Burden of COVID-19: Disability-Adjusted Life Years (DALYs) across 16 European countries.

OBJECTIVE: The aim of this study is to measure and compare the burden of disease of COVID-19 pandemic in 16 EU/EEA countries through the estimation of Disability-Adjusted Life Years (DALYs) over a long period of time. MATERIALS AND METHODS: The observational study was based on data from ECDC and WHO databases collected from 27 January 2020 to 15 November 2020. In addition to the absolute number of DALYs, a weekly trend of DALYs/100,000 inhabitants was computed for each country to assess the evolution of the pandemic burden over time. A cluster analysis and Kolmogorov-Smirnov (KS) test were performed to allow for a country-to-country comparison. RESULTS: The total DALYs amount to 4,354 per 100.000 inhabitants. YLLs were accountable for 98% of total DALYs.  Italy, Czechia and Sweden had the highest values of DALYs/100,000 while Finland, Estonia and Slovakia had the lowest. The latter three countries differed significantly from the others - in terms of DALYs trend over time - as shown by KS test. The cluster analysis allowed for the identification of three clusters of countries sharing similar trends of DALYs during the assessed period of time. These results show that notable differences were observed among different countries, with most of the disease burden attributable to YLLs. CONCLUSIONS: DALYs have proven to be an effective measure of the burden of disease. Public health and policy actions, as well as demographic, epidemiological and cultural features of each country, may be responsible for the wide variations in the health impact that were observed among the countries analyzed.

Development of a checklist as self-assessment tool to evaluate the reprocessing of endoscopic instruments in an Italian teaching hospital.

OBJECTIVE: The present study aims to develop a checklist, as a self-assessment tool, for evaluating all the items involved in the endoscope reprocessing that could be useful for the improvement and/or development of a safety endoscope reprocessing system. MATERIALS AND METHODS: A three-step modified Delphi method, with an embedded qualitative component, was adopted to develop the checklist. According to it, corrective actions were performed before its further re-administration. Contextually, the microbiological surveillance of the endoscopes and of the wash disinfector machine was carried out. RESULTS: Five areas were included in the checklist. After the 1st checklist application, only one of three wards reached the excellent scores in all the items. The other two wards showed an improvement in the Traceability and Endoscope Reprocessing areas after corrective actions. The McNemar's test reported significant difference in the proportion of satisfactory results before and after the 1st and 2nd checklist application. The microbiological surveillance, conducted after the 1st administration, showed unsatisfactory results for the 2 bronchoscopes available in the Intensive Care Unit and for 2 automated endoscope reprocessors. The analysis performed after the 2nd administration showed good results. CONCLUSIONS: The periodic administration of the checklist is functional for a self-assessment of quality reprocessing procedures carried out in the large endoscopic services and in the wards occasionally providing those services, according to the good practice guidelines and for any corrective actions to increase the safety.

Gemelli decision tree Algorithm to Predict the need for home monitoring or hospitalization of confirmed and unconfirmed COVID-19 patients (GAP-Covid19): preliminary results from a retrospective cohort study.

OBJECTIVE: To develop a deep learning-based decision tree for the primary care setting, to stratify adult patients with confirmed and unconfirmed coronavirus disease 2019 (COVID-19), and to predict the need for hospitalization or home monitoring. PATIENTS AND METHODS: We performed a retrospective cohort study on data from patients admitted to a COVID hospital in Rome, Italy, between 5 March 2020 and 5 June 2020. A confirmed case was defined as a patient with a positive nasopharyngeal RT-PCR test result, while an unconfirmed case had negative results on repeated swabs. Patients' medical history and clinical, laboratory and radiological findings were collected, and the dataset was used to train a predictive model for COVID-19 severity. RESULTS: Data of 198 patients were included in the study. Twenty-eight (14.14%) had mild disease, 62 (31.31%) had moderate disease, 64 (32.32%) had severe disease, and 44 (22.22%) had critical disease. The G2 value assessed the contribution of each collected value to decision tree building. On this basis, SpO2 (%) with a cut point at 92 was chosen for the optimal first split. Therefore, the decision tree was built using values maximizing G2 and LogWorth. After the tree was built, the correspondence between inputs and outcomes was validated. CONCLUSIONS: We developed a machine learning-based tool that is easy to understand and apply. It provides good discrimination in stratifying confirmed and unconfirmed COVID-19 patients with different prognoses in every context. Our tool might allow general practitioners visiting patients at home to decide whether the patient needs to be hospitalized.