Summary

• Conventional disease burden measures, such as QALYs, DALYs, and WELLBYs assume that each has the same societal value.
• Most people place a greater weight on health gains to those who are currently disadvantaged.
• As such, we have a problem- the current measures of disease burden, on which we base our cost effective analyses (CEAs) and decisions of where to use our limited resources, do not account for these equity considerations that many people care about.
• There may be sensible ways of incorporating equity weightings into CEAs
• This is an understudied field, and one that will require more time and research   before equity considerations are more widely adopted.

Building equity into analyses

If you were to ask a room of people to pick between two interventions, which both cost the same amount of money, assuming all else is equal:

• A QALY gain of 9 years for someone living in Nigeria, which has an average life expectancy of 55.
• A QALY gain of 10 years for someone living in the UK, which has an average life expectancy of 81.

I would posit that most people would pick the former. Although the above example is somewhat contrived, there is a moderate amount of literature to show that a majority of the general population has a preference for placing greater weight to health gains that are gained by those who are currently disadvantaged or marginalised (Gorden-Hecker et al. 2020), such as those from low-income countries and those with lower life expectancies (Dolan et al. 2004). In simple terms, people place some importance on equity, as well as efficiency.

The current QALY approach assumes that all QALYs, regardless of who they are gained by, have the same societal value. In simple terms, a QALY gained in Nigeria is the same as a QALY gained in the UK.

When thinking about where to use our limited resources, equity and efficiency often overlap. In general, cost-effective interventions tend to affect those who are disadvantaged , since they die younger, have the most potential for health improvement and are disproportionately affected by infectious diseases with inexpensive cures (Shillcutt et al 2012). However, there may be cases where equity and efficiency diverge, as summarised in the health equity impact plane in Figure 1 below.

To promote equity, an equity-efficiency trade-off may be required, which may result in sacrifice of health gains in order to achieve greater distributional equity of health gains (Whitehead and Ali, 2010). For example, it may be more efficient (in terms of lives saved or QALYs gained) to implement a mental health intervention in an easier to reach and higher income country than it is in a low or middle income country. The possible divergence between efficient and equitable distribution of resources is not a hypothetical- it is a real problem affecting policymakers (Ottersen et al. 2008). 

Figure 1 Health equity impact plane; from Cookson et al (2017)

This justifies the need to consider how we might adjust, or weight, our cost effectiveness analyses (CEAs) to include a consideration of equity; this idea is also commonly termed distributive justice, a principle of prioritarianism. This is a really important concept to think about; it affects what interventions we think are worth starting, which are worth funding, and how healthcare systems nationally and globally decide what they should spend money on. I think that even if you ascribe to a wholly utilitarian world value system, there are a significant amount of people, including funders and policy makers, who might hold a prioritarianist value set, so this is an important area to consider.

Common opposition to effectiveness adjustments

A common opposition to any weighting adjustment or deviation from efficiency based calculations is its subjectivity and potentially problematic implications. Before 2010, the WHO applied an age adjustment in their CEAs, whereby they weighted  QALY gains differently depending on the age of the individual. The basis of this was that people of different ages have different capacities to contribute to society (very young and very old people have less capacity to contribute than someone in their 20s and 30s). They removed this age adjustment in 2010, citing that it devalued the lives of “non-productive” members of society. Sebastian Roig writes, in a blog post on Giving What We Can: 

But if we open up for the possibility of weighting according to social roles, shouldn’t we also weight individuals across professions and income? What about doctors and nurses? Since people depend on them for care shouldn’t they also be given a higher weight? It turns out this kind of weighting is quite problematic. By weighting people according to their age we open up many other issues that would have to be considered before we could justify the application of age-weights. Furthermore, numerous critics voiced concerns about the universalism of a human life. 

Although I believe that this is very valid and important argument, I think that there are a few reasons that I disagree with these arguments and think that equity considerations are different from age considerations:

1. If our unifying principle is the universalism of a human life, then it should be concerning to us that the current state of humanity produces a system in which based arbitrarily on where you are born, there are vast differences in the quality and length of your life. A particular concern for those with lower lifetime health prospects is a common principle of prioritarianism.
2. At its core, this seems like a slippery slope argument. These discussions about whether and what ways to weight measures of disease burden are hard, and we shouldn’t rush them. If we did hypothetically decide to add in an equity weighting based on life expectancy (as will be discussed below), that does not mean that we would all of a sudden start adding a bunch of other weightings at the same time.
3. It is important to consider the rationale behind the weighting- Taking the age weighting as an example, its basis was on the financial, social and economic contribution that people of different ages make. That seems quite fundamentally different to adjusting based on life expectancy, which does not seek to make judgements on the worthiness of different lives and people but rather on the state and fairness of the world in which they find themselves.
    

How to create equity informed CEAs, and a simple example

The place where someone lives, their gender, wealth and their age will all affect the opportunities that they have in life. In response to this, there is an emerging field called distributional CEAs (DCEAs), a science concerned with the application single or multivariate equity indicators to CEAs-  a primer on this has been written by Asaria et al (2016). In a systematic review of 54 articles describing equity-informed CEAs, equity indicators can be applied in a broad range of contexts, and are perceived to provide significant value (Avanceña and Prosser, 2021).  For instance, a systematic review of equity-related indicators for rotavirus vaccination in LMICs (Boujaoude et al. 2018) found 18 unique indicators used in CEAs, including wealth and income (e.g. Atkinson index), social welfare (e.g. Kolm Index) deprivation and gender based adjustments.

Let us take one very simplified illustrative example of a  potential weightage adjustment that may be useful; using the example at the start of this article, in deciding whether to pick an intervention in Nigeria or UK, it may be appropriate to include an adjustment weighting depending on the average life expectancy of people within a region.

DCEA = cost effectiveness * life expectancy/100

                    Why life expectancy?

The choice of life expectancy as the weighting factor in this specific circumstance  is fairly arbitrary. Other considerations included burden of disease (DALY / 100 000 population). I am fairly uncertain that life expectancy is the best metric to use for this DCEA, but on an initial search, it was the one that seemed most intuitive to me. In addition, from a non-comprehensive search of the literature, I identified one paper that applied a similar approach (Ottersen et al. 2008). They took health planners from Tanzania to explore their distributional preferences at a district and regional level. Respondents ranked health programmes with different target groups, and selected and ranked the reasons they thought should be given most importance in priority setty. A majority consistently assigned higher rankings to programmes where the initial life expectancy of the target group was lower. A high proportion of respondents considered “affect those with least life expectancy” to be the most important reason in priority setting.

                  Are there better ways of doing this?

There are certainly a number of different ways to approach DCEAs:

1. As highlighted above, depending on the context, there may be more appropriate or representative measures than life expectancy, such as social welfare, income, gender or a combination of these. Some ideas for these might be found in the articles by Cookson et al (2017) and Asaria et al (2016).
2. Instead of quantitatively weighting equity considerations into our model, one could do this qualitatively. For example, when building a model to consider where to spend our resources, we often look at a number of factors, such as scale, neglectedness, tractability, quality of evidence and cost effectiveness. It may be easier and more adaptable to include equity considerations as another factor, and operationalise it depending on the specific question being asked.

Where to from here?

Equity informed CEAs, or DCEAs,  are a new field. At this time, there is not a scientific consensus or clear roadmap about how to adequately include equity considerations into decisions about how to use our limited resources to do the most good. But just because this is a difficult area, and one that can sometimes be difficult to talk about, does not mean it is one that we should shy away from. More than anything, I hope that this article may serve as a springboard for more  people to weigh in about what they think about this issue, and share their ideas for how to think about this.
 

References

1. Asaria M, Griffin S, Cookson R. Distributional Cost-Effectiveness Analysis: A Tutorial. Med Decis Making. 2016;36(1):8-19. doi:10.1177/0272989X15583266
2. Avanceña ALV, Prosser LA. Examining Equity Effects of Health Interventions in Cost-Effectiveness Analysis: A Systematic Review. Value Health. 2021 Jan;24(1):136-143. doi: 10.1016/j.jval.2020.10.010. Epub 2020 Dec 3. PMID: 33431148.
3. Boujaoude, MA., Mirelman, A.J., Dalziel, K. et al. Accounting for equity considerations in cost-effectiveness analysis: a systematic review of rotavirus vaccine in low- and middle-income countries. Cost Eff Resour Alloc 16, 18 (2018). https://doi.org/10.1186/s12962-018-0102-2
4. Cookson R, Mirelman AJ, Griffin S, et al. Using Cost-Effectiveness Analysis to Address Health Equity Concerns. Value Health. 2017;20(2):206-212. doi:10.1016/j.jval.2016.11.027
5. Dolan, P., Shaw, R., Tsuchiya, A. and Williams, A. (2005), QALY maximisation and people's preferences: a methodological review of the literature. Health Econ., 14: 197-208. https://doi.org/10.1002/hec.924
6. Ottersen. T.,  Mbilinyi D., Mæstad O., Norheim O.F. Distribution matters: Equity considerations among health planners in Tanzania, Health Policy, Volume 85, Issue 2, 2008, Pages 218-227
7. Roig, S. What values do we need to keep in mind about the DALY? Part I; Giving What We Can:https://www.givingwhatwecan.org/post/2015/01/what-values-do-we-need-keep-inmind-about-daly-part-i/
8. Shillcutt, S.D., Walker, D.G., Goodman, C.A. et al. Cost Effectiveness in Low- and Middle-Income Countries. Pharmacoeconomics 27, 903–917 (2009). https://doi.org/10.2165/10899580-000000000-00000
9. Whitehead S,, Ali S. Health outcomes in economic evaluation: the QALY and utilities, British Medical Bulletin, Volume 96, Issue 1, December 2010, Pages 5–21,
10. Wailoo A, Tsuchiya A, McCabe C. Weighting must wait: incorporating equity concerns into cost-effectiveness analysis may take longer than expected. Pharmacoeconomics. 2009;27(12):983-9.