WEek3 Instructions For this assignment, you will create an annotated spreadsheet describing the cost analysis strategies and decision-making approaches of

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For this assignment, you will create an annotated spreadsheet describing the cost analysis strategies and decision-making approaches of your selected governmental entity. 
You will also write a 2 to 3-page paper discussing the following:

Evaluate the tools used by the entity to calculate current and project future costs.
Consider the mix of mandatory and discretionary spending.
Assess the alignment of budget decisions and policy goals.
Explain the entity’s approach to capital planning.
Discuss prioritization, compromise, and decision-making approaches.
Provide specific examples where appropriate.
Evaluate the effectiveness of this process; make two or more recommendations to improve the process.

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Length: 1 spreadsheet with a 2 to 3-page explanatory brief, not including title and reference pages
References: Include a minimum of 3 scholarly resources.
The completed assignment should address all of the assignment requirements, exhibit evidence of concept knowledge, and demonstrate thoughtful consideration of the content presented in the course. The writing should integrate scholarly resources, reflect academic expectations and current APA standards, and adhere to Northcentral University’s Academic Integrity Policy Phelps and Madhavan Cost Eff Resour Alloc 2018, 16(Suppl 1):48


Resource allocation in decision support
Charles Phelps1* and Guruprasad Madhavan2

From Priority Setting in Global Health Symposium Boston, MA, USA. 5–6 October 2016


Background: Cost–benefit and cost-effectiveness analysis place limits on the dimensions of value that the models
can incorporate. Cost–benefit analysis requires monetization of all measures of value (including life), a task sometimes
deemed either difficult to accomplish or even repugnant. Cost-effectiveness analyses include health care gains in
natural units (e.g., quality-adjusted life years or QALYs) rather than purely monetizing them (e.g., in dollars) and offers
an efficiency perspective based on the ratio of cost per QALYs or similar health measures. These two methods use
different rules for investment. Cost–benefit analysis says to invest whenever benefits exceed costs. Cost-effectiveness
analysis says to invest if the intervention has a cost per QALY that meets—or is below—a designated cutoff value.

Methods: Multi-criteria frameworks expand decision analyses by considering value tradeoffs from decision makers,
and then producing a synthetic measure that summarizes the performance of investment options. This evaluation is
done across all chosen dimensions of value, based on the weights provided by the decision makers, but this flexibility
comes at a cost. To date, no approach is widely accepted to suggest how much to invest (how to determine a budget
constraint) using multi-attribute models. Moreover, there is no agreed-upon method to measure willingness to pay for
incremental multi-attribute value improvements. Our paper proposes a way forward.

Results: Based on existing dollar estimates of willingness to pay for QALYs, our concept creates a comparable cutoff
for multi-criteria value measures. Our proposed method expands the acceptable cost per QALYs in proportion to how
much of the total measure is accounted for by the QALY component. Agreed-upon values for cost per QALY are thus
extrapolated to account for extra value created by non-QALY attributes of each intervention.

Conclusion: Using our proposed methods, the cost per QALY cutoff can serve as a benchmark toward creating a
resource allocation cutoff in multi-criteria frameworks.

Keywords: Multi-criteria decision analysis (MCDA), Cost effectiveness analysis (CEA), Cost–benefit analysis (CBA),
Budget constraints, Quality-adjusted life years (QALYs), Policy development, Investment planning, Portfolio analysis

© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
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provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,
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Methods to evaluate and allocate societal investments
have evolved over time. Cost–benefit analysis grew from
the work of French engineer-economist Jules Dupuit in
1844 [1], later formalized by economist Alfred Marshall
[2]. The US government began the specific requirement

of cost–benefit analyses for water navigation projects
in 1936, further codified in the 1939 Flood Control Act,
which embodied into law an operative investment deci-
sion rule: Invest when “the benefits to whomever they
accrue [be] in excess of the estimated costs” [3]. This
illuminates an important limitation of the cost–benefit
analysis: it cannot consider the distribution of those ben-
efits and costs, yet issues of distribution and equity are
the center of many public policy debates.

Open Access

Cost Effectiveness and
Resource Allocation

*Correspondence: charles.phelps@rochester.edu
1 University of Rochester, Rochester, NY, USA
Full list of author information is available at the end of the article





Page 86 of 98Phelps and Madhavan Cost Eff Resour Alloc 2018, 16(Suppl 1):48

Planners and analysts of health care have been reluc-
tant to fully embrace the concept of cost–benefit analy-
sis, since it requires an explicit statement by the analyst
of the value of a human life or life-year. Over time, cost-
effectiveness emerged as an appealing criterion, wherein
the analyst can evaluate the incremental costs and health
benefits of various medical interventions and then report
their ratio. Decision makers then set the cutoff value for
approval of investments.

The proof that this approach flowed directly from
a single person’s lifetime utility maximization pro-
gram came only in 1997 [4]. Until that point, the intui-
tive appeal of cost-effectiveness was all that supported
its legitimacy. The use of cost-effectiveness significantly
expanded during the latter part of the twentieth century,
most notably within the British National Health Service
which—through its National Institute for Health and
Care Excellence—evaluates medical interventions using
a cutoff value currently set around £30,000 per quality
adjusted life-years (QALYs) [5]. The World Health Organ-
ization recommends the use of modified cost-effective-
ness analysis to evaluate health care interventions: using
disability-adjusted life years (DALYs), it recommends a
cutoff value of one to three times per-capita gross domes-
tic product to guide resource allocation [6].

Once a cutoff value for an acceptable investment has
been made, cost-effectiveness analysis and cost–benefit
analysis are virtually equivalent, with the difference being
the choice for the value measure, be it lives, life-years,
QALYs or DALYs [7]. That said, how does one establish
the cost-effectiveness cut-off? A general discussion of
this issue appears in the most recent cost-effectiveness
“handbook” [8]. A complication arises when a health
system announces one cost-effectiveness threshold (e.g.,
$100,000 per QALY) but establishes a budget that is
insufficient to fund all technologies passing the estab-
lished threshold. This creates an affordability conundrum.

This issue has recently even entered the British court sys-
tem with a lawsuit over prescription drugs facing a conflict
between cost-effectiveness thresholds and affordability [9].
If the budget is too tight to fund all approved technolo-
gies, then that implies a more stringent threshold in actual
use. In all that follows, we intend to use the constraint
that binds more tightly (typically the budget). Embedded
in a tight budget the “shadow price” for QALYs—the price
that really matters. So if the official cutoff is $100,000 per
QALY and the budget only would fund activities with cost-
effectiveness ratios of $80,000 per QALY, then we would
intend that the $80,000 value be used.

Both cost–benefit and cost-effectiveness approaches
share a common defect: they are narrow, and cannot
include practical critical factors such as the distribu-
tion of benefits and costs, broader impact of societal

programs, public values and perceptions, and related
matters [10]. The importance of these distributional
issues have been considered [8, 11–13]. One model meas-
ures the distribution of health benefits and costs across
various population subgroups [14] but does not provide
a mechanism to synthesize this information into a unified
value measure.

More comprehensive decision support systems such as
multi-criteria decision analysis can include these issues
such as equity and social distribution directly and trans-
parently [15–17]. Multi-criteria approaches have demon-
strated their value, especially when decision makers have
various—and often competing—priorities [18].

Multi-criteria models differ in an important way from
standard economic analyses. Economists typically esti-
mate the structure of people’s preferences from observed
choices they make using the formal tools of utility maxi-
mization and demand theory. These “revealed prefer-
ences,” as economists call it, are inferred from actual
choices. Multi-criteria decision analysis does something
completely different: it elicits the preferences of the deci-
sion makers and the trade-offs that they are willing to
make. Subsequently, most of these approaches use a sim-
plified method for approximating the total value of any
portfolio, often using linear approximation. These “attrib-
utes” might include considerations relating to finances,
health gains, social justice, or patient preferences to avoid
certain side effects of treatments some of which could (in
theory) be included in formal cost-effectiveness models
(with relevant data) but often are omitted for simplicity
and practicality.

Multi-criteria analyses do come at a cost. In their
current form, unlike cost–benefit and cost-effectiveness
analyses, multi-criteria models do not explicitly guide
resource allocation. No widely accepted rule exists for
multi-attribute approaches that match the logical ease
and spirit of “invest when the net benefits are positive”
(as in cost–benefit analysis) or “approve the project if
the cost per unit of health gained falls below some pre-
determined cutoff ” (as in 1X to 3X per capita GDP for
cost-effectiveness analysis). A recent task force of the
International Society for Pharmaceutical and Outcomes
Research (ISPOR) concluded that the best practices to
support the use of multi-criteria decision analysis to
consider budget constraints is “still unclear, and further
research should focus on this topic” [19]. This paper
seeks to contribute to that discussion.

As a starting point for discussion, resource allocation
relates to how basic measurements are done. With a
fixed budget, if all one desires is a prioritized rank order

Page 87 of 98Phelps and Madhavan Cost Eff Resour Alloc 2018, 16(Suppl 1):48

of investments, then ordinal scales (placing choices in
the desired order) or interval scales (such as Fahrenheit
and Celsius temperatures) suffice. Many forms of multi-
criteria decision analysis produce interval scales. In this
setting, investments are made until the fixed budget is

A more refined approach might allocate a fixed invest-
ment budget across scalable investments by choosing
how large or small each potential project might be to
maximize the overall value of the investments. Assign-
ing appropriate budget shares to each potential invest-
ment option requires that the multi-attribute model
provide a ratio scale, not just an interval scale. Finally,
if one wishes to have a clear decision about whether or
not to invest—analogous to the outcome of cost–ben-
efit analysis—then benefits and costs must be measured
in the same monetary units (e.g., dollars, euros, yuan or
rupees). Since multi-criteria models do not automati-
cally convert benefit measures to monetary units, and
often only determine ordinal priority ranks, they do not
yet provide generalizable resource allocation rules.

In some settings, resource allocation does not enter
the picture. For example, an individual health care
patient choosing among insured treatment alternatives
need not consider resource costs, but rather selects
among the options provided by the patient’s health
plan. But in most real decisions—such as insurance
coverage decisions and health technology assessment—
budget constraints invariably enter the picture. In our
own recent work focused on systems analysis for pri-
oritizing new vaccine research and development, the
issue of choosing investments within a fixed budget—
or determining the level of such a budget—was not a
design factor [15, 20, 21]. But in some settings, more
guidance about resource allocation is needed.

The recent ISPOR task force assessing multi-criteria
decision analysis models discussed this challenge, sum-
marizing previous work on the topic and offering three
alternatives (none of which they deemed wholly satis-
factory), and urged further research on the issue [19].
The approaches they identified from that literature sur-
vey included the following:

Directly include costs
One approach to solving this problem directly includes
cost as an attribute in the multi-criteria analysis—lower
costs being better—with a user-assigned weight within
the model. This has the equivalent effect of asking the
decision maker (when establishing the weights) to evalu-
ate willingness to pay for the benefits. But as the ISPOR
task force report notes “stakeholders do not have the
knowledge to estimate the benefits that would have to be
forgone to fund an alternative. Instead, this would require

the forgone alternatives to be identified and evaluated
using the same” multi-criteria framework.

Score comparable interventions
The scoring approach seeks to find existing interventions
that might be eliminated to free-up funds for the new
investment, hence identifying the “opportunity cost” of
the new intervention. The scores for the candidates are
generated by the multi-criteria model providing a com-
parative view of their performances. But this approach
contains circular logic: how does one know in which pro-
grams to disinvest until all programs have been evaluated
in the same multi-criteria metric? Those interventions
that may seem available for elimination in cost-effec-
tiveness analysis may look very good in a multi-criteria
model, and vice versa. So the selection of a group of
comparable interventions is an incomplete and defective
approach. Rankings of investment priorities can read-
ily shift importantly when other criteria—such as public
fear during a disease outbreak—enter the analysis beyond
cost-effectiveness [10].

Modified cost–benefit calculations
This approach omits “cost” in the multi-criteria model,
evaluates each of the various options, and then calcu-
lates a cost–benefit ratio, similar to an incremental cost-
effectiveness ratio. The only difference is that here, the
“benefit” metric has multiple dimensions—unlike the
unidimensional QALY, DALY, or similar health benefit
measure in cost-effectiveness analysis. Multi-attribute
models create an index specific to each decision makers’
preferences, and thus such indexes are not comparable
with one another. But in this situation, unless all multi-
attribute evaluations use the same ratio scale measure-
ment, comparing cost–benefit ratios is impossible. Yet,
forcing all measurements into a single multi-attribute
framework defeats its very purpose—that is, allowing dif-
ferent stakeholders the ability to specify their own prefer-
ence functions.

In a similar fashion, one recent analysis recommends
calculating the ratio of multi-criteria value scores to cost
[22]. This approach then sorts the available choices from
the most favorable to the least favorable, and then pro-
ceeds until the investment budget is exhausted. Unfor-
tunately, this approach does not provide advice on the
proper investment budget size—it is exogenous in their
analysis. Nor does it allow for the possibility that at least
some of the possible investments are scalable, which
would introduce further investment options beyond
those originally considered. This rule is akin to calcu-
lating QALYs per cost (the inverse of the usual metric)
and investing in the most favorable until the budget

Page 88 of 98Phelps and Madhavan Cost Eff Resour Alloc 2018, 16(Suppl 1):48

is exhausted. It provides neither a cutoff rule—as has
become common in cost-effectiveness analysis—nor a
mechanism for budget setting mechanism; so while it
places organizations on the efficient frontier, it does not
identify the best part of that frontier. Economists call this
technical or “X-efficiency” but it is an incomplete meas-
ure of overall efficiency, since it ignores “allocative effi-
ciency” [23].

In real world settings, budgets do not ultimately
descend from the top: somebody has to work through
and determine the desired level of investment. Thus
something further is needed to guide such decisions. To
make further headway, we need an approach to guid-
ing either cutoff values or a mechanism for shaping an
optimal budget. The two tasks cannot be done indepen-
dently—one implies the other.

League tables
One could also construct a league table for multi-attrib-
ute models of various interventions to provide guidance,
just as early cost-effectiveness analysis used league tables
to guide resource allocation before people became com-
fortable with choosing a cutoff value. Earlier use of league
tables for cost-effectiveness contains a strong assump-
tion, namely that previous decisions about health care
interventions were made with implicit cost-effectiveness
tradeoffs in mind. But if those league tables also included
(even informally) the value of other attributes, then they
could overstate the willingness to pay for a QALY. This
approach is logically equivalent to one of the approaches
evaluated by the ISPOR task force and it contains the
same defect. One does not know ex ante which technolo-
gies appropriately belong in the league table and which
are simply out of bounds.

Willingness to pay and accept
As a proxy for resource allocation, one could also sim-
ply survey the measures of willingness to pay (WTP)
and willingness to accept (WTA), as is common, for
example, in environmental policy [24]. However, behav-
ioral scientists cast serious doubt on the validity of such
approaches, arguing that the responses may reflect atti-
tudes, but do not represent true willingness to pay [25].
A prominent concern is that of framing, where responses
depend on the way the question was posed. The distinc-
tion often hinges on WTP (as in “how much would you
be willing to pay to avoid unpleasant situation X”) versus
WTA (as in “how much would you need to be paid before
you would accept unpleasant situation X”). Answers dif-
fer greatly in these issues depending on the framing:
whether or not the object is currently owned, and is
available or not.

A review comparing numerous WTA and WTP studies
on the same economic area (e.g., health, environmental)
concluded that WTA values regularly exceed WTP val-
ues, the gap highest for non-market goods. The authors
of this summary concluded that the less the good is like
an ordinary market good—that is, it cannot be readily be
bought and sold—the higher the ratio [26]. They reported
a typical WTA/WTP ratio of 7.2 for analyses carried out
in a number of different subject areas (including health,
environmental, water resources and others). A more
recent review found a WTA/WTP ratio of 5.1 for goods
involving health and safety [27]. These results appear to
show that WTA studies—such as those involving wage
premiums for risky occupations—severely overstate the
more desirable measure of willingness to pay. If so, then
relying on these WTA measures instead of agreed-upon
cost per QALY measures would be inappropriate.

Since “health” is perhaps the quintessential non-mar-
ket good, one might expect the WTA/WTA ratios for
the value of life and life years to possibly be significantly
higher than for typical market goods. However, some
proponents of the use of labor force studies (wage dif-
ferentials for risky occupations) to measure value of life
argue that the gap is not nearly so large as this literature
suggests, if properly interpreted, thus seeking to restore
confidence in the large value of life measures found in the
health and safety literature [28].

Our approach requires that any multi-criteria decision
model contain a component of health benefits for which
there is at least some agreement regarding a proper cut-
off for cost-effectiveness analysis. Suppose that the health
benefits measure (e.g., QALY or DALY) has a weight
w, and all other attributes combined have a weight of
(1 − w). If the agreed upon cost-effectiveness cutoff
is to accept any intervention with cost per QALY (or
DALY) ≤ K, then the proper cutoff in the multi-criteria
model is K/w. K is the more binding of the announced
cost-effectiveness threshold or the implicit and more
stringent value from a tight budget.

Using QALYs (or DALYs) as a standard of value, we can
scale the total willingness to pay for the aggregate ben-
efit by using the fraction of the total benefit attributable
to QALYs (or DALYs). Our proposal therefore leverages
previous agreement about proper cost-per-QALY thresh-
old into a new threshold for the newly defined portfolio
of benefits. The value of QALY serves as the numeraire.

Suppose two decision makers have created their
respective multi-attribute models where QALYS account
for different percentages of the total value weight. In
Table  1, these two decision makers are presumed to
agree on the proper cutoff for a cost-effectiveness model

Page 89 of 98Phelps and Madhavan Cost Eff Resour Alloc 2018, 16(Suppl 1):48

at $100,000 per QALY. This generalizes to the situation
where they have different initial cutoffs, as shown in
Table 2. In both cases (Tables 1 and 2) once we know the
decision maker’s cutoffs for cost-effectiveness and their
weights assigned to health outcomes in the multi-criteria
analysis, then we can infer the proper cutoff for decision
making using multi-attribute models.

Returning to the affordability conundrum raised earlier,
the notion that one should be willing to pay more for an
item with greater value seems incontestable. However, if
a fixed budget health care system suddenly introduces an
expanded multi-criteria measure of value (and logically,
a greater willingness to pay for that expanded concept of
value) then the budget constraint will likely become more
binding, and the gap between the stated willingness to
pay and the shadow price in the budget can widen.

To see how this works, suppose that a health care sys-
tem introduced a multi-criteria model with two attrib-
utes of value—QALYs gained and the extent to which
disease burden of identified disadvantaged populations
is equitably reduced. Some interventions that might
not meet a population-wide cost-effectiveness criterion
might now have higher priority. Tuberculosis prevention
or treatment might provide a good example—low priority
in a general population but high priority in a disadvan-
taged population. Using the new measure of value would
increase the desire to fund (in our example) the tuber-
culosis-treatment program, hence further stressing the
overall budget for the health care system.

To bring things into alignment logically one of three
things must occur: (a) new resources must be added to
the budget (b) some previously funded activities must be
defunded, or (c) the threshold for accepting interventions
must tighten (or some combination of these options).

In using this approach in  situations with a fixed budget
(or until a budget can be adjusted to accommodate new
items of value), it is important to use the appropriate
threshold, which may well be more stringent than the
announced threshold, and which will change even further
as the extra value of non-QALY items is introduced into
the analysis.

One can ask under what circumstances our simple
extrapolation procedure remains valid beyond a simple
linear utility model. As a starting point, the extrapolation
of the value for QALYs to the entire multi-criteria model
remains valid whenever the decision maker’s assumed
utility function has constant budget shares (proportions
of the total budget spent on a particular good). Cobb–
Douglas utility models have this feature—the budget
shares are constant over all incomes and prices. A more
general set of utility functions—those with constant elas-
ticity of substitution—assures that the method is globally
correct while allowing incomes to change, but holding
relative prices of QALYs and the other goods constant.

In more generalized utility structures, budget shares
vary with changes in income and relative prices of
goods. In such cases, the simple proportional extrapo-
lation from the value of a QALY to the value of a more
complex multi-criteria bundle will require adopting
a specific functional form for the utility structure and
then calculating the appropriate extrapolation method.

Multi-criteria models in general are meant to help
structure problems for decision makers and to pro-
vide general guidance, not to provide precise measures
of value. There is always a tradeoff between accuracy
and simplicity, and most practitioners of multi-criteria

Table 1 Comparison of multi-criteria cut-off points for 2 hypothetical decision makers with same starting point cost-
effectiveness cut-offs

Weight on QALYs Weight on other

($/QALYs) cutoff


Decision maker A 0.5 0.5 $100K $200K

Decision maker B 0.666 0.333 $100K $150K

Table 2 Comparison of multi-criteria cut-off points for 2 hypothetical decision makers with different starting point cost-
effectiveness cut-offs

Weight on QALYs Weight on
other attributes

($/QALYs) cutoff


Decision maker A 0.5 0.5 $80K $160K

Decision maker B 0.666 0.333 $100K $150K

Page 90 of 98Phelps and Madhavan Cost Eff Resour Alloc 2018, 16(Suppl 1):48

decision analysis generally opt for simplicity when possi-
ble. Under what circumstances our extrapolation method
remains valid can and should be a topic for further

Since much of the literature on choosing a cutoff for an
acceptable cost per QALY has focused on its relationship
to income, this suggests that our extrapolation method
will be reasonably useful even if ignoring differences in
relative prices for QALYs and other goods in the multi-
criteria bundle.

We also note that even the standard model of cost-
effectiveness analysis and the associated “acceptable
technology” cutoff rules are not invariant to changes in
economic conditions. The current debate about how to
incorporate “affordability” into cost-effectiveness analy-
sis highlights this issue. If a new technology emerges
that has widespread use yet its cost-effectiveness ratio is
“acceptable” by current norms, the situation can easily
arise where the technology is both acceptable and unaf-
fordable (with a fixed budget). If the underlying economic
conditions change markedly (income, prices, or techno-
logical opportunities), then the original behavioral rules
that emerge (e.g., a cost per QALY rule) must be revised.
This is true both in a pure QALY-based model and in our
more general model that incorporates both QALYs and
other goods.

The basic idea of our approach is straightforward: If one
knows the value of part of a package of valuable items,
and one knows the proportion of overall value of the
package attributable to that particular part of the pack-
age, then one can readily deduce the overall value of the
package. In the realm of health, the most likely com-
ponents to serve this purpose appear to be QALYs or
DALYs. The benefit of using existing cutoff measures
such as cost per QALYs is simply that a considerable liter-
ature exists on determination of those values. We note—
referring to the obvious—that the difficulty in reaching
agreement about the proper cost per QALY threshold
suggests that reaching consensus about a cutoff for multi-
attribute decision models may be even more difficult.

Authors’ contributions
Both authors contributed to the conceptualization of the ideas herein, both
contributed to the writing and editing of the manuscript. Both authors read
and approved the final manuscript.

Author details
1 University of Rochester, Rochester, NY, USA. 2 National Academies of Science,
Engineering and Medicine, Washington, DC, USA.

The views expressed in this article are those of the authors and not necessarily
of the National Academies of Sciences, Engineering, and Medicine. This work
was supported in part by grant to Charles Phelps from the National Cancer
Institute of the National Institutes of Health, U01CA183081.

Competing interests
The authors declare that they have no competing interests.

Availability of data and materials
Not applicable.

Consent for publication
Not applicable.

Ethics approval and consent to participate
Not applicable.

Publication funding
The publication costs for this article were funded by Mark O’Friel, the Brinson
Foundation, and the Payne Family Foundation.

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