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Context-Specific Economic Evaluation for Molecular Pathology Tests: An Application in Colorectal Cancer in the West of Scotland

Published online by Cambridge University Press:  11 July 2019

Janet Bouttell*
Affiliation:
University of Glasgow, Glasgow, United Kingdom
Yun Yi Tan
Affiliation:
NHS Greater Glasgow and Clyde, Beatson West of Scotland Cancer Centre, Glasgow, United Kingdom
David Creed
Affiliation:
Merck Serono Ltd, Bedfont Cross, Stanwell Road, Feltham, Middlesex, United Kingdom
Gillian McGaffin
Affiliation:
NHS Scotland, Pathology Department, Level 3, Laboratory and Services Management Building, Queen Elizabeth University Hospital, Glasgow, United Kingdom
Neil Hawkins
Affiliation:
University of Glasgow, Glasgow, United Kingdom
Ruth McLaughlin
Affiliation:
Glasgow Molecular Pathology Node, University of Glasgow, Laboratory and Services Management Building, Queen Elizabeth University Hospital, Glasgow, United Kingdom
Graeme Smith
Affiliation:
NHS Scotland, Pathology Department, Level 3, Laboratory and Services Management Building, Queen Elizabeth University Hospital, Glasgow, United Kingdom
Paul Westwood
Affiliation:
NHS Scotland, Pathology Department, Level 3, Laboratory and Services Management Building, Queen Elizabeth University Hospital, Glasgow, United Kingdom
Nicola Williams
Affiliation:
NHS Scotland, Pathology Department, Level 3, Laboratory and Services Management Building, Queen Elizabeth University Hospital, Glasgow, United Kingdom
Janet Graham
Affiliation:
University of Glasgow, Glasgow, United Kingdom NHS Greater Glasgow and Clyde, Beatson West of Scotland Cancer Centre, Glasgow, United Kingdom
*
Author for correspondence: Janet Bouttell, E-mail: janet.bouttell@glasgow.ac.uk
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Abstract

Objectives

The cost-effectiveness of molecular pathology testing is highly context dependent. The field is fast-moving, and national health technology assessment may not be relevant or timely for local decision makers. This study illustrates a method of context-specific economic evaluation that can be carried out in a limited timescale without extensive resources.

Methods

We established a multi-disciplinary group including an oncologist, pathologists and a health economist. We set out diagnostic and treatment pathways and costs using registry data, health technology assessments, guidelines, audit data, and estimates from the group. Sensitivity analysis varied input parameters across plausible ranges. The evaluation setting was the West of Scotland and UK NHS perspective was adopted. The evaluation was assessed against the AdHopHTA checklist for hospital-based health technology assessment.

Results

A context-specific economic evaluation could be carried out on a timely basis using limited resources. The evaluation met all relevant criteria in the AdHopHTA checklist. Health outcomes were expected to be at least equal to the current strategy. Annual cost savings of £637,000 were estimated resulting primarily from a reduction in the proportion of patients receiving intravenous infusional chemotherapy regimens. The result was not sensitive to any parameter. The data driving the main cost saving came from a small clinical audit. We recommended this finding was confirmed in a larger population.

Conclusions

The method could be used to evaluate testing changes elsewhere. The results of the case study may be transferable to other jurisdictions where the organization of cancer services is fragmented.

Information

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Copyright
Copyright © Cambridge University Press 2019 
Figure 0

Table 1. Population, Intervention, Comparator, and Outcome Measure for Case Study

Figure 1

Fig. 1. Decision tree illustrating current and proposed KRAS/NRAS/BRAF test and relevant first-line treatment pathway for colorectal cancer in the West of Scotland. PICC, peripherally inserted central catheter; IVI, intravenous infusional chemotherapy; Oral + IV, oral and intravenous chemotherapy; CRC, colorectal cancer. Diamond represents a decision node. Circle represents a probability node. Percentages represent the base case estimates (see Table 1). Solid arrow represents branches where treatment pathways are identical between the current and proposed strategies; striped arrows represent changed treatment pathways.

Figure 2

Table 2. Inputs to the Model and Data Sources

Figure 3

Table 3. Base-Case Results and Sensitivity and Threshold Analyses

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