We investigated the faecal carriage prevalence of extended-spectrum β-lactamase production in Escherichia coli (EP-EC) and/or Klebsiella pneumoniae (EP-KP) and risk factors associated with carriage among adult study subjects in Finland, Germany, Latvia, Poland, Russia and Sweden (partner countries). The aim was to get indicative data on the prevalence of ESBL-carriage in specific populations in the region. Faecal samples were collected from four study populations and screened on ChromID-ESBL and ChromID-OXA-48 plates. Positive isolates were further characterised phenotypically. Our results show a large variation in carrier prevalence ranging from 1.6% in Latvia to 23.2% in Russia for EP-EC. For the other partner countries, the prevalence of EP-EC were in increasing numbers, 2.3% for Germany, 4.7% for Finland, 6.6% for Sweden, 8.0% for Poland and 8.1% for all partner countries in total. Carriers of EP-KP were identified only in Finland, Russia and Sweden, and the prevalence was < 2% in each of these countries. No carriers of carbapenemase-producing isolates were identified. This is the first study reporting prevalence of carriers (excluding traveller studies) for Finland, Latvia, Poland and Russia. It contributes with important information regarding the prevalence of EP-EC and EP-KP carriage in regions where studies on carriers are limited.

Washing hands is one of the most important ways to prevent infectious diseases, including COVID-19. The World Health Organization (WHO) has published hand-washing guidelines. This paper presents a large real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Pauls Stradins Clinical University Hospital. There are 3185 hand-washing episodes in total, each of which is annotated by up to seven different persons. The annotations classify the washing movements according to the WHO guidelines by marking each frame in each video with a certain movement code. The intention of this “in-the-wild” dataset is two-fold: to serve as a basis for training machine-learning classifiers for automated hand-washing movement recognition and quality control, and to allow to investigation of the real-world quality of washing performed by working medical staff. We demonstrate how the data can be used to train a machine-learning classifier that achieves classification accuracy of 0.7511 on a test dataset.

Background: Severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2, the causative agent of coronavirus disease 2019 (COVID-19)] outbreak has been declared a global pandemic by the World Health Organization. The COVID-19 pandemic has highlighted problems of sustainable infection prevention and control measures worldwide, particularly the emerging issues with an insufficient supply of personal protective equipment. The aim of this study was to provide an action plan for mitigation of occupational hazards and nosocomial spread of SARS-CoV-2 through a failure mode analysis based on observations during in situ simulations. Methods: A multicenter, cross-sectional, observational, simulation-based study was performed in Latvia from March 2 to 26, 2020. This study was conducted at 7 hospitals affiliated with Riga Stradiņš University. The presentation of a COVID-19 patient was simulated with an in situ simulations, followed by a structured debrief. Healthcare Failure Modes and Effects Analysis is a tool for conducting a systematic, proactive analysis of a process in which harm may occur. We used Healthcare Failure Modes and Effects Analysis to analyze performance gaps and systemic issues. Results: A total of 67 healthcare workers from 7 hospitals participated in the study (range = 4-17). A total of 32 observed failure modes were rated using a risk matrix. Twenty-seven failure modes (84.4%) were classified as either medium or high risk or were single-point weaknesses, hence evaluated for action type and action; 11 (40.7%) were related to organizational, 11 (40.7%) to individual, and 5 (18.5%) to environmental factors. Conclusions: Simulation-based failure mode analysis helped us identify the risks related to the preparedness of the healthcare workers and emergency departments for the COVID-19 pandemic in Latvia. We believe that this approach can be implemented to assess and maintain readiness for the outbreaks of emerging infectious diseases in the future.

Background: Thousands of people die every day around the world from infections acquired in a hospital. Hands are the main pathways of germ transmission during healthcare. Hand hygiene monitoring can be performed using various methods. One of the latest techniques that can combine all is a neural network-based hand hygiene monitoring system. Methods/Design: Each participant performed 3 hand-washing trials, each time receiving different type of feedback. The order in which each participant of the study used the developed applications was strictly defined, thus each hand-washing study session started with performing hand washing using application A, B and C accordingly. All captured videos of hand-wash episodes were saved and later analysed with neural networks. In the end, both evaluation results were compared and evaluated. Results show that when the participants use Application Type A, they perform hand washing much faster, as well as in comparison of Application Type A versus application type C. However, the longest time spent for the hand washing was detected while using the application type B. Conclusion: Study shows that structured guidance provided during the real time hand washing could be associated with better overall performance. The Application C has confirmed its effectiveness. Proving its advantage among other applications, the Application C can be integrated into the clinical environment