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VP21 Factors Associated With Recommendations On Drugs For Rare Diseases
Published online by Cambridge University Press: 03 January 2019
Abstract
In Canada, reimbursement recommendations on drugs for common and rare indications (for example, orphan drugs) are made through the pan-Canadian Oncology Drug Review (pCODR) and the Common Drug Review (CDR). However, some stakeholders have called for a separate mechanism for orphan drugs, arguing that existing processes place too much weight on their high price tags. The purpose of this study was to examine factors associated with positive recommendations on drugs for rare diseases.
Information was extracted from CDR and pCODR recommendations on drugs for diseases (prevalence of less than 1 in 2,000) up to April 2018. Univariate and multivariate logistic regression models were applied to explore the influence of the following variables on recommendations: year; prevalence; clinical safety and effectiveness (safety, quality of life, symptoms, surrogate outcomes, and survival); quality of evidence (availability of comparative data, external validity, and bias); unmet need; treatment cost; and incremental cost-effective ratio (ICER). Two-way interactions were also tested.
Of 128 recommendations, fifty-four (77 percent) and forty (69 percent) were positive for cancer and non-cancer indications, respectively. For cancer indications, all submissions reporting meaningful improvements in surrogate, quality of life, and survival outcomes were significantly more likely to have a positive recommendation. Submissions showing a lack of external validity were significantly less likely to receive a positive recommendation. For non-cancer indications, more recent submissions and those presenting no safety issues were associated with positive recommendations. Prevalence, treatment cost, and ICER were not determinants of positive or negative recommendations.
For both cancer and non-cancer orphan drugs, impact on clinical safety and effectiveness, rather than cost, appears to be a key factor in the formulation of recommendations.
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