RTI template by MikeJenny

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									                Medicare, Medicaid, and Managed Care Analysis
                             (MMMCA) Project
                                                      Presented by
                                         Alexander Cowell, PhD, RTI International
                                              Kay Miller, Thomson Medstat

                                              Presented at
                Joint Conference on Mental Health Block Grant and National Conference on
                                        Mental Health Statistics
                                            Washington, DC
                                             May 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
    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


2
                         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




3
                               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



4
                       Types of Analyses

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


5
                           Deliverables

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




6
            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
       “What’s New” and “Contact Us” links




7
                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




8
            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




9
Online Technical Assistance Training
                           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


11
                   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 state’s Medicaid data
        Our experience can help them use these data



12
                     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



13
                   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


14
                            Audience

        Primary audience: MH/SA agency personnel who
         want to work with their own state’s 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




15
                  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)


16
     Steps to Policy Research


                  Define the Research Question
                  and Develop an Analysis Plan


                  Acquire and Assess Data

                  Define Samples and Design
                  the Analytic Database

                  Build Analytic Files and Tables

                  Report Results




17
         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

18
                     Example Slide: Session 1
                      Potential 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



19
         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


20
           Example Slide: Session 2
     Step 1A: Define the Research Question


                          Identify the Issue

                          List the Research Questions

                          Choose the Primary
                          Research Questions

                          Organize the Primary
                          Research Questions




21
          Example Slide: Session 2
     Step 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




22
          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



23
                      Example Slide: Session 3
                             Data 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




24
                        Example Slide: Session 3
                                Data Assessment:
                          Step 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


25
         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



26
                    Example Slide: Session 4
                       Keys 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



27
                     Example Slide: Session 4
             Sample Selection Criteria: MH/SA Examples

        Diagnosis code                     Revenue/cost center code
            Major depressive disorder          Psychiatric or
            Alcoholic psychoses                 detoxification room
        Procedure code                         Drug/alcohol rehabilitation
            Detoxification                 Prescription drug code
            Psychotherapy                      Antidepressant
        Service/provider type code
            Inpatient psychiatric
             facility
            Mental health clinic
            Psychiatrist/psychologist

28
                    Example Slide: Session 4
             Organize 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

29
     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 5
      Building Analytic Files: Data Processing Flowchart

                        Medicaid                   Medicaid
                         Claims                   Enrollment




          Step 1:                                             Step 3:             Step 4:
                                     Step 2:
          Identify                                         Build Analytic      Build Person
                                   Select Final
     Disease-Specific                                      Claim-Level        Summary Files
                                    Samples
         Samples                                               Files              (PSFs)



                                      Final                     Analytic
        Claimant                                                             Annual     Historical
                                       ID                      Claim-Level
         ID File                                                   File       PSF         PSF
                                      File




31
                        Example Slide: Session 5
         Select 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

32
                    Example Slide: Session 5
             Create 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
          Inpatient                       Ambulatory facility
            Inpatient psychiatric         Lab/x-ray
            Other long-term care          Pharmacy
            Physician                     Premium payments

33
                  Example Slide: Session 5
              Keys 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

34
                      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?



35
Analytic Tables
                           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



37
                         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




38
         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




39
       Table Findings: Prescription Drugs (II)
                Michigan Average Prescription Drug Payment Per Drug Claimant

     $1,800

     $1,600

     $1,400

     $1,200

     $1,000                                                                    Random Sample
      $800                                                                     MH/SA Sample

      $600
      $400

      $200

        $0
              1994       1995         1996        1997         1998



40
     Table Findings: Prescription Drugs (III)

           Michigan Medicaid Drug Payments as a Percentage of Total Payments

     16%

     14%

     12%

     10%
                                                                          Random Sample
     8%
                                                                          MH/SA Drugs
     6%

     4%

     2%

     0%
           1995           1996            1997            1998



41
       Table Findings: Prescription Drugs (IV)

              Michigan Average Prescription Drug Payment Per Drug Claimant

 $1,800

 $1,600

 $1,400

 $1,200

 $1,000                                                                      Random Sample
     $800                                                                    MH/SA Sample

     $600
     $400

     $200

       $0
            1994       1995         1996        1997         1998



42
         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


43
Analytic Reports
               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
        Children’s mental health services




45
               Analytic Reports: Topics (II)

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


46
               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.




47
              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 Claims—
             Diagnosis 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.



48
              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.

49
             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.




50
              Analytic Reports: Examples (V)

        Children’s Mental Health Services
            Larson, M.J., S. Sharma, K. Miller, and R. Manderscheid. 2004.
             ―Children’s 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.




51
             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.




52
             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.


53
             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.




54
             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.




55
              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.




56
             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.



57
Analytic Report: Medicaid Prescription Drug
  Utilization and Costs for Dual-Eligible
                Individuals
          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




59
         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




60
         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.




61
           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?


62
                   Analytic Report Methods


        1999 prescription drug data from Michigan, New
         Jersey, Pennsylvania, and Washington
        FFS recipients in 1999 MAX data
        MMMCA acquired Thomson’s 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”



63
         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%)




64
                     Analytic Report Prescription Drug Expenditures:
                      MH/SA and Random Sample Dual Eligibles


                    $4,000
                                                        $3,654
                    $3,500                                                                  $3,280
                                                                          $3,071
                    $3,000
                                               $2,571
                                                                 $2,365
     $ per Capita




                    $2,500            $2,266                                       $2,205
                             $1,880                                                                  Random Sample
                    $2,000
                                                                                                     MH/SA Sample
                    $1,500

                    $1,000

                     $500

                       $0
                                Michigan         New Jersey       Pennsylvania       Washington




65
     Analytic Report Psychiatric Drug Expenditures: MH/SA
        Beneficiaries With and Without Dual Eligibility

                        $1,800
                                                                                           $1,618
                        $1,600
                                                   $1,380              $1,375
                        $1,400

                        $1,200
       $ per Capita .




                        $1,000
                                                             $798                                    $802
                         $800
                                 $480                                            $562
                         $600
                                         $312
                         $400

                         $200

                           $0
                                 Dual   Non-Dual   Dual     Non-Dual    Dual    Non-Dual   Dual     Non-Dual
                                   Michigan          New Jersey         Pennsylvania         Washington



66
     Analytic Report Other Drug Expenditures: MH/SA
      Beneficiaries With and Without Dual Eligibility
                      $2,500
                                                   $2,274


                      $2,000
                               $1,786
                                                                       $1,696              $1,662
                                                            $1,606
     $ per Capita .




                      $1,500


                                                                                                     $974
                      $1,000
                                         $819                                    $763

                       $500


                         $0
                                Dual    Non-Dual    Dual    Non-Dual    Dual    Non-Dual    Dual    Non-Dual
                                  Michigan           New Jersey         Pennsylvania         Washington



67
          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




68
Analytic Report: The Impact of Medicaid
      Managed Care on Medicaid
     Fee-for-Service Expenditures
                 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




70
                         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




71
                             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




72
                       Medicaid Enrollment
                  Percentage Change (19931997)


          State            Total Medicaid    AFDC/TANF   Disabled

     Michigan                    –9             –26        21
     New Jersey                  –1             –9         10
     Pennsylvania*                 0            –5          7
     Washington                    7             1         19
     *Pennsylvania data are for 1994–1997.




73
                                 Managed Care Penetration

                         Michigan Managed Care Penetration                           Pennsylvania Managed Care Penetration

                0.60                                                          0.60
                0.50                                                          0.50
                0.40                                                          0.40
      Percent




                                                                    Percent
                                                             AFDC                                                               AFDC
                0.30                                                          0.30
                                                             DIS                                                                DIS
                0.20                                                          0.20
                0.10                                                          0.10
                0.00                                                          0.00
                       1993    1994    1995   1996    1997                            1994      1995          1996      1997
                                       Year                                                            Year




                       New Jersey Managed Care Penetration                           Washington Managed Care Penetration

                0.90                                                          0.80
                0.80                                                          0.70
                0.70                                                          0.60
                0.60
                                                                              0.50
     Percent




                                                                    Percent
                0.50                                         AFDC                                                               AFDC
                                                                              0.40
                0.40                                         DIS                                                                DIS
                0.30                                                          0.30
                0.20                                                          0.20
                0.10                                                          0.10
                0.00                                                          0.00
                       1993    1994   1995    1996    1997                           1993    1994      1995      1996    1997
                                      Year                                                             Year




74
                                  Michigan Trends by County:
                                      Medicaid Disabled
               Michigan Absolute Change in MC and Percentage Change in FFS$ (1993–1997), Medicaid Disabled
                2.0


                1.5


                1.0


                0.5
     Percent




                0.0
                      1   4   7   10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82
               -0.5


               -1.0


               -1.5
                                                                  County Number

                                        Change in MC Penetration Rate   % Change MH/SA$   % Change FFS$




75
                                   Michigan Trends by County:
                                    Medicaid AFDC/TANF

                          Michigan Absolute Change in MC and Percentage Change in FFS$ (1993–1997), AFDC

               2.0


               1.5


               1.0
     Percent




                                                                                                     y = 0.006x + 0.0974
               0.5                                                                                       R2 = 0.2154


               0.0                                                                          y = -0.0016x + 0.0586
                      1    4   7                                                                 R2 = 76 79 82
                                   10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 730.0093

               -0.5


               -1.0
                                                                       County

                                   MC Penetration Rate Change   % Change MH/SA$            % Change FFS$
                                   Linear (% Change FFS$)       Linear (% Change MH/SA$)




76
     Analytic Report Washington Trends by County:
                                             Medicaid Disabled
      Washington Absolute Change in MC and Percentage Change in FFS$ (1993–1997), Medicaid Disabled

                1.5


                1.0


                0.5
                                                                                                        y = 0.0191x - 0.4349
                                                                                                            R2 = 0.2988
      Percent




                0.0
                  69

                       45

                             19

                                  33

                                        57

                                              55

                                                     59

                                                          11

                                                               13

                                                                    23




                                                                                    51




                                                                                              37

                                                                                                   75

                                                                                                        61

                                                                                                              17




                                                                                                                          25
                                                                          3

                                                                               1




                                                                                          5




                                                                                                                     7
                -0.5
                                                                                                        y = -0.0005x - 0.7575
                                                                                                             R2 = 0.0012
                -1.0


                -1.5
                                                                    County Number

                            Change in MC Penetration Rate      % Change MH/SA$                 % Change FFS$
                            Linear (% Change FFS$)             Linear (% Change MH/SA$)




77
                             Pennsylvania Trends by County:
                                   Medicaid Disabled
     Pennsylvania Absolute Change in MC and Percentage Change in FFS$ (1993–1997), Medicaid Disabled

                4.0

                3.5
                3.0
                2.5
                2.0
      Percent




                1.5                                                                                  y = 0.0203x + 0.0569
                                                                                                          R2 = 0.4144
                1.0
                0.5
                0.0                                                                                  y = -0.0048x + 0.157
                       1 3                                                                                R2 = 61 63
                             5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 0.0394 65 67
                -0.5
                -1.0
                -1.5
                                                                County Number

                             Change in MC Penetration Rate   % Change MH/SA$                 % Change FFS$
                             Linear (% Change FFS$)          Linear (% Change MH/SA$)




78
                          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



79
     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)




80
                             Objective

        Wide-spanning report on trends in utilization and
         payments for the 1995–1998
        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

81
                  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



82
                  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

83
    Wrap-Up

Alexander Cowell, PhD
 Project Director, RTI

								
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