In a 2009 Value in Health paper, Joseph Lipscomb and colleagues concisely summarized some of the common critiques of use of quality adjusted life years (QALYs) in economic and policy analysis. Below is an even more concise summary, informed by their paper.
QALYs are summations of terms involving four kinds of measures: (1) a health state (e.g., having a stroke or not, though probably more detailed than that), (2) the probability of being in that health state, (3) the value of that health state (e.g., how much better or worse it makes one’s life, in some precise sense), and (4) a discount factor (e.g., how much less you care about being in that state next year vs. this year).
Most debates about QALYs focus on defining health states, assessing their valuation, or how to weigh QALYs against other ethical or distributional concerns not included in their calculation. (I’m also aware debates have arisen over the discount rate.)
The authors raise these main concerns, among others:
- Value of health states can be assessed in many ways. There are several, common measurement systems for health-related quality of life. They provide “similar but not identical trends,” so “they will yield different QALY scores and thus possibly different conclusions about the cost-utility of interventions of interest.”
- QALYs ignore or assume away some issues, like fairness and distributional concerns. If a QALY-based approach suggested that health interventions for men yield higher QALYs than for women, should we fund health care accordingly? Few would find that fair.
- A related area of concern is motivated by the question: Whose preferences are embodied in QALYs and whose should be? Should QALYs reflect the preferences of individuals experiencing various health states or the preferences of individuals imagining different health states? Or, should QALYs reflect societal value of health?
- In the US, QALYs aren’t that relevant to policy-making. Are they not relevant because of methodological issues (like those in #1, above) or cultural issues (which arise in #2)? A related concern is that QALYs might be biased by financial conflicts of interest or applied in a biased way.
The authors go on to address these concerns, suggesting ways to modify QALYs or adapt them into a process that is sensitive to some of their limitations. If nothing else, the paper serves as a handy reference to a lot of QALY and QALY limitations literature. This post is not intended to be anything close to a complete enumeration of those limitations.