Review of the Last Lecture

1. Review of the Last Lecture • Are looking at program evaluation in healthcare • Three methods: CBA, CEA, CUA • discussed CBA, problem: need shadow prices, which are difficult to generate • First alternative to CBA: CEA, discussed advantages and disadvantages (vis-à-vis CBA) • Today: finish our discussion of CEA then discuss CUA

2. Future HC Costs and CEA • suppose two programs cost the same and each saves 20 life years • However, for program A  lifetime follow-up costs = $10,000 (annual check-up plus prescription costs for a subset of patients) • for program B  lifetime follow-up costs = $300,000 (e.g expensive anti-rejection drugs for an organ transplant) • society not indifferent between these two outcomes. • BASIC RULE: subsequent HC costs that are a direct consequence of the initial program should be included in the CEA • DON’T include future HC costs not associated with the program ///

3. Two Fundamental Problems with CEA • can’t compare cost-effectiveness across projects with different outputs  different units! • quality of life is ignored in life years saved (would expect that 10 life years saved in full health is preferred to 10 life years saved in poor health). • SOLUTION TO BOTH PROBLEMS: Cost-utility analysis  CUA ///

4. Cost Utility Analysis: CUA • CUA is the second alternative to CBA, and is really CEA using a Quality Adjusted Life Year (QALY) as the unit of output • QoL = w, w  1, where w is a measure of the quality of life (QoL) associated with a given health state (thus HRQoL) • full health: w = 1, dead: w = 0 • if w < 0, utility of ill health state is worse than death, e.g. extreme pain that cannot be treated. • conceptually, can convert any state of ill health into a w value • then QALYs associated with n years in that state of ill health is wn  can compare cost-utility across programs with different real program outputs, since all outputs, e.g., pain prevented, mobility improved can, in principle, be converted to QALYs ///

5. Computing Utility Values: Rating Scale • rating scale (visual analogue scale)  two cases: • 1. Death is the worst state (see diagram): all w > 0 • 2. Death is not the worst state: w could be < 0 • if there are n years for which w’s are being computed  • QALYs = w1 + w2 + … +wn • wi i = 1,…,n • rating scale gives cardinal (not ordinal) values  additive • in standard utility theory  utility is ordinal (ranking of levels of well being  not additive) ///

6. Problems with Rating Scale, RS • respondents tend to shy away from the two end points • QALYs combine length of life with quality of life to form one outcome  however, the RS technique determines quality of life independently of length of life. • this raises the question of whether a person really is indifferent between say 4 years of life at w = 0.25, or 1 year of life at w = 1. • both cases represent 1 QALY, but is the person really indifferent between the two? Will be if indiff curves are rectangular hyperbolas • this method assumes that the indifference curves defined over quality of life and length of life are rectangular hyperbolas (indifference curve diagram) • No reason why they should be

7. Computing Utility Values: Time Trade Off (TTO) • here ask the patient to choose between t years in chronic state of ill health followed by death: Uill = U(wt) (where w = quality of life weight) and x years of life in full health, where x < t, Uwell= U(x) (since w =1) • vary x until indifferent between the two states, i.e., Uill = Uwell , i.e., U(wt) = U(x) • if U(wt) = U(x) then wt = x  w = x/t • QALYs = wi = (x/t)i = healthy year equivalent = HYE, where i = 1, …, n is the number of years left to live ///

8. Time Trade Off (TTO): Problems • TTO assumes utility is based on the product of quality of life and length of life U = U(wt)  see diagram • it is conceivable that w and t enter the utility function separately  U = U(w, t) and that this utility function is not a rectangular hyperbola see diagramfor slide 6  now x/t need not be w. • However, could simply use healthy year equivalents (HYE’s) as the measure of a program’s output, e.g. ask patient what the HYE is for their current health status before treatment and what it is after treatment and use the increment in HYE as the output measure for the treatment, ie., use HYE’s rather than QALY’s

9. Computing Utility Values: Standard Gamble: SG • classical method of measuring cardinal utility • based on work by von Neuman and Morgenstern (1953) • SG  considers a choice between staying in the current state of chronic ill health for n years followed by death, or accepting what could be considered a lottery ticket. • the lottery ticket consists of accepting treatment  one of two outcomes will occur: • i) full health for n years then painless death, with a probability of p ii) instant painless death, with a probability of (1 – p) (diagram)

10. SG Method Continued • subject is confronted with the choice of staying in chronic state of ill health or accepting the lottery ticket (treatment) • obviously if ill health very bad and p of success very high  accept treatment • at some value pe for p (prob of successful treatment) the person is indifferent between treatment (lottery ticket) and no treatment: i.e., • U(HSill) = pe U(full health) + (1 – pe) U(death) • set U(death) = 0, U(full health) = 1  U(HSill) = pe • U(HSill) is the fraction, pe , of the utility of full health U(full health) • NB. These fractions are additive  QALYs =  pe i i = 1,…,n ///

11. Problems with the SG Method • applying this technique to subjects experiencing the state of ill-health can be problematic (stressful for patients) • applying this technique to subjects who are not ill requires them to understand the true extent of the state of ill health (also problematic) • subjects may not be able to judge the true significance of different probability values.

12. Discounting QALYs • basic issue  is saving 10 lives at a given HRQoL for 1 year equivalent to saving 1 life for 10 years at the same HRQoL? • really two issues here: • An equity issue: if we can save 10 QALY’s are we indifferent between saving 1Qaly for each of 10 people, or 10 QALY’s for 1 person (we will ignore this distributional issue) • whether QALYs that accrue in the future as a result of HC today should be discounted or not. ///

13. Arguments Against Discounting QALYs • discounting implies inter-temporal choice  ability to trade off future benefits for present ones and vice versa  at the individual level inter-temporal choice is feasible for quality of life but not for life years  can’t give up a life year now for more tomorrow  thus argued  don’t discount • while an individual has a finite time horizon and thus discounts the future (may not be alive next year, or two years from now so present is more valuable than the uncertain future); society could be considered infinitely lived  thus no time preference  thus future as important as the present  no discounting. ///

14. Arguments for Discounting QALYs • the flow of HC costs over time is always discounted  symmetry requires that QALYs also be discounted  society unlikely to be indifferent between two projects with the same PV of HC costs and the same undiscounted QALYs gained but where the gain is far into the future for one project and the gain is immediate for the other.=> discounting QALYs takes care of this problem • statistically can trade-off lives inter-temporally! HOW?  • borrow for expanded HC services today  save lives. • later when the debt must be serviced  curtail HC services to balance the budget  lives could be lost. ///