Decisions in 4 EU countries*

Transcription

1 Exploratory analysis of the main variables driving the Drug Reimbursement Decisions in 4 EU countries* Laia Maynou Pujolràs, PhD Prof. John Cairns AES, Pamplona 29 th May 2014 Research funded under the European 7 th FP: Advance-HTA * Please do not quote without authors permission

2 Presentation structure 1. Introduction 2. Main variables defining the HTA 2.1. System-level variables 2.2. Product-specific variables 3. Application 4.1. Descriptive 4.2. HTA decisions by country 4. Analysis 4.3. HTA characteristics by country 4.4. ICER/Decisions by country 5. Methods 5.1. Model specification 5.2. Results of the model 6. Discussion

3 1. Introduction Drug reimbursement and pricing (R&P) decisions are one of the foremost uses of Health Technology Assessment (HTA). It encompasses the entire process from the submission of a reimbursement request to the final decision. In the last stage of the process, the decision making body will take a Favourable, Favourable with restrictions or Non- Favourable decision.

4 1. Introduction Across the European Union, it exists different HTA systems. As a result, the final decision of reimbursement can differ across countries. The main motivation behind this paper is to explain the differences in the final decision across these countries. This objective will be achieved through an empirical analysis which will determine the main variables affecting the decision making process.

5 2. Main variables characterising the HTA Description of the drug reimbursement decision processes. Taking into account the Hutton Framework and a detailed analysis of 15 EU countries. Our taxonomy follows the Framework proposed by Hutton and colleagues, but we have grouped their essential variables differently. Distinction between system-level and product-specific variables.

6 2.1. System-level variables A. Organisational Evidence: production and review of evidence is done internally or externally. Independence: HTA body independent or inside MoH. Recommendation or decision? Decision level: decision and recommendation national, regional, freedom of implementation? B. Process Initiator: manufacturer submission/department of Health/automatic. Stakeholders involvement Degree of transparency Appeal process

7 2.1. System-level variables C. Method Economic Evaluation analysis required in the appraisal. Budget impact requirement. Pricing location: decision inside MoH or external body. Pricing decision: based on a negotiation, reference pricing or set by manufacturer.

8 2.2. Product-specific variables A. Drug characteristics Type of patient: adult, children or both. Orphan drug: designed by the EMA (5 in ). Number of patients eligible (rate of incidence). Disease stage: early or late treatment. ICER: below or above per QALY. End of life treatment by NICE (2009) B. Drug-country characteristics Managed Entry Agreement (MEA): financial, outcome or both. Alternative drug already in the positive list Number of decisions in the last 6 months. Decision time difference (week) to NICE

9 3. Application We have analysed the HTA system of 15 EU countries. But we have collected decisions from 4 countries (Scotland, England, Belgium and Sweden). Start with cancer drugs. 159 drug-indications analysed based on SMC. Sample: includes the pharmaceutical technology appraisals for cancer drugs from 2002 to March 2014.

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11 4.2. HTA decisions by country Scotland England Prostate cancer Pancreatic cancer Ovarian cancer Myeloma Melanoma Bone cancer Brain cancer Soft tissue Thyroid sarcoma cancer Skin cancer Breast cancer Prostate cancer Pancreatic cancer Ovarian cancer Cervical cancer Colorectal cancer Endocrine cancer Myeloma Melanoma Bone cancer Brain cancer Soft tissue Thyroid Skin cancer sarcoma cancer Breast cancer Cervical cancer Colorectal cancer Endocrine cancer Lymphoma EpCam carcinoma Lymphoma Head and Neck cancer ITP Kidney cancer EpCam carcinoma Head and Neck cancer ITP Kidney cancer Lung cancer Liver cancer Leukaemia Lung cancer Liver cancer Leukaemia

12 4.2. HTA decisions by country Sweden Belgium Prostate cancer Pancreatic cancer Ovarian cancer Myeloma Melanoma Bone cancer Brain cancer Soft tissue Thyroid Skin cancer sarcoma cancer Breast cancer Cervical cancer Colorectal cancer Myeloma Melanoma Endocrine cancer Prostate cancer Pancreatic cancer Ovarian cancer Bone cancer Soft tissue Thyroid Brain cancer Skin cancer sarcoma cancer Breast cancer Cervical cancer Colorectal cancer Endocrine cancer Lymphoma Lung cancer Liver cancer Leukaemia EpCam carcinoma Lymphoma Head and Neck cancer ITP Kidney cancer Lung cancer Liver cancer Leukaemia EpCam carcinoma Head and Neck cancer ITP Kidney cancer

13 4.3. HTA characteristics by country Evidence Bodyindependency Decisionlevel Initiator Stakeholders Transparency Appeal Economicevaluation Budgetimpact Pricinglocation Pricingdecision Scotland Evidence Bodyindependency Decisionlevel Initiator Stakeholders Transparency Appeal Economicevaluation Budgetimpact Pricinglocation Pricingdecision Sweden (TLV) Evidence Bodyindependency Decisionlevel Initiator Stakeholders Transparency Appeal Economicevaluation Budgetimpact Pricinglocation Pricingdecision Belgium Evidence Bodyindependency Decisionlevel Initiator Stakeholders Transparency Appeal Economicevaluation Budgetimpact Pricinglocation Pricingdecision England

14 4.4. ICER / Decision by country 28% of NICE decisions (ICER non-costeffective) are considered End of Life drug indications

15 5. Methods 5.1. Model specification Dependent variable treated as an ordered categorical variables. Model specified as an Ordered Probit. Two levels: drug-indication (i) within each country (j). Multi-level model. P(Y ij = J) = a ij + b 1ij ( weektonice ) ij + b ( 2ij timediff ) ij + b ( 3ij mea ) ij + b ( 4ij alternative ) ij + b 5i ( icer ) i + b ( 6i endlife ) i + b ( 7i orphan ) i + b ( 8i diseasestage ) i + b ( 9i typeofpatient ) it + u ij Model estimated using a Bayesian framework, through integrated nested Laplace approximation (INLA).

16 5. Methods 5.2. Results of the model Ordered Probit (OR) Intercept (0.393) Time difference to NICE (0.125) Alternative (0.109) ICER cost-effective 1.288* (0.162) ICER non-submission (0.152) ICER no data 1.601*** (0.172) Orphan (0.124) Patient (children) 1.303* (0.177) Patient (both) (0.500) mean (std. Dev.) * p<0.1, ** p<0.05, *** p<0.01

17 6. Discussion This presentation is a first application of what we want to analyse. This research is still in progress. We cannot show significant results from our analysis. More countries need to be included to increase variability. The final aim of this project is to present an empirical analysis of decisions with respect to a number of anti-cancer drugs. Demonstrate the utility of the designed approach before extending it to other clinical areas.

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