the health economics of genomic sequencing

The health economics of genomic

sequencing

Helseøkonomikonferansen 2019 i Bergen, 27th May 2019

Dr James Buchanan

[email protected] | @jbuchanan_ox | https://healtheconomicsandgenomics.com

Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK

Plan

1. Scientific background

2. Current genomic testing context in the UK

3. Existing health economics evidence base

4. Economic evaluation challenges

5. Two applied studies

a) Microcosting study of genome sequencing in cancer and rare diseases

b) DCE investigating stakeholder preferences for genome sequencing in

inherited cardiovascular disease

Background

Genetics and genomics

What is a gene?

• Genes are defined sections of DNA that are stored within cells in your body and which carry instructions for particular traits

• Everybody has around 20,000 genes. Your complete set of genes is your genome

What is genetics?

• The study of genes, their functioning and composition, and the way in which traits and conditions are passed between generations

What is genomics?

• The study of genomes, and the inter-relationships between all genes, in order to identify their combined influence on health and disease

What are genetic tests?

• Targeted to specific genes of interest

• e.g. newborn screening for cystic fibrosis

What are genomic tests?

• Provide information across whole genome; can identify multiple genetic changes

Genome sequencing

2003 – first human genome sequenced

2008 – next generation sequencing

approaches enter the research setting

• Genome sequencing / Exome sequencing /

Targeted panels

• Sequencing is quick, sensitive and

conducted at depth

• Multiple genetic changes can be detected

simultaneously vs genetic tests (often

targeted at specific genes)

• This genomic information can inform

diagnosis / prognosis / clinical management

e.g. in cancer and rare diseases

Genome sequencing in the UK

World leader

2008 onwards – genomic tests increasingly used in research setting

2013 – 100,000 Genomes Project launched

• Aim: sequence 100,000 whole genomes from patients with a rare disease or

cancer

• Completed in December 2018

October 2018 – NHS Genomic Medicine Service launched

• UK Government aims to sequence 5 million genomes in 5 years

What about cost-effectiveness?

Is genome sequencing cost-effective? If so, for which conditions?

Health economic evidence base in the UK to support this decision is limited

There is a Genomics England Clinical Interpretation Partnership (GeCIP) for Health Economics

• Unfunded / little work done to date

Some initial unpublished calculations performed for target conditions to estimate potential cost savings

No evidence on value

The international evidence base

Evidence on costs

No evidence that the cost of exome sequencing is falling over time

Limited evidence that the cost of genome sequencing is decreasing

Limited information on how cost estimates were generated

Many estimates either reflected commercial prices or were hypothetical

threshold costs

The lowest estimates were also the worst quality estimates

Evidence on health outcomes

Most common outcome measure was diagnostic yield

• Not widely accepted by health technology assessment agencies

Few studies reported health outcome measures

recommended for economic evaluations

• e.g. survival, quality of life

• Only 2 studies presented data on QALYs

Evidence on cost-effectiveness

First author

(Year)

Country Investigation Comparison Disease(s) Type of

economic

evaluation

Outcome measure Economic evaluation

results

Bennette

(2015)

USA Genome and

exome

sequencing

Returning

incidental findings

or not

Cardiomyopathy,

colorectal cancer,

healthy individuals

CUA QALYs £32,187-£82,623 per

QALY gained

Buchanan-

Hughes (2015)

UK Bacterial

genome

sequencing

Current testing

pathway

Urinary tract infections CUA QALYs Genome sequencing

dominated by current

practice

Sagoo (2017) UK Exome

sequencing

Current testing

pathway

Variety of conditions CEA Number of positive

diagnoses

£2,230-£3,213 per

additional diagnosis

Soden (2014) USA Genome and

exome

sequencing

Current testing

pathway

Paediatric

neurodevelopmental

disorders

CEA Number of diagnoses N/A (cost threshold

analysis)

Schofield

(2017)

Australia Exome

sequencing and

gene panel

Current testing

pathway

Childhood-onset

muscle disorders

(suspected congenital

muscular dystrophy or

nemaline myopathy)

CEA Number of diagnoses Cost-saving

Stark (2017) Australia Exome

sequencing

Three strategies

of integrating

exome

sequencing into

the current

testing pathway

Paediatric suspected

monogenic disorders

CEA Number of diagnoses £1,030-£3,830 per


Tsiplova

(2016)

Canada Genome and

exome

sequencing

Chromosomal

microarray

Paediatric autism

spectrum disorder

CEA Number of diagnoses £13,912-£106,590 per


Van

Nimwegen

(2017)

Netherlands Genome and

exome

sequencing

Current testing

pathway

Paediatric neurological

disorders

CEA Number of diagnoses Cost saving to £8,319

per additional diagnosis

Preferences (1)

Preferences (2)

11 studies identified

• 6 DCEs, 1 Contingent Valuation, 1 TTO, 3 Mixed methods

Marshall (2016):

• Ranking exercise to determine what people are willing to pay for genome sequencing information

• 38% would not pay for actionable genomic information, and 3% would pay more than $1,000

• 55% would not pay for genomic information for which medical treatment is currently unclear, and 7% would pay more than $400

Regier (2015):

• Preferences for the return of secondary findings from NGS

• This information is valued, but WTP depends on type of finding

Relevance? Secondary findings not a major focus of large scale sequencing projects

Health economic challenges

Analytical approach

Measuring costs

Measuring outcomes

Measuring effectiveness

Health economic challenges

Genomic test timing is critical – standard testing practice evolves

continuously

There are no national pricing tariffs for genomic tests

Disease-specific and preference-based outcome measures are limited

Capturing information on personal utility is important, but difficult

Effectiveness data for genomic interventions are challenging to incorporate

into standard health economic analyses

Oxford Microcosting Study

Oxford microcosting study

Aim: estimate the cost of using genome sequencing to identify

pathogenic variants in cancer or rare disease cases, using the

Illumina HiSeq 4000

Microcosting approach

• Costs assigned to individual resource use to generate aggregate costs

Study conducted in Oxford Molecular Diagnostics Centre

(NHS laboratory)

Costing based on annual throughput of 399 samples

Data collected from June 2016 to December 2017

Methods and data (1)

Collected information on resource use (equipment, staff, consumables) using questionnaires/interviews, then attached unit costs to generate an overall cost

Costed all steps in genome sequencing pathway, from sample reception to reporting and archiving

Same steps for cancer and rare diseases

Methods and data (2)

Unit cost data provided by laboratory staff or equipment suppliers

Equipment costs discounted at 3.5%

All salaries inflated by 20% to incorporate National Insurance and

Superannuation

Total costs inflated by 20% to account for overheads

All data assumed to be stored for 5 years

All parameters varied in one-way sensitivity analysis

All costs are 2016 values

Costs were calculated at the case level and also per genome:

• Cancer case size: 2 samples (tumour and germline)

• Rare disease case size: 3 samples (proband and both parents)

Microcosting results

Cost category (% of total cost before overheads)Total

Equipment Consumables Staff Overheads

Cancer (A)£694(12%)

£4,126(72%)

£880(15%)

£1,140£6,841

£3,420/genome

Rare diseases (B)£1,042

(18%)

£4,022(68%)

£811(14%)

£1,175£7,050

£2,350/genome

Difference (B-A) £348 -£105 -£69 £35 £209

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Pro

port

ion o

f to

tal te

st c

ost

s

Cancer

Rare diseases

Two-way sensitivity analysis (1)

Joint changes in annual throughput and consumable costs for genome sequencing in rare diseases

(results expressed as the cost per case)

£0

£2 000

£4 000

£6 000

£8 000

£10 000

£12 000

0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 2 000

Co

st p

er c

ase

Throughput

Consumable cost = £100


Consumable cost = £1,000

Consumable cost = £4,023 (base case)

Annual throughput of 399 (base case)

Two-way sensitivity analysis (2)

Joint changes in annual throughput and consumable costs for genome sequencing in rare diseases

(results expressed as the cost per genome)

£0

£500

£1 000

£1 500

£2 000

£2 500

£3 000

£3 500

£4 000

0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 2 000

Co

st p

er g

eno

me

Throughput




Consumable cost = £1,341 (base case)

Annual throughput of 399 (base case)

Microcosting conclusions

Our estimates are generally lower than existing estimates in the literature

Limitations – costs are sequencer/country specific

The costs of sequencing are yet to meet the desired £1,000/$1,000 per

genome figure when testing is performed on relatively small numbers of

patients with cancer or a rare disease in a single centre with modest

throughput

High throughput – commensurate with a national-scale facility – combined

with bulk discounts on consumable costs will likely have the greatest

impact on the overall cost of sequencing going forward

Is this likely?

DCE in inherited cardiovascular disease

Background

Patient access to genomic tests often strictly controlled

Preferences of healthcare professionals may influence the translation of genomic tests into clinical practice

Case study: genomic testing in inherited cardiovascular disease (CVD)

Several genomic testing approaches are options in inherited CVD

• Genome sequencing / Exome sequencing / Panel tests

No consensus on the best option

We conducted a DCE in health professionals in UK who order or who have ordered genetic or genomic tests for patients selected for mutation analysis in the context of inherited CVD

Aims:

• To understand the relative preferences of health professionals for the different attributes of genomic testing

• To consider how these preferences might impact on test uptake in the UK NHS

Attributes and levels

Test attribute

Possible levels for each testing alternative

Genome sequencing Exome sequencing Cardiac panel testGenetic testing not

indicated

Ability of the test to

identify pathogenic

mutations

Pathogenic mutation is identified in 20 out of every 100 cases

Pathogenic mutation is

identified in 0 out of every

100 cases




Ability of the test to

identify variants of

unknown significance

Variant of unknown significance is identified in 10 out of every 100 casesVariant of unknown

significance is identified in 0

out of every 100 cases

Variant of unknown significance is identified in 20 out of every 100 cases

Variant of unknown significance is identified in 30 out of every 100 cases

Test cost

£1,000 £500 £150

£0£3,000 £1,500 £300

£5,000 £2,500 £450

£7,000 £3,500 £600

Quantity of counselling

received

40 minutes 10 minutes

0 minutes50 minutes 20 minutes

60 minutes 30 minutes

Disclosure of secondary

findings

No secondary findings disclosed

No secondary findings disclosedSubset of well-characterised secondary findings disclosed

Choice question

Experimental design

Modelled the probability that health professionals would select a specific

genomic test

Used model averaging approach in Ngene

Designs generated for both parts of the choice tasks – combined to create

overall d-efficient design

• Design for second part of choice task given weighting of 0.05

• Best estimate: only 5% of respondents would select the opt-out

Sampling and survey administration

Target respondents: healthcare professionals who order / have ordered

genetic and/or genomic tests for patients with a suspected inherited

cardiovascular disease

Sampled from two populations

• GeCIP in Cardiovascular Disease (N=200)

• UK Association for Inherited Cardiac Conditions (N=350)

Piloted in both populations

Survey conducted online

Respondents asked to rank attributes before/after completing choice tasks

Information collected on respondent characteristics

Data analysis

Evaluated observed uptake and existence of dominant preferences

Used mixed logit regression analysis

• Analysis 1: Evaluated responses to first part of choice question

• Analysis 2: Used responses to part two of choice question if respondent selected opt-out (only undertaken if >5% selected opt-out)

Evaluated impact of attributes/levels on test uptake

Also:

• Evaluated predictive power

• Calculated willingness-to-pay (WTP)

• Estimated marginal rates of substitution (MRS)

• Predicted future uptake

• Explored heterogeneity

Respondent characteristics

37 respondents

Key characteristics:

• Male (65%)

• Aged 35-44 years (41%)

• Cardiologists (40%) or clinical geneticists (31%)

• Saw one patient with suspected or confirmed inherited cardiovascular disease

every working day

• Most (95%) had previously ordered a genetic or genomic test

• Most (95%) thought that genomic tests for CVD patients should be made

available in the NHS

• Median time to complete DCE: 15 minutes

Responses to choice questions

57% selected exome sequencing in a practice question in which this was

the ‘best’ option

Panel test chosen most often (66% of all choices)

• Genome sequencing 18%, exome sequencing 14%, opt-out 1%

Five respondents (14%) always selected the choice alternative that

identified the most pathogenic mutations

Four respondents (11%) selected the panel test in every choice question

• These 4 respondents also selected panel testing in the practice question

Mixed logit

Attribute Β-coefficient SE Lower CI Upper CI PWillingness-to-

payLower CI Upper CI

Random parameters

ASC_WGSMean 3.194 2.700 -2.097 8.486 0.237 £486.27 -£198.45 £1,170.98

SD 0.386 0.758 -1.099 1.871 0.610 - - -

ASC_PANELMean 4.851 2.071 0.791 8.911 0.019 £738.52 £300.39 £1,176.65

SD 4.254 1.196 1.910 6.599 0.000 - - -

PATHOGENICMean 0.768 0.238 0.301 1.235 0.001 £116.90 £99.14 £134.65

SD 0.797 0.290 0.229 1.365 0.006 - - -

UNKNOWNMean -0.213 0.083 -0.375 -0.050 0.010 -£32.38 -£48.22 -£16.53

SD 0.304 0.110 0.087 0.520 0.006 - - -

COSTMean -0.007 0.002 -0.011 -0.002 0.003 - - -

SD 0.015 0.005 0.005 0.025 0.003 - - -

Fixed parameters

ASC_WES Mean 5.879 2.944 0.109 11.649 0.046 £894.94 £280.22 £1,509.66

COUNSEL Mean -0.097 0.043 -0.181 -0.012 0.025 -£14.72 -£24.30 -£5.15

SECONDARY Mean -0.052 0.474 -0.981 0.876 0.912 -£7.95 -£148.17 £132.26

Pseudo R2 0.24

Respondents willing to tolerate a 36% increase in VUS if the mutation detection

rate increases by 10%

Model predicts 99% of choices made by respondents

Test uptake

Test

Test attributes and levels Utility scoreProbability of

uptake

Pathogenic mutation identified

in X out of every 100 cases

VUS identified in X out of every

100 casesCost Counselling

Secondary findings

disclosedMean SD Mean SD

Genome sequencing 50 55 £5,000 60 mins Subset -18.5 30.8 0.1% 0.6%

Exome sequencing 45 35 £2,500 60 mins Subset 9.0 15.2 1.4% 4.1%

Panel testing 40 15 £300 30 mins None 33.9 15.9 98.1% 6.2%

None 0 0 £0 0 mins None -0.1 0.0 0.4% 2.3%

Conclusions

Healthcare professionals in this clinical context have strong preferences for

genomic testing, if the alternative is no testing at all

Uptake of genomic testing is more likely if the pathogenic mutation rate increases,

fewer variants of unknown significance are identified, or if tests decrease in cost

Respondents prefer panel testing to genome or exome sequencing

Either scepticism or lack of awareness of the benefits of genome and exome

sequencing, or awareness of the limitations of these tests in this context

• E.g. more VUS are identified using genome or exome sequencing, but few of these are

likely to convert to pathogenic mutations

Caveats: specific clinical context / single hypothetical scenario

Tentative conclusion: uptake of genome and exome sequencing might be limited if

these tests have a high yield of VUS

Where next?

1. How much do genomic tests cost?

• Need more microcosting studies in different settings and at scale

• Need to better understand all the costs that patients incur across their clinical

pathway, before and after testing

2. What are benefits of genome sequencing in terms of health outcomes?

• Survival and quality of life data are required

• Must collect data that can feed into current health technology assessment

processes

3. Is genome sequencing cost-effective?

• Urgent need for economic evaluations of different applications of genome

sequencing

Acknowledgements

Current and former* members of

the genomics team at the Health

Economics Research Centre

• Lars Asphaug*

• Sarah Briggs

• Ana Cruz

• Brett Doble*

• Patrick Fahr

• Jilles Fermont*

• Liz Morrell

• Laurence Roope

• Katharina Schwarze*

• Apostolos Tsiachristas

• Sarah Wordsworth

Microcosting study co-authors

• Pavlos Antoniou

• Carme Camps

• Helene Dreau

• Jilles Fermont

• Steve Harris

• Samantha Knight

• Erika Kvikstad

• Alistair Pagnamenta

• Melissa Pentony

• Niko Popitsch

• Anna Schuh

• Katharina Schwarze

• Jenny Taylor

• John Taylor

• Mark Tilley


DCE study co-authors and

collaborators

• Edward Blair

• Elizabeth Ormondroyd

• Jenny Taylor

• Kate Thomson

• Hugh Watkins


Takk!

Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK

Extra slides

One way sensitivity analysis

Most changes in staff-related variables had no effect on test costs

HiSeq 4000 sequencing machine was the most expensive piece of

equipment (£474,373, annual maintenance £55,641)

• Varying the cost of the sequencer +/- 50% changed test costs by +/- 3-4%

• Half of the sequencer cost per cancer or rare disease case (£279 for cancer

cases and £418 for rare disease cases) is the annual maintenance cost

Changing the duration of data archiving had no effect on test costs

Reducing the family size for rare disease cases to 2.4 reduced test costs

by 20% to £5,650

Qualitative work

Literature review → Long-list of 16 potential attributes

Interviews with healthcare professionals (clinicians, laboratory

scientists, genetic counsellors)

• Ranked attributes by importance

• Five attributes selected

• Also discussed testing alternatives and attribute levels

Attribute rankings

Attribute Before completing choice questions After completing choice questionsMean score (SD) Ranking Mean score (SD) Ranking

Ability of the test to identify pathogenic mutations

1.0 (0.0) 1 1.0 (0.2) 1

Test cost 2.7 (0.9) 2 2.5 (0.9) 2Quantity of counselling received

3.4 (1.0) 3 3.5 (0.9) 4

Ability of the test to identify variants of unknown significance

3.6 (1.1) 4 3.4 (1.1) 3

Disclosure of secondary findings

4.4 (0.8) 5 4.5 (0.7) 5

the health economics of genomic sequencing

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