A. Introduction to

Health Economics

Dr Alan Haycox

Reader in Health Economics

Health Economics Unit

University of Liverpool Management School

Introduction to Health Economics –

Programme The programme will be broken down into

four sections:

1. Introduction to health economics

2. Economics modelling: Theory & Practice

3. Value of new drugs including new cancer

drugs: Scottish Medicines Consortia (SMC)

Scotland

4. Value of new drugs including new cancer

drugs: NICE (England)

Methods of economic evaluation

and other techniques The four types of health economic

evaluation are:

◦ CMA

◦ CEA

◦ CUA

◦ CBA

We will also cover measuring health related

quality of life as well as economic modelling

The Four Methods of economic

evaluation Cost Minimisation Analysis (CMA)

Cost Effectiveness Analysis (CEA)

Cost Utility Analysis (CUA)

Cost Benefit Analysis (CBA)

Cost Minimisation Analysis (CMA)

Simplest of all methods of economic

evaluation

Does not mean benefits are ignored –

they have to be proven to be equivalent

Once benefits have been proven to be

equivalent, analysis needs only to consider

costs

Example - CMA of generic

formulations and different treatments

Two drugs with exactly the same

pharmaceutical components with differing

costs, e.g. different formulations of

paclitaxel

Two approaches to cancer surgery with

similar outcomes but different costs, i.e.

one approach more invasive requiring

more extensive analgesia

Cost-Effectiveness Analysis (CEA)

Health benefits are measured in natural

units reflecting a single dominant

therapeutic goal

◦ Reduction in blood pressure (treatment)

◦ Increase in cases detected (screening)

CEA is only useful and undertaken if a

single dimension dominates the health

outcome to be compared

Example - CEA of alternative

Approaches to cervical screening How much more does the more effective

screening system cost? (incremental costs)

How many more cases are detected by the more effective screening system? (incremental effectiveness)

What is the incremental cost-effectiveness ratio (ICER)? ◦ ICER = incremental cost/incremental

effectiveness

(-)

Incre

men

tal co

sts

(+) Existing

technology

dominates

(-) Incremental effectiveness (+)

Cost-

effectiveness

ratio

(additional cost

per additional

success)

Cost-

effectiveness

ratio

(cost saved per

reduced

success)

New

technology

dominates

The Cost-effectiveness Plane

Pro

bab

ilit

y c

ost

-eff

ecti

ve

Ceiling ratio

1

0.5

0

0 £50,000

Incorporating cost-effectiveness

thresholds (CEAC’s) for decision-making

£30,000

0.3

£20,000

0.8

Cost Utility Analysis (CUA)

Incorporates the effects on morbidity (quality of life) and mortality (quantity of life)

The most commonly used index is the quality-adjusted life-year (QALY)

A QALY is calculated by aggregating the number of years gained from a health care intervention, weighted by the relative value attached to each future health state

Issues underlying outcome analysis for CUA are explored in detail in the next session

Measuring Quality of Life (QoL)

QoL weights reflect the subjective level of wellbeing experienced in different health states; the more preferable a health state the higher will be its associated ‘value’

Perfect health = 1

Death = 0

We will return shortly to the methods used to help determine QoL. In the meantime, give a brief overview to determine QALY gains

Preference elicitation methods

There are three main methods for direct

measurement used in cost utility analysis.

◦ Visual Analogue Scale (VAS)

◦ Standard Gamble (SG)

◦ Time Trade-off (TTO)

Visual Analogue Scale (VAS)

Individuals are asked to indicate where on

the line between the best and the worst

imaginable health states they would rate a

pre-defined health state

We would like you to indicate on this

scale how good or bad is your health

today, in your opinion. Please do this

by drawing a line from the box below

to wherever point on the scale

indicates how good or bad your

current health state is

100

60

50

40

30

20

10

0

70

80

90

Your own health state today

VAS

Alternative 1:

Health state H1

with certainty

Alternative 2:

Gamble

With probability p:

Full health, H2

Choice

With probability (1-p):

Death, H3

Standard Gamble

90 10

% Chance % Chance

PERFECT

HEALTH DEATH

Some problems in moving about

No problems with usual activities

Moderate pain or discomfort

Not anxious or depressed

Choice A

Choice B

No problems with self-care

Standard Gamble board

Years

Value of health

QOLA=1

0

QOLB

LOLA LOLB

QALYA QALYB =

The time trade-off method

Cost-benefit analysis

This requires all costs and benefits to be measured in the same unit – money

In cost-benefit analysis an activity should be undertaken if the sum of the benefits are greater than the sum of the costs

The difficulties of converting all benefits (pain, anxiety, disability, death) to a monetary equivalence implies that CBA is rarely used in health economic analyses

Summary

Which tool for which analysis? What is the context of the analysis?

What is the nature of the comparison

being made?

What is the nature of the ‘outcome’

arising from the competing options?

Conclusion - What Health

Economics aims to achieve Efficiency: Does the allocation of scarce

resources maximise the achievement of

health outcomes?

Equity: Is the sharing of health care

resources fair between people?

The manner in which we are attempting to

achieve these aims is explored in the following

presentations

What is Health Related Quality of

Life?

A multi-dimensional concept that

encompasses the physical, emotional and

social components associated with an

illness or its treatment

Pain

Disability

P2

P1

P3

P4

P0

D0 D1 D2 D3

A = P0D0 = Normal health

B = P4D3 = Total disability

& severe pain

B

A

Measuring Health-Related Quality of

Life (HRQoL)

What are Q of L ‘weights’?

Such weights reflect the subjective level of

wellbeing experienced in different health

states; the more preferable a health state

the higher will be its associated weight.

Perfect health = 1

Death = 0

TIME

ONSET OF ILLNESS

1

0

INTERVENTION

= Health gain

Prognosis with intervention

Prognosis without intervention

Measuring Health Gain in Theory

DEATH

QUALITY OF LIFE

Two ‘types’ of Measure

Generic instruments

◦ Designed to have broad application across a wide range of disease states

◦ eg sickness impact profile, Nottingham health profile, EuroQol

Disease specific instruments

◦ designed to assess the impact of specific disease states

◦ eg arthritis impact measurement scale, back pain disability questionnaire

Calculating QALYs – A Simple

Example Survival and associated health states

◦ With treatment ‘X’ 10 years in

improved health

◦ Without treatment ‘X’ 8 years in

poorer health

Preference weights for health states

◦ With treatment ‘X’ 0.7

◦ Without treatment ‘X’ 0.5

QALY Analysis for Treatment ‘X’

Without treatment X

Survival = 8 years

Q of L = 0.5

QALY = (8 X 0.5) = 4.0

With treatment X

Survival = 10 years

Q of L = 0.7

QALYs = (10 X 0.7) = 7.0

QALY gain = 3.0 Q.A.L.Y’s (7.0-4.0)

Cost of intervention = £45,000

Cost per QALY = £15,000

QALYs – For and against their use

in health economic evaluations For ◦ Generic multi-dimensional

◦ Easy to apply

◦ Provides practical guidance in allocating health care resources between very different therapeutic interventions

Against ◦ Too superficial to measure the full benefits from

health care?

◦ Insufficiently sensitive to capture small changes in the patient’s Q of L

◦ Can we really measure quality of life in only five questions?

Time

Improved survival

(increased length of life)

only

Measuring gains from different types

of intervention

Quality

of life

= Health gain

Time

Improved quality of life

only

Measuring gains from different types

of intervention

Quality

of life

= Health gain

Time

Improved survival and

improved quality of life

Measuring gains from different types

of intervention

Quality

of life

= Health gain

Time

Improved survival at

expense of decreased

quality of life

Measuring gains from different types

of intervention

Quality

of life

= Health gain

= Health loss

Conclusion of this section

Accurate health outcome measurement is vital in determining the value and hence priority that should be placed on competing healthcare interventions. For cancer, this includes screening, initial management (adjuvant treatment, surgery, radiotherapy), management of advanced disease and end of life

The need for sensitivity and practicality may pull in different directions

QALYs assume that all health interventions aim either to make us live longer (quantity) or live better (quality)

B. Economic Modelling

Theory & Practice

Therapeutic interventions are

messy and complex Limited understanding of how things work ◦ Disease/Treatments/Services

Limited evidence of effectiveness ◦ A better treatment? How much better and is it better

for all patients?

Evidence limited in time and place ◦ Are RCTs valid for other situations and in other

countries?

Variable quality and limited availability of evidence ◦ How to fill gaps?

◦ What is the comparative value of RCTs, observational data and ‘expert’ opinion?

Hence we need to model in order

to… 1. Extrapolate beyond the results of a trial

2. Link intermediate clinical endpoints to final

outcomes

3. Generalise to alternative settings

4. Synthesise head-to-head comparisons where

relevant trials do not exist

1. Extrapolating beyond the

results of a trial Economic evaluations require long term

analyses to comprehensively assess the costs

and benefits arising from an intervention

Techniques

A range of techniques are available to

extrapolate outcome data into the future e.g.

constant benefits or linear extrapolation

2. Linking intermediate endpoints

to final outcomes where necessary

Where RCTs only report intermediate clinical endpoints e.g. ◦ Hypercholesterolaemia (changes in HDL/LDL)

◦ Response rates to length of survival

◦ Disease free progression to length of survival

Economic evaluations in comparing cost-effectiveness attempt to consider ‘harder’ outcomes ◦ Life-years gained

Techniques Logistic equations and other methods are used to try

and determine impact on length of survival

3. Generalising to alternative

settings Costs

◦ Costs differ from one setting (e.g. country) to another

Techniques Adapt analyses to take account of local unit costs, comparators and

patterns of care

Efficacy

◦ Patients are carefully selected in clinical trials

◦ Compliance in trials is artificially high

Techniques Develop an ‘impact model’ that identifies factors underlying the success of

a healthcare intervention and dichotomise between ‘locally specific’ and ‘generalisable’

4. Synthesising head-to-head

comparisons

RCTs do often compare an active drug vs. Placebo; alternatively an ‘add-on’ drug to an existing regimen and not a replacement. Clinicians need to know whether a new drug is superior to existing therapeutic interventions – not as an ‘add on’ especially when scarce resources

Techniques

Modelling allows for the results of more than one trial to be incorporated thus facilitating indirect comparisons between drugs

Stages in developing an economic

model 1. Define the problem and your objective

2. Identify all relevant factors and how they inter-relate

3. Search for data/information to quantify those relationships

4. Choose an appropriate methodology/structure

5. Construct and calibrate the model

6. Test/validate model

7. Revise/correct model (return to stage 5 as required)

8. Apply model results to problem/decision

Knowledge requirements for

modelling Epidemiological:

◦ Population at risk, mortality, effects

Medical:

◦ Nature of the disease and how well do the

treatment and comparators work?

Economic

◦ Resources consumed at each stage of the

treatment process

Data requirements for modelling

Parameter estimates for each possible

outcome or health state

Probabilities of occurrence of each

outcome or health state

Cost for each resource consumed during

the process of care provision

Types of model

1. Decision Tree

Model all possible treatment paths and

outcomes

Each alternative is shown as a branch

Each branch is connected by a decision

(choice) node

Outcomes are connected to branches by

probability (chance) nodes

Terminal health states / outcomes totalled

for costs & benefits

Types of model

2. Markov Chain

Based on movements between defined health states caused by events

Individuals may enter the system at one or more source states

Individuals progress from one state to another according to a set of transition probabilities

Transitions occur at predetermined intervals (cycle period)

Model may include one or more sink or terminal states (no exit)

pn = transitional probability

Asymptomatic

disease

Progressive

disease Death Patient

p1

1-p1-p3 1-p2 1

p2

p3

Example of a simple Markov Model

How ‘robust’ are health economic

analyses? Issue to be addressed:

◦ Do limitations in either the quality or availability

of evidence affect the recommended decision?

◦ If the decision is not altered despite ‘reasonable’

variations in key assumptions/parameters, then

the analysis can be considered to be ‘robust’

Two types of uncertainty:

◦ Structural (is the model design correct?)

◦ Parameter (are the values correct?)

Techniques for handling uncertainty

Structural: scenario analysis

◦ Re-run the analysis with alternate assumptions and model structures

Parameter: sensitivity analysis (SA)

◦ Re-run the analysis with different parameter values

◦ One-way SA,

◦ Multi-way SA,

◦ Extreme values SA,

◦ Probabilistic SA

-5000

-4000

-3000

-2000

-1000

0

1000

2000

3000

-0.05 0 0.05 0.1 0.15

Incremental QALY

Increm

en

tal C

ost

Presentation of results of sensitivity analysis

1. Cost-Effectiveness Plane

0

0.2

0.4

0.6

0.8

1

£0 £10,000 £20,000 £30,000 £40,000 £50,000 £60,000

Value of ceiling ratio

Pro

ba

bil

ity

co

st-

eff

ecti

ve

Presentation of results of sensitivity analysis

2. CE Acceptability Curve

Using the results of modelling

A model simply provides a structure

(good or bad) that organises complex

relationships and data enabling them to

be interpreted and manipulated

By predicting and comparing costs and

outcomes of competing interventions, it

enables decision-makers to address

problems in a more systematic manner

Good economic modelling practice

A good model provides a structure that

allows data to be interpreted and used.

However, to maximise the value of the

model, certain principles should be followed:

◦ Keep analyses simple

◦ Keep analyses transparent

◦ Make explicit the quality of the underlying data

◦ Keep a focus on uncertainty

◦ Compare the results obtained in your model to

others

Conclusion - converting ‘numbers’

to ‘knowledge’ Remember:

◦ Numbers are meaningless

◦ Data = numbers with meaning and a source of

integrity

◦ Information = data interpreted

◦ Knowledge = information in action

C. Value of new drugs including

new cancer drugs:

Scottish Medicines Consortia

(SMC), Scotland

Ref: Andrew Walker and Ailsa Brown EACPT 2009

Only limited number of new products

having reasonable health gain

SMC recently analysed their guidance for 281 new products and indications (all drug classes) issued between April 2002 and September 2008

Data extracted from base case QALY gain estimates provided by the manufacturers showed:

Overall median health gain - 0.1 QALY

Mean health gain - 0.5 QALYs (standard deviation 1.72)

This broken down as:

◦ 22% offered no benefit

◦ 28% offered >0 – 0.1 QALY

◦ 25% offered >0.1 -0.5 QALY

◦ 13% offered >0.5- 1.0 QALY

◦ 12% offered >1 QALY

Recent examples of new drugs not

recommended by SMC as economic concerns

Drug Disease Reason for

rejection Cost/ QALY

Sunitinib

(SUTENT) GIST and mRCC

Economic case not

proven £34000 - £81000

Aliskiren

(RASILEZ)

Essential

hypertension

High costs with

comparable efficacy

£11-14/ year

(generic ACEi) vs.

£257-309

Pemetrexed

(ALIMTA)

Metastatic NSCL

cancer

Economic case not

proven Up to £53,000

AVASTIN and

ERBITUX

Metastatic ca

colon/ rectum

Economic case not

proven £24000 –£93000

Rimonabant

(ACCOMPLIA) Obesity

Economic case not

proven

Not assessed - no

comparator

Ref: SMC website

SMC and new anti-cancer medicines

recently reviewed 61 cancer medicines reviewed

◦ 36 for advanced/metastatic cancer

◦ 25 for earlier/adjuvant treatment

Median QALY gain (over current treatment)

◦ 0.38 for advanced cancer

◦ 0.30 for earlier/adjuvant treatment

Mean QALY gain (over current treatment)

◦ 0.52 for both groups

What do these ‘mean and median’

QALY gains imply in reality? Median health gain

◦ 6 months with quality of life 70% of normal

Mean health gain

◦ 8-9 months with QoL 70%

Only 6 drugs (10%) offered ≥1 QALY

22 drugs (36%) offered ≤0.2 QALY

◦ = ≤3 months at 70% of normal QoL

Overall

Some individual cancer drugs had

considerable health gain Some of the greatest health-gains are with

really innovative drugs: ◦ Trastuzumab – 2.4 QALYs

◦ Nilotinib – 2.1 QALYs

◦ Bortezomib – 1.1 QALYs

Even if these are expensive, they may offer good ‘value-for-money’

The issue subsequently becomes affordability and opportunity costs (workshop)

Health gain with cancer drugs

similar to other disease area Anti-cancer drugs are much like new drugs

for other disease areas ◦ Musculoskeletal (11) – 0.66 QALY

◦ Infections (33) – 0.11 QALY

◦ Endocrine (24) – 0.07 QALY

◦ Cardiovascular (33) – 0.05 QALY

◦ CNS and pain (55) – 0.04 QALY

Overall new drugs in general do not appear to be as valuable as many would like to think!

D. Value of new drugs including

new cancer drugs:

NICE (England)

What does NICE mean by

cost-effective?

More effective and less costly

More effective and more costly AND additional effect is worth the extra cost

Less effective and less costly AND the cost saving is large enough to compensate for the loss of effect

What is the cost-effectiveness threshold for acceptance?

NICE ‘does not use a precise ICER threshold above which a technology would automatically be defined as not cost effective or below which it would’

Why do NICE use a cost-

effectiveness threshold?

“The appropriate threshold to be used is that of the opportunity cost of programmes displaced by new, more costly technologies”

If most plausible estimate is below £20,000 per QALY gained: cost effective use of NHS resources

Above £20,000: are there benefits not captured by the QALY? Has quality of life aspect been adequately measured?

Above £30,000 “…need to identify an increasingly stronger case for supporting the technology as an effective use of NHS resources”

End of life care: The NICE criteria

Introduced 5 January 2009, revised July 2009

Three criteria in order to qualify:

◦ The treatment is indicated for patients with a short life expectancy, normally <24 months

◦ There is sufficient evidence to indicate that the treatment offers an extension to life, normally of at least an additional 3 months, compared to current NHS treatment

◦ The treatment is licensed or otherwise indicated for small patient populations

End of life care: The NICE process

For eligible treatments, the Committee will consider: ◦ The impact of giving greater weight to QALYs

achieved in the later stages of terminal diseases, using the assumption that the extended survival period is experienced at the full quality of life anticipated for a healthy individual of the same age

◦ The magnitude of the additional weight that would need to be assigned to the QALY benefits in this patient group for the cost-effectiveness of the technology to fall within the current threshold range

Committee requires that the assumptions used in the reference case economic modelling are plausible, objective and robust

End of life care:

Specifying the comparator The comparator for the technology being

assessed is very important because the choice to a large extent determines the incremental costs and incremental effects (and thus the cost per QALY)

Relevant comparators might include:

◦ Therapies routinely used in the NHS

◦ Current best practice

◦ What is expected to be replaced (SMC)

◦ ‘Do nothing’ (e.g. best supportive care)

End of life care:

Measurement of health benefit The incremental QALYs as a result of a

treatment have two components: ◦ Changes in survival

◦ Changes in health-related quality of life

The main challenge with estimating changes in survival arises because the data on clinical effectiveness typically means that long-term overall survival must be extrapolated from short-term progression-free survival data

Two challenges recur with quality of life data 1. The absence of data

2. Unsatisfactory measure of quality of life

Case study: Cetuximab for locally advanced

squamous cell cancer of head and neck

Cetuximab with radiotherapy versus radiotherapy alone in patients considered unsuitable for chemotherapy

RCT showed significant improvement in duration of locoregional control, overall and progression-free survival, and overall response rate for the combination than for radiotherapy alone (Bonner et al, NEJM 2006)

Manufacturer estimated a cost per QALY of £6,390

Committee rejected the submission highlighting uncertainties regarding the clinical evidence (e.g. RT regimens used in trial not typical of UK current practice, high proportion of patients in trial suitable for chemotherapy, and no clinical benefit demonstrated in patients with poor performance status)

End of life care:

The importance of sub-groups

Cost-effectiveness generally varies across sub-groups

Important because ICER for entire patient group may be above the threshold but there may be sub-groups for whom the intervention is cost-effective

Similarly, an ICER below the threshold for the patient group as a whole may hide ICERs for particular sub-groups above the cost-effectiveness threshold

RCTs often under-powered to assess treatment effects in sub-groups

Additional analysis presented

following appeal

Karnofsky

performance

status

Hazard Rate Confidence

Interval

Cost effectiveness

100 0.61 0.28 to 1.31 £13,200

90 0.58 0.39 to 0.88 £4,500

80 1.11 0.69 to 1.77 £58,200

70 1.22 0.53 to 2.78 RT dominant

<70 3.41 0.65 to 17.7 £37,000

NICE recommendation (June 2008)

The Committee concluded that

Cetuximab in combination with

radiotherapy is clinically and cost-effective

in patients with locally advanced

squamous cell cancer of the head and

neck who have a Karnofsky performance

status score of 90% or greater and for

whom platinum-based chemoradiotherapy

treatment is contraindicated

NICE evaluation:

A summary ICERs and cost-effectiveness

Understanding the economic model

Key elements to watch out for:

◦ Appropriate comparators

◦ Relevant sub-groups

◦ Measurement of health benefit

◦ Analysis of uncertainty

The importance of HTA:

Conclusion No health system can afford to fund all new

healthcare interventions so we inevitably have to prioritise and choose

HTA simply attempts to identify the healthcare interventions that provide sufficient clinical benefit to justify their cost

HTA enables health systems to optimise the amount of patient benefit obtained from the limited resources available to the healthcare system

HTA also enables an informed debate to be undertaken with the industry concerning the importance of linking drug pricing to drug effectiveness