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Overview of the CDC Chronic Disease Cost Calculator

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On January 18, 2008, Eric Finkelstein gave an overview of the CDC Chronic Disease Cost Calculator at the State Healthcare Quality Improvement Workshop. This is the text version of the event's slide presentation. Please select the following link to access the slides: (PowerPoint® File, 1.0 MB; PDF File, 360 KB; PDF Help).

Slides: 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37

Slide 1: Introduction to the CDC/RTI Chronic Disease Cost Calculator

Presented by: Eric Finkelstein, Ph.D.

This slide has a white background. The top banner is blue and darkens gradually from left to right. The banner reads, "turning knowledge into practice." The bottom banner is also blue that gradually lightens from left to right and contains "" in the left corner. In the body of the slide, logos of CDC, National Association of Chronic Disease Directors, NPC, RTI International, and AHRQ surround the title.

This presentation uses a white background with two banners. The top banner is blue that darkens gradually from left to right. The bottom banner is also blue that gradually lightens from left to right and contains "" in the left corner and the RTI logo in the right corner.

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Slide 2: Investigators

  • RTI Investigators
    • Susan Haber
    • Eric Finkelstein
    • Justin Trogdon
  • CDC Investigators:
    • Diane Orenstein
    • Isaac Nwaise
    • Florence Tangka
    • Kumiko Imai
    • Louise Murphy

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Slide 3: Other Collaborators

  • Agency for Healthcare Research and Quality (AHRQ)
  • National Association of Chronic Disease Directors (NACDD)
  • National Pharmaceutical Council (NPC)

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Slide 4: Overview

  • Project Goals.
  • Why are burden estimates needed?
  • Why examine state-specific total and Medicaid costs?
  • Project description: objectives, methodology, strategy, estimation, preliminary results.
  • Screen shots.
  • Next Steps.

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Slide 5: Project Goals

  • Apply a consistent framework to calculate state-specific Total and Medicaid costs for persons diagnosed and/or treated for heart diseases, stroke, hypertension, congestive heart failure, diabetes, cancer, [completed] arthritis and major depression [ongoing]
  • Calculate the proportion of state Total [ongoing] and Medicaid costs for these diseases [completed]
  • Develop a user friendly calculator to estimate prevalence-based state-specific Total [ongoing] and Medicaid [completed] cost estimates for all states without having to analyze claims data
  • Expand the toolkit to include indirect costs and a forecasting module [ongoing]
  • Disseminate our methodology and results to key stakeholders [ongoing]

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Slide 6: Why are burden estimates needed?

There is a circle located in the center labeled "Burden & Cost of Illness." Two arrows protruding from the circle point upwards to the text, "Planning/Forecasting, Prevention, and Resource Allocation." Four arrows are located above each word and point upwards toward a box containing the following text: Public Health Policy & Decisions.

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Slide 7: Why are burden estimates needed (cont.)?

  • Evidence-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.
  • Enhance understanding of the burden of disease 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.

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Slide 8: Why Chronic Diseases?

  • Chronic diseases are leading causes of mortality and morbidity.
  • Over 33% of adults have some form of cardiovascular disease.
  • 9.6% of adults have diabetes.
  • Over 3% of population has history of cancer.
  • Some estimates suggest that chronic diseases account for 83% of total healthcare expenditure in the general population.

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Slide 9: Why Examine Costs at the State Level?

  • State estimates are important because much of the prevention dollars are allocated at the state level.
    • Indirect costs may also be important for resource allocation decisions.
  • Chronic Disease directors, state policy makers, and partners have been requesting this information.

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Slide 10: Why Examine Medicaid Costs Separately

  • Approximately 22% of all state spending is for Medicaid expenditures.1
  • Research has not examined the cost burden of chronic diseases to state Medicaid programs in a consistent manner across states.
  • Medicaid directors and others have been requesting this information.
  • It is feasible to estimate Medicaid costs using claims data, however, it is complicated, expensive and not without limitations.

1. National Governors Association and National Association of State Budget Officers. Fiscal Survey of States, June 2007. Accessed from November 21, 2007.

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Slide 11: Federal, State and Total Medicaid Spending, 1965-2014

The x-axis on this graph represents the year from 1966 until 2014. The y-axis represents a dollar amount in millions from 0 to 700. The graph has two projections that increase over time; both start at 0 in 1966 and in 2014, the federal spending projection terminates at 325 whereas the state spending projection ends at 600, representing the total, of which the State portion is about 275.

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:

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Slide 12: Why not use existing estimates?

  • Existing estimates are based on inconsistent data and methods.
  • Results are often contradictory.
    • Different populations.
    • Different data sets.
    • Different methodology.
    • Lots of double counting.
  • Toolkit and estimation approach presents a transparent and evidence-based strategy for calculating costs.

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Slide 13: Estimation Approach

  • 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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Slide 14: MEPS Data

  • 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.
  • Identifies all medical conditions for which a participant sought treatment during the survey period and for selected chronic conditions.
  • AHRQ granted access to state identifiers to quantify state-level adjustment factors.

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Slide 15: Advantages of MEPS

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

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Slide 16: Disadvantages of MEPS

  • Sample size may be inadequate for some diseases/payers/population stratifications.
    • Pooling years can help.
    • Combined, 2000-2003 MEPS includes approximately 125,000 people, and 25,000 Medicaid recipients.
  • Data do not include institutionalized population.

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Slide 17: Data—Medicaid 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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Slide 18: 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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Slide 19: 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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Slide 20: Data—Strategy

  • 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 State-specific total and Medicaid costs.
    • Prevalence data can be provided by the user or estimated from the model.

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Slide 21: 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.

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Slide 22: 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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Slide 23: 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.
    • With appropriate calculation, avoids double-counting of costs for co-occurring diseases.
    • Can run model separately on total or Medicaid population.

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Slide 24: Avoiding double counting

  • Commonly-used econometric models also lead to double counting of costs across diseases (Trogdon, Finkelstein and Hoerger 2007).
  • Occurs when expenditures for co-occuring diseases (e.g., heart disease with cancer) are not properly allocated across the two diseases.
    • Typically results in inflated estimates.
  • We developed a strategy to estimate the expenditures associated with co-occuring diseases and reallocate these expenditures to the individual diseases.
    • Methodology forthcoming in HSR.
    • Used in Trogdon et al. (2007) Health Promotion Practice article and in the toolkit.
  • Note — explains why our estimates are generally lower than what is in the literature.

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Slide 25: Estimation Strategy

  • Determine appropriate functional form for empirical models.
  • Estimate separate models for annual expenditures in five categories.
    • Inpatient
    • Outpatient
    • Office-based
    • Rx
    • Other
  • Calculate per capita cost for each disease and combination of diseases.
  • Use the coefficients from the model, which provide information about the relative importance of each disease on expenditures, to reallocate costs associated with co-occurring diseases.

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Slide 26: Estimation Strategy cont.

  • Combine results to produce a national estimate of per capita costs for each disease.
  • Use regional/state level adjustment factors to generate per capita costs for each state.
  • Multiply costs by prevalence estimates (either user supplied or estimated from the model) to generate Total (Medicaid) costs.
  • Compare estimates to those generated directly from 4 states Medicaid claims data.

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Slide 27: Medicaid Results: Cardiovascular Disease

  • Annual costs per person with disease attributable to the disease to Medicaid.
    • Congestive heart failure, $4,180
    • Hypertension, $1,610
    • Stroke, $1,550
    • Other heart disease, $1,500
  • Source: Trogdon et al. (2007)

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Slide 28: Publications

Use of Econometric Models to Estimate Expenditure Shares

Justin G. Trogdon, Eric A. Finkelstein, Thomas J. Hoerger

Forthcoming at Health Services Research (CDC-funded through RTI-UNC Center of Excellence in Health Promotion Economics)

The Economic Burden of Cardiovascular Disease for Major Insurers

Justin G. Trogdon, Eric A. Finkelstein, Isaac Nwaise, Florence Tangka, and Diane Orenstein

Health Promotion Practice 2007;8(3):234-242.

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Slide 29: Screen Shots

This slide contains a screenshot of the Welcome screen of the RTI Chronic Disease Cost Calculator.

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Slide 30

This slide contains a screenshot of the Main Switchboard of the RTI Chronic Disease Cost Calculator.

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Slide 31

This slide contains a screenshot of the Select State window of the RTI Chronic Disease Cost Calculator.

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Slide 32

This slide contains a screenshot of the Select Diseases window of the RTI Chronic Disease Cost Calculator.

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Slide 33

This slide contains a screenshot of the Number of Medicaid Beneficiaries in Your State window of the RTI Chronic Disease Cost Calculator.

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Slide 34

This slide contains a screenshot of the Prevalence of Chronic Diseases in Your State window of the RTI Chronic Disease Cost Calculator.

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Slide 35

This slide contains a screenshot of the Cost Per Person With Chronic Disease in Your State window of the RTI Chronic Disease Cost Calculator.

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Slide 36

This slide contains a screenshot of the Calculated Costs window of the RTI Chronic Disease Cost Calculator.

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Slide 37: Next Up

  • Hands on demonstration of the toolkit.
  • Policy discussion surrounding the question: 'How should the estimates generated from the toolkit be used?'

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