Establishing an integrated prevention and control system to ensure zero nosocomial infections - an analysis of the logistics support in controlling nosocomial infections during the hospital's fight against the COVID-19 epidemic.

Aim: To analyze the role of the logistics support services in nosocomial infection control during emergency periods, with a focus on job responsibilities including the organization of vehicle parking, supply of hospital meals, washing of medical bedding and clothing, disposal and management of medical sewage and waste, elevator services, disinfection of air conditioning systems, disinfection and cleaning of ambulances, management of hospital buildings, storage of sterilization supplies, reception and delivery of oxygen cylinders and protection of staff health as examples. Methods: The adjustment and optimization of the emergency support system and working mode as part of hospitals' response to major public emergencies were summarized, and the vital supporting role of the logistics support services in nosocomial infection control was analyzed. Results: The logistics support services played a crucial role in ensuring the high-performance operations of the hospitals and control of nosocomial infections, resulting in the excellent outcome of "zero infection" among hospital staff. Conclusion: Establishing a safe, flexible and efficient system for the logistics support services is important in ensuring an effective response by hospitals to health emergencies.

Factors determining different death rates because of the COVID-19 outbreak among countries.

BACKGROUND: During the coronavirus disease 2019 (COVID-19) pandemic, all European countries were hit, but mortality rates were heterogenous. The aim of the current paper was to identify factors responsible for this heterogeneity. METHODS: Data concerning 40 countries were gathered, concerning demographics, vulnerability factors and characteristics of the national response. These variables were tested against the rate of deaths per million in each country. The statistical analysis included Person correlation coefficient and Forward Stepwise Linear Regression Analysis (FSLRA). RESULTS: The FSLRA results suggested that 'days since first national death for the implementation of ban of all public events' was the only variable significantly contributing to the final model, explaining 44% of observed variability. DISCUSSION: The current study suggests that the crucial factor for the different death rates because of COVID-19 outbreak was the fast implementation of public events ban. This does not necessarily mean that the other measures were useless, especially since most countries implemented all of them as a 'package'. However, it does imply that this is a possibility and focused research is needed to clarify it, and is in accord with a model of spreading where only a few superspreaders infect large numbers through prolonged exposure.