The process of healthcare is undertaken so that people can benefit from the intervention. An economic evaluation looks at all the implications of deciding to choose one way of providing care over another, not just the costs. This means that any effect the service, good or bad, has on the patient or customer needs to be investigated

1. 1
Pharmacoeconomics Lectures 3+4
Measuring patient outcomes for use in economic evaluations
Introduction
The process of healthcare is undertaken so that people can benefit from the intervention.
An economic evaluation looks at all the implications of deciding to choose one way of
providing care over another, not just the costs. This means that any effect the service,
good or bad, has on the patient or customer needs to be investigated (1)
.
Benefits, outcomes and consequences refer to the effect on the patient, not the effect
on people providing the service. Cost is not an outcome measure.
In order to assess the benefit of healthcare, correct identification and measurement of
patient outcomes is essential in health economics. This lecture defines and describes the
main categories of outcome measure used: effectiveness, quality of life, utility, and
expressing benefits as monetary values, and appraises the importance of using the
appropriate outcome measure (1)
.
1-Effectiveness
Effectiveness is the outcome of an intervention or service measured in natural units.
These can be general outcome measures, such as:
-Cases successfully diagnosed.
-Cases successfully treated.
-Life years saved.
It is also possible to use clinical indicators, such as:
-Number of asthma attacks avoided
-Pain-free days
-Change in infection rate
-Percentage reduction in blood pressure
-Effect on nausea and vomiting frequency.
These measures are relatively simple to use and are often reported in clinical trials of
interventions. They are therefore the most common type of outcome measure and the
most frequently used in economic evaluation. They are sometimes called intermediate
outcome measures because there is the implication that changes in them will extrapolate
to an effect on the patient's ultimate health status. For example, a study of diet-based
lipid-lowering therapy in the prevention of coronary heart disease could use the drop in
plasma cholesterol level to assess the effectiveness of this intervention. It is assumed that

2. 2
by reducing a person's cholesterol level, the risk of developing coronary heart disease is
reduced (1)
.
Quality of effectiveness information
Effectiveness information is generally available from the medical and health services
research literatures. The quality of that information is critical; poor information means
that any economic analysis using that information will be of poor quality also (1)
.
Bias and randomized controlled trials
Randomized controlled trials (RCTs) are considered to be the highest grade of evidence.
Proper randomization of patients to control and treatment groups in clinical trials is
essential to minimize bias. Randomization is what enables the trial to detect the efficacy
of the intervention while controlling for variations in patient factors like age, sex or
health status (1)
.
Effectiveness versus efficacy
Efficacy is the consequence (benefit) of a treatment under ideal and controlled
conditions (ideal trial study). However, real life does not behave like an ideal trial study.
In practice, different types of patients from those in the trial may receive the intervention
(1)
.
Effectiveness is the therapeutic consequence of a treatment in real-world conditions.
The effectiveness of a treatment or service is often lower than its efficacy, and so using
information from an ideal clinical trial may overestimate the impact of the intervention (1)
Limitations of effectiveness measures
A problem with effectiveness measures is that:
1-There may be more than one outcome reported for a particular treatment or
service. For example, an analgesic may be more effective than another at relieving pain,
but cause more nausea (1)
.
So, effectiveness measures are limited because they only measure one part of an
outcome and may not reflect the overall impact of the intervention on the patient's
health-related quality of life (HRQoL) (1)
.
Also, we do not know whether the treatments were associated with unwanted side
effects. There may be side effects that are sufficiently bad to affect the patient's health
status, or so bad that they refuse to have the treatment at all.
2-Many effectiveness measures are disease specific, such as lung function, and so
cannot be used to compare outcomes for different disease states. For example, it would
not be possible to use lung function to assess the outcome of an operation for a ruptured
Achilles tendon (1)
.

3. 3
2-Mortality used as an effectiveness measure
Mortality has been used to measure the effectiveness of treatments in patients. Examples
of this are the studies looking at the use of aspirin after myocardial infarction, lipid-
lowering agents in coronary heart disease, and the treatment of hypertension in
patients with diabetes. Mortality is a useful outcome measure because it is objective and
easy to measure. However, there are problems associated with it.
1- People may die from causes other than that of interest to the study, which can
mask mortality linked to the intervention of interest and thus confound the results.
2- Most illnesses affect quality of life rather than mortality, and so quality of life
improvements due to interventions will not be detected or included in the economic
evaluation.
3- Mortality requires a study with many patients followed up over a long period of
time.
4- People of different ages and sex have different risks of mortality, so it is important
that patient groups have similar age and sex profiles if they are to be compared (1)
.
Example of mortality used as an effectiveness measure
Table 4.2 reports the results from a trial that examined the mortality of men aged 45-54
with pre-existing coronary heart disease, and assessed the impact of lipid-lowering
drugs on that mortality. This table shows that using these drugs reduces mortality in
this patient group. If 1000 men were treated for 10 years, three deaths would be
prevented (1)
.
Everybody dies eventually, so it is actually premature mortality we are trying to reduce,
such that individuals are able to live an acceptable lifespan. Therefore, if a premature
death is prevented — for example by lipid-lowering drugs - we have saved years of life
for an individual. Therefore, mortality can be converted into life-years saved or life-
years gained (1)
.

4. 4
Worked example 4.1
A theoretical worked example of using effectiveness measures.
Patients with chronic renal failure who are on haemodialysis suffer from profound
anaemia, which is often extremely debilitating. This is due to a reduction in their
production of erythropoietin and loss of blood during haemodialysis. Historically, these
patients have been managed by the use of blood transfusions. Now, synthetic
erythropoietin is available. It is considered to be highly effective, but is very expensive.
So, the alternatives are either to give erythropoietin or to give blood transfusions when
the patient's haemoglobin level is below 8g/dl.
Effectiveness data for the two alternatives available from the literature suggest that
erythropoietin can maintain haemoglobin levels above 8g/dl for 91 % of the year,
whereas blood transfusions maintain levels for 76% of the year (1)
.
1. What is the implicit assumption being made by the use of this outcome measure?
The assumption is that this is a desirable outcome because the reversal of anaemia will
increase the patient's energy levels and hence their quality of life.
2-What is the difference in effectiveness of the two alternatives, for 1000 patients?
Erythropoietin keeps the Hb level >8g/dl for 15% more of the year than do blood
transfusions = 54.75 days per patient per year = 54 750 days per 1 000 patients per year
(1)
.
3-Quality of life
Most modern medicine improves quality rather than quantity of life (1)
. The limitations
associated with effectiveness measure led researchers to develop ways of measuring the
whole impact of a disease or treatment on a patient (1)
.
Definitions: it may be helpful to distinguish between the terms quality of life (QoL) and
health-related quality of life (HRQoL). The first term, QoL, is a broad concept with many
aspects that measures people's overall perception of their lives. QoL includes both health-
related and non-health-related aspects of their lives (e.g., economical, political, cultural).
HRQoL is the part of a person's overall QoL that "represents the functional effect of an
illness and its therapy upon a patient, as perceived by the patient."
In health-related research and articles, the term QoL is often used interchangeably with
the term HRQoL, and both might be used to indicate the narrower definition pertaining to
a person's health (2)
.
Importance: The use of HRQoL measures has been increasing since the mid-1980s.
Traditionally, health has been considered from a biomedical point of view. A broad
definition of health proposed by the World Health Organization more than 50 years ago
is: "Health is a state of complete physical, mental and social well-being and not
merely the absence of disease or infirmity." (2)
.

5. 5
Measuring QoL
There are many functional, social, psychological, cognitive and subjective factors that
affect QoL.
QoL measures can be divided into generic and disease specific.
A-Disease-specific measures have been developed for patients with
chronic diseases; such measures can only be used to assess patients or treatments
within those disease states (1)
. In this case, condition- or disease-specific measures are
often used to collect more narrowly focused patient views on the impact of the disease.
Examples of specific areas investigated with disease-specific questionnaires include
nausea and vomiting for cancer treatment, and range of movement for arthritis
treatment. Examples of disease-specific health status instruments are found in Table 8.4,
and some questions from Asthma Quality of Life Questionnaire are listed in Table 8.5.
The Quality of Life Instruments Database website (http://www.qolid.org) summarizes
many of the health status instruments.
Table 8.5. Examples of items from the asthma quality of life questionnaire (AQLQ)
‫لالطالع‬

6. 6
B-Generic measures
Are more useful when looking at groups of patients who may have different illnesses,
and can be used to compare outcomes in different patient groups. One of the most widely
used is the Short Form (SF)-36 health survey. This looks at:
1- Physical functioning, physical role, body pain, general health
2- Vitality, social functioning, emotional role, mental health.
Typical questions asked include: Does your health limit you in these activities?
Table 8.3. Examples of items from the SF-36 general HRQOL instrument ‫لالطالع‬
Using these types of tool can give a much better indication of the impact of the treatment
or service on the patient's QoL than using effectiveness measures.
In the example of the study of lipid-lowering drugs in men aged 45-54 with pre-existing
heart disease, a tool such as the SF-36 health survey could assess the effect of reducing
the incidence of coronary heart disease on the QoL of the patients who have the treatment
(1)
.

7. 7
4-Utility
(Utility is a measure of the relative preference for various options (2)
. Utility is the value
attached by an individual to a specific level of health or a specific health outcome.
Different individuals may attach different values to the same health state. For example,
some people may be prepared to tolerate a lot of nausea to allow them to be pain free.
Others may prefer to tolerate more pain and reduce the level of nausea. The important
concept here is that utility measurement allows patients to value their health status
based on their own preferences (1)
.
Like generic QoL measures, utility can be used when looking at groups of patients who
may have different illnesses, and can be used to compare outcomes in different
patient groups. Utility measures go beyond generic quality of life measures because
they enable quantitative comparison. Simply, utility is used to "attach a numerical
value to the value a person has for a particular health state.
Imagine that treatment A improves a group of patients' health by an average of 6 points
on a utility scale, and that treatment B improves a group of patients' health by an average
of 3 points. Treatment A can be said to be twice as effective as treatment B. However,
treatment A might be surgery for a ruptured Achilles tendon and treatment B might be
rhDNase for cystic fibrosis. This example shows that utility can be used to compare
outcomes for very different treatments in very different patient groups (1)
.
Attaching values to health states can be carried out using standard gamble, time trade-
off methods, or a rating scale. The last is rarely used (1)
.
A- Rating scale
To understand these methods it is necessary to be familiar with visual analogue scales
(VAS). Figure 4.1 illustrates a typical VAS.
1- Mark on the scale where you think indicates how you feel now.
2- Mark on the VAS where you would value your health state if you had pneumonia.
The difference between (1) and (2) is the difference in your health state, as valued by
you. So, if you had pneumonia, and you were given some antibiotics to cure it, that
difference in health state would be the health gain obtained by the drugs (1)
.

8. 8
B-Standard gamble
The standard gamble is considered by some health economists to be the gold standard
for utility valuation. In this approach, an individual is asked to choose between the
following:
1-The certainty of surviving for a fixed period in a defined health state (1)
(Choice A—livings in health state i (a chronic health state between perfect health and
death) with certainty) (3)
.
2- Choice B: A gamble between a probability (p) of surviving for the same period
without disability or a probability (1 — p) of immediate death (1, 3)
.
The probability (p) is varied until the person shows no preference (is indifferent)
between the certain option and the gamble (1)
.
As an example, a person considers two options: a kidney transplant with 20% probability
of dying (80% chance of returning to normal health) during the operation (alternative B)
or certain dialysis for the rest of his or her life (alternative A) (2)
.
If the person says he or she would have the operation if the chance of the successful
operation p is 80% (chance of immediate death 20%), the percent chance of success is
decreased until the person reaches his or her point of indifference (the point where the
two options are nearly equal and the person cannot decide between the two).
If the person says he or she would not have the operation if the percent chance for
success was 80% (chance of dying, 20%), the percent chance of success is increased until
the person reaches his or her point of indifference.
Let us say that the first person chooses a 70% chance (p) of a successful operation (with
a 30% chance [1-p] of immediate death) as the point of indifference between having a
kidney transplant and living with kidney dialysis for life.
The utility score for this person for this disease state or condition (kidney dialysis)
would be calculated as the probability (p) of living a normal life after the operation,
or 0.7 (2)
.

9. 9
The limitations of the standard gamble are that it is time-consuming, that people have
difficulty understanding probabilities, and that how people value health states can be
influenced by how the questions are phrased or presented (1)
.
A variety of other problems with the gamble have become apparent. For example,
treatment of most chronic diseases does not approximate the gamble. There is no
known product that will cure a patient with arthritis nor one that is likely to kill him. In
other words, the decision-making experience of the patient is not likely to include an
option that has a realistic gamble (3)
.
C-Time trade-off (TTO)
The third technique for measuring health preferences or utilities is the TTO method
(Figure 6.3). Again, the subject is offered two alternatives.
Alternative 1 is a certain disease state for a specific length of time (t) the life expectancy
for a person with the disease, and then death.
Alternative 2 is being healthy for time x, which is less than t. Time x is varied until the
respondent is indifferent between the two alternatives. The utility score for the health
state is calculated as x divided by t.
For example, a person with a life expectancy of 40 years is given two options:
Alternative 1 is being blind for 40 years, and alternative 2 is being healthy (including
being able to see) for 25 years followed by death. If the person says he or she would
rather be blind for 40 years than sighted for 25 years, the number of years (x) of sight
(healthy state) is increased until the person is indifferent between the two alternatives. If
the person would rather be sighted for 25 years than blind for 40 years, the number of
years (x) of sight is decreased until the person is indifferent between the two alternatives.
Let us say that for a person who expects to live 40 more years, the person's point of
indifference is 30 years of sight versus 40 years of being blind. The utility score would be
x/t = 30/40 or 0.75 (2)
.

10. 10
Figure 6.3. Time tradeoff (TTO). This TTO schematic represents the choice a respondent makes
about trading off years of life for better health for a shorter period of time. The respondent is
given the choice of living a full life (to time t) with a specific condition or living fewer years (to
time x) without this condition (being healthy). The time of living healthy is varied until the respondent is
indifferent between living in full health x years and living with the condition for t years. The utility
calculated for the condition is x/t
References:
1-Rachel Elliot and Katherine Payne .Essential of economic evaluation in healthcare. 2005
pharmaceutical press.
2- Karen L.Rrascati. Eessentials of Pharmacoeconomics. copyright 2009 Lippincott Williams and
Wilkins.
3- Bootman JL, Townsend RJ, McGhan WF, (Eds.), Principles of Pharmacoeconomics, Harvey
Whitney Books Company, Cincinnati.