14 September 2017
This blog has been written by guest authors Zornitza Stark (Murdoch Children’s Research Institute; Melbourne Genomics Health Alliance), Deborah Schofield (Murdoch Children’s Research Institute; Faculty of Pharmacy, University of Sydney; Garvan Institute of Medical Research) and Clara Gaff (Department of Paediatrics, University of Melbourne; Melbourne Genomics Health Alliance).
The recent health economic evaluation of whole-exome sequencing in clinical genetics published by the PHG Foundation provides a valuable UK perspective to the emerging body of health economics studies in rare disease genomics.
The diagnostic and clinical utility of genomic sequencing for rare disease diagnostics is well recognised. However there is a need to develop sustainable funding models and provide evidence of cost effectiveness to support the widespread implementation of genomic sequencing in routine healthcare, as advocated in the recent report by the UK’s Chief Medical Officer, Generation Genome.
Few cost effectiveness studies in genomics have been published to date. Those that have focus on patients where there is evidence of clinical benefit. In the main children with either a variety of rare disorders or neurological conditions.
Most studies have looked at the costs of investigations in patients who have had a ‘diagnostic odyssey’, which can include sequencing of one or more candidate genes. The results show that the cost of the traditional diagnostic pathway is high, with per patient costs ranging from AU$4,013 (£2,398) in conditions affecting the peripheral nerves to US$19,100 (£14,779) in children with complex conditions.
These studies have concluded that substantial cost savings could have been made if these ‘diagnostic odyssey’ patients had received genomic testing and a diagnosis earlier. Many investigations would then have been rendered unnecessary.
Genomic testing: ‘last resort’ or ‘first line’?
In this genomic era, clinicians have the choice of first performing standard investigations and reserving genomic sequencing as a test of ‘last resort’ or replacing some or all other diagnostic investigations with a genomic sequencing test as a ‘near first line’ test.
The recent study from Sagoo et al. reports on a subset of previously investigated but undiagnosed patients. Whole exome sequencing (WES) achieved a 42% diagnostic yield. They conclude that at current prices, WES was more cost effective when used as a first line test (£2,201 per additional diagnosis) than in addition to standard genetic testing (£3,171 per additional diagnosis).
However, this study does not consider similar patients who did receive a diagnosis through standard testing. As it is generally difficult to predict which patients are likely to receive a diagnosis with standard investigations when they first present, the outcomes for all patients should be considered in calculations of cost effectiveness.
An Australian retrospective study of people with childhood onset neuromuscular disorders included both patients who received a diagnosis through standard care as well as those who did not for a period averaging about seven years. This study demonstrated that whole exome sequencing saved USD$10,024 (£7,716) for each additional diagnosis over standard diagnostic care.
Ideally such studies should be prospective, with all patients who have not yet had standard investigations receiving WES at the beginning of their diagnostic trajectory, thus allowing a direct comparison of the outcomes and costs.
Two studies have conducted genomic sequencing prospectively in parallel with standard diagnostic investigations. Both confirmed that genomic sequencing yielded more diagnoses and reduced expense.
Our study of infants with a range of suspected monogenic disorders found that WES, when applied as a first-tier sequencing test, more than tripled the diagnostic rate to 62.5%, and resulted in a saving of AUD$2,182 (£1,304) for each additional diagnosis compared to standard diagnostic pathway. While it may be appropriate to perform genomic sequencing as a ‘near-first tier’ test in place of a large number of other investigations in medically stable children presenting in the outpatient setting, it would not be suitable for acutely unwell patients unless it can be performed with rapid turnaround times (5 days or less) rather than the current averages of 4-6 months.
A prospective Dutch study has similarly found that replacing all diagnostic efforts in children with complex neurological disorders by WES increased the diagnostic yield four-fold, while reducing the expenses for genetic testing by E744 (£655).
There are several limitations common to all the studies reported. Study samples have been small, ranging from 17 (5) to 150 patients (6), making estimates of uncertainty difficult. Studies would generally benefit from estimation of confidence intervals. To date these have only been provided in two studies.
Other sources of uncertainty in health economic analyses of genomic testing relate to the inter-provider variability and change over time in the cost of testing, Sagoo et al. provide a valuable insight by examining numerous scenarios in the sensitivity analysis for the cost of WES and clinician services.
Health economic assessments in clinical genomics have been limited to short-term outcomes, such as ‘cost per diagnosis’. In contrast, health economic assessments in most other areas of medicine incorporate longer-term health outcomes, typically measured in cost per additional quality adjusted life year gained (QALYs) as well as the cost of change in management (cost utility studies).
Genomic testing differs from many other medical interventions in that the impact of rare genetic disease diagnosis extends to the family. Thus, it is important to capture the costs and benefits of outcomes for other family members, which can include:
The models would also be further improved by capturing broader outcomes and cost impacts, such as societal costs incurred by families as part of the diagnostic odyssey (e.g. lost productivity and travel expenses).
While the study by Sagoo et al. limits analysis to impact on genetic testing budget only, and is not prospective, it is unique in reporting a budget impact analysis, extrapolating the cost of to the health system, in addition to reporting costs for the patients within the study.