Measuring Health Outcomes

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 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 savedcost per life-year saved ($/LY saved)

Evolution of Output Units

cost per “case”cost per life savedcost per life-year savedcost per quality-adjusted life year

($/QALY saved)

QALYs = area under this curve

QALE = average number of QALYs experienced

by a cohort of the same starting age

and quality of life

00

1.01.0

qualityquality ofof lifelife

additional years of lifeadditional years of lifenownow deathdeath

Ideal outcome:

Longer life, and higher quality of life, so QALYs gained is larger.

00

1.01.0

qualityquality ofof lifelife

additional years of lifeadditional years of lifenownow deathdeath

Suppose: intervention changes life path from this point

Ideal outcome:

Longer life, and higher quality of life, so QALYs gained is larger.

00

1.01.0

qualityquality ofof lifelife

additional years of lifeadditional years of lifenownow deathdeath

QALYs gained

00

1.01.0

qualityquality ofof lifelife

additional years of lifeadditional years of lifenownow deathdeath

Shorter life, but higher quality... total QALYs may be greater or smaller

00

1.01.0

qualityquality ofof lifelife

additional years of lifeadditional years of lifenownow deathdeath

Shorter life, but higher quality... total QALYs may be greater or smaller

QALYs gained

QALYs lost

00

1.01.0

qualityquality ofof lifelife

additional years of lifeadditional years of lifenownow deathdeath

Longer life, but lower quality mostly... QALYs may be larger or smaller

Disease Specific General Health

non-preference

physical measures

Many! e.g.

joint countstotal cholesterol

?

rating scalesMany!e.g., Roland Scale, VFQ-25

SIP, Rand GHS, COOP, MOS short forms EVGFP

preference based

indexed?

QWB, HUI, EQ-5D

patient’s own prefs. ad hoc ad hoc

Disease-specific measure...

more sensitive to the particular dysfunctionoften seem objectivedesigned to be sensitive to changes from

treatment for a specific diseaseacceptable 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)

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-128 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

Quality of Life

high

low

less morea1 a2

q1

q2

How much of Awould you 'trade'to improve your

quality of life fromq2 to q1?

A

Life Expectancy Time tradeoff

Probability of survival Standard Gamble

Time Tradeoff (TTO)

Life A: Health state

10 yr0

Life B: Excellent health

X

10=

Weight for

health state

X

Vary X until Life A ~ Life B

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

Standard Gamble

Life B:P %

1-P %

Live remaining LE in excellent health

Die immediately

Vary P until Life A Life B,

then P = health state weight

Life A: For remaining life expectancy

Profile 235

Standard Gamble

Method directly from decision theory incorporating attitudes about risk

Has been used with apparent success in many settings

Many report hard to understandNot representative of decision at handWeights 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 subjectsAge range: 54 – 99, Average Age: 7725 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.]

10

12

5

15

18

19

16

14

11

19.5

18.5

17

13

15.5

14.5

11.5

10.5

8

2

9

6

4

1

9.5

8.5

7

5.5

4.5

3

1.5

0.5

Remaining Years of Life

Yes

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Standard GambleSuppose 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.

17

35

65

85

50

60

25

75

90

95

80

70

55

98

92

77

73

58

52

40

15

45

30

20

5

48

42

27

23

10

2

Percent Chance of Death

Yes

No

No

No

NoNo

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

Yes

No

No

No

No

No

No

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

0

20

40

60

80

100

120

Sc

ore

20/20 - 20/40 & no AMD

20/20 - 20/50

20/60 - 20/100

> 20/100

GH = General HealthGV = General VisionOC = Ocular PainNA = Near ActivitiesDA = Distance ActivitiesSF = Social FunctioningMH = Mental HealthRD = Role DifficultiesD = DependencyDR = DrivingCV = Color VisionPV = Peripheral VisionPCS-12 = Physical Component Score (SF-12)MCS-12 = Mental Component Score (SF-12)

Utility-BasedTime Tradeoff vs Standard Gamble

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

TTO SG

Uti

lity

20/20 - 20/40 & no AMD

20/20 - 20/50

20/60 - 20/100

> 20/100

TTO SG PCS-12 MCS-12 VFQA_GH VFQA_GV VFQA_OC VFQA_NA VFQA_DA VFQA_SF VFQA_MH VFQA_RD VFQA_D VFQA_DR VFQA_CV

TTO -----SG 0.49 -----PCS-12 0.20 0.14 -----MCS-12 0.02 0.12 -0.09 -----VFQA_GH 0.33 0.35 0.71 0.11 -----VFQA_GV 0.38 0.31 0.36 0.08 0.43 -----VFQA_OC 0.25 0.12 0.48 0.21 0.43 0.29 -----VFQA_NA 0.39 0.26 0.39 0.06 0.35 0.85 0.30 -----

VFQA_DA 0.39 0.34 0.41 0.02 0.40 0.84 0.22 0.85 -----VFQA_SF 0.30 0.31 0.37 0.10 0.40 0.68 0.14 0.64 0.74 -----VFQA_MH 0.43 0.39 0.50 0.15 0.44 0.71 0.30 0.77 0.69 0.58 -----VFQA_RD 0.39 0.38 0.59 0.01 0.46 0.76 0.34 0.80 0.77 0.58 0.80 -----VFQA_D 0.43 0.37 0.41 0.06 0.40 0.73 0.29 0.75 0.68 0.62 0.68 0.77 -----VFQA_DR 0.42 0.27 0.32 0.06 0.28 0.69 0.14 0.69 0.70 0.38 0.63 0.62 0.56 -----VFQA_CV 0.21 0.14 0.18 0.11 0.32 0.50 0.16 0.40 0.53 0.79 0.31 0.32 0.57 0.27 -----VFQA_PV 0.31 0.18 0.51 -0.02 0.45 0.60 0.27 0.56 0.70 0.64 0.53 0.54 0.49 0.55 0.45

Correlations

Only PCS is significantly correlated with VFQs

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

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

Any ideas???

More literature review Health outcome measurement Investigate the limitations of each measurement

Try to link to HCI