Medicine, Health Care and Philosophy
https://doi.org/10.1007/s11019-022-10118-8
SCIENTIFIC CONTRIBUTION
Covid‑19 and age discrimination: benefit maximization, fairness,
and justified age‑based rationing
Andreas Albertsen1
Accepted: 20 September 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2022
Abstract
Age-based rationing remains highly controversial. This question has been paramount during the Covid-19 pandemic. Analyzing the practices, proposals, and guidelines applied or put forward during the current pandemic, three kinds of age-based
rationing are identified: an age-based cut-off, age as a tiebreaker, and indirect age rationing, where age matters to the extent
that it affects prognosis. Where age is allowed to play a role in terms of who gets treated, it is justified either because this is
believed to maximize benefits from scarce resources or because it is believed to be in accordance with the value of fairness
understood as (a) fair innings, where less priority is given to those who have lived a full life or (b) an egalitarian concern
for the worse off. By critically assessing prominent frameworks and practices for pandemic rationing, this article considers
the balance the three kinds of age-based rationing strike between maximizing benefits and fairness. It evaluates whether
elements in the proposals are, in fact, contrary to the justifications of these measures. Such shortcomings are highlighted,
and it is proposed to adjust prominent proposals to care for the worse off more appropriately and better consider whether the
acquired benefits befalls the young or the old.
Keywords Distributive justice · Health care rationing · Age rationing · Fair Innings · Covid-19
Introduction
As the Covid-19 pandemic illustrates, healthcare systems
sometimes face extreme shortages. How we decide to balance patients’ competing interests may effectively determine
the length of their lives.1 Therefore, we must give serious
thought to which policy to pursue. This article addresses the
rationing of ventilators under the Covid-19 pandemic with
specific attention to the role of age in rationing decisions.
The allocation of ventilators is discussed because it is a
scarce resource of immense importance to those hit hardest by the disease. A Covid-19 infection can diminish the
lungs’ capacity to provide sufficient oxygen to vital organs.
In such situations, patients need to admission to an ICU.
Here a ventilator can support breathing while the body
fights off the infection. But this is only possible if there is
* Andreas Albertsen
aba@ps.au.dk
1
Department of Political Science, Aarhus University
and the Centre for the Experimental-Philosophical Study
of Discrimination, CEPDISC, Aarhus University, Aarhus,
Denmark
a ventilator available. As ventilators are a scarce resource,
questions regarding their distribution quickly became a topic
for discussion as the number of seriously infected rose across
countries.
One particular aspect of this discussion pertains to the
role of age in rationing decisions. The relevance of this is
underscored by the fact that age is a significant predictor of
mortality among those infected by Covid-19 (Jordan et al.
2020; Zhou et al. 2020; Wu et al. 2020; Wu and McGoogan 2020). Age-based rationing is also important in broader
debates about ethics and healthcare rationing (Bognar 2016).
Empirical studies show that many people support some
age-based rationing (Busschbach et al. 1993; Johannesson
and Johansson 1997; Rodríguez and Pinto 2000; Tsuchiya
1999)—also, in a Covid-19 context (Wilkinson et al. 2020).
However, in the philosophical debate over healthcare rationing, many consider age-based criteria problematic and/or
discriminatory (Daniels 2008; Giordano 2005; Farrelly
2008; Kilner 1989; Rivlin 1995). Others allow that age
1
As an anonymous reviewer pointed out, the discussion shares
many features of the broader discussion of ethical theory in disaster
situations. For this literature, see: (Mallia 2015; Wagner and Dahnke
2015; Zack 2010).
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A. Albertsen
could, for various reasons and to different degrees, play
a role, directly or indirectly, when allocating healthcare
resources (Bognar 2008; Shaw 1994; Williams 1997b). In
the specific debate over age-based rationing under the Covid19 pandemic, we can also identify both sides of this debate.
Recent contributions have questioned age-based rationing
(Cesari and Proietti 2020; Farrell et al. 2020a, b; Farrell
et al. 2020a, b; Jecker 2022), while others have considered
them more permissible—at least in the dire circumstances of
a global pandemic (Nielsen 2020; Lippert-Rasmussen 2020).
Also, in this debate, whether age-based rationing is a form of
problematic discrimination is a recurrent theme (den Exter
2020; Popescu and Marcoci 2020).2
This article adds to our understanding of the role of age
in priority decisions under extreme scarcity. Specifically,
it asks whether the justifications offered for the different
age-based rationing policies are in accordance with the proposed policies. The proposals for rationing are identified in
the contemporary literature on Covid-19 and rationing, with
particular attention paid to how age-based rationing is justified. The article takes its starting point in situations where
we have to choose between two persons when assigning a
ventilator. It is assumed that the persons are in equal need
in the sense that treating them would not be futile and that
they are unlikely to live without access to a ventilator. It is
further assumed that we have already done what we could
to increase ICU capacity in terms of ventilators and that the
persons under consideration do not differ in their exercises
of responsibility for becoming ill in the first place.3 The final
assumption is that both persons want the treatment and have
not issued directives against receiving the treatment in the
past. These assumptions aim to isolate age and discuss it
alongside clinical factors such as prognosis.
suggested that “An age limit for the admission to the ICU
may ultimately need to be set” (Vergano et al. 2020, p. 471).
Interviews with Italian doctors suggest that they employed
an age-based cut-off point as a rationing criterion (Rosenbaum 2020).4 The SIIARTI guidelines were the subject of
heated debate and drew severe criticism due to the age-based
rationing (Craxì et al. 2020).
An influential review of issued guidelines for Covid-19
rationing conducted by Joebges and Biller-Andorno demonstrates that these approach age-based rationing quite
differently.5 Some guidelines issued do not mention age.
This is the case with the guidelines issued by NICE in the
UK. Similarly, the guidelines from the Austrian Society for
Anesthesiology, Reanimation and Intensive Care (OEGARI)
were also silent on this (Joebges and Biller-Andorno 2020).
Elsewhere, guidelines were specifically against age-based
rationing. This was the case with the guidelines issued by
the Belgian Society of Intensive Care Medicine, and those
issued by several German intensive care professional associations and the German Academy for Ethics in Medicine
(AEM) (Joebges and Biller-Andorno 2020). Finally, as mentioned, SIIARTI in Italy and guidelines issued by the Swiss
Academy of Medical Sciences leave the door open for agebased rationing (Joebges and Biller-Andorno 2020). The
role of age in guidelines issued by the Swiss Academy of
Medical Science developed in a noteworthy way. In the first
and second versions, both issued in March 2020 age above
85, is listed as an exclusion criterion (i.e. excluded from ICU
access) under the direst resource scarcity (Swiss Academy
of Medical Sciences 2020a; 2020b).6 This cut-off approach
was altered in the third version of the guidelines issued in
November 2020. In these age is not directly employed as a
rationing criterion, but is so indirectly because people above
the age of 85 are excluded if their so-called frailty score7 is
Proposals, guidelines, and practices: age
rationing and Covid‑19
This section briefly presents the role assigned to age in
rationing in a selected set of proposals, guidelines, and
practices.
In Italy, a country hit so hard by Covid-19 in the spring
of 2020 that it served as an influential tale of caution for
many European countries, age quickly became part of the
public discussion. In March, the Italian College of Anesthesia, Analgesia, Resuscitation, and Intensive Care (SIIARTI)
2
For a discussion of age based discrimination and ageism in the
broader societal response to the pandemic, see (Fraser et al. 2020).
3
This assumption is made to avoid responsibility-sensitive intuitions
such as those expressed in the application of luck egalitarianism to
healthcare rationing (Segall 2010, 2013; Albertsen 2020; Albertsen
and Knight 2015; Albertsen 2015).
13
4
Furthermore, a retrospective study of all ICU admissions in Lombardia in Northern Italy found, that as pressure on ICU capacity
increased, the likelihood of being admitted to the ICU for people
above 70 years of age dropped significantly compared to the decrease
experienced by other groups (Trentini et al. 2022).
5
A similar comparison of more countries deems age-based rationing
to be a contested issue (Jöbges et al. 2020).
6
The so-called Stage B was defined as: ‘No ICU beds available—
Resource management through decisions on discontinuation of treatment’ (Swiss Academy of Medical Sciences 2020a, p. 4). This contrasts with stage A where ‘: ICU beds available, but capacity limited’
(Swiss Academy of Medical Sciences 2020a, p. 4). Stage A was from
the third version of the guidelines redefined as: ‘ICU beds available,
but national capacity limited, with a risk of ICU beds becoming unavailable in the next few days’ (Swiss Academy of Medical Sciences
2020c, p. 6).
7
The employed frailty score is the so-called clinical frailty score
(Swiss Academy of Medical Sciences 2020c, p. 5).
Covid‑19 and age discrimination: benefit maximization, fairness, and justified age‑based…
too high (Swiss Academy of Medical Sciences 2020c).8 The
reasoning behind this is that age affects prognosis.
Age also plays a significant role in two of the most prominent ethical frameworks for rationing under Covid-19,
developed during the pandemic by ethicists. One of these,
the Multiprinciple Allocation Framework, was adopted at a
number of US hospitals (White and Lo 2020a). In this proposal, everyone in need of critical resources is deemed eligible. The proposal does not include a categorical exclusion;
however, those in need receive a priority score based on their
likelihood of surviving with treatment and their life expectancy after discharge. This is translated into a score from 1
to 8, with 1 being eligible for the highest priority. Age enters
the picture as a tiebreaker. Priority is given to those who
have gone through the fewest of life’s cycles (White and Lo
2020a). In a longer document, the following age groups are
recommended for this: ages 12–40, ages 41–60; age 61–75;
and older than age 75 (White 2020). Thus, in that proposal,
age, or rather age groups, functions as a tiebreaker if we
must choose between people with similar prognoses.9
The final framework to be presented here is the one developed by Emanuel et al. (Emanuel et al. 2020). This framework identifies six values: “maximizing benefits, treating
equally, promoting and rewarding instrumental value, and
giving priority to the worst off” (Emanuel et al. 2020, p.
2051). Following from these, several recommendations are
developed.10 The framework provides an indirect role for age
rationing because age affects prognosis—which is relevant
when benefits are maximized—and sees this as further justified by a concern for the worst off. There is a limit, however, in terms of the scope of the benefits taken into account.
Emanuel et al. write:
Limited time and information in a Covid-19 pandemic
make it justifiable to give priority to maximizing the
number of patients that survive treatment with a reasonable life expectancy and to regard maximizing
improvements in length of life as a subordinate aim.
The latter becomes relevant only in comparing patients
whose likelihood of survival is similar. (Emanuel et al.
2020, p. 2052)
In this framework, age is not a tiebreaker. My interpretation is that we should first look at the likelihood that we
will save that person’s life (short-term prognosis), then, if
equal, the length of this life (long-term prognosis) matters,
and finally, if still equal, lots are to be drawn (Emanuel et al.
2020, p. 2053).11 This leaves an indirect role for age, as age
affects both long- and short-term prognosis.
Based on the brief presentation above of prominent practices and recommendations, we can say that those who allow
for age-based rationing provide three distinct roles for age:
– Age-based cut-off: Here, treatment is not offered to
those over a certain age. This was seemingly the practice
employed in some places in Italy, part of the SIIARTI
guidelines and the early guidelines provided by the Swiss
Academy of Medical Sciences.
– Age as a tiebreaker: Here, age is evoked as a tiebreaker
between those deemed to have a similar prognosis. This
is the main role of age in the framework from White and
Lo.12
– Indirect age rationing: Here, age is indirectly relevant
because of its interaction with prognosis and life expectancy. This is the key role afforded to age by Emanuel
et al., but it is also part of the framework from White and
Lo and later guidelines issued by the Swiss Academy of
Medical Sciences.
The next section takes a closer look at the justifications
for these different roles given to age in rationing.
8
The required score differs with age in the third version of the
guidelines from November 2020, this is described as follows: Stage
A (cf. footnote 6) people above the age of 65 is excluded if they
have a frailty score of 7 or above. People older than 85 is excluded
if their frailty score is equal or above 6. As scarcity increases (i.e.
scenario B) the restrictions become tighter. Here those older than 65
are excluded if they have a frailty score equal to or above 6 (Swiss
Academy of Medical Sciences 2020c, p. 7). These remain in place in
the newest version of the guidelines issued in November 2021 (Swiss
Academy of Medical Sciences 2021).
9
In different presentations of the proposal, it is not entirely clear
whether age is the first or the second tiebreaker (White 2020). The
other suggested tiebreaker is to give priority to those who are vital
to the healthcare system’s response to the pandemic. For the discussion about what society owe this particular group, see (Albertsen and
Thaysen 2017; Sokol 2006; Reid 2005).
10
These are:: “maximize benefits; prioritize health workers; do not
allocate on a first-come, first-served basis; be responsive to evidence;
recognize research participation; and apply the same principles to all
Covid-19 and non–Covid-19 patients” (Emanuel et al. 2020, p. 2051).
Justifying age‑based rationing
Analyzing the presented policies and frameworks, we can
see that different justifications for age-based rationing are
offered. This section presents these justifications in the
frameworks and guidelines allowing for such rationing.
11
Do note that they argue for priority to be given to those who participate in research and to frontline healthcare workers. The latter
seem, in fact, to be eligible for absolute priority.
12
According to Bognar and Hirose the tie-breaker approach was also
recommended by The Critical Care Society of Southern Africa, The
Canadian Medical Association and The Australian and New Zealand
Intensive Care Society (Bognar and Hirose 2022, pp. 102–3).
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A. Albertsen
The first justification prominently linked to age-based
rationing is benefit maximization. While the age limit in
the proposal received a lot of attention, it is important to
understand that it was proposed alongside other concerns,
specifically prognosis (Cesari and Proietti 2020), and that it
was motivated by the utilitarian concern of maximizing the
number of lives and life years saved (Vergano et al. 2020;
Craxì et al. 2020). In the SIAARTI guidelines, the age limit
is proposed with a utilitarian justification. “The underlying principle would be to save limited resources which
may become extremely scarce for those who have a much
greater probability of survival and life expectancy, in order
to maximize the benefits for the largest number of people”
(Vergano et al. 2020, p. 471). This suggests that maximizing benefits provides a reason for age-based rationing. The
theme is also clearly present in the framework presented by
Emanuel et al. Here “maximizing the benefits produced by
scarce resources” is described as something which may lead
to priority for the youngest (Emanuel et al. 2020, p. 2051).13
The prominence of maximizing benefits is unsurprising, but
it conflicts and interacts with age-based rationing in several
ways, which will be presented shortly. Some of these are
highly relevant to what we might call the scope of the idea
of maximizing benefits (i.e., which benefits to count).
The second justification for age-based rationing is fairness. Fairness is fleshed out in two different ways. One is an
egalitarian concern for the worst off, and the other is a kind
of fair innings approach. In White and Lo’s framework, the
reference to life phases suggests a fair innings reasoning for
employing age as a tiebreaker. The idea of fair innings has
a long pedigree in the philosophical debates over healthcare rationing (Williams 1997a; Bognar 2008; 2015; Harris
1985). It refers to the sentiment that when people have had
their fair share of life, their claim to assistance weakens. As
Harris puts it:
‘The fair innings argument requires that everyone be
given an equal chance to have a fair innings, to reach the
appropriate threshold but, having reached it, they have
received their entitlement. The rest of their life is the sort of
bonus which may be canceled when this is necessary to help
others reach the threshold.’ (Harris 1985, 91).
In one prominent interpretation, this means that beyond a
specific cut-off point (i.e. when the threshold is reached), a
person no longer has the same claim for treatment as those
13
The importance of maximizing benefits is also found in the multiprinciple framework. There, it is, however, not tied to age (White and
Lo 2020a).
13
below this cut-off have.14 In what follows, this kind of fairness concern is deemed fair innings fairness.
The worst-off interpretation of fairness is tied to age in
the above arguments. This is clearest in the framework from
Emanuel et al. Here, the worst-off condition refers, in particular, to the unfairness of some having lived shorter lives
than others.15 In this framework, age is considered relevant
as part of the worst-off condition (even if it is mostly connected to maximizing benefits).16 In their specification of
maximizing benefits, the authors underscore that this will
(often) also correspond to what is required by a concern for
the worst off “in the sense of being at risk of dying young
and not having a full life” (Emanuel et al. 2020).
But plausibly, we must suggest that having lived fewer
years is only one instance of being worse off—just as benefitting the younger is only one instance of maximizing
benefits. It cannot be the full meaning of the concern for
the worse off. Following this, the use of worse off in what
follows will also consider broader egalitarian concerns.17
In what follows, this kind of fairness concern is deemed
egalitarian fairness. These specifications of the justifications make it clearer that two distinct readings of fairness are
used here. Note that, at least in principle, they do not imply
14
Such a cut-off point version of the fair innings approach, is arguably the canonical formulation of the fair innings argument. For this
reason it is also often the starting point for discussions of fair innings
(Bognar and Hirose 2014; Dunlop 2002; Farrant 2009; Harris 1985;
Nielsen 2020; Rivlin 2000). But, as an anonymous reviewer points
out, this is not the only interpretation of the fair innings argument.
A point also raised by Bognar (2016). The fair innings idea could
be interpreted to mean that people’s claim for assistance weakens as
they grow older (i.e., comes closer to having had a fair inning). This
is the version Bognar defends based on a prioritarian weighting of
life-years, where later life-years have less weight (Bognar 2015) [see
also, (Adler et al. 2021; Dunlop 2002)]. This is different from having
a cut-off point, and, according to Bognar, avoids some of the associated difficulties. Harris seemingly prefers the cut-off version, even
if he allows for the fact that deciding the cut-off is difficult (Harris
1985, p. 94). However, he is clearly aware that the reasoning behind it
could be used to reach the conclusion drawn by Bognar. In the above
the cut-off version will be discussed. Both because of its prominence
but also because it is most clearly distinct from one interpretation of
the worse-off value employed by the framework (where the youngest
is all else equal the worse off). For an illuminating discussion of Bognar’s view, see (Davies 2016). For further developments of the fair
innings approach, see also (Nord 2005). While some of the verdicts
reached by what is termed fair innings fairness below, would not have
been reached on other specifications, the discussion of each of these
is not possible here—any many of the judgement would be similar.
15
Though they mention that another interpretation could be to aid
those who are most sick.
16
According to the authors, the youngest should be provided
resources first if this produces a reduced infection rate. But the example they provide where this might be the case is vaccines (Emanuel
et al. 2020).
17
Something that later work by White and Lo also calls for (White
and Lo 2020b).
Covid‑19 and age discrimination: benefit maximization, fairness, and justified age‑based…
the same age-based rationing. All else being equal egalitarian fairness requires us to treat the youngest (all else being
equal), while fair innings fairness only does if one person in
the comparison is above a certain threshold.
Maximizing benefits, fairness, and the three
proposals
This section assesses the policies outlined earlier to determine whether they are in accordance with fairness and
maximizing benefits. The purpose of this discussion is not
an assumption that it is likely that a policy will achieve
both, as they are likely to conflict. The purpose is rather to
understand the relationship between the justifications and
the extent to which the various policies end up prioritizing
one over the other due to the role they provide age in rationing decisions. When we consider to what degree a specific
policy reflects the justifications described above, there are
several things that should be stressed in advance. While
age affects prognosis, it is far from perfect in predicting it.
As proponents of the so-called frailty score would submit,
numerous other factors affect this (Lewis et al. 2021). The
same is true for the benefit acquired from the expected lifespan after discharge. Again, age provides some guidance for
how long a person might live after discharge—but an imperfect one, nonetheless. Think of those who face a distinct,
shortened lifespan because of co-existing conditions. This
includes those with co-existing diseases such as hypertension, diabetes, cardiovascular disease, or chronic lung disease (Jordan et al. 2020). These circumstances affect and
complicate the assessment of both Covid-19 patients and
policies.
Age‑based cut‑off
Consider first the age-based cut-off policy identified in the
SIIARTI guidelines. This policy introduces the principle that
whenever we must choose between two patients where only
one is above a certain age, we must treat the younger one.
Thus, the policy does not always amount to choosing the
youngest, but it does so when one of the persons in question
is above the threshold and one is below.
How does this policy fare in terms of maximizing benefits
and achieving fairness? One element in the policy speaks in
favor of this in terms of benefit, namely the life expectancy
of those whose lives it saves. As those who are allowed treatment under this policy are younger than those for whom
treatment is denied, the former group surely has a longer
life expectancy, all else being equal. There are two other
ways this policy might maximize benefits: if those treated
are more likely to benefit and if those offered treatment are
more likely to be cured after a shorter time span. The latter
is important because it would mean that the ventilator could
benefit another once one person’s treatment has ended.
Despite this, all is not well from the perspective of maximizing benefits. As clarified above, co-existing diseases
affect prognosis. For this reason, an age-based cut-off may
not maximize benefits. A person with a failed kidney might
be younger and below the threshold, yet still have a shorter
expected lifespan and a worse prognosis than someone above
the cut-off. This becomes even more likely when we compare people where one is slightly above the cut-off, and one
is slightly below. This should provide us with at least enough
to reconsider whether the age-based cut-off is the best way of
maximizing benefits during the pandemic. It bears mentioning that an age-based cut-off is also silent on several comparisons. It does not allow us to choose between two people
below the threshold. Here, maximizing benefits might not
be silent due to co-existing diseases.
The next thing to assess is how the age-based cut-off fares
in terms of the two kinds of fairness. From the perspective
of fair innings fairness, there is much to be said for a cut-off
policy—at least if the cut-off is set at a point that reasonably
reflects a full lifespan. Of the policies under consideration
here, a cut-off policy seems to be the purest fair innings
policy because it involves a cut-off and is silent about the
scenarios that do not involve two people on either side of the
cut-off. This policy is, therefore, strongly recommended by
the fair innings approach.
Consider, then, egalitarian fairness. Here, a wide range of
concerns can be taken into the discussion. While the policy
in question may not adequately capture some of these, it
does seem to capture some important aspects of fairness.
It prioritizes those who have lived the shortest lives. This
seems to express a relevant kind of fairness; as long as we
compare people who are otherwise equal, it is reasonable to
suggest that those who have lived the shortest lives are the
worst off.
However, these justice-based assessments become much
more complicated once we allow for the plausible fact that
there may be justice-relevant differences between those of
a similar age. One such difference springs from the previously mentioned co-existing diseases, affecting prognosis
and post-treatment lifespan. This means that also, out of a
concern for the worse off, there is something problematic
with a policy that is silent when considering patient pairs
below or above the cut-off point. The argument for counting
a co-existing disease towards status as being worse off is that
co-existing diseases, which adversely affect one’s prognosis
in terms of Covid-19, may also plausibly affect other aspects
of one’s health and wellbeing. Therefore, we should say that
a person with a co-existing disease is worse off from the
egalitarian perspective of fairness.
This has two implications for the age-based cut-off policy. Firstly, if those with co-existing diseases are unjustly
13
A. Albertsen
disadvantaged from the perspective of egalitarian justice,
then the policy makes a mistake when it cannot choose
between two young people or two older people where only
one has a co-existing disease. Secondly, if one of the persons in question suffers from other unjust disadvantages
(i.e., socio-economic disadvantages), then it may also be
the case that the recommended prioritizations are not just
from the perspective of egalitarian fairness. This is likely
to be the case when we consider people of the same age,
similarly affected by co-existing diseases are unequal in
terms of socio-economic position. But it is less likely so
when we make the comparison between people of large age
differences.
White and Lo: age‑based tiebreaker
How should we evaluate the proposal from White and Lo
that age should be employed as a tiebreaker for people who
have an equal score? The priority score, ranging from 1 to
8, reflects a combination of prognosis for a successful treatment and life expectancy after the treatment. Thus, in the
multiprinciple framework, short-term prognosis and longterm prognosis work in tandem to determine the priority
score. The overall principle is that lower SOFA scores,
which correspond to a lower likelihood of short-term mortality, receive higher priority, which is then supplemented
by long-term mortality. Here, a shorter expected lifespan
(< 1 years) decreases the likelihood of receiving priority,
while a longer expected lifespan increases (> 5 years) the
likelihood of receiving priority. For similar prognoses, age
is the tiebreaker. In terms of choosing between two patients,
this policy amounts to treating the person from the youngest
age group whenever we have to choose between two people
with a similar short-term and long-term prognosis. How
does this proposal fare in balancing maximizing benefits
and fairness?
Consider first the aim of maximizing benefits. Here, the
policy fares well because it clearly gives high priority to
maximizing benefits. Age only becomes part of the decisionmaking in cases where the benefits expected to be gained
from choosing one or the other patient are relevantly similar.
As such, this policy gives very high weight to maximizing
benefits. In doing so, it avoids some of the disadvantages,
from a maximizing benefit perspective, identified with the
policy of a cut-off point. This is the case because when the
starting point is not whether people have passed a specific
age, the effects co-existing diseases have on prognosis and
lifespan matter in ways that are sensible from the perspective of maximizing benefits. We do not risk choosing the
younger person if we have reasons to expect that this will
not maximize benefits.
Now consider the two kinds of fairness. Here, it is less
clear that the proposal achieves these. That is predictable
13
due to the primacy given to prognosis and the less elevated
role provided to age. From the perspective of fair innings
fairness, the minor role provided to age seems problematic.
It means that people who have gone through the life phases
will get priority over those who have not when the shortterm and long-term prognoses of the latter are slightly worse
than that of the former.
This, of course, also raises problems from the perspective of egalitarian fairness. It means that all else being
equal, those who have lived the fewest life years may not
be afforded the opportunity to experience more of these if
they suffer from a co-existing disease and are compared to
an older person who is not. It also means if socio-economic
position affects the likelihood of suffering from co-existing
diseases, which affect prognosis—that the broader unfair
social distribution of goods affects who gets treated. Simply
put, on the 1–8 scale, we might feel that people can receive
almost the same score for reasons that are quite different
and do not take age into account. The SOFA scale employed
to provide priority, in combination with the assessment of
longer-term prognosis, allows for this.
Emanuel: indirect age rationing and restricting
the role of long‑term prognosis
In the proposal from Emanuel et al., age is not a tiebreaker
between people with similar prognoses. As above, if age
(or co-existing disease) affects short-term prognosis, then
the primacy given to maximizing benefits means that the
young will be treated. The proposal, due to the role it affords
maximizing benefits, thus allows what we might call an indirect age-based rationing. Note, however, that in this proposal, there is a curtailment of which benefits are taken into
account, which limits the extent of indirect age-based rationing. Long-term prognosis is only taken into account when
short-term prognosis is similar. In those cases, long-term
prognosis functions as a tiebreaker (Emanuel et al. 2020,
p. 2052). From the perspective of maximizing benefits, it is
clear that this limit in the framework makes it the case that
the framework does not maximize benefits. Doing so would
require giving a more direct and elevated role to future life
expectancy—a role at least as prominent as the one afforded
to it in the multiprinciple framework described above.
From the perspective of fairness, in either interpretation,
the evaluation of the framework is, of course, affected by
how much it allows co-existing diseases to affect prognosis
and priority and that it declines to give age a direct role
in the rationing procedure. This means that insofar as age
affects prognosis—long or short term- it is only allowed to
count if it affects the short term. All else being equal, this
decreases the likelihood that maximizing benefits will correspond to treating the youngest.
Covid‑19 and age discrimination: benefit maximization, fairness, and justified age‑based…
There are other elements as well which may be problematic from the perspective of fairness. In the fair innings
interpretation of fairness, drawing lots when people are
equal on both long-term and short-term prognosis is clearly
problematic. It means—at least in principle—that we could
end up treating someone who has lived 65 years rather than
someone who has lived 40.
In terms of egalitarian fairness, problems arise similar to
those discussed above. This is the case because once again,
prognosis can be influenced by a range of factors and coexisting diseases, which are unequally distributed across the
population and related to social position. From the perspective of egalitarian fairness, several aspects are thus overlooked. For these reasons, it seems reasonable to suggest that
the policy does not sufficiently consider several important
aspects of what it means to be worse off.
Proposal: justice‑adjusted priority
and age‑adjusted benefit
In light of the above, I believe to have identified specific
points in the valuable and elaborate frameworks where they
do not adequately pursue fairness (or rather, where they
provide it with too minor a role) and where they allow a
problematic role to maximizing benefits. There are feasible
ways of correcting this. The easiest way of illustrating the
corrections is to identify how they would change the multiprinciple framework as this is the one that is most elaborately described.
The first alteration is to give a larger role to egalitarian
fairness and the concern for the worst off in ascribing priority points.18 One way of doing so would be to subtract 1
priority point from those who come from the most deprived
backgrounds. The purpose of doing so would be to attempt
to mitigate the fact that co-existing diseases affect people’s
long and short-term prognoses in a significant way. With this
addition, the general ranking is kept, and maximizing benefits is still a very important concern, but we would ensure
that a fairer chance is given to those whose prospects are
predictably worsened by their socio-economic circumstances
or co-existing diseases. This would go against maximizing
benefits but leave more room for a fairness-based concern for
the worst off. One worry with this proposal would be that for
some comparisons, the difference in the SOFA score would
mean that we give priority to people who have too small a
chance to survive treatment. If the worse-off adjusted priority score moves people too much, the alternative would be a
worse-off adjusted SOFA score, which would move people
18
This is something that White and Lo are open to themselves
(White and Lo 2020b).
less. Nevertheless, do note that the framework seemingly
already allows people with a high SOFA score and good
long-term prognosis to be treated before those with low
SOFA scores and a bad long-term prognosis.
The second alteration is directly about what the role of
age should be. Many of the problems identified above reflect
that age is given too indirect a role. We achieve, as it were,
fair innings fairness by circumstance because age-based circumstances will often affect the benefits acquired in the short
and long term. However, as the examples above showed,
this might not always be the case. Sometimes, the circumstances would not produce age-based rationing. This is clear
when the Emanuel et al. framework allows lots to be drawn
between people of very unequal ages, and when small differences in prognosis mean that the multiprinciple framework
does not get to employ the age-based tiebreaker but rather
opts for treating the oldest with the slightly better prognosis. Both proposals involve the principled view that a life
year gained weights equally no matter who receives it. This
may seem initially plausible, but it produces the implausible
priority decisions just described. For both frameworks, the
adjustment would affect how long-term prognosis is taken
into account. In the multiprinciple proposal, it could mean
adding different points for long-term prognosis based on age,
and in Emanuel et al.’s proposal, it would mean differentiating how long-term prognosis functions as a tiebreaker
depending on the age of the persons in question.
Conclusion
The above identified three kinds of age-based rationing:
an age-based cut-off, age as a tiebreaker, and indirect age
rationing, where age matters to the extent that it affects
short-term prognosis. The reasons provided for such policies
are to maximize benefits from scarce resources or because it
is believed to be in accordance with the fairness understood
as (a) fair innings, where less priority is given to those who
have lived a full life or, (b) an egalitarian concern for the
worse off, where it is considered fair to aid those who have
lived the fewest years (and/or suffers other unjust disadvantages). Several shortcomings were highlighted in the three
policies in terms of realizing benefit maximization and fairness. In light of this, it was proposed to adjust the proposals to care more for the worse off and to take into account
whether the acquired benefits are given to the young or the
old. While these adjustments are perhaps most relevant
under the extreme scarcity presented to us in the pandemic,
they may also be relevant under the more usual scarcity facing healthcare systems.
Acknowledgements I am grateful for comments received by Didde
Boisen Andersen, Anne-Marie Søndergaard Christensen, Anna
13
A. Albertsen
Christina Hjuler Dorf, Kasper Lippert-Rasmussen, Greg Bognar, Axel
Gosseries, Søren Midtgaard, Jens Tyssedal, Sigurd lauridsen, Jake
Lehrle-Fry, Thomas Søbirk Petersen, and Lasse Nielsen.
Funding Funding Statement: Work on this article was supported by the
Danish Research Council: Grant Number: 9037-00007B and Danish
Research Fund (DNRF114).
Declarations
Conflict of interest The authors declare that they have no conflict of
interest.
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