affect Services for Behavioral Health? An
  Examination of Utilization in Private Health
  Plans. Report to SAMHSA.
• This study examines the utilization experiences
  of individuals enrolled in employer sponsored
  private health plans, which are categorized as
  fee-for-service (FFS) plans, managed health plans
  like preferred provider organizations (PPO), or
  capitated health plans like health maintenance
  organizations (HMO). The general approach is to
  compare various utilization measures across these
  three different types of health plans, for
  selected patient subgroups.

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Analytic Report Medicaid Prescription Drug
Utilization and Costs for Dual-Eligible
Individuals
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Analytic Report Background on MMA

• Medicare Modernization Act of 2003 (MMA)
• Part D prescription drug benefit
• January 1, 2006, start date
• 43 million beneficiaries are eligible
• CMS oversees implementation of Part D
• Numerous private prescription drug plans
• Vary on formulary restrictions, cost-sharing
  measures, utilization review, premiums
• CMS guidance on structure of formularies
• Voluntary enrollment and choice of drug plan

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Analytic Report Example Dual-Eligible
Beneficiaries

• Medicare/Medicaid jointly enrolled
• Prescription drug benefit shifting to Part D
• Automatically enrolled and can choose or change
  drug plan
• States will make clawback payments to federal
  government
• States are not required but may continue some
  pharmacy benefits
• 7 million beneficiaries out of 43 million
• Low-income seniors and disabled
• 6 million have full benefits

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Analytic Report Psychiatric Medications

• 41 therapeutic categories and 137 associated
  pharmacologic classes on common framework
• Model guidelines at least two drugs from each
  class should be included on formulary
• CMS clarification substantially all
  antipsychotic and antidepressant medications
  should be included
• Benzodiazepines are not covered by Part D

Four other classes are anticonvulsant,
anticancer, immunosuppressant, and HIV/AIDS.
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Analytic Report MMMCA Analyses

• What are the characteristics of Medicaid
  beneficiaries to be shifted to Medicare drug
  plans? What proportion have MH/SA conditions?
• How do the prescription drug expenditures of dual
  eligibles with MH/SA conditions compare to all
  dual eligibles?
• How do the drug expenditures of the MH/SA
  beneficiaries shifted to Part D compare to
  beneficiaries who will remain under Medicaid
  benefits?

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Analytic Report Methods

• 1999 prescription drug data from Michigan, New
  Jersey, Pennsylvania, and Washington
• FFS recipients in 1999 MAX data
• MMMCA acquired Thomsons Red Book classification
  system to assign drugs to therapeutic classes
• Identified dual eligible as at least one claim
  during the year with a Medicare deductible or
  coinsurance paid by Medicaid

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Analytic Report MH/SA vs. Random Samples

• MH/SA sample more likely to be dual eligible in 3
  of 4 states (33 vs. 28)
• Among dual-eligible enrollees, MH/SA vs. random
  sample
• More likely to be disabled (62 vs. 47)
• Less likely to be in a nursing home full year
  (4.6 vs. 5.3)

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Analytic Report Prescription Drug Expenditures
MH/SA and Random Sample Dual Eligibles
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Analytic Report Psychiatric Drug Expenditures
MH/SA Beneficiaries With and Without Dual
Eligibility
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Analytic Report Other Drug Expenditures MH/SA
Beneficiaries With and Without Dual Eligibility
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Analytic Report Discussion and Next Steps

• There is variation in pharmacy expenditures
  within states across subgroups (MH/SA vs. random
  dual samples MH/SA dual vs. non-dual)
• Movement toward a more uniform prescription
  benefit may reduce some of this variation
• Subgroups with higher prescription drug
  expenditures may be disproportionately affected
  by transition to Part D
• Preparing two publications (1) MH vs. random
  sample dual eligibles, (2) dual vs. non-dual MH/SA

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Analytic Report The Impact of Medicaid Managed
Care on Medicaid Fee-for-Service Expenditures
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Managed Care Penetration

• In the 1990s, a growing proportion of Medicaid
  recipients moved from fee-for-service (FFS)
  coverage into managed care
• Two dominant forms of managed care
• Primary care case management (PCCM)
• Enrollment into separate health plans

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Research Question

• Assess whether average Medicaid FFS expenditure
  rates changed between 1993 and 1997 as a result
  of increasing managed care penetration
• Unsure of answer because of several factors
• Lower rates due to biased risk selection
• Higher rates due to spillover effects
• Tumultuous period for Medicaid

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Methods

• Key measures
• Proportion of enrollees in managed care within
  each county for each state (penetration rate)
• Total payments per FFS enrollee
• MH/SA payments per FFS enrollee

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Medicaid Enrollment Percentage Change (1993?1997)
Pennsylvania data are for 19941997.
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Managed Care Penetration
Michigan Managed Care Penetration
0.60
0.50
0.40
AFDC
0.30
Percent
DIS
0.20
0.10
0.00
1993
1994
1995
1996
1997
Year
New Jersey Managed Care Penetration
0.90
0.80
0.70
0.60
AFDC
0.50
Percent
0.40
DIS
0.30
0.20
0.10
0.00
1993
1994
1995
1996
1997
Year
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Michigan Trends by County Medicaid Disabled
Michigan Absolute Change in MC and Percentage
Change in FFS (19931997), Medicaid Disabled
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Michigan Trends by County Medicaid AFDC/TANF
Michigan Absolute Change in MC and Percentage
Change in FFS (19931997), AFDC
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Analytic Report Washington Trends by
CountyMedicaid Disabled
Washington Absolute Change in MC and Percentage
Change in FFS (19931997), Medicaid Disabled
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Pennsylvania Trends by CountyMedicaid Disabled
Pennsylvania Absolute Change in MC and Percentage
Change in FFS (19931997), Medicaid Disabled
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Conclusions

• FFS general health care expenditures are either
  neutral or increasing with managed care
  penetration
• But FFS MH/SA expenditures are either neutral or
  decreasing with managed care penetration
• Managed care experience clearly varies across and
  within states
• Larger counties have greater managed care
  penetration

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Analytic Report Trends in Number of and Payments
for Persons with Mental Health and Substance
Abuse Disorders in Public and Private Sector
Health Plans (draft submitted to appear in
Mental Health, United States, 2004)
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Objective

• Wide-spanning report on trends in utilization and
  payments for the 19951998
• Uses all three sources Medicare, 4 Medicaid
  states, and MarketScan
• Trends presented and discussed
• All sample claimants
• MH/SA claimants
• Claimants with co-occurring MH and SA conditions
• MH/SA claimants with prescription drug medication

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Summary of Findings (I)

• MarketScan and Medicaid decreasing effective
  sample size because of increased managed care
  penetration
• But we know from other work by the project team
  that this may not have unduly altered average
  payments for MH/SA services
• An increasing proportion of claimants in Medicaid
  and Medicare had an MH or SA condition

83
Summary of Findings (II)

• Proportion of MH/SA claimants with co-occurring
  MH and SA disorders has remained stable or
  decreased over time
• Their average payments have remained stable or
  increased over time
• Average prescription drug payments for Medicaid
  MH/SA claimants have remained consistently higher
  than payments for a random sample of all
  claimants
• The increase in payments for MH/SA claimants in
  step with that for a random sample of claimants

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Wrap-Up

• Alexander Cowell, PhDProject Director, RTI