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Which factors enhance positive drug reimbursement recommendation in Scotland? A retrospective analysis 2006-2013
Objective
To identify the factors that influence the Scottish Medicines Consortium (SMC) in deciding whether to accept pharmaceutical technologies for use within the Scottish health care system
Methods
A database of SMC submissions between 2006 and 2013 was created, containing a range of clinical, economic, and other factors extracted from published health technology assessment reports. A binomial outcome variable was used, defined as the decision to “accept for use” or “not recommend” a technology. Univariate and multivariate analyses were conducted to assess the impact by means of odds ratios (ORs) of the submitted evidence on the recommendation decision.
Results
Out of 463 applications, 265 were accepted for use (57%) and 198 (43%) were not recommended for use within National Health Service Scotland. Univariate analyses showed that 13 variables significantly affected the SMC decision. Of these 13 variables, 7 variables were shown to have a meaningful impact in the multivariate analysis. Four of these concerned the outcome of cost-effectiveness analyses; the fact that a submission was supported by a cost-minimization analysis was the strongest positive variable (OR = 10.30) and a submission showing a product not being cost-effective (i.e., incremental cost-effectiveness ratio above £30,000/quality-adjusted life-year gained) was the strongest negative predictor (OR = 0.47). The other variables concerned whether the submission was related to a product indicated for a nervous system disease (OR = 0.41), whether it was indicated for nonchronic use (OR = 1.66), and whether the submission was performed by a big company (OR = 2.83).
Conclusion
This study demonstrated that the outcome of cost-effectiveness analyses is an important factor affecting the SMC’s reimbursement recommendation decision.
Authors
M Charokopou, I M Majer, J D Raad, S Broekhuizen, M Postma, B Heeg
Journal
Diabetic Medicine
Therapeutic Area
Other
Center of Excellence
Health Economic Modeling & Meta-analysis
Year
2015
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