Background and objectivesExternal reference pricing (ERP) is a price regulation tool widely used by policy makers in the European Union (EU) Member States (MS) to contain drug cost, although in theory, it may contribute to modulate prices up and down. The objective of this article was to summarise and discuss the main findings of part of a large project conducted for the European Commission (‘External reference pricing of medicinal products: simulation-based considerations for cross-country coordination’; see www.ec.europa.eu/health/healthcare/docs/erp_reimbursement_medicinal_products_en.pdf) that aimed to provide an overview of ERP systems, both on processes and potential issues in 31 European countries (28 EU MS, Iceland, Norway, and Switzerland).MethodsA systematic structured literature review was conducted to identify and characterise the use of ERP in the selected countries, to describe its impact on the prices of pharmaceuticals, and to discuss the possible cross-country coordination issues in EU MS. This research was complemented with a consultation of competent authorities’ and international organisations’ representatives to address the main issues or uncertainties identified through the literature review.ResultsAll selected countries applied ERP, except the United Kingdom and Sweden. Twenty-three countries used ERP as the main systematic criterion for pricing. In the majority of European countries, ERP was based on legislated pricing rules with different levels of accuracy. ERP was applied either for all marketed drugs or for specific categories of medicines; it was mainly used for publicly reimbursed medicines. The number of reference countries included in the basket varied from 1 to 31. There was a great variation in the calculation methods used to compute the price; 15 countries used the average price, 7 countries used the lowest price, and 7 countries used other calculation methods. Reported limitations of ERP application included the lack of reliable sources of price information, price heterogeneity, exchange rate volatility, and hidden discounts. Spill-over effect and downward price convergence have often been mentioned as ERP's consequences leading to pricing strategies from pharmaceutical companies.ConclusionWhile ERP is widely used in Europe, processes and availability of price information vary from one country to another, thus limiting ERP implementation. Furthermore, ERP spill-over effect is a major concern of pharmaceutical firms leading to implementation of the so-called ‘launch sequence strategies’.

Background: Primary biliary cholangitis is an autoimmune disease affecting the interlobular bile ducts. Limited information is available on its epidemiology and treatment in Italy. Aims: To describe primary biliary cholangitis epidemiology and investigate treatment patterns for Italian patients with this disease. Methods: Electronic medical records from 900 general practitioners (part of the QuintilesIMS TM Longitudinal Patient Databases) were examined. Demographics were compared with those from the Italian National Institute of Statistics dataset. The International Classification of Diseases, Ninth Revision, biliary cirrhosis code 571.6 was used for diagnosis, and data on comorbidities, concomitant medications, medical examinations, specialist referrals, and treatments were collected. Results: This dataset was representative of the Italian population. Point prevalence of primary biliary cholangitis was calculated as 27.90 per 100,000 and incidence as 5.31 per 100,000 inhabitants/year. Some associations between the disease and comorbidities were sex specific. The most common laboratory assays requested were for liver enzymes, and the majority of patients were not referred to a specialist. Ursodeoxycholic acid was the most common therapy. Conclusion: This can be used as a benchmark for monitoring and identifying unmet needs to improve treatment in Italy.

Background Several studies have been focusing on the potential role of atmospheric pollutants in the diffusion and impact on health of Covid-19. This study’s objective was to estimate the association between ≤10 μm diameter particulate matter (PM10) exposure and the likelihood of experiencing pneumonia due to Covid-19 using individual-level data in Italy. Methods Information on Covid-19 patients was retrieved from the Italian IQVIA® Longitudinal Patient Database (LPD), a computerized network of general practitioners (GPs) including anonymous data on patients’ consultations and treatments. All patients with a Covid-19 diagnosis during March 18th, 2020 – June 30th, 2020 were included in the study. The date of first Covid-19 registration was the starting point of the 3-month follow-up (Index Date). Patients were classified based on Covid-19-related pneumonia registrations on the Index date and/or during follow-up presence/absence. Each patient was assigned individual exposure by calculating average PM10 during the 30-day period preceding the Index Date, and according to GP’s office province. A multiple generalized linear mixed model, mixed-effects logistic regression, was used to assess the association between PM10 exposure tertiles and the likelihood of experiencing pneumonia. Results Among 6483 Covid-19 patients included, 1079 (16.6%) had a diagnosis of pneumonia. Pneumonia patients were older, more frequently men, more health-impaired, and had a higher individual-level exposure to PM10 during the month preceding Covid-19 diagnosis. The mixed-effects model showed that patients whose PM10 exposure level fell in the second tertile had a 30% higher likelihood of having pneumonia than that of first tertile patients, and the risk for those who were in the third tertile was almost doubled. Conclusion The consistent findings toward a positive association between PM10 levels and the likelihood of experiencing pneumonia due to Covid-19 make the implementation of new strategies to reduce air pollution more and more urgent.

BACKGROUND: Several studies have been focusing on the potential role of atmospheric pollutants in the diffusion and impact on health of Covid-19. This study’s objective was to estimate the association between ≤10 micrometers diameter particulate matter (PM10) exposure and the likelihood of experiencing pneumonia due to Covid-19 using individual-level data in Italy.METHODS: Information on Covid-19 patients was retrieved from the Italian IQVIA® Longitudinal Patient Database (LPD), a computerized network of general practitioners (GPs) including anonymous data on patients’ consultations and treatments. All patients with a Covid-19 diagnosis during March 18th, 2020 – June 30th, 2020 were included in the study. The date of first Covid-19 registration was the starting point of the 3-month follow-up (Index Date). Patients were classified based on Covid-19-related pneumonia registrations on the Index date and/or during follow-up presence/absence. Each patient was assigned individual exposure by calculating average PM10 during the 30-day period preceding the Index Date, and according to GP’s office province. A multiple generalized linear mixed model, mixed-effects logistic regression, was used to assess the association between PM10 exposure tertiles and the likelihood of experiencing pneumonia.RESULTS: Among 6,483 Covid-19 patients included, 1,079 (16.6%) had a diagnosis of pneumonia. Pneumonia patients were older, more frequently men, more health-impaired, and had a higher individual-level exposure to PM10 during the month preceding Covid-19 diagnosis. The mixed-effects model showed that patients whose PM10 exposure level fell in the second tertile had a 30% higher likelihood of having pneumonia than that of first tertile patients, and the risk for those who were in the third tertile was almost doubled.CONCLUSION: The consistent findings toward a positive association between PM10 levels and the likelihood of experiencing pneumonia due to Covid-19 make the implementation of new strategies to reduce air pollution more and more urgent.

Background and ObjectivesWith 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).MethodIn 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.ResultsAccording 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.ConclusionsThe results of this forecast suggested a decrease in pharmaceutical expenditure in the studied period. The model was sensitive to pharmaceutical policy decisions.