Anne-Lise Vataire

Analyse coût-efficacité d’un test prédictif de la chimiothérapie dans le cancer du sein (Oncotype DX®) en France

BACKGROUND In breast cancer, adjuvant chemotherapy is often prescribed as a precautionary measure and sometimes unnecessarily. A diagnostic test based on estimating the risk of recurrence at 10 years for women with breast cancer in early stage has been developed (Oncotype DX(®)). METHOD A Markovs model was adapted to evaluate the long-term effect of this test in terms of costs and life-years gained in French clinical practice for patients with ER+, HER2-, node-negative early-stage breast cancer. Input data were obtained from an international meta-analysis evaluating the proportions of patients in which the genetic test led to changes in the oncologists decision. Costs and epidemiological data were specific to France. The analysis was conducted in accordance with methodological recommendations from the Haute Autorité de santé. RESULTS The test is associated with net cost savings of €570 per patient (€1,600 with productivity loss) from societal perspective and gains of 0.15 life-years and 0.14 quality-adjusted life-years per patient. CONCLUSIONS The use of the test represents efficient use of health care resources in French practice. This test provides an opportunity to optimize treatment prescription by avoiding unnecessary chemotherapies and by prescribing chemotherapy to women who would not have received it based on standard decision criteria.

EU pharmaceutical expenditure forecast

Background and Objectives With constant incentives for healthcare payers to contain their pharmaceutical budgets, forecasting has become critically important. Some countries have, for instance, developed pharmaceutical horizon scanning units. The objective of this project was to build a model to assess the net effect of the entrance of new patented medicinal products versus medicinal products going off-patent, with a defined forecast horizon, on selected European Union (EU) Member States’ pharmaceutical budgets. This model took into account population ageing, as well as current and future country-specific pricing, reimbursement, and market access policies (the project was performed for the European Commission; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). Method In order to have a representative heterogeneity of EU Member States, the following countries were selected for the analysis: France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. A forecasting period of 5 years (2012–2016) was chosen to assess the net pharmaceutical budget impact. A model for generics and biosimilars was developed for each country. The model estimated a separate and combined effect of the direct and indirect impacts of the patent cliff. A second model, estimating the sales development and the risk of development failure, was developed for new drugs. New drugs were reviewed individually to assess their clinical potential and translate it into commercial potential. The forecast was carried out according to three perspectives (healthcare public payer, society, and manufacturer), and several types of distribution chains (retail, hospital, and combined retail and hospital). Probabilistic and deterministic sensitivity analyses were carried out. Results According to the model, all countries experienced drug budget reductions except Poland (+€41 million). Savings were expected to be the highest in the United Kingdom (−€9,367 million), France (−€5,589 million), and, far behind them, Germany (−€831 million), Greece (−€808 million), Portugal (−€243 million), and Hungary (−€84 million). The main source of savings came from the cardiovascular, central nervous system, and respiratory areas and from biosimilar entries. Oncology, immunology, and inflammation, in contrast, lead to additional expenditure. The model was particularly sensitive to the time to market of branded products, generic prices, generic penetration, and the distribution of biosimilars. Conclusions The results of this forecast suggested a decrease in pharmaceutical expenditure in the studied period. The model was sensitive to pharmaceutical policy decisions.

PHP6 QUANTITATIVE ANALYSIS OF THE INFLUENCE OF DISEASE AND PRODUCT CHARACTERISTICS ON ORPHAN DRUG PRICES IN EUROPE

PHP2 VALIDITY OF THE ADHERENCE ESTIMATOR IN THE PREDICTION OF PERSISTENCE WITH CHRNONIC MEDICATIONS ASSESSED OVER 14 MONTHS Gadkari A, McHorney C Merck & Co., Inc., North Wales, PA, USA OBJECTIVES: Our objective was to assess the predictive validity of the Adherence Estimator® (AE), a three-item instrument designed to estimate patients’ propensity to adhere to prescription medications for chronic disease. METHODS: The AE was part of a larger survey mailed to adults who had an index prescription fill for one of five chronic diseases. Persistence over time was assessed using the continuous measure of medication gaps (CMG) from pharmacy claims data. The Wilcoxon rank sum test was used to assess differences in median PDC between pairs of the adherence risk groups (low/medium/high risk). Sensitivity, specificity, and positive predictive value (PPV) of the AE in the prediction of non-persistence were calculated at 45 days, and three, six, nine, 12, and 14 months. Multiple linear regression was used to assess whether the AE was a significant predictor of persistence at different time intervals, controlling for demographics and comorbidity. RESULTS: There were 1,674 usable responses for an overall survey response rate of 24.3%. Overall, 42.4% of the respondents were classified as being at low risk for non-adherence; 35.1% at medium risk; and 22.5% at high risk. At 14 months, median therapy gaps (CMG) for the low risk group (38.2%) were significantly lower than those for the medium-risk (48.7%) and high-risk groups (59.1%). At 14 months, sensitivity, specificity, and PPV for the AE were 61.1%, 50.5%, and 73.9%, respectively. The sensitivity and the specificity of the AE remained constant over time; the PPV increased as more respondents fell off therapy. Starting at three months, the AE risk groups (low vs. medium/high risk) significantly predicted subsequent gaps in therapy as measured by the CMG. CONCLUSIONS: Patients’ propensity to adhere to prescription medications for chronic disease as measured by the AE significantly predicted patients’ adherence behavior from three to 14 months post-index fill.

Core discrete event simulation model for the evaluation of health care technologies in major depressive disorder.

OBJECTIVE A review of existing economic models in major depressive disorder (MDD) highlighted the need for models with longer time horizons that also account for heterogeneity in treatment pathways between patients. A core discrete event simulation model was developed to estimate health and cost outcomes associated with alternative treatment strategies. METHODS This model simulated short- and long-term clinical events (partial response, remission, relapse, recovery, and recurrence), adverse events, and treatment changes (titration, switch, addition, and discontinuation) over up to 5 years. Several treatment pathways were defined on the basis of fictitious antidepressants with three levels of efficacy, tolerability, and price (low, medium, and high) from first line to third line. The model was populated with input data from the literature for the UK setting. Model outputs include time in different health states, quality-adjusted life-years (QALYs), and costs from National Health Service and societal perspectives. The codes are open source. RESULTS Predicted costs and QALYs from this model are within the range of results from previous economic evaluations. The largest cost components from the payer perspective were physician visits and hospitalizations. Key parameters driving the predicted costs and QALYs were utility values, effectiveness, and frequency of physician visits. Differences in QALYs and costs between two strategies with different effectiveness increased approximately twofold when the time horizon increased from 1 to 5 years. CONCLUSION The discrete event simulation model can provide a more comprehensive evaluation of different therapeutic options in MDD, compared with existing Markov models, and can be used to compare a wide range of health care technologies in various groups of patients with MDD.

Evaluation of the costs and resource use associated with adjuvant chemotherapy for breast cancer in France

Abstract Objectives: There is a paucity of recent data on breast cancer costs, particularly on the burden of chemotherapy. The present study was designed to estimate resource use and costs associated with the current standard of care for adjuvant chemotherapy for breast cancer. Methods: Costs and resource use were assessed by retrospective analysis of medical records at a single comprehensive cancer care center, Hôpital Tenon, Paris, France. Data were extracted from files of female patients having undergone surgical resection of breast cancer between January–July 2010. Patients were included if they received chemotherapy at the hospital and had medical records available. Patients were followed from the start of adjuvant chemotherapy (including pre-chemotherapy) to the end of treatment. Costs were collected for each resource use item from a societal perspective using standard, published sources and expressed in 2011 Euros (€). Limitations of the analysis included the single-center study design and the use of pre-defined questionnaires on resource use (which may conservatively estimate costs). Results: A total of 62 patients were included in the study with a mean age of ∼54 years. Most patients had stage II (50.8%) or stage III (40.7%) disease. Anthracycline plus taxane-based chemotherapy regimens were most commonly prescribed (77% of patients). Mean cost of adjuvant chemotherapy was estimated to be ∼€15,740 per patient from a societal perspective. The acquisition costs of chemotherapy agents were responsible for 26% of the total, with lost productivity (27%), chemotherapy administration (19%), and adverse events (16%) also contributing substantially. Conclusions: Evaluation of costs in patients with non-metastatic breast cancer in France has shown that the costs of adjuvant chemotherapy are substantial. The main components of total cost were the cost of chemotherapy agents, lost productivity, chemotherapy administration, and management and prevention of adverse events.

Determinants of orphan drugs prices in France: a regression analysis

BackgroundThe introduction of the orphan drug legislation led to the increase in the number of available orphan drugs, but the access to them is often limited due to the high price. Social preferences regarding funding orphan drugs as well as the criteria taken into consideration while setting the price remain unclear. The study aimed at identifying the determinant of orphan drug prices in France using a regression analysis.MethodsAll drugs with a valid orphan designation at the moment of launch for which the price was available in France were included in the analysis. The selection of covariates was based on a literature review and included drug characteristics (Anatomical Therapeutic Chemical (ATC) class, treatment line, age of target population), diseases characteristics (severity, prevalence, availability of alternative therapeutic options), health technology assessment (HTA) details (actual benefit (AB) and improvement in actual benefit (IAB) scores, delay between the HTA and commercialisation), and study characteristics (type of study, comparator, type of endpoint). The main data sources were European public assessment reports, HTA reports, summaries of opinion on orphan designation of the European Medicines Agency, and the French insurance database of drugs and tariffs. A generalized regression model was developed to test the association between the annual treatment cost and selected covariates.ResultsA total of 68 drugs were included. The mean annual treatment cost was €96,518. In the univariate analysis, the ATC class (p = 0.01), availability of alternative treatment options (p = 0.02) and the prevalence (p = 0.02) showed a significant correlation with the annual cost. The multivariate analysis demonstrated significant association between the annual cost and availability of alternative treatment options, ATC class, IAB score, type of comparator in the pivotal clinical trial, as well as commercialisation date and delay between the HTA and commercialisation.ConclusionThe orphan drug pricing is a multivariate phenomenon. The complex association between drug prices and the studied attributes and shows that payers integrate multiple variables in decision making when setting orphan drug prices. The interpretation of the study results is limited by the small sample size and the complex data structure.

Novel methodology for pharmaceutical expenditure forecast

Background and objective The value appreciation of new drugs across countries today features a disruption that is making the historical data that are used for forecasting pharmaceutical expenditure poorly reliable. Forecasting methods rarely addressed uncertainty. The objective of this project was to propose a methodology to perform pharmaceutical expenditure forecasting that integrates expected policy changes and uncertainty (developed for the European Commission as the ‘EU Pharmaceutical expenditure forecast’; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). Methods 1) Identification of all pharmaceuticals going off-patent and new branded medicinal products over a 5-year forecasting period in seven European Union (EU) Member States. 2) Development of a model to estimate direct and indirect impacts (based on health policies and clinical experts) on savings of generics and biosimilars. Inputs were originator sales value, patent expiry date, time to launch after marketing authorization, price discount, penetration rate, time to peak sales, and impact on brand price. 3) Development of a model for new drugs, which estimated sales progression in a competitive environment. Clinical expected benefits as well as commercial potential were assessed for each product by clinical experts. Inputs were development phase, marketing authorization dates, orphan condition, market size, and competitors. 4) Separate analysis of the budget impact of products going off-patent and new drugs according to several perspectives, distribution chains, and outcomes. 5) Addressing uncertainty surrounding estimations via deterministic and probabilistic sensitivity analysis. Results This methodology has proven to be effective by 1) identifying the main parameters impacting the variations in pharmaceutical expenditure forecasting across countries: generics discounts and penetration, brand price after patent loss, reimbursement rate, the penetration of biosimilars and discount price, distribution chains, and the time to reach peak sales for new drugs; 2) estimating the statistical distribution of the budget impact; and 3) testing different pricing and reimbursement policy decisions on health expenditures. Conclusions This methodology was independent of historical data and appeared to be highly flexible and adapted to test robustness and provide probabilistic analysis to support policy decision making.