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The Role ofPatient Reported
Outcomes Measures (PROMS)
andHealth-Related Quality-of-Life
(HRQoL) inEconomic Analysis
WilfredIfeanyiUmeojiako, AhmerMansuri,
Katherine-HelenHurndall, andChristopherRao
6
An Introduction toEconomic
Evaluation
Rising healthcare cost and nite resources have
opened a debate on what can and should be
funded by health systems in the developed world.
Healthcare innovations such as new drugs,
devices, or screening and diagnostic tests must
demonstrate clinical efcacy and safety before
being approved for use in clinical practice.
However, questions then remain about whether
they represent “value-for-money” due to the
additional resources required to provide these
services [1]. In a climate of static investment in
W. I. Umeojiako
Lewisham and Greenwich NHS Trust, University
Hospital Lewisham, London, UK
e-mail: wilfred.umeojiako@nhs.net
A. Mansuri
Barking, Havering and Redbridge University
Hospitals NHS Trust, Queen’s Hospital,
Romford, UK
e-mail: a.mansuri@nhs.net
K.-H. Hurndall
Royal Free London NHS Foundation Trust, The
Royal Free Hospital, London, UK
e-mail: katherine-helen.hurndall1@nhs.net
C. Rao (*)
North Cumbria Integrated Care NHS Foundation
Trust, The Cumberland Inrmary,
Carlisle, Cumbria, UK
Imperial College London, London, UK
e-mail: christopher.rao@imperial.ac.uk
healthcare the provision of new services will necessarily displace existing services from a system
[2]. This has resulted in an increasing acceptance
of the importance of economic evaluation of
healthcare interventions [3, 4].
Economic Analysis, Economic Evaluation,
Technology Appraisal and Cost-Effectiveness
analysis are commonly used synonyms in the literature for the process of simultaneously evaluating the costs and benets of an intervention.
Historically benets of a healthcare intervention
have been evaluated in several ways [4].
Types ofEconomic Analysis
Cost-minimisation analysis assumes that alternative interventions are equally effective and interventions are compared simply on the basis of cost
[5]. The term cost-effectiveness analysis is confusingly also commonly applied to a subset of
economic analysis in which the effect can be a
clinical parameter such as, “cost per episode free
day” [6], or “cost per case detected” for diagnostic tests [7]. This allows comparison of alternative interventions for the same disease within the
same eld, however, it is not possible to compare
the cost-effectiveness of interventions for different diseases [8]. In cost-benet analysis both the
costs and effects are expressed in monetary units
[8] and therefore facilitates comparison of health
care interventions with other public spending, for
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
T. Athanasiou et al. (eds.), Patient Reported Outcomes and Quality of Life in Surgery,
https://doi.org/10.1007/978-3-031-27597-5_6
77
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W. I. Umeojiako et al.
educational or infrastructure projects [9]. Finally,
Cost-utility analysis uses quality adjusted measures of effect for example, Health-Adjusted Life
Years (HALY), Disability-Adjusted Life Years
(DALY), and most commonly in the healtheconomic literature, Quality-Adjusted Life Years
(QALY) [9]. Cost-utility analysis allows consideration of both mortality and morbidity from all
causes when evaluating the effectiveness of an
intervention and facilitates comparison of costeffectiveness between healthcare disciplines [8,
9]. Unlike cost-benet analysis, it avoids the ethi-
cal and practical problems associated with valuing morbidity and mortality explicitly in
monetary terms [8–10]. As the QALY is the most
commonly used measure of effectiveness in the
health-economic literature, and its use recommended by many bodies responsible for technology evaluation such as the National Institute for
Health and Care Excellence (NICE) the remainder of this chapter will focus on the QALY.
The Quality-Adjusted Life-Year
The QALY had been widely used since the 1970s
and accounts for the effect an intervention on
either the length or quality of life by multiplying
the change in Health-Related Quality of Life
(HRQoL) (quantied using a utility score) by the
change in the length of life [1, 10]. Utility can be
thought of as a method of quantifying the strength
of an individual’s preference for a health state or
outcome. Conventionally a utility of 1 is deemed
to be equivalent to perfect health and 0 is deemed
to be equivalent to death.
It has been argued that QALY do not reect
societal preferences. For example, implicit in
cost-effectiveness analysis is the assumption that
QALY are equally valuable no matter at what age
and to whom they are assigned. Whilst this may
appear egalitarian, society may prefer to assign
QALY to a patient who is very ill rather than to a
patient who is comparatively well; or to a patient
who has been ill most of their life, rather than to
a patient who has been well most of their life [8].
However most health-economists feel that QALY
represents a close enough approximation of indi-
vidual and societal preference to justify their use
[11], and in the absence of functional robust
alternatives they are favoured by bodies engaged
in technology appraisal and widely used in costeffectiveness analysis [4, 12].
The necessity to calculate QALY in costutility analysis and therefore to quantify HRQoL
has resulted in the development of several methods to measure and instruments that have been
designed to measure and describe global HRQoL.
Empirical Methods toMeasure
Quality-of-Life Directly
HRQoL can be measured empirically using several different methods. All methods are grounded
in economic decision theory. Consequently, their
validity and the validity of the resulting QALY
are dependent on several assumptions. Firstly,
that individuals will behave rationally to maximise their personal satisfaction or
HRQoL.Secondly, that they are willing to trade
years of life in each health state for fewer years of
life in a better health state. Finally, it is assumed
that individuals are risk neutral [4]. Several
authors suggest that these assumptions may not
be valid in clinical practice. Studies have suggested that not only is there considerable variation between participants in their attitude to risk,
but the same participants often have different attitudes to risk in different circumstances or even in
the same circumstance when questioned differently. In the absence of robust, validated alternatives, however, the following methods continue
to be used [13].
The standard gamble (SG) is routed in von
Neumann-Morgenstern utility theory, and
requires an individual to choose between remaining in their current health state or undergoing a
medical intervention with a dened probability
of either returning them to perfect health or killing them [14]. The time trade-off (TTO) approach
was originally conceived as a pragmatic and
more intuitive way of replicating utility estimates
generated using the SG, however often yields different estimates of HRQoL to the standard gamble. It requires an individual to determine what
6 The Role of Patient Reported Outcomes Measures (PROMS) and Health-Related Quality-of-Life (HRQoL…
79
proportion of their life they would be willing to
forego in order to return to perfect health [15].
Neither the SG or TTO method are intuitive to
patients, they are time-consuming, and can only
really be used in clinical practice with the assistance of a trained researcher [3, 16, 17]. Visual
analogue scales (VAS) require respondents to
value a state of health on a visual scale between a
point representing death and a point representing
perfect health [18]. Whilst VAS are unquestionably intuitive their theoretical basis is questioned
by many health economists [3].
Generic Patient-Reported Outcome
Measures (PROMS) toMeasure
Quality-of-Life
As a consequence of the difcultly in applying
empirical methods for measuring HRQoL in clinical practice a number of Patient-Reported
Outcome Measures (PROMS) have been developed to quantify the impact of disease states on
HRQoL that are both more descriptive and intuitive [16, 17]. These instruments can be thought of
as being either generic to all health states or
disease- specic (Fig.6.1 and Table6.1) [45].
The EQ-5D
The EQ-5D instrument is one of the most com-
generic PROMS has a basis in empirical methods
for measuring HRQoL such as the TTO [19, 20].
It was developed by the multidisciplinary
EuroQol group and designed to be so intuitive it
could be sent out as a postal questionnaire. The
originally EQ-5D-3L had ve attributes: mobility, self-care, usual activity, pain/discomfort, and
anxiety/depression. Each of these attributes has
three levels namely: no problem, some problem,
and major problem [20]. Econometric modelling
based on population-based TTO valuations was
used to generate a summary utility value. An
updated instrument, the EQ-5D-5L has been
developed, with ve rather than three levels, to
improve the ability to discriminate between
smaller changes in HRQoL in reduced sample
sizes [21]. Mapping tools exist to map results
obtained using the older EQ-5D-3L to the newer
EQ-5D-5L [24]. The EQ-5D-3L, however,
remains the preferred tool for National Institute
for Health and Care Excellance (NICE)
Technology Evaluation [12, 46].
Fig. 6.1 Whilst disease specic instruments to measure
health-related quality of life (HRQoL) are more sensitive
and will detect small differences in HRQoL, they are less
generalisable than organ specic or generic instruments to
measure HRQoL.Mapping tools can be used to translate
HRQoL measurements made using disease specic and
organ specic HRQoL instruments to generic scales to
facilitate comparison with other health-care interventions
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W. I. Umeojiako et al.
Table 6.1 A Summary of all methods to derive HRQoL
(utility) values for use in economic evaluation of cardiovascular technology and practice
Method/instrument Mapping tools
Empirical methods
Standard Gamble (SG)
[14]
Time Trade-Off (TTO)
[15]
Visual Analogue Scales
(VAS) [18]
Generic instruments
EuroQol Instruments
(EQ-5D)
EQ-5D-3L [19, 20]
EQ-5D-5L [21]
Short Form Questionnaire
(SF)
SF-6D to summarise
SF-36 [25, 26]
SF-6D to summarise
SF-12 [26, 27]
The Health Utilities Index
[32]
Version 2 (HUI-2)
Version 3 (HUI-3)
The Quality of WellBeing Scale (QWB) [34]
Assessment of Quality of
Life [35]
6 dimensions
(AQoL-6D)
8 dimensions
(AQoL-8D)
15D instrument [36] 15D to EQ-5D [22, 23]
Examples of disease specic instruments
The Seattle angina
questionnaire (SAQ) [37]
The Minnesota living with
heart failure questionnaire
(MLWHF) [40]
MacNew Heart Disease
Quality of Life
Questionnaire [43]
EQ-5D to HUI-3 [22],
QWB [22], AQoL-8D
[22], 15D [22] and
SF-6D [23]
EQ-5D-3L to EQ-5D-5L
[24]
SF-36 or SF-12
descriptor systems to
EQ-5D [28–31]
HUI-2 to EQ-5D [33]
HUI-3 to EQ-5D [22, 23]
QWB to EQ-5D [22]
AQoL-8D to EQ-5D [22]
SAQ to EQ-5D [38, 39]
MLWHF to EQ-5D [41,
42]
MacNew to EQ-5D,
SF-6D, HUI-3, QWB,
15D and AQoL-8D [44]
vitality, physical functioning, bodily pain, general health perceptions, physical role functioning,
emotional role functioning, social role functioning, and mental health. The scores are scaled and
are the weighted sums of the questions in their
section. Each score is transformed into a 0 (maximum disability) to 100 (no disability) scale [47].
Several studies have suggested that the SF-36
compares favourably with other descriptive tools
such as the Nottingham Health Prole [49–51].
The weakness of the SF-36 and SF-12 instruments were that, unlike the EQ-5D, they do provide a summary value that can be used to calculate
quality-adjusted life years (QALYS) and therefore are of more limited utility in economic analysis and technology evaluation. To overcome this
problem several mapping tools were developed
to obtain EQ-5D utility estimates from the SF-36
or SF-12 descriptor systems [28–31] it has been
suggested that whilst these mapping tools generally perform well there is a tendency to overpredict very severe health states [52]. To overcome
problems that are associated with mapping tools
the SF-6D instrument was developed to summarise SF-36 [25, 26] and SF-12 [26, 27]
responses based on a population preferencebased valuation of health states using the SG, to
facilitate their use in economic analysis.
Other Generic Instruments
The Health Utilities Index version 2 (HUI-2) and
version 3 (HUI-3), like the EQ-5D based on population based TTO valuations of health states
have been widely used in the literature [32]. The
Quality of Well-Being Scale (QWB) [34],
AQoL-8D [35], and 15D instrument [36] are
other less commonly used generic instruments.
The SF-36
Disease-Specic Patient-Reported
Outcome Measures (PROMS)
The SF-36 [47] and its abbreviated form, the
SF-12 [48] are widely used descriptive PROMS,
which perform much better than the EQ-5D as a
qualitative tool to describe global patient
HRQoL. The SF-36 consists of eight sections:
toMeasure Quality-of-Life
Disease-specic tools are designed to assess the
HRQoL of individuals with specic diseases
such as myocardial infarction, atrial brillation,
6 The Role of Patient Reported Outcomes Measures (PROMS) and Health-Related Quality-of-Life (HRQoL…
81
and heart failure. Compared with other types of
assessment tools, these measures provide a more
detailed assessment for specic diseases and are
also likely to be more sensitive to specic
treatment- related changes in HRQoL. They are
designed to be more sensitive to the ways patients
and clinicians perceive the effects of the diseases
on functioning and well-being (Fig. 6.1). For
example, in patients with coronary artery disease
and heart failure treated with revascularisation
and medical treatments, improvement in HRQoL
was detected using disease-specic instruments
but not with the generic instruments. Importantly,
however, the focused nature of these instruments
means that they cannot be compared with other
instruments from other cohorts [53]. To overcome this problem several mapping tools have
been developed to map the results of commonly
used disease specic instruments to generic indices of HRQoL (Table6.1) [41]. Whilst this may
facilitate economic evaluation of technology
where a generic preference-based instrument has
not been used, mapping functions may be subject
to error and it is therefore preferential to collect
generic assessments of HRQoL directly [54].
Examples ofDisease Specic PROMS
Used inEconomic Analysis
The Seattle angina questionnaire (SAQ) is a
19-item instrument covering physical limitations,
angina stability, angina frequency, treatment satisfaction and QOL/disease perception [37]. It has
been used in several randomised controlled trials
such as COURAGE [55], SYNTAX [56], and
FREEDOM [57]. Mapping tools have been
developed to map the results from the SAQ to the
EQ-5D index [38, 39].
The Minnesota living with heart failure
(MLWHF) [40], has been widely used in randomised controlled trials including CARE-HF
[58], MERIT-HF [59], BEST [60], CIBIS II [61],
CHARM [62], REMATCH [63] and MIRACLE
[64]. The MLWHF instrument is a 21-item questionnaire covering heart failure symptoms, physical functioning, sleep, role function, sex,
recreation, appetite, psychological/emotional,
adverse effects of medication, hospitalisation,
and medical cost [40]. Results from the MLWHF
have been mapped to the EQ-5D including during an economic analysis associated with the
CARE-HF trial [41, 42].
Finally, the MacNew Heart Disease Quality of
Life Questionnaire is a validated modication of
the quality of life after myocardial infarction
(QLMI) questionnaire suitable for selfadministration by patients [43]. Whilst it has not
been used in randomised controlled trials [53]
mapping tools are available to map results from
the MacNew into the EQ-5D, SF-6D, HUI-3,
QWB, 15D and AQoL-8D instruments [44].
Discussion
In the developed world, aging populations,
increasing expectations of healthcare and the cost
of modern medical practice stretch the nite
resources available for healthcare. Consequently,
there is considerable pressure to apply economic
evaluation to rationalise health resources allocation. Furthermore, the potential population health
benets of resource optimisation arguably place
an ethical responsibility on clinicians and
researchers to consider these issues [4]. Economic
evaluation, in particularly cost-utility analyses,
are now frequently published in the literature [42,
65–67]. Estimates of HRQoL, informing cost-
utility analysis, are a central tenant of NICE
guidance in numerous areas of medicine such as
coronary revascularisation, diagnostic tests for
ischaemic heart disease, and the management of
heart failure [68–70]. The measurement of
HRQoL, practically always using either generic
or disease-specic PROMS (Table 6.1 and
Fig.6.1), will remain central to the evaluation of
emerging technology and resource allocation as
long as cost-utility analysis retains its central
position.
The choice of instrument is important. Generic
instruments facilitate comparison of the impact
of an intervention on HRQoL between conditions
and disease areas, they are however less likely
than disease-specic instruments to detect
changes in HRQoL, particularly in small
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W. I. Umeojiako et al.
population samples [53]. It is therefore important
when the expected changes to HRQoL are uncertain or unknown to perform pilot studies prior to
any measurement of HRQoL. This also may
facilitate the assessment of the tolerability and
compliance of patients to PROMS instruments
which can be problematic in some patient populations [71]. Whilst symptom and disease- specic
PROMS instruments do not facilitate comparison
between disease areas, mapping tools are available to facilitate conversion of measured HRQoL
with these instruments to generic scales. This
may facilitate comparison of the efcacy of interventions between disease areas, technology
appraisal and economic analysis. There are however, often practical and theoretical problems
with the application of mapping tools and consequently they should not be used in preference to
generic instruments when available [45, 54].
When published estimates of HRQoL are utilised
for economic modelling in addition to ensuring
that the most appropriate instrument has been utilised it is also essential to ensure the relevance to
the population, disease and intervention of
interest.
In conclusion the measurement of HRQoL
using PROMS is now a central tenant of economic evaluation. It remains critically important
however to understand how estimates of qualityof- life are derived; to select appropriate estimates, measured in appropriate populations, with
appropriate instruments; and to understand the
strengths and limitations of each instrument.
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Quality ofLife Following Bariatric
andMetabolic Surgery
AlanAskari, ChanpreetArhi,
andRavikrishnaMamidanna
7
Introduction
Over the last three decades, obesity has become a
global health pandemic, the worrying trajectory
of which is set to worsen. The World Health
Organisation (WHO) estimates around 2.8 million deaths annually worldwide as a result of
being overweight or obese [1]. More recently this
disease which was once thought to be affecting
the rich and developed nations has made its presence worldwide. It has been predicted that more
than half of the world population will be obese by
the year 2030 [2]. In response, ever increasing
numbers of patients are undergoing bariatric and
metabolic surgery. Bariatric and Metabolic
Surgery (BMS) has been shown to be effective in
achieving and maintaining weight loss and potentially reversing some of the comorbidities associated with obesity and metabolic syndrome [3].
BMS has been shown in various large studies to
be responsible for sustained long term weight
loss as well as a decrease in overall mortality as
A. Askari
Bedfordshire Hospitals NHS Foundation Trust,
Luton, UK
e-mail: alan.askari@nhs.net
C. Arhi
West Hertfordshire Hospitals NHS Trust, Watford, UK
e-mail: c.arhi@nhs.net
R. Mamidanna (*)
Lewisham and Greenwich NHS Trust, London, UK
e-mail: r.mamidanna@nhs.net
compared to obese population that has not had
this intervention [4, 5].
In particular, BMS is more effective than lifestyle interventions in reducing the risk of cardiovascular morbidity such as myocardial infarction
and kidney disease in diabetic patients [6–8].
There is evidence to suggest a decreased risk of
hormone related cancers such as breast, endometrium and prostate following BMS in obese
patients [9]. BMS is also considered an effective
treatment for Type II Diabetes Mellitus,
Hypertension and Obstructive Sleep Apnoea in
patients who are overweight [10, 11].
This has also resulted in vast amounts of medical literature exploring the Quality of Life (QoL)
amongst patients who have undergone bariatric
surgery. The tools used to assess QoL are perhaps
as numerous as the range of issues investigated
although in recent years, there has been a growing effort to use standardised QoL scoring checklists and questionnaires in the hope of obtaining
meaningful comparisons between studies.
Certainly, obesity comes with its own QoL
impacts whether the patient is pre- or
post-surgery.
Quality ofLife (QoL)
The WHO denes health as “a state of complete
physical, mental and social well-being, and not
merely the absence of disease and inrmity”.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
T. Athanasiou et al. (eds.), Patient Reported Outcomes and Quality of Life in Surgery,
https://doi.org/10.1007/978-3-031-27597-5_7
85
86
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A. Askari et al.
QoL has been dened as “an overall general
well-being that comprises objective descriptors
and subjective evaluations of physical, material,
social, and emotional well-being together with
the extent of personal development and purposeful activity, all weighted by a personal set of values” [12]. From a patient’s perspective it is
essentially a sensation of well-being and a
judgement of satisfaction with life. Numerous
denitions and interpretations of QoL are available in the literature. This has led to the development of various scales that can objectively
measure a patient’s perception of their current
health. Health Related Quality of Life (HRQoL)
examines wellbeing in various domains such as
physical, mental and social health [12]. HRQoL
has been dened as “those aspects of selfperceived well- being that are related to or
affected by the presence of disease or treatment”
[13]. In order to be able to meaningfully compare
HRQoL between two or more groups of patients
various questionnaires have been developed [14,
15]. Such tools aim at assessing a combination of
aspects such as physical and social functioning,
pain, mental wellbeing and ability for self-care.
One must acknowledge however that HRQoL
should ideally be assessed from the patient’s
point of view. This means that the values can
uctuate over time and that there are differences
in how it is perceived and hence reported by people of various ages and cultural backgrounds. The
terms QoL, health status and HRQoL have been
used interchangeably in literature and various
efforts have been made to underpin the subtle differences in these denitions [12].
HRQoL After surgery
It has been proposed that HRQoL should be the
metric of choice in a clinical setting as it not only
focusses on health, but also on disease [16, 17].
Traditionally the success of a surgical procedure
was based on the complications and survival following surgery. With the advances in treatment
modalities and surgical techniques the complica-
tions have reduced and survival has improved
over time. Herein comes the importance of understanding the patients’ perception of success following an operation which is well captured by
HRQoL measures [17]. However, there are also
critics who are sceptical about the use of such
measures at their face value. It is true that there
are limitations in being able to accurately gauge
QoL due to the subjectivity and perception or
interpretation of health and wellbeing between
different patient cohorts. Hence one must be very
careful while comparing HRQoL outcomes
depending on the disease process that has necessitated surgery, for example curative versus palliative surgery for cancer.
HRQoL After Bariatric andMetabolic
Surgery
The effectiveness of BMS in reduction or remission of obesity related complications and overall
mortality in obese population has been adequately shown [3–5]. In addition to the resolution
of metabolic syndrome, BMS also aims at providing a signicant improvement in overall QoL
of the patients. This is expected to continue many
years following surgery. Certainly, patients themselves would measure the success of BMS based
on their own evaluation of the difference in QoL
pre and postoperatively [18, 19]. On both the
physical and mental fronts, studies have shown
that obese patients who underwent BMS had an
improvement in QoL and this was sustained for
many years after surgery [20, 21].
However, it remains somewhat unclear as to
whether different approaches of surgery result in
different levels of satisfaction and QoL.The main
aim of this chapter is to summarise the existing
literature with regards to differences in QoL
(both physical and mental) following bariatric
surgery. The secondary objectives will be to compare different surgical operations and endoscopic
procedures to determine whether there are differences in QoL depending on the procedure
undertaken.
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