Measuring Health Outcomes

1. Measuring Health Outcomes Thitima Kongnakorn Community of Scholars October 9, 2002

2. Measuring Health Outcomes • Clinical Decision Analysis • drug choice, specialty care, disease management program • Cost Effectiveness Analysis • economic aspects • Health Technology Assessment • Drug evaluation, screening tests, surgical interventions, medical devices, health promotion technology

3. Terminology • Health Status Measure • Used generally to refer to all of these measures • Health Profile • A health status measure that is a vector of scores on different dimensions (e.g. SF-12) • Quality of Life Measure • Preference based health status measures

4. Terminology HALYs: Health-Adjusted Life Years Using a health status measure for health weights QALYs: Quality-Adjusted Life Years A type of HALY computed using a HRQOL measure for health weights

5. Evolution of Output Units • Cost per “case” (e.g., $/cancer found)

6. Evolution of Output Units • cost per “case” • cost per life saved ($/life saved)

7. Evolution of Output Units • cost per “case” • cost per life saved • cost per life-year saved ($/LY saved)

8. Evolution of Output Units • cost per “case” • cost per life saved • cost per life-year saved • cost per quality-adjusted life year ($/QALY saved)

9. 1.0 quality of life 0 now death additional years of life QALYs = area under this curve QALE = average number of QALYs experienced by a cohort of the same starting age and quality of life

10. Suppose: intervention changes life path from this point 1.0 quality of life 0 now death additional years of life Ideal outcome: Longer life, and higher quality of life, so QALYs gained is larger.

11. QALYs gained 1.0 quality of life 0 now death additional years of life Ideal outcome: Longer life, and higher quality of life, so QALYs gained is larger.

12. 1.0 quality of life 0 now death additional years of life Shorter life, but higher quality... total QALYs may be greater or smaller

13. 1.0 quality of life 0 now death additional years of life QALYs gained QALYs lost Shorter life, but higher quality... total QALYs may be greater or smaller

14. 1.0 quality of life 0 now death additional years of life Longer life, but lower quality mostly... QALYs may be larger or smaller

15. Disease Specific General Health non-preference Many! e.g. ? joint counts total cholesterol physical measures SIP, Rand GHS, COOP, MOS short forms EVGFP Many! e.g., Roland Scale, VFQ-25 rating scales preference based QWB, HUI, EQ-5D ? indexed ad hoc ad hoc patient’s own prefs.

16. Disease-specific measure... • more sensitive to the particular dysfunction • often seem objective • designed to be sensitive to changes from treatment for a specific disease • acceptable to clinicians because focused on aspects of one health condition -- often measure things they strive to change with treatment.

17. But disease-specific measures may miss things • Many people (especially when older) have multiple health conditions • Many treatments have unintended effects (arthritis & hearing)

18. Why an interest in measures of General Health?(aka “generic measures”) • Allows many comparisons: • across diseases • in people with multiple conditions • across studies • Needed for cost-effectiveness studies

19. Medical Outcomes Study -- “short forms” • Derived from Rand General Health Survey • Originally 250+ questions • Published short forms that are in use: • SF-12 • SF-20 • SF-36

20. SF-36 & SF-12 • 8 components, scaled worst=0 to best=100 • Physical functioning • Role function (from physical limitation) • Pain • General Health • Vitality • Social functioning • Role function (from emotional limitation) • Mental health

21. New Scaling for SF-36 & SF-12 • PCS : physical component scale • MCS: mental component scaleproprietary scoring systems that combine the 8 scales into 2.

22. Measuring Health State Utility • Methods that require the subjects to explicitly trade health against something else that they value • Measure of QOL • Use in calculating QALYs

23. Life Expectancy  Time tradeoff Probability of survival  Standard Gamble Making Choices – Measuring Utility

24. X 0 10 yr Life A: Health state Life B: Excellent health Vary X until Life A ~ Life B Time Tradeoff (TTO) Weight for health state X = 10

25. TTO • Scaled to be “QALY”-like • Related to choice • Easier to use than SG

26. Problems with TTO • Difficult to apply to “short-term” health states (e.g. radiologic diagnostic tests) • Unrealistic for a patient to visualize himself/herself in an excellent health state and compare to a short-term unpleasant health state

27. Standard Gamble Profile 235 For remaining life expectancy Life A: Live remaining LE in excellent health P % Life B: 1-P % Die immediately Vary P until Life A  Life B, then P = health state weight

28. Standard Gamble • Method directly from decision theory incorporating attitudes about risk • Has been used with apparent success in many settings • Many report hard to understand • Not representative of decision at hand • Weights often very near 1.0

29. Results from Empirical Data • Questionnaire Based • SF-12 (Generic) • VFQ-25 (Disease-Specific) • Visual Functioning Questionnaire (25 questions) • Utility-Based • TTO (easy to understand) • Standard Gamble (hard to understand)

30. Subjects • 66 subjects • Age range: 54 – 99, Average Age: 77 • 25 males, 41 females • 4 Groups (classified by visual acuity) • 20/20 – 20/40 (n = 31) • 20/20 – 20/50 with AMD (n = 14) • 20/60 – 20/100 with AMD (n = 9) • Worse than 20/100 with AMD (n = 12)

31. Time Trade-Off Assume that your current life expectancy is 20 years from now.   Suppose there is a technology that can return your eyesight to perfectly normal in both eyes. The technology always works but your length of life will be decreased to 10 years. So, would you be willing to give up 10 years of your life to receive this technology and have perfect vision for your remaining years?   [The question continues by increasing or decreasing length of life with bisection technique until reaching an indifferent point.]

32. Standard Gamble Suppose there is a technology that can return your eyesight to normal. When it works, patients respond perfectly and have normal vision in both eyes for the rest of their lives. When it doesn’t work, however, the technology fails and patients do not survive (for example, death under anesthesia). Thus, it either restores perfect vision or causes immediate death. If there is a 50 percent chance of death, will you accept or refuse to take this technology? The question continues by increasing or decreasing percent chance of death with bisection technique until reaching an indifferent point.

33. Questionnaire-BasedDisease-Specific(VFQ-25) vs Generic (SF-12)

34. Utility-BasedTime Tradeoff vs Standard Gamble

35. SG is significantly correlated with TTO, and VFQs TTO is significantly correlated with SG, and VFQs VFQs are significantly correlated with TTO, SG, and PCS Only PCS is significantly correlated with VFQs Correlations

36. Conclusions • VFQ-25 is sensitive to measure outcomes for patients with visual impairment • When using generic measure, SF-12, people did not really take their visual impairment into account • People try to avoid “chance of death” rather than “losing remaining years of life” • VFQs, TTO, and SG are significantly correlated

37. Any ideas??? Future Steps • More literature review • Health outcome measurement • Investigate the limitations of each measurement • Try to link to HCI