Estimating the Cost of Chronic Diseases

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Estimating the Cost of Chronic Diseases

• National Center for Chronic Disease Prevention
• and Health Promotion (NCCDPHP), CoCHP
• Centers for Disease Control and Prevention
• In collaboration with Chronic Disease Directors,
  National Pharmaceutical Council, and Agency for
  Healthcare Research and Quality

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Project Task Order

• CDC Technical Monitors Diane Orenstein PhD
  (DHDSP)
• Isaac Nwaise MA Economist (DHDSP)
• Florence Tangka PhD Economist (DCPC)
• Kumiko Imai PhD Economist (DDT)
• Research Triangle Institute (RTI International)
• Project Investigators Susan Haber ScD
• Eric Finkelstein PhD
• Justin Trogdon PhD
• Phase I October 2004 September 2006

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Overview

• Impact for CDC
• Why do we need this information?
• Project Goals
• Why examine Medicaid costs?
• Project description objectives, methodology,
  strategy, estimation, preliminary results
• Use of toolkit
• Benefits to CDD and Medicaid Directors

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Impact for CDCConsistent with Goals

• Health impact focus - by quantifying the
  economic burden of chronic disease, we are better
  positioned to demonstrate the value added of
  prevention and health promotion activities
• Customer-centricity - by translating findings
  into toolkits for states and other key
  stakeholders use
• Leadership - by partnering with other divisions
  across the chronic center as well as key external
  partners we leverage expertise and partnerships
• Accountability - the project makes efficient and
  effective use of resources by examining cross
  cutting issues in a consistent and collaborative
  manner

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Why Do We Need this Information?
Public Health Policy Decisions
Planning/Forecasting Prevention Resource
Allocation
Burden Cost of Illness

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Why Do We Need this Information (cont.)?

• Project created in response to a stated need from
  State Chronic Disease Directors, with support
  form the collaborating partners
• States need to have a comprehensive understanding
  of the fiscal impact of chronic diseases
• Information provides evidence-based data for
• Advocacy
• Policy development
• Budgetary planning and resource allocation

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Percent of All Deaths Due to Five Major Chronic
Diseases By State, 2001
Diseases of the heart, all cancers, stroke,
chronic lower respiratory disease, and diabetes.
Source Centers for Disease Control and
Prevention, 2001 mortality data obtained from the
National Vital Statistics System, The Burden of
Chronic Diseases and their Risk Factors, 2004
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Six Diseases

• Hypertension, Heart disease, Stroke, Congestive
  heart failure, Diabetes, and Cancer
• Leading causes of morbidity and mortality
• U.S. Prevalence
• Cardiovascular diseases 71 million
• (65 million-hypertension, 13.2 million- heart
  disease, 5.5 million stroke, 5 million congestive
  heart disease)
• Diabetes 20.8 million
• Cancer 10 million
• Medical Costs
• Cardiovascular diseases256 billion
• Diabetes 92 billion
• Cancer 72 billion

Source 1. AHA statistics update, 2006
2. ADA fact sheet, 2005 3. NCI
cancer trends progress report, 2005 update
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Project Goals

• To calculate state-specific Medicaid costs for
  persons diagnosed and/or treated for heart
  diseases, stroke, hypertension, congestive heart
  failure, diabetes, and cancer in six states
• To calculate the proportion of costs for these
  diseases to total state Medicaid budgets in six
  states
• To develop a cost prediction model and a toolkit
  to calculate prevalence-based state-specific
  Medicaid cost estimates for these 6 diseases for
  all states
• To develop an alternative cost estimation
  methodology using Medical Expenditure Panel
  Survey (MEPS) data, which are publicly available
  from the Agency for Healthcare Research and
  Quality (AHRQ)

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Why Examine Medicaid Costs?

• Medicaid accounted for approximately 22 of all
  state spending in 20031
• State Medicaid spending is growing annual
  increase more than doubled from FY 2004 to 2005
  (4.8 to 11.7)2

• National Governors Association and National
  Association of State Budget Officers. Fiscal
  Survey of States, June 2005. Accessed from
  http//www.nasbo.org/Publications/fiscalsurvey/fss
  pring2005.pdf November 29, 2005.
• Kaiser Commission on Medicaid and the Uninsured
  Survey of State Medicaid Officials conducted by
  Health Management Associates, June and December
  2003

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Federal, State and Total Medicaid Spending,
1965-2014
Source Centers for Medicare and Medicaid
Services, National Health Expenditures (NHE)
Amounts by Type of Expenditure and Source of
Funds Calendar Years 1965 -2015, available at
www.cms.hhs.gov/ statistics/nhe/projects
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Chronic Diseases in Medicaid

• Chronic diseases account for 83 of total
  healthcare expenditure in the general population
• Many national estimates of the costs of chronic
  diseases exist, often with conflicting results
• Different populations (e.g., national, Medicare)
• Different data sets
• Different methodology
• Lots of double counting

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Chronic Diseases in Medicaid cont.

• Research has not examined the cost burden of
  chronic diseases to state Medicaid programs in a
  consistent manner across states
• Medicaid has a high prevalence of chronic
  diseases
• The lack of research is problematic given that
  most prevention efforts occur at state or local
  levels

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Project Objectives

• Develop a toolkit for states to estimate Medicaid
  costs for select chronic diseases at the state
  level
• Use a consistent methodology across states
• Avoid double-counting disease costs
• User-friendly but flexible
• Does not require states to crunch through
  Medicaid claims data (both labor and computer
  intensive)
• Long-term goals
• Increase the number of diseases in the model
• Provide costs for other chronic diseases, payers
  and indirect costs
• State total
• Medicare

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Methodology

• Data
• Nationally Representative Data Medical
  Expenditure Panel Survey (MEPS)
• State Representative Data Medicaid MAX
  fee-for-service claims
• Estimation approach
• Econometric (regression-based) modeling

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MEPS

• Nationally-representative survey of the US
  civilian non-institutionalized population
• Quantifies annual medical spending by payer
• Includes information on health insurance status
  and demographic characteristics
• Allows for identifying any medical condition for
  which a participant sought treatment during the
  survey period and for select chronic conditions
• AHRQ granted us access to state identifiers to
  quantify state-level adjustment factors

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MEPS (cont.)

• Advantages
• Includes payments for most medical services,
  including Rx drugs
• Nationally-representative dataset with state
  identifiers
• Single data source for all states
• Allows for stratification by payer (sample-size
  permitting)
• Data is free and publicly available

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MEPS (cont.)

• Disadvantages
• Sample size may be inadequate for some
  diseases/stratifications
• Pooling years can help
• Combined, 2000-2003 MEPS includes approximately
  125,000 people, and 25,000 Medicaid recipients
• Data does not include institutionalized
  population
• MEPS estimates of annual medical expenditures are
  approximately half the corresponding estimates
  from National Health Accounts

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DataMedicaid MAX Files (state Medicaid data)

• Made available by CMS in a uniform format across
  states
• Used for research on Medicaid population
• Includes person-level eligibility records with
  demographic (Enrollment file) and claims data
• Available variables include
• Chronic disease flags based on diagnosis codes
• Demographic information (e.g., age, gender,
  race/ethnicity)
• Months of eligibility during the year
• An indicator for dual eligibility
• Medicaid payments, in total and broken out by
  type of service

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Medicaid MAX Files (cont.)

• Advantages
• Includes Rx claims
• Includes long-term care population (unlike MEPS)
• Single source for state-specific Medicaid
  prevalence, demographic, and cost data
• Very large number of observations
• Available for all states

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Medicaid MAX Files (cont.)

• Disadvantages
• Misses payments for dual eligibles
• Misses payments for non-covered services
• Data are incomplete for states with high Medicaid
  managed care enrollment
• Data are costly and analyses are labor and
  computer intensive
• Incomplete coding on long-term care claims may be
  problematic for some analyses

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DataStrategy

• Use MEPS to generate annual per capita disease
  costs for non-institutionalized populations
• Better controls for confounders
• Single data source for all states
• Can use state-level inflators to adjust for
  regional price variation
• Can test results using the 4 states MAX data
• Use MAX data for estimating per capita disease
  costs for institutionalized populations
• Combine unit costs with prevalence data to
  generate total Medicaid costs
• Prevalence data can be provided by the user or
  estimated from the model

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MAX State Selection Criteria

• Data quality
• Relatively low enrollment in Medicaid managed
  care
• Good reporting of diagnosis data (especially on
  crossover claims for dual eligibles)
• Current Study states
• IL (n1,754,113)
• IN (n749,853)
• KS (n255,163)
• LA (n904,701)
• Note data from South Carolina and Massachusetts
  are still being processed

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Estimation Approaches

• Accounting Approach sum payments for all events
  with the disease listed as the primary diagnosis
• May either understate or overstate costs
  attributable to the disease of interest
• Understate does not include attributable costs
  when disease of interest (e.g., diabetes) is
  listed as a secondary diagnosis
• Overstate may include costs attributable to
  secondary diagnoses
• Including primary plus secondary diagnoses
  results in additional problems
• Likely to result in double counting
• We chose to pursue an econometric approach

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Econometric Approach

• Use multivariate regression analysis to estimate
  marginal costs associated with each disease while
  controlling, to the extent possible, for other
  observable characteristics that affect costs
• Annual f (diseases of interest,
  socio-demographic characteristics, other medical
  conditions)
• Diseases of interest heart disease, stroke,
  hypertension, CHF, diabetes, cancer
• Sociodemographic characteristics gender, race,
  age, education, income
• Additional high prevalence or high cost
  conditions

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Econometric Approach

• This approach has several major advantages over
  other approaches
• Regressions control for covariates (e.g., age,
  gender, comorbidities)
• Allows flexibility in the modeling
• Avoids double-counting of costs for co-occurring
  diseases

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Estimation Strategy

• Determine appropriate functional form for
  empirical models
• Estimate separate models for annual expenditures
  in five categories
• Inpatient
• Outpatient
• Office-based
• Rx
• Other
• Combine results to produce a national estimate of
  per capita costs for each disease

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Estimation Strategy cont.

• Use regional/state level adjustment factors to
  generate per capita costs for each state
• Multiply costs by prevalence estimates for each
  states Medicaid population (either user supplied
  or estimated from the model)
• Compare estimates to those generated from
  Medicaid claims data

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Preliminary results

• Estimates of Medical Costs Attributable to Cancer
  in the U.S.
• Florence Tangka, Eric A. Finkelstein, Ian C.
  Fiebelkorn, Donatus Ekwueme, and Gayle Clutter
• Presented at the World Cancer Congress in
  Washington D.C. in June 2006
• Estimating State Costs For Chronic Diseases Using
  Medicaid Data
• Susan Haber, ScD, Boyd Gilman, PhD, Florence
  Tangka, PhD, Diane Orenstein, PhD, Isaac Nwaise,
  MA, Daniel Crespin, BA
• Health Services Research for their special issue
  on State-Level Health Service Delivery, Access,
  and Practice Improving Research and Policy

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Benefits to Chronic Disease Directors and Partners

• MEPS estimation provides a consistent
  methodology for approximating disease related
  coststo share among CDDs partners, and
  stakeholders
• Toolkit and MEPS estimation provides CDC, CDD,
  and partners with evidence-based strategy for
  calculating state Medicaid costs for chronic
  diseases
• It is feasible to estimate state Medicaid costs
  (using state MAX data) however, it is
  complicated, expensive and not without
  limitations

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Implications

• Evidenced-based recommendations to inform policy
  decisions
• Cost containment
• Potential solutions prevention and control
  programs at the state and national levels
  supported by many partners
• Advocacy to increase for prevention efforts
• Expand partnership between state CDD and CMS
  directors

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Benefits to CMS

• Enhance understanding of the burden of chronic
  diseases to state Medicaid program and spending
  budgets
• Evidence-based data to support resource
  allocation for state budgets
• Collaborate with state health departments to
  share strategies to prevent and control chronic
  diseases implement disease management,
  prevention and wellness initiatives