COMPARISON BETWEEN CANCER DRUGS ASSESSMENT IN PORTUGAL AND UNITED KINGDOM
Author(s)
Carmo S1, Ramos R2, Crespo N1
1ISCTE-IUL, Lisbon, Portugal, 2INFARMED, I.P., Lisbon, Portugal
Presentation Documents
The number of new cancer drugs and expenditure on oncologic diseases are increasing year after year. Nevertheless reimbursement policies for the same cancer drugs vary among countries, even though they rely on the same clinical evidence due to different processes and factors taken in consideration for the evaluation. This study aims to assess the differences between Portugal and United Kingdom in terms of their respective processes of assessment. The National Institute of Health and Care Excellence (NICE), in the United Kingdom, carries out a centralized evaluation and makes decision about funding, based on the assessment submitted by the manufacturer and their decisions are follow by the National Health Service in a similar way of the INFARMED – National Authority of Medicines and Health Products, I.P., the national agency responsible for health technologies evaluations. The similarities between the two organizations just got bigger with the creation, in 2015, of the National System of Health Technology Assessment (SiNATS), and more recently, the creation, under SiNATS, of the Health Technology Assessment Committee (CATS), in Portugal, with the objective to analyze the added value of technology and economic evaluation studies, contribute to the revision of the methods for health economic evaluation and issue recommendations. In order of this new paradigm, the focus of this analysis is to compare all the assessments from 2010 to 2015, for new cancer drugs submitted to NICE and INFARMED, I.P., and critically reviews the number of drugs assessed, the decision taken and the timelines concerning both organizations’ procedures at the time.
Conference/Value in Health Info
2016-10, ISPOR Europe 2016, Vienna, Austria
Value in Health, Vol. 19, No. 7 (November 2016)
Code
PRM218
Topic
Methodological & Statistical Research
Topic Subcategory
Confounding, Selection Bias Correction, Causal Inference
Disease
Oncology