Benefit-Transfer: Practice and Prospects

Workshop

22 November 2007

Applications of Benefit–Transfer in Health

Kees van GoolCentre for Health Economics Research and Evaluation

University of Technology Sydney

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Outline

• Setting the scene– Economic evaluation and health care policy– Economic evaluation and benefit transfer in health

• Examples: – Screening for cystic fibrosis – Health impact of noise– Cancer treatment protocols

• Conclusions

Setting the scene

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Economic evidence and health care policy• Evidence of use at a central level, e.g.

– PBAC/MSAC in Australia

– NICE in the UK

– CDR in Canada

• Very limited evidence of use at a local level (e.g. public hospitals)

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Central level use of economic evidence• Pharmaceutical Benefits Scheme (PBS):

– Australia’s most famous example of economic evaluation use

– Used to make decisions about which (new) drugs to list on the PBS and receive public subsidies.

– Mandatory use of economic evidence since 1993 (world first)

– Formal nexus between decision-making and economic evidence

– Pharmaceutical Benefits Advisory Committee (PBAC) recommendation binds the Minister for Health:

• Minister cannot list drugs that have been rejected by PBAC• Minister can reject drugs that have been recommended by

PBAC

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Central level use of economic evidence• Applicants (drug companies) conduct economic

evaluation based on guidelines published by PBAC– Focus on “how much it would cost to achieve additional health

outcomes with the new therapy compared with the existing therapy that would be replaced”

– Australian context

• Use of randomised clinical control trial data, but:– Lack of resource data (estimation)– Insufficient duration (extrapolation)– Trial population differ from real population (application)– Non-patient-relevant outcomes of treatment (transformation)

Translation (akin to benefit-transfer)

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Central level use of economic evidence

• Independent consultants check modeling• Department of Health re-checks• In 2006, PBAC made 187 decisions (137

positive recommendations)• PBAC administrative costs around $11m

– $60,000 per decision

• PBAC economic evidence not publicly available due to commercial-in-confidence

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Local level use of economic evidence

• Hospitals and local health regions make many resource allocation decisions

• Very little use of economic evidence at this level– Lack health economics expertise/resources

at local level– Perception of bias in studies– Lack of relevance to local setting– Budget rigidities

• Is the published economic evidence useful for decision makers?

Some examples of current work

Example I

Screening for Cystic Fibrosis

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Economic evaluation of cystic fibrosis screening program• Cystic Fibrosis (CF) is one of the most common

serious genetic disease in Caucasians• Incidence of 1 in 2500 and carrier frequency of 1 in 25.

• In Australia, over 70 babies with CF are born mostly to parents with no known family history – No organized community based prenatal testing

programs– Calls for community screening of CF carriers

• Population screening strategies:• Preconception (before pregnancy)• Prenatal (during early pregnancy)• Neonatal (new born)

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Aims

• Analyse the cost-effectiveness of a community-based cystic fibrosis (CF) carrier screening program– the cost of CF carrier screening per CF birth

averted.• Use decision analysis techniques• Attempted to look at literature

– Economic evidence– Transferability of existing evidences

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Results - Economic Evidence

• 29 economic studies were included• North America (12), Europe(15), Asia/ Australia (2)• Only 14 studies focussed on preconceptional/ prenatal

screening

• Wide ranging Incremental Cost Effectiveness Ratio(ICER) – Cost per carrier couple detected ranged from

US$33,504 to US$295,121• Inconsistent net savings results (cost CF care

minus cost of CF screening) • Literature offers decision makers with limited

information and great uncertainty• How can we make better use of this evidence?

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Deconstructing the model (1)Carrier (+)

Screening acceptance

(Single)

Carrier(-)

Normal Foetus

Screening acceptance (partner)

Carrier (+) Couple at

Risk

Acceptance for foetal diagnosis

CF affected foetus

Termination with no further reproduction

CF birth averted

Termination with healthy

foetus replacement

Delivery of affected CF

Child

Lifetime cost of care

for a CF child

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Deconstructing the model (2)• Set inputs

– Carrier incidence (1/25 amongst Caucasians)– Carrier couple incidence (1/625 amongst Caucasians)– Foetus CF status (1/4 amongst CF+ carrier couples)

• Behavioural inputs– Screening participation:

• Preconception - 10% to 100%• Prenatal - 50% to 100%

– 15 -25% refrained from having children (preconception)– 75-100% make use of prenatal diagnosis– 80- 95% therapeutic termination rates– Decisions like in vitro fertilization ignored– Therapeutic termination range from 30 – 100%

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Deconstructing the model (3)• Cost inputs

– Pre-screening stage• Mass communication - US$35k (in school screening) to

between US$297k - $562k (general population)– Screening stage

• Cost of per test – US$28 to US$240 – Post-test stage

• Counselling cost/carrier couple (US$17.2 to US$1188),CF foetal diagnosis(US$249 to US$2120),termination - US$206 to US$3486 and replacement (US$4,696)

– Lifetime cost of care of CF patients• Range from US$329k to US$1.3m• Estimated in several ways (specific to age, severity &

symptoms) and included different cost items (e.g. non-hospital costs)

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Deconstructing the model (3)

• Identify fixed inputs• Identify behavioural inputs

– Assess the likelihood of variation with local setting– Where necessary substitute using

• Existing local evidence• New evidence where none exist

• Use local cost data from existing sources and standard methods

Example II

State of the art on the economics valuation of noise

project undertaken for the Department of Environment

and Climate Change NSW

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Project aims

• Identify and assess methods for measuring the economic impact of noise pollution

• Appraise the potential for these methods for measuring noise in NSW

• Assess the applicability of empirical results of noise pollution to the NSW context– Here we focus on health

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Project framework

• The nature of noise pollution– Multiple sources and impacts

• Context specific• Some evidence uncertain, others clear• Some impacts well-known, others unknown

– Multiple valuation techniques• Revealed preferences• Stated preferences• Physical linkages

– Preferred for the purposes of measuring health impact

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Model deconstructionStep 1

Measure/model noisesource and levels

Step 2Estimate noise dispersion

by source and levels

Step 3Through exposure-response functions calculate impact

Step 4Estimate monetary value of

noise (e.g. welfare loss)

Step 1AMeasure change in noise levels

Step 3AMeasure change in impact

Step 2AMeasure change in noise

dispersion

Step 4AMeasure change in welfare

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Empirical results: health costs

• Total health impact is a function of:– noise level– noise distribution– prevalence of disease– attributable fraction– cost of disease

• Health impact – Life years lost

– WTP to avoid disease

• Health care costs (cost of treatment/management)• Productivity costs (e.g. cost of days absent)

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Exposure functions

• Noise can have an impact on:– Physiological responses including

stress and annoyance– Sleep disturbance – Hearing loss– Mental health– Child health– Cardiovascular disease– Performance and learning in children

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Noise exposure functions

• State of the evidence– Good evidence on ‘annoyance’, ‘sleep

disturbance’ and ‘hearing loss’.– Some evidence on cardiovascular disease

and child learning and performance– Little or no evidence on serious mental

health and child health

• Future prospects of better and more conclusive evidence of relationships

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Measuring the impact

• Staatsen et al (2004) estimated the monetary values for each health impact associated with noise as the sum of:

i. WTP to avoid each type of episode of ill health.

ii. health care costs of treatment when relevant; and

iii. productivity loss.

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Empirical results: health costs

Source: Staatsen et al (2004)

Example III

Economic evaluation of Standard Cancer Treatment

Protocols UNSW and UTS

NHMRC Health Services Research Grant

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Government spending on cancer drugs as a percentage of total (PBS/RBS)

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

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Cancer-related pharmaceutical spending in public hospitals• $127m through Section 100 – highly

specialised drugs• $124m on drugs related to cancer

separations• 52% related to chemotherapy• Average pharmaceutical cost per chemo

separation:– 1996/97 = $165– 2004/05 = $479

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The challenge in public hospitals• Capped budget; limited resources

– Maximise health– Very little information– Very little effective coordination

within/between hospitals

• Community and provider expectations– Teaching hospitals need to be at the cutting

edge– Clinical trials and Special Access Scheme

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The challenge for public hospitals (and PHI)• Many new drugs• Far more costly • Far more complex

– Adoption decision– Cost-effective diffusion

• Introduced into a dysfunctional decision-making system

• Possible strategic behaviour by pharmaceutical companies

CI-SCAT

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Cancer Institute– Standard Cancer Treatment protocols (NSW)

– Online resource that lists over 450 protocols– Information on target patient group, how to

administer the chemo drug, summary of evidence, dose calculation and side effects.

– Developed by multidisciplinary reference groups

www.cancerinstitute.org.au – But no economic evidence

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Aims of the project

• Aim 1: Developing economic evidence for CI-SCaT clinical guidelines– similar to those produced for PBS funding– to present models that illustrate the costs

and consequences of implementing cancer treatment guidelines

– using existing data on cancer treatment pathways, as well as resource costs, to construct an economic “base case” against which new interventions can be compared.

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Aims of the project• Aim 2: Developing economic evidence

applicable to local settings – work with local decision makers to adapt the

decision analytic models to the particular context of their locality.

– use local data to populate key model parameters – Models to set out the conditions necessary to

ensure that a new treatment remains cost-effective in practice.

• estimate the economic impact if prescribing patterns go beyond the intended patient groups,

• if treatment is not halted once certain clinical indicators have been reached.

• Aim 3: Have aims 1 and 2 had an impact?

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The challenge

• 455 protocols (and counting); • Small budget, few health economists

and five years.• Economic evidence to be

– High quality– Timely– Relevant to local setting– Easily interpreted

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The approach

• The decision context: – what type of chemotherapy to give – what additional (health) benefits can we expect at

what additional cost?• Outcome:

– survival but can also include quality of life– evidence from trials

• Resource use:– Cost of the drug - – Cost of administering – broad categories– Cost of managing side effects – general

econometric model

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The approach

• Model to be available on web• Updated as new evidence is released• Estimates of additional resources (e.g. number of

chairs, nursing time)• Estimate of cost burden (e.g. federal government,

PHI, public hospital)• Local users can adapt model to take into account:

– Local population parameters– Local unit costs (e.g. wages)– Comparator– Alternative scenarios

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Conclusions• Health economists place too much emphasis on the

results rather than the mechanics of the model.• Deconstruction would be useful – with more emphasis

on making general models available.• CI-SCAT project aims to produce economic evidence

that is: – Relevant and adaptable– High quality– Widely disseminated– Timely and regularly updated– Produced efficiently

• Will availability of economic evidence have an impact on decision-making?