Innovation Rating in Italy: Analysis of 77 Drug/Indication Reports (2017-2020) from AIFA
Author(s)
Berto P*1;Aiello A2, Sironi E3
1Certara, Verona, VR, Italy, 2Certara, Milano, MI, Italy, 3Università Cattolica del Sacro Cuore, Milano, Italy
OBJECTIVES: In 2017 the Italian Medicines Agency (AIFA) published its revised model to assess the degree of innovation for new drugs/indications. High rating allows funding from a dedicated 1B€ fund (0.5B€ each allocated to oncology and non-oncology indications), and faster access to local/regional formularies. As of June 2020, 77 reports were published on the AIFA website, allowing examination of some determinants of the Agency’s decision-making.
METHODS: An ordinal regression model was applied to address determinants of innovation status. According to AIFA’s reports, innovation status is a categorical variable ranked into the following descending categories: full innovation (FI), conditional innovation (CI), no innovation (NI). The set of main predictors supposedly affecting innovation status includes: 1) Therapeutic Need (TN), 2) Added Therapeutic Value (ATV), 3) Quality of Evidence (QE) (rated according to the GRADE method). Two additional control variables are also considered: orphan drug designation (ODD) (yes/no) and therapeutic macro-area (oncological vs. non oncological).
RESULTS: Of 77 published drug/indication submissions, 50 (65%) are oncological; 32 (42%) are orphan. 28 (36%) drug/indications were classified as FI; 23 (30%) CI and 26 (34%) NI. Regression estimates confirm that QE is fully significant (p<0.001) in determining innovation status, with high quality of evidence being positively associated to innovation status. TN and ATV are also positively associated to innovation status, even if multicollinearity undermines estimates for a model including both indicators. Conversely, orphan designation and therapeutic macro-area seem not to affect innovation status, when controlling for TN, ATV and QE.
CONCLUSIONS: Although the sample size is still relatively limited, our results show that QE, rooted in the application of the objective standardized GRADE methodology, represents an important determinant of Innovation Status. Multicollinearity of TN and ATV highlights strict inter-correlation between the two variables. ODD or oncology macro-area per se do not grant positive innovation rating in Italy.
METHODS: An ordinal regression model was applied to address determinants of innovation status. According to AIFA’s reports, innovation status is a categorical variable ranked into the following descending categories: full innovation (FI), conditional innovation (CI), no innovation (NI). The set of main predictors supposedly affecting innovation status includes: 1) Therapeutic Need (TN), 2) Added Therapeutic Value (ATV), 3) Quality of Evidence (QE) (rated according to the GRADE method). Two additional control variables are also considered: orphan drug designation (ODD) (yes/no) and therapeutic macro-area (oncological vs. non oncological).
RESULTS: Of 77 published drug/indication submissions, 50 (65%) are oncological; 32 (42%) are orphan. 28 (36%) drug/indications were classified as FI; 23 (30%) CI and 26 (34%) NI. Regression estimates confirm that QE is fully significant (p<0.001) in determining innovation status, with high quality of evidence being positively associated to innovation status. TN and ATV are also positively associated to innovation status, even if multicollinearity undermines estimates for a model including both indicators. Conversely, orphan designation and therapeutic macro-area seem not to affect innovation status, when controlling for TN, ATV and QE.
CONCLUSIONS: Although the sample size is still relatively limited, our results show that QE, rooted in the application of the objective standardized GRADE methodology, represents an important determinant of Innovation Status. Multicollinearity of TN and ATV highlights strict inter-correlation between the two variables. ODD or oncology macro-area per se do not grant positive innovation rating in Italy.
Conference/Value in Health Info
2020-11, ISPOR Europe 2020, Milan, Italy
Value in Health, Volume 23, Issue S2 (December 2020)
Code
HTA3
Topic
Health Policy & Regulatory, Health Technology Assessment
Topic Subcategory
Decision & Deliberative Processes, Pricing Policy & Schemes, Reimbursement & Access Policy
Disease
Multiple Diseases