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.

Abstract According to System of Health Accounts (SHA) data total health expenditures in Austria have grown on average by 3.9% per year from 2000 to 2019. This was followed by high growth rates of 5.1% and 11.9% in 2020 and 2021, respectively. The steep increase was caused by the global COVID-19 pandemic. However, quantifying the effect of the pandemic on health expenditures is not straightforward and significant uncertainties remain about how future health expenditures will be affected and how they can most accurately be projected, particularly given recent high economic uncertainties. In this project we developed several models to predict Austrian health expenditures. Most importantly, we compare models based on demand and cost driving factors with a simple GDP-linked model. We assess the performance of the different models by predicting past health expenditure data up to 2019. Comparing the model predictions for the pandemic years with the actual SHA data provides an approach to estimate the net impact of the pandemic. Several different dynamics play a role in how COVID-19 has been affecting health expenditures. We find that the pandemic has caused sizeable one-time costs in the years 2020-2022, but “regular” health expenditures partially decreased during that time due to forgone elective and preventive care. As COVID-19 becomes an endemic disease it also becomes an additional long-term burden on the health system, but the main driver of health expenditure is currently inflation. The shock of the pandemic has created a structural break for the health sector with many not yet well-known consequences such as systematic changes in work conditions or salaries for health sector employees. With the currently available data and information, many of these effects cannot be directly built into the prediction models. However, we compare our models in their projections of future health expenses until 2030 with past projections from 2019 for the year 2030 under different scenarios. Key messages • The pandemic has caused sizeable one-time costs in the years 2020-2022, but “regular” health expenditures partially decreased during that time due to forgone elective and preventive care. • As COVID-19 becomes an endemic disease it also becomes an additional long-term burden on the health system, but the main driver of health expenditure is currently inflation.

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. 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

In response to the SARS-CoV-2 pandemic, the Austrian governmental crisis unit commissioned a forecast consortium with regularly projections of case numbers and demand for hospital beds. The goal was to assess how likely Austrian ICUs would become overburdened with COVID-19 patients in the upcoming weeks. 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. Here, we report on three key contributions by which our forecasting and reporting system has helped shaping Austria’s policy to navigate the crisis, namely (i) when and where case numbers and bed occupancy are expected to peak during multiple waves, (ii) whether to ease or strengthen non-pharmaceutical intervention in response to changing incidences, and (iii) how to provide hospital managers guidance to plan health-care capacities. Complex mathematical epidemiological models play an important role in guiding governmental responses during pandemic crises, in particular when they are used as a monitoring system to detect epidemiological change points.

Cross-border health care (CBHC) collaborations, using EU funding, may represent a lever for local and regional policymakers by which to balance questions of access and quality of health care against economic concerns in health. An analysis of existing collaborations analyses CHBC against the three core dimensions of European Union health policy: fiscal policy, economic policy, or social cohesion policy. We carried out a literature review and a systematic analysis of online data bases on EU-funded CBHC collaborations for the period 2007-2017. Identified projects were classified as referring to CBHC as an element of either of the three dimensions. Out of 1167 identified projects, 423 EU-funded projects were selected. Projects not primarily concerned with economic concerns (internal market) and fiscal aspects predominate. Results indicate the importance of shared historical ties and geographical proximity for CBHC collaborations. Yet, they also show the need for further mixed methods research to investigate whether EU policies in health are more likely to be in line with the needs of policymakers in member states, if they focus on local and regional demands for high-quality, accessible health care rather than on internal market concerns or fiscal aspects.

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.

Many studies provide evidence for the so-called weekend effect by demonstrating that patients admitted to hospital during weekends show less favourable outcomes such as increased mortality, compared with similar patients admitted during weekdays. The underlying causes for this phenomenon are still discussed controversially. We analysed factors influencing weekend effects in inpatient care for acute stroke in Austria. The study analysed secondary datasets from all 130 public acute care hospitals in Austria between 2010 and 2014 (Austrian DRG Data). The study cohort included 86,399 patient cases admitted with acute ischaemic stroke. By applying multivariate regression analysis, we tested whether patient, treatment or hospital characteristics drove in-hospital mortality on weekends and national holidays. We found that the risk to die after an admission at weekend was significantly higher compared to weekdays, while the number of admissions following stroke was significantly lower. Adjustment for patient, treatment and hospital characteristics substantially reduced the weekend effect in mortality but did not eliminate it. We conclude that the observed weekend effect could be explained either by lower quality of health care or higher severity of stroke admissions at the weekend. In depth analyses supported the hypothesis of higher stroke severity in weekend patients as seen in other studies. While DRG data is useful to analyse stroke treatment and outcomes, adjustment for case mix and severity is essential.

Health care is one of the largest and fastest growing service sectors in OECD countries and a significant contributor to climate change. Health care is also indispensable for human well-being. It is therefore crucial to understand how the health care sector can reduce its emissions without undermining its service quality. We break down the carbon emissions of Austrian health care in unprecedented detail over a decade starting in 2005. We calculated the carbon footprints of Austrian health care providers and further decomposed the emissions attributable to hospitals, the largest health care provider in Austria. We estimated detailed life cycle assessments of the carbon emissions attributable to energy use, the use of selected pharmaceuticals and medical goods and induced private travel. The Austrian health carbon footprint amounted to 6.8 million tons of CO2 in 2014, a decline of 14% since 2005, mainly due to the rising shares of renewables in the Austrian energy sector. Complementary calculations of the carbon emissions from energy use by Austrian health care providers confirm this finding. Goods purchased by hospitals, pharmaceuticals and other medical non-durables stand out as especially large contributors to the health carbon footprint. Carbon emissions attributable to induced travel increased by 15%, indicating the need to better align planning of health care provision with transport and spatial planning. Concluding, we argue that many untapped possibilities for reducing the carbon footprint of health care exist and propose six concrete steps towards sustainable health care that are applicable to most industrial countries.

Austria has a long history of social protection through the social health insurance (SHI) system. Insurance coverage is mandatory and the assignment to a particular insurance fund is determined by law, depending on the place of occupation, type of occupation and occupational status (unemployed, retired person, and so on). This means that there is no regulated competition between SHI funds in Austria and the level of SHI coverage is very high. Around 99.9 per cent (8.8 million residents in 2017) of Austrians are covered by SHI. The Austrian healthcare system in general and the hospital sector in particular are characterized by a high degree of fragmentation. Competencies in the field of inpatient care lie with the nine federal states; financing is split between the federal level, the federal states and SHI.

Abstract Background In order to reduce avoidable consultations but also avoidable self-referrals to hospital outpatient departments, the Austrian authorities agreed to establish a voluntary telephone-based triage system in the course of the health reform 2013. Three regions piloted the system in early 2017. Methods An economic evaluation aimed to assess the impact on health service demand after having consulted the telephone-based system. For analysing the impact, we used the conceptual model of “shift cases” from one particular service setting such as outpatient clinic to another (e.g. GP office) and calculated savings realised through patient shifts. Based on potential savings in private and public costs and running costs of the service, we identified threshold values for cost-effective operation. Results In total, approximately 45,000 completed telephone consultations were registered in the pilot phase. 2,500 persons were advised to conduct self-care whereas more than 40,000 were recommended to contact a health service provider with differing levels of priority. Adherence to the initial recommendation of the provider setting was 70%, the level of priority was met in 90%. With regard to the economic impact, public savings of shift cases range from €31 (self-care instead of GP consultation) to €198 (GP/specialist instead of hospital outpatient clinic). Public costs ranged between €10 and €50 depending on the degree of capacity utilisation, contingency costs and duration of calls. Therefore, economic net gains can be realized if approximately 15% to 25% of callers choose a lower care setting due to the consultation service. Conclusions The tele-triage service has shown to be a potentially cost effective tool but largely depends on user uptake, patient adherence and local maintaining costs. In order to exploit the full potential of the system, policy makers are advised to promote the use of the system in general and to evaluate the used algorithm. Key messages Telephone-based triage systems are a potentially cost-effective strategy in order to reduce avoidable encounters both on a primary care level but also at hospital outpatient clinics. The public savings of a shift in the provider setting vary substantially depending on the level of service delivery with diminishing savings for shifts in lower levels.

stewardship program with a multidisciplinary team using telemedicine.CCPM is a children cardiological center located in Taormina hospital (220 beds), born in Sicily in partnership with The Bambino Gesu `Children's Hospital of Rome (OPBG).CCPM includes an icu, an operating room with a Hybrid Cath lab and performs high complex surgical procedures, on children in critical conditions and low birth-weight requiring hospitalwide support for long periods. Results:Comparing the period before the intervention (1 January 2014-1 March 2015) to a post-intervention period (1 March 2015-1 March 2016) we observed a decrease in antibiotic consumption(-30%), a lower isolation rate of multi drug resistant bacteria (104 vs 79 x 1000 person days, p = 0.01) and a reduction in the incidence of hospital infections (9.5 vs 6.5 x 1000 person days).Following the good results in the fight against infections we decided to extend the telemedicine program to other specialized branches (radiology, neonatal surgery, genetics, etc): in 2016 and 2017, 42 and 59 telemedicine services were provided, respectively, including training sessions in the field of nursing education.Lessons:The health system needs to promote cost reduction, network organization and clinical specialization.The information technology offers tools to support professional training and patient safety.We believe that telemedicine is a very useful system to guarantee high-quality services at sustainable costs, especially in contexts where geographical distances make difficult developing residential training programs. Key messages:telemedicine can be an economically sustainable tool for developing the high-level clinical specialties and health networks, especially in sub-urban areas.multidisciplinary antimicrobial stewardship program can promote the appropriate use of antibiotics.

OBJECTIVES: One of the main policy targets in many European health care systems is to provide constant quality of health care across time and space without discrimination of patients. Many studies show that the risk of mortality after admission to a hospital following acute diseases such as stroke is significantly higher on weekends than on weekdays. However, from a medical point of view the burden of disease should be consistent throughout the week. Literature calls this phenomenon weekend effect, which has caused great attention by experts and the public since the 1970s. The independent effect has been found in all inpatient settings of care irrespective of elective or emergency care. The weekend effect is well documented, however the reasons are still discussed controversially. Evidence for variation of health outcomes overnight, at holidays or at the weekend is still scarce and in many cases speculative. This study aims to analyse whether there is constant service quality measured by health outcomes in inpatient care across weekdays when controlling for patient and hospital characteristics in Austrian acute care hospitals. *** METHODS: The study analyses secondary datasets from all public acute care hospitals in Austria from 2010 to 2014 (Austrian DRG Data). The study cohort includes all patient episodes suffering from acute ischaemic stroke admitted to public Austrian acute care hospitals (approx. 88,500 episodes in 130 hospitals). The data sources contain patient characteristics and aspects of quality of care allowing a retrospective study by performing multivariate regression analysis controlling for important patient-level as well as hospital characteristics. The primary outcome variable is case fatality rate within 30 days after admission to an acute care hospital and the primary independent variable is admission on weekends (Saturday, Sunday, and holidays) versus weekdays. *** RESULTS: Admissions following stroke on weekends are significantly lower than on weekdays. At the same time, the risk to die after an admission at a weekend is significantly higher. The results show a significant weekend effect on stroke mortality. Hospital as well as patient characteristics on weekends differ significantly with regard to treatment, patient demographics, infrastructure and staffing. The weekend effect might be attributed to higher stroke severity in weekend patients (case mix on weekends) but further analysis and rigorous risk adjustment is needed to show clearer evidence. *** DISCUSSION: Patients admitted to Austrian acute care hospitals following stroke show a higher mortality than patients admitted on weekdays. Disparities in quality and patient characteristics may explain the observed differences in weekend mortality. The dataset allows only the calculation of covariates that mix quality and severity proxies which makes a clear attribution challenging. The findings should initiate further research and critical evaluation whether resources, expertise and staff should be provided for critical care in the same quantity and quality throughout the week.