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Using Clinical Information To Project Federal Health Care Spending*

Using Clinical Information To Project Federal Health Care Spending*. Michael J. O’Grady, PhD, Senior Fellow. NORC /University of Chicago Principal, O’Grady Health Policy, LLC Study funded by the National Changing Diabetes Program, Novo Nordisk A/S.

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Using Clinical Information To Project Federal Health Care Spending*

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  1. Using Clinical Information To Project Federal Health Care Spending* Michael J. O’Grady, PhD, Senior Fellow. NORC /University of Chicago Principal, O’Grady Health Policy, LLC Study funded by the National Changing Diabetes Program, Novo Nordisk A/S *Health Affairs, web exclusive, September 1, 2009

  2. Potential Implications of Diabetes Simulation Models • Diabetes is perhaps the prototypical chronic condition for demonstrating what epidemiological modeling can do for cost-estimating. • Natural history of diabetes has been assessed and modeled extensively for over a decade. • The baseline progression of major complications are well documented. • The effect of treatment interventions are generally well understood (but evolving). • Multiple scientific organizations have created diabetes models (NIH, CDC, UK and Europeans).

  3. The 10-Year Budget Window, Disease Progression, and Effect of Treatment – The NIDDK Model Conventional Protocol The 10-Year “Budget Window” Intensive Protocol Source: National Changing Diabetes Programs: Federal Health Care Cost Estimating: A Look at Current Practice and the Implications for Assessing Chronic Disease Prevention Proposals.

  4. Advance in disease progression one year Retinopathy Module (Clarke, 2004) Nephropathy Module (UKPDS 33, 1998) Alive Simulate natural history of diabetes progression according to patient characteristics Assign Initial Patient Characteristics Mortality Module (Vital Statistics) Neuropathy Module (Clarke, 2004) Coronary Heart Disease Module (Stevens, 2001) Dead Stroke Module (Kothari, 2002) Select next patient Modeling Diabetes Complications

  5. Policy Projections • Modeled a prototypical diabetes treatment improvement intervention that is similar to current well-designed disease management programs. • Intensify the treatment of individuals with prevalent and incident diabetes aiming to improve • Glucose control • Blood pressure control • Cholesterol control • Use of beneficial preventive therapies (aspirin, ACEI) • Expected benefits from program based on meta-analyses and national diabetes QI program experience.

  6. Diabetes Quality Improvement Intervention (25-year spending) Diabetes Spending w/o intervention (baseline) Source: Derived from the authors’ own analyses/computations.

  7. Conclusions • In selected instances, include the best epidemiologic data and modeling in baseline and intervention estimates: • Allow the modeling of obesity trends and their interaction with chronic illnesses, like diabetes. • Incorporation of consensus “lessons learned” from clinical trials. • Challenge to the epidemiological community – be sure the data and trials meet rigorous standards for inclusion in the policy debate.

  8. Conclusions(continued) • In certain instances, look beyond the traditional 10-year budget window, if the data indicates a better understanding for policymakers. • For most proposals a 10-year window is appropriate, but if there’s a well established natural history of the disease exceptions should be possible. • Cuts both ways – CBO may find that for many proposal a longer window would show ballooning spending in the out years.

  9. Thanks…. • The research team: • Elbert Huang, M.D., M.P.H., Assistant Professor of Medicine University of Chicago • Anirban Basu, Ph.D. Assistant Professor Center for Health and the Social Sciences, University of Chicago • The funder: • http://www.ncdp.com/ • Dana Haza, Senior Director, National Changing Diabetes Program, Novo Nordisk A/S Michael J. O’Grady, Ph.D. Senior Fellow. NORC /U. of Chicago & Principal, O’Grady Health Policy, LLC James C. Capretta, MA Principal and Director of Health Policy Consulting Civic Enterprises, LLC

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