Should a treatment’s impact on inequality affect it’s value?admin
In general, health economists would like to have health
insurers cover treatments that are welfare improving in the Pareto sense. This means, if a treatment provides more
expected benefits than costs and no one is worse off (in expectation), then
this treatment should certainly be covered.
It could be the case, however, that people care who gains the
benefits. For instance, consider the
case of a new technology that helped people with serious diseases move around
more easily inside a mansion. Assume
this technology had more benefits than cost.
Some (many) people, however, may not like covering a treatment that only
benefits people who are very well-off.
This issue is especially relevant in single payer systems—like the
United Kingdom’s National Health Service (NHS)—which are funded by taxpayers.
One option is to consider both the average net health
benefits (i.e., benefits less cost) to a population as well as it’s affect on
inequality. If a society doesn’t care
at all about inequality, then this reduced to just measuring net health benefit
overall; if a society has a strong preference for equality, treatments that
provide benefits to only the better off will be considered less valuable.
A paper by Love-Koh et al. 2019 provides a nice quantitative way to estimate these tradeoffs. The approach uses both the Atkinson inequality index and the Kolm index. In the Atkinson index, N is the number of people, Qi is the gain in quality adjusted life expectancy (QALE) for person i, Q_bar is the average QALY for the whole population, and ε is the inequality aversion parameter. The Kolm index is measured similarly. Whereas the Atkinson index measures relative inequality, the Kolm index measures inequality on an absolute scale; also the inequality aversion for the Kolm index is α rather than ε.
One can use these indices to calculate the equally distributed equivalent (EDE), which is the level of population health (in QALYs) in a completely equal distribution that yields the same amount of social welfare as the distribution under investigation.
Using this approach, the authors find
Twenty-seven interventions were evaluated. Fourteen interventions were estimated to increase population health and reduce health inequality, 8 to reduce population health and increase health inequality, and 5 to increase health and increase health inequality. Among the latter 5, social welfare analysis, using inequality aversion parameters reflecting high concern for inequality, indicated that the health gain outweighs the negative health inequality impact.
Despite the attractive features of this approach
analytically, there are issues related to how it would be implemented. In this case, inequality is based solely on
quality-adjusted life expectancy.
However, others could take a more holistic approach and look at socioeconomic
status including other factors (e.g., income, employment, etc.). In theory, one could perform the same
exercise measuring individual overall utility including these other aspects,
but few (rightly) would want the government to assess how well people are
overall. Second, the authors qualify
expected life expectancy by patients’ sex, primary diagnosis and postcode. Thus, you could have a system that
prioritizes treatments for men—since men’s life expectancy is generally less
than women. Third, this model assumes
diseases is exogenous. In many cases
this is true, but the in some cases individual behavior could increase the
likelihood of having a disease. For instance,
would people want to discount treatments for diseaess that are preventable
(e.g., lung cancer due to smoking, diabetes due to poor eating
habits/exercise). Typically, there are
no diseases that are fully exogenous or fully at fault of the individual, so
this is a slippery slope.
What the Love-Koh paper contributes is a easy to implement method
for quantifying how inequality preferences should affect the value of different
treatments. What the paper does not
answer is whether this approach should be implemented.
- Love-Koh J,
Cookson R, Gutacker N, Patton T, Griffin S. Aggregate
Distributional Cost-Effectiveness Analysis of Health Technologies. Value in
Health. 2019 May 1;22(5):518-26.