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

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

Terminology Health Status Measure Health Profile 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

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

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

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

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

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

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

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

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

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

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

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

Disease Specific General Health non-preference preference based 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 patient’s own prefs. ad hoc ad hoc

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.

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

Allows many comparisons: 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

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

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

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

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

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

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

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

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

For remaining life expectancy 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

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

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)

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)

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.]

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.

Questionnaire-Based Disease-Specific(VFQ-25) vs Generic (SF-12)

Utility-Based Time Tradeoff vs Standard Gamble

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

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

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