Introduction: Biosimilar medicines are considered promising alternatives to new biologicals with high price tags. The extent of savings resulting from biosimilar use depends on their price and uptake, which are largely shaped by pricing, reimbursement, and demand-side policies. This article informs about different policy measures employed by European countries to design the biologicals market and explores potential savings from the increased use of biosimilar medicines in Germany.Methods: Policy measures that target the price and uptake of biosimilar medicines were identified based on a prefilled questionnaire survey with public authorities in 16 European countries, who were the members of the Pharmaceutical Pricing and Reimbursement Information network (July 2020). Potential savings that could have been generated in Germany if different measures identified in the surveyed countries had been implemented were calculated for six publicly funded biological molecules. Price data of the Pharma Price Information service and German consumption data for 2018 were used for the calculation of five scenarios.Results: Several countries use a price link policy, setting the biosimilar price as a percentage of the price of the reference biological. Also lowering the price of the reference biological upon market entry of a biosimilar is less frequently used. While tendering of biosimilar medicines in the inpatient setting is the norm, it is rarely employed for biosimilars in outpatient use. Reference price systems and INN prescribing of medicines are the commonly used policy measures in the off-patent market, but some countries define exemptions for biologicals. Substituting biosimilars at the pharmacy level is rather an exception. Potential savings in Germany ranged from 5% (simple price link) to 55% (prices at the level of other countries) for the six studied molecules.Conclusion: Despite some differences, there are discernible tendencies across European countries with regard to their applications of certain policy measures targeting the price and uptake of biosimilar medicines. The potential for savings of some of these policies was clearly demonstrated. Monitoring and evaluation of these rather recent measures is key for obtaining a more comprehensive picture of their impact.

No abstract

Since the start of the 2019 pandemic, wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the prevalence of SARS-CoV-2. With methods and infrastructure being settled, it is time to expand the potential of this tool to a wider range of pathogens. We used over 500 archived RNA extracts from a WBE program for SARS-CoV-2 surveillance to monitor wastewater from 11 treatment plants for the presence of influenza and norovirus twice a week during the winter season of 2021/2022. Extracts were analyzed via digital PCR for influenza A, influenza B, norovirus GI, and norovirus GII. Resulting viral loads were normalized on the basis of NH4-N. Our results show a good applicability of ammonia-normalization to compare different wastewater treatment plants. Extracts originally prepared for SARS-CoV-2 surveillance contained sufficient genomic material to monitor influenza A, norovirus GI, and GII. Viral loads of influenza A and norovirus GII in wastewater correlated with numbers from infected inpatients. Further, SARS-CoV-2 related non-pharmaceutical interventions affected subsequent changes in viral loads of both pathogens. In conclusion, the expansion of existing WBE surveillance programs to include additional pathogens besides SARS-CoV-2 offers a valuable and cost-efficient possibility to gain public health information.

Background. The corona crisis hit Austria at the end of February 2020 with one of the first European superspreading events. In response, the governmental crisis unit commissioned a forecast consortium with regularly projections of case numbers and demand for hospital beds. Methods. We consolidated the output of three independent epidemiological models (ranging from agent-based micro simulation to parsimonious compartmental models) and published weekly short-term forecasts for the number of confirmed cases as well as estimates and upper bounds for the required hospital beds. Findings. Here, we report om four key contributions by which our forecasting and reporting system has helped shaping Austria's policy to navigate the crisis and re-open the country step-wise, namely (i) when and where case numbers are expected to peak during the first wave, (ii) how to safely re-open the country after passing this peak, (iii) how to evaluate the effects of non-pharmaceutical interventions and (iv) provide hospital managers guidance to plan health-care capacities. Interpretation. Complex mathematical epidemiological models play an important role in guiding governmental responses during pandemic crises, provided they are used as a monitoring system to detect epidemiological change points. For policy-makers, the media and the public, it might be problematic to distinguish short-term forecasts from worst-case scenarios with undefined levels of certainty, creating distrust in the legitimacy and accuracy of such models. However, when used as a short-term forecast-based monitoring system, the models can inform decisions to ease or strengthen governmental responses.

Background The protection of vulnerable populations is a central task in managing the Coronavirus disease 2019 (COVID-19) pandemic to avoid severe courses of COVID-19 and the risk of healthcare system capacity being exceeded. To identify factors of vulnerability in Austria, we assessed the impact of comorbidities on COVID-19 hospitalization, intensive care unit (ICU) admission, and hospital mortality. Methods A retrospective cohort study was performed including all patients with COVID-19 in the period February 2020 to December 2021 who had a previous inpatient stay in the period 2015–2019 in Austria. All patients with COVID-19 were matched to population controls on age, sex, and healthcare region. Multiple logistic regression was used to estimate adjusted odds ratios (OR) of included factors with 95% confidence intervals (CI). Results Hemiplegia or paraplegia constitutes the highest risk factor for hospitalization (OR 1.61, 95% CI 1.44–1.79), followed by COPD (OR 1.48, 95% CI 1.43–1.53) and diabetes without complications (OR 1.41, 95% CI 1.37–1.46). The highest risk factors for ICU admission are renal diseases (OR 1.76, 95% CI 1.61–1.92), diabetes without complications (OR 1.57, 95% CI 1.46–1.69) and COPD (OR 1.53, 95% CI 1.41–1.66). Hemiplegia or paraplegia, renal disease and COPD constitute the highest risk factors for hospital mortality, with ORs of 1.5. Diabetes without complications constitutes a significantly higher risk factor for women with respect to all three endpoints. Conclusion We contribute to the literature by identifying sex-specific risk factors. In general, our results are consistent with the literature, particularly regarding diabetes as a risk factor for severe courses of COVID-19. Due to the observational nature of our data, caution is warranted regarding causal interpretation. Our results contribute to the protection of vulnerable populations and may be used for targeting further pharmaceutical interventions.