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A METHODOLOGY FOR MEASURING THE COST- UTILITY OF EARLY CHILDHOOD DEVELOPMENTAL INTERVENTIONS. Quality of improved life opportunities (QILO) . Why Do We Need Economic Analysis?. Evaluate political policies (economic perspective)
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A METHODOLOGY FOR MEASURING THE COST- UTILITY OF EARLY CHILDHOOD DEVELOPMENTAL INTERVENTIONS Quality of improved life opportunities (QILO)
Why Do We Need Economic Analysis? • Evaluate political policies (economic perspective) • Realisation of benefits for participants, their families, and the community • Attract further investment (private and public) • To evaluate the efficiency of programs (prioritisation of resources) • Effectiveness = $ Savings and Benefits
Current methods • Cost Analysis • Cost-Effectiveness Analysis • Cost-Savings Analysis • Cost-Benefit Analysis
Governmental Perspective • Governmental Perspective • Short and long term benefits or savings to government “Might government funds invested early in the lives of children yield compensating decreases in government expenditures”?
Problems with this approach • Unable to measure disparate programs • Frames the case for developmental prevention too narrowly • Disjuncture between program objectives and program evaluation criteria • Neglects potential benefits with important consequences for society at large • Improved public safety • Living an effective an productive life
The societal perspective • Identifying a common metric outcome • A holistic approach to valuing benefits on an individual level. • Measures benefits across multiple domains, at different times, but still on an individual level. • Possibility of valuing alternative life courses.
Adaptation of QALYs • Help us make better resource allocation decisions • Choices between groups (clientele/patient) competing for specific forms of care are made explicit • Prioritisation of resources • Provides another piece in the complex jigsaw puzzle.
Concept behind QALYs • Common metric for assessing the extent of benefits gained from a variety of interventions • Quantity and quality of life gains
Quantity and quality of life • A QALY places a weight on time in different health states • Quantity of life is expresses in terms of survival or life expectancy • Quality of life embraces not only health status but also a range of other health states e.g. physical and mental capacity
Measuring Quality of life • Standard gamble • Time trade-off • Rating scales • Mulitattribute utility functions
Utilities • Utilities produced represent valuations attached to each health state • Continuum between 0 &1
Instruments that produce utilities • EQ-5D • SF-36 • Health Utilities Index (HUI1)(HUi2)(HUI3) • EuroQol-5D
Arriving at QALY Benefits (QALYs)= Number of QALYs saved through preventing or delaying premature death + number of QALYs gained through illness avoided or delayed - number of QALYs lost from side effects - loss of benefits from substitution for other drugs or treatment regimes
Arriving at QALY • Amount of time spent in health state is weighted by the utility score given to that state
Arriving at QALY • Data relating to Health Related Quality of Life = Impact of intervention on an individual patient (with and without intervention)
Cost utility ratio • Difference between the costs of two interventions divided by the difference in the QALYs they produce
Cost utility ratio 2 • E.g. Treatments for a disease T1 & T2 T1 generates 0.5111 QALYs T2 generates 0.6016 QALYs • T1 QALY – T2 QALY = 0.0905 QALYs • 33 days of perfect health (0.0905 x 365) • Addition cost of T2 = $220/patient • Therefore it costs $2,431 (220/0.0905) to generate an additional QALY by using T2
Where to? • Adapting the methodology (QILO) • Framing and designing the study • Estimating costs (using methodology developed by Manning, 2004) • Discounting costs • Identifying relevant outcomes • Describing the element states and their possible course over time for individuals who receive the intervention and for those who receive each alternative. • Attain weights for various states (survey) • Combining the elements of each state (measures of effectiveness) into a single number reflecting the value assigned to that state (Multi-attribute utility) • Integrating the values assigned to the states • Estimating the probabilities of each outcome • computing a numerical average outcome for each of the alternatives being compared.