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Health outcome valuation study in Thailand
Sirinart TongsiriResearch degree studentHealth Services Research Unit, Public Health & Policy DepartmentLSHTM
Supervisor: Professor John Cairns
17 November 2006
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Outline
Introduction Research question Objectives Methods Budget & Timetable Conclusion
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Introduction
Resources are limited Market failures in Health Economic Evaluation
ICER = Cost Outcome
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Cost-utility analysis (CUA)
Outcome in CUA Quality-adjusted Life Year Impact on health: Quality of life &
Quantity of life Compare across different health
interventions
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Quality-Adjusted Life Year (QALY)
Quality of life (QoL)
life expectancy
Before treatment
After treatment
Health interventions
0
1
Q0
Q1
T0 T1
QALY gain = Q1T1 – Q0T0
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Recommendations from the NICE and the US Panel on Cost-Effectiveness in Health and Medicine
A tariff estimated from the general population
No tariff estimated from the Thai general population
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A national tariff for preference-based health measure: Why?
UK = -0.098Denmark = 0.101Zimbabwe = 0.400Japan = 0.031Thailand ?
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Research question:
A tariff for health outcomes from the Thai perspective
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How to elicit preferences over health states?
Torrance (1986) Prepare health state descriptions Selection of subjects Use a utility measurement
instrument
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Health
Complex Encompass many dimensions Individuals perceive differently A number of generic health
descriptive systems, e.g. the EQ-5D, the SF-36 and the HUI
The EQ-5D will be used in the research
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The EQ-5D
5 Dimensions - Mobility - Self-care - Usual activities - Pain and Discomfort - Anxiety and Depression
3 Levels - No problem - Some problems - Severe problems
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The EQ-5D
5 Dimensions - Mobility - Self-care - Usual activities - Pain and Discomfort - Anxiety and Depression
3 Levels - No problem - Some problems - Severe problems
11223
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The EQ-5D
5 Dimensions - Mobility - Self-care - Usual activities - Pain and Discomfort - Anxiety and Depression
3 Levels - No problem - Some problems - Severe problems
243 health states
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Problem 1:
Is the EQ-5D an appropriate tool to capture a concept of “health” in the Thai population?
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Preference, Utility, Value
What different between these terms?
Different methods to derive preferences, e.g. VAS, SG and TTO
Different methods give different values
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Assumption
A fully informed rational person is the best judge of one’s own welfare
Individual utility can be aggregated and comparable.
An interval scale is needed
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An interval scale
The difference between score 20 and 10 (10) is equal to the difference between 30 and 20 (10).
The difference between the state with score 0.4 and 0.3 (0.1) is equal to the difference between the state 0.6 and 0.5 (0.1)
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How to “quantify” preference?
Health states ranking, the VAS and the TTO methods will be used to elicit preferences of respondents in the study
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Whose preferences should be elicited? Patients or Population
Population Aim to use in decision making at the
societal perspective Generalizability
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Debates
Whose values should be counted? Preferences are “elicited” or
“constructed”? Preferences are “labile”. Simple Heuristics Framing and labelling effects
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UtilityPerceived Risk attitudestate attributes Time preference
Health state Health preferences
Description Cognition
Elicitation Utility procedure measurement
Emotions and prejudices NumeracyRandom errorLogical errorCross method inconsistencyAnchoring on single values
How individuals respond to the preference elicitation methods
Lenert et al. 2000
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Problem 2:
Do the elicitation methods appropriate for the Thai population?
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Pre-pilot study in London
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Pre-pilot study in London
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Cognitive burden
How to minimize cognitive burden of Thai respondents?
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Respondents can value not more than 13 health states
How all 243 health states will be scored?
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Problem 3:
Existing statistical models from various countries
Do these models fit with preferences observed from the Thai population?
What is an appropriate model for the Thai population?
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Thailand
A majority of population is Buddhist
Religious belief 1 : the perfect health in this life guarantee the perfect health in next life
Religious belief 2: inferior health results from bad kamma from previous life (no preferences on different inferior health)
Does these beliefs influence TTO?
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Are Buddhism beliefs influence preferences on health of the Thai general population ?
The study by Chirawatkul (2005)
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Bad Kamma
GermsDrunkeness
Poisoning Carelessness
Aging Disease Congenital AccidentBad luck
Supernatuaral factors
Disability
Mild Moderate Severe
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Objectives
1. Elicit preferences on health states from a Thai general population
2. Identify appropriate statistical models to explain respondents’ preferences over health states
3. Whether the Thai EQ-5D adequate to capture health concept of the Thai general population
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Methods
Objective 1:- Health states ranking- Visual Analog Scale- Time trade-off
Pre-testing and piloting the survey questionnaire and process
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Pre-testing the questionnaire
What, from Thais, are “usual activities”, “self-care”?
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Pilot interview
To test the interview procedure Cognitive burden
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Sample
Randomly selected from the Thai general population
Household registration database The National Statistical Office,
Thailand Health Welfare Survey (addresses
and maps are included) Regional level: 5 provinces in the
central region
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1 region (Central region)
5 provinces: Ratchaburi, Phetchaburi, Nakorn-Nayok,Nakorn-Pathom andPrachuab Kirikhan Multi-stage sampling
Sample size = 1,000
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Interview procedure
Replicate from the Measurement and Valuation of Health (MVH) study in the UK (Dolan et al 1995)
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Interview procedure
Complete the EQ-5D with own health Ranking own health Ranking 15 different health states Score each state using the VAS Score each state using the TTO Personal details: age, gender, education
and socioeconomic status
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Thai EQ-5D questionnaire
Mobility
Self-care
Usual activities
Pain/Discomfort
Anxiety/Depression
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Thermometer scale
The best health imagination
The worst health imagination
Your health today
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Example of health state card
ข้�าพเจ้�า ไม่สาม่ารถไปไหนได้�และจ้�าเป�นต้�องอยู่�บนเต้�ยู่ง ม่�ป�ญหาในการอาบน�!าหร"อการแต้งต้#วบ�าง ไม่สาม่ารถทำ�าก&จ้กรรม่ทำ�'ทำ�าเป�นประจ้�าได้� ไม่ม่�อาการเจ้(บปวด้หร"ออาการไม่ส)ข้สบายู่ ร� �ส*กว&ต้กก#งวลหร"อซึ*ม่เศร�าม่ากทำ�'ส)ด้
Moderate32313ข้�าพเจ้�า ม่�ป�ญหาในการเด้&นบ�าง ไม่สาม่ารถอาบน�!าหร"อแต้งต้#วด้�วยู่ต้นเองได้� ม่�ป�ญหาในการทำ�าก&จ้กรรม่ทำ�'ทำ�าเป�นประจ้�าอยู่�บ�าง ม่�อาการเจ้(บปวด้หร"ออาการไม่ส)ข้สบายู่ม่ากทำ�'ส)ด้ ร� �ส*กว&ต้กก#งวลหร"อซึ*ม่เศร�าปานกลาง
Severe23232
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Health states ranking
11111
3333332331
1221322231
11213
22133
33211
1123311323
22333
13131
21212
11111 - anchor
11213
11233
11323
11213
12213
22231
22133
33211
32231
32331
33333 - anchor
Bisection method
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Time trade-off question
1. Imagine that you live in a state for 10 years and die
2. If you can choose to live in healthy life and die sooner than 10 years, how many years you would sacrifice?
Preference is subjective. To compare preference between states,Years of life in perfect health will be compared
The shorter duration in perfect health, the less preferred state(use years of life to “buy” a better state)
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Time trade-off score transformation
Duration of life (yrs)
Health status
1
0
10
X
Preference = x 10
Better than death
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Time trade-off score transformation
Duration of life (years)
Health status
10X
1
0
Value of health state:
-x (10-x)
Worse than death
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Statistical Modelling
To estimate preferences for 243 health states from the observational data of 42 health states
Econometrics methods Use existing models to fit new data STATA 9
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Estimate preference from TTO
Better health states have higher preferences
11211 is “better” than 11222
Overall preference is the result of the addition of sub-preference in each dimension
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Example of models
Dolan 1997
322222 11109876
54321
NAPUSM
ADPDUASCMOy
R2 = 0.46Mean absolute difference = 0.46
Dolan et al 2002
ijijijijij ANYxxcy 1321 '2
'1
R2 = 0.55Mean absolute difference = 0.03
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Estimate preference from health states ranking
Salomon (2003)
^'jxij
Parameters are predicted using the conditional logit regression model
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Timetable
Activities When?1. Proposal, budget and questionnaire preparation
November 2006 – January 2007
2. Preference elicitation interview
February – June 2007
3. Identify appropriate modelling to predict preferences from TTO and VAS
July - August 2007
4. Qualitative survey September – December 2007
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Budget
Preparation 10,000 baht Preference interview 737,000 baht Qualitative survey 18,400 baht
Total: 765,400 baht
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Potential funding organizations
The International Health Policy and Programs, Thailand
The Health promotion for the Disabled project, Thailand
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Conclusion
Can the EQ-5D health description system capture the concept of health in Thais?
A tariff of the Thai EQ-5D to be used in the economic evaluation in Thailand
How existing models can fit the new data
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How Buddhist beliefs influence preference on health states
Contribution of preference scores to a new version of the EQ-5D
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Acknowledgement
Prof. John Cairns Louise Longworth Dr.Viroj Tangcharoensathien Dr.Wachara Riewpaiboon My fellow PhD students My family
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