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Economic Studies in Motor Neurone Disease: A Systematic Methodological Review
Alan Moore1, Carolyn A Young2, Dyfrig A Hughes3
1Centre for Health Economics and Medicines Evaluation, Bangor University, Ardudwy, Holyhead Road, Bangor, LL57 2PZ, UK.
Background:
Motor neurone disease (MND) is a devastating condition which greatly diminishes patients' quality of life and limits life expectancy. Health technology appraisals of future interventions in MND need robust data on costs and utilities. Existing economic evaluations have been noted to be limited and fraught with challenges.
Objective:
The aim of this study was to identify and critique methodological aspects of all published economic evaluations, cost studies, and utility studies in MND.
Methods:
We systematically reviewed all relevant published studies in English from 1946 until January 2016, searching the databases of Medline, EMBASE, Econlit, NHS Economic Evaluation Database (NHS EED) and the Health Economics Evaluation Database (HEED). Key data were extracted and synthesised narratively.
Results:
A total of 1830 articles were identified, of which 15 economic evaluations, 23 cost and 3 utility studies were included. Most economic studies focused on riluzole (n = 9). Six studies modelled the progressive decline in motor function using a Markov design but did not include mutually exclusive health states. Cost estimates for a number of evaluations were based on expert opinion and were hampered by high variability and location-specific characteristics. Few cost studies reported disease-stage-specific costs (n = 3) or fully captured indirect costs. Utilities in three studies of MND patients used the EuroQol EQ-5D questionnaire or standard gamble, but included potentially unrepresentative cohorts and did not consider any health impacts on caregivers.
Conclusion:
Economic evaluations in MND suffer from significant methodological issues such as a lack of data, uncertainty with the disease course and use of inappropriate modelling framework. Limitations may be addressed through the collection of detailed and representative data from large cohorts of patients.
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