Introduction: Determinants of orphan drugs (ODs) pricing have been extensively investigated in several publications, with various studies identifying different relationships between the price of ODs and the prevalence of the indication.

Objectives: To summarise recent OD policy developments in England and Italy and analyse the correlation between annual treatment cost (ATC) and disease prevalence with a new dataset.

Methods: HTA agency websites were reviewed to identify relevant OD policies. A five-step process was adopted to analyse the relationship between ATC and prevalence: (1) details of the regulatory approval (2006-2020) and reimbursement status of ODs in Italy and England were collected (2) ex-factory list prices for selected ODs were extracted from the IHS-Markit POLI database; (3) calculation of ATC; (4) extraction of disease prevalence from Orphanet, for multiple indications the most prevalent was reported; and (5) regression analysis of disease prevalence on ATC. The role of unmet need in oncology indications was investigated using 5-year overall survival (OS) data.

Results: AIFA introduced new mechanisms to balance between ensuring rapid access to OD for patients and improving the long-term sustainability of the system, whilst in England different routes to access reflect the historical differences in OD assessment and prioritisation. One hundred and sixteen ODs (89 indications) were identified. Regression analysis identified negative association between ATC and prevalence which was statistically significant consistently for Italy and England. Exploratory analyses identified weaker association between ATC and prevalence in oncology relative to non-oncology assets and non-significant association between OS and ATC for oncology assets. The weaker association for oncology ODs may be due to the limited duration of treatment and disease severity.

Conclusions: A statistically significant negative association was established between ATC and prevalence for Italy and England. Further research is required into other factors such as disease severity, public pressure and available treatment alternatives.