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