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Cost-Effectiveness Analysis: New methods for old limitations?

Cost-Effectiveness Analysis: New methods for old limitations?. R Scott Braithwaite, MD, MSc, FACP Yale University School of Medicine Connecticut VA Healthcare System. CEA Limitations. Difficulty interpreting “number” (ICER) Is $59,000/life-year good or bad?

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Cost-Effectiveness Analysis: New methods for old limitations?

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  1. Cost-Effectiveness Analysis: New methods for old limitations? • R Scott Braithwaite, MD, MSc, FACP Yale University School of Medicine Connecticut VA Healthcare System

  2. CEA Limitations • Difficulty interpreting “number” (ICER) • Is $59,000/life-year good or bad? • A policy tool in search of a U.S. policy “lever” • Other countries use it (e.g. Canada, U.K., Australia, Netherlands) but U.S. payers don’t • Does not incorporate quality of evidence • An analysis based on a high-quality study may appear to have same certitude as analysis based on expert opinion

  3. New CEA Methods • Decision rules for interpreting results • Linking results to policy “levers” in U.S • Incorporating quality of evidence

  4. Question: How can we use CEA to inform statin formulary decisions ? • Case 1. 64 year-old female diabetic, prior MI, pre-treatment LDL 137 • Case 2. 41 year-old male, no cardiac risk factors or history of cardiovascular disease, pre-treatment LDL 167 • Note: Treatment of both patients endorsed by current NCEP guidelines

  5. Research to address limitations of cost-effectiveness analysis • Decision rules for interpreting results • Linking results to policy “levers” in U.S • Incorporating quality of evidence

  6. Introduction • CEA results estimate value • Value allows maximizing health given budget • What is high-value for one society at one time may be low-value for another society • So how to interpret CEA in the U.S. currently? • CEA results often presented together with simple acceptability thresholds (e.g. “$50,000 per QALY”) • Individual thresholds have little validity • Ranges (e.g. $50,000 per QALY to $100,000 per QALY) may be more valid and feasible

  7. Objective • To inform decision rules for CEA interpretation based on health care purchasing choices in US • Two distinct but complementary analyses to estimate upper- and lower-bounds of range

  8. Methods • Analysis #1: Estimate cost-effectiveness of “modern” health care • Individuals prefer cost/benefit of modern health care to cost/benefit of pre-modern care • If willing to pay for modern care, then should be willing to pay for services that have same or better cost-effectiveness as modern care • May inform lower bound for decision rule

  9. Methods • Analysis #2 :Estimate cost-effectiveness of unsubsidized health insurance • Individuals prefer cost/benefit of no insurance to cost/benefit of unsubsidized insurance • Free rider effect: Pay 1/10 costs, get 2/3 benefit • If not willing to pay for unsubsidized insurance, then should be not willing to pay for services that have same or worse cost-effectiveness • May inform higher bound for decision rule

  10. % Willing to Pay for Health Insurance No Employer Subsidy Employer Subsidy % Poverty Line <100% 100%-199% 200%-299% 300%-399% ≥400% Income (Family of 4) <$19K $19K-$37K $38K-$56K $57K-$75K >$75K

  11. Cost-effectiveness of modern health care • Incremental benefits of modern care • 53% of observed mortality decrease • 4.7 life-years • Incremental lifetime costs of modern care • $452,000 • Incremental cost-effectiveness of modern care • $96,000 per life-year • Approximately $100,000 per QALY Braithwaite RS et al, Med Care 2008; 46:349-356

  12. Cost-effectiveness of unsubsidized insurance • Incremental benefits of buying (for 1 year) • Mortality reduced by 18% • LE increased by 0.020 years • Incremental costs of buying (for 1 year) • $4100 • Incremental cost-effectiveness of buying unsubsidized insurance • $204,000 per life-year • Approximately $300,000 per QALY Braithwaite RS et al, Med Care 2008; 46:349-356

  13. Interpreting cost-effectiveness results • Cost-effectiveness of modern health care • Inform lower (less inclusive) bound for rule • ≈ $100,000/QALY • Cost-effectiveness of unsubsidized insurance • Inform higher (more inclusive) bound for rule • ≈ $300,000/QALY • $50,000/QALY unlikely to be valid • Acceptability range ($100,000/QALY to $300,000/QALY) likely more valid and feasible Braithwaite RS et al, Med Care 2008; 46:349-356

  14. Question: How can we use CEA to inform statin formulary decisions ? • Case 1. 64 year-old female diabetic, prior MI, pre-treatment LDL 137 • Incremental cost-effectiveness <$10,000/QALY • Favorable • Case 2. 41 year-old male, no cardiac risk factors or history of cardiovascular disease, pre-treatment LDL 167 • Incremental cost-effectiveness $420,000/QALY • Unfavorable

  15. Research to address limitations of cost-effectiveness analysis • Decision rules for interpreting results • Linking results to policy “levers” in U.S • Incorporating quality of evidence

  16. Background • Cost-sharing becoming a standard “volume knob” to control utilization in U.S. • Cost-sharing has • Great potential to control costs • Great potential to cause harm • Increasing calls to link cost-sharing to value (e.g. value-based insurance design) • No cost-sharing for high-value services • Same or increased cost-sharing for low-value services

  17. Background • Possible way to link cost-effectiveness results to cost-sharing • > $300K/QALY: Increase cost-sharing • $100K-300K/QALY: No Δ cost-sharing • < $100K/QALY: Waive cost-sharing • Cost-saving: Share cost-savings? • Braithwaite RS et al, Ann Intern Med. 2007; 146: 602-605

  18. Purpose • To estimate the impact of value-linked cost-sharing if it were applied systematically across US health system

  19. Methods • From RAND, we can estimate the impact of cost-sharing amount on health service demand • Results confirmed by >100 observational studies

  20. New year = new “pull” from cost-effectiveness distribution Age N Health expenditure Uninsured Insured Insured, value-based cost-sharing Value increases, lowers, or maintains health expenditures Prevailing cost-sharing maintains health expenditures High cost-sharing lowers health expenditures Mortality Age N+1

  21. Life Expectancy Gain (Years) No cost-shar, Expand insurance Value-based cost-shar Expand insurance Value-based cost-shar Current insurance Current cost-shar Expand insurance Current cost-shar Current insurance 20% 30% 40% 50% Copayment if low-value

  22. Annual per-capita cost (2003 $) No cost-shar, Expand insurance Current cost-shar Expand insurance Current cost-shar Current insurance Value-based cost-shar Expand insurance Value-based cost-shar Current insurance 20% 30% 40% 50% Copayment if low-value

  23. Conclusions • Value-linked cost-sharing may increase life expectancy from health care while reducing costs • Costs may be lowered sufficiently to offset incremental expenditures from expanding health insurance coverage

  24. Question: How can we use CEA to inform statin formulary decisions ? • Case 1. 64 year-old female diabetic, prior MI, pre-treatment LDL 137 • Incremental cost-effectiveness <$10,000/QALY • Favorable • No cost-sharing • Case 2. 41 year-old male, no cardiac risk factors or history of cardiovascular disease, pre-treatment LDL 167 • Incremental cost-effectiveness $420,000/QALY • Unfavorable • Higher cost-sharing (30%-35%)

  25. Research to address limitations of cost-effectiveness analysis • Decision rules for interpreting results • Linking results to policy “levers” in U.S • Incorporating quality of evidence

  26. Problem • Clinicians and policy-makers often wonder “what goes into the model” • Current methods for cost-effectiveness analysis take into account uncertainty from random variation but not from low-quality evidence

  27. Objective • Can we augment standard cost-effectiveness analysis methods to develop a sensitivity analysis based on quality of evidence?

  28. Methods • Basic concept of our approach • When potential information sources have insufficient quality of evidence, don’t use them • Instead, assume that little is known by using uninformative distributions over wide range • Don’t obscure questionable data under a “false veneer of mathematical certitude” • Warning! If you set evidence standards very high, not much of the available evidence may qualify.

  29. Methods • Assess quality of evidence using USPSTF guidelines • Study design • Design differs from controlled experiment • Internal validity • Results represent truth in study population • External validity • Results represent truth in target population • Our approach can be used with any evidence-evaluation hierarchy • We chose USPSTF guidelines because of ubiquity not because of rigor

  30. Methods • Set minimum standard in each evidence domain • These can be “dialed” up or down at will • Evaluate each possible source of evidence • If source meets evidence criterion, use its 95% confidence interval in the analysis • If evidence does not meet criterion, do not use it in the analysis • Instead use uninformative (“wide”) distribution

  31. Test Case: Directly observed therapy for HIV antiretrovirals • Base Case: No evidence criteria • All 17 data sources eligible for parameter estimation • Study Design set to highest standard (“1”) • 13 out of 17 sources were eligible • Internal Validity set to highest standard (“good”) • 9 out of 17 sources were eligible • External Validity set to highest standard (“high”) • 5 out of 17 sources were eligible • All three criteria set to highest standards • 3 out of 17 sources were eligible

  32. Results: All Evidence Braithwaite RS et al, Ann Intern Med 2007; 146:133-141

  33. Results: Internal Validity “Good” Braithwaite RS et al, Ann Intern Med 2007; 146:133-141

  34. Results: Study Design “1” Braithwaite RS et al, Ann Intern Med 2007; 146:133-141

  35. Results: External Validity “High” Braithwaite RS et al, Ann Intern Med 2007; 146:133-141

  36. Results: All 3 Criteria Braithwaite RS et al, Ann Intern Med 2007; 146:133-141

  37. Conclusions • Quality of evidence may have profound impact on the precision and estimates of CEAs • Stricter evidence criteria may produce more uncertain results because there are fewer studies to base assumptions on • Approach shows when evidence is not good enough for decision making • Need higher-quality information on HIV DOT

  38. Question: How can we use CEA to inform statin formulary decisions ? • Case 1. 64 year-old female diabetic, prior MI, pre-treatment LDL 137 • Incremental cost-effectiveness <$10,000/QALY • Value: Favorable • Likely <$100,000/QALY if strict evidence std? Yes • Decision: No cost-sharing

  39. Question: How can we use CEA to inform statin formulary decisions ? • Case 2. 41 year-old male, no cardiac risk factors or history of cardiovascular disease, pre-treatment LDL 167 • Incremental cost-effectiveness $420,000/QALY • Value: Unfavorable • Likely ≥$300,000/QALY if strict evidence std? Yes • Decision: Higher cost-sharing (30%-35%)

  40. Cost-Effectiveness Analysis Limitations (and possible solutions) • Difficulty interpreting end-result • Give policy makers decision rules demarcating low from intermediate from high cost-effectiveness • A policy tool in search of a U.S. policy “lever” • Link CEA results to level of cost-sharing • Does not incorporate quality of evidence • Let policymakers and payers specify minimum threshold of evidence for decision making

  41. Questions ?????? • Special thanks to • Mentors • Amy C Justice, MD, PhD • Mark S Roberts, MD, MPP • Funders • NIAAA • RWJ • Co-authors, including • David O. Meltzer, MD, PhD • Joseph King, MD • Alison B. Rosen, MD, ScD • John Concato, MD, MSc

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