Vomiting of nurses while working in isolation wards: a cross-sectional study in China during COVID-19 (preprint)

Background: The early phases of COVID-19 infection are highly transmissible and can be life-threatening, with infected individuals requiring isolation for proper treatment. Healthcare workers, particularly nurses, who provide care in such areas must take precautionary measures by donning personal protective equipment. Unfortunately, these nurses are also at elevated risk for developing adverse psychological outcomes, such as fear, anxiety, burnout, and post-traumatic stress disorder. Objective: This study aimed to examine the characteristics and identify the risk factors associated with vomiting syndrome among nurses working in a COVID-19 isolation ward. The objective of the investigation is to provide valuable information to support the development of effective management strategies to minimize the occurrence of this syndrome. Design: This study employed a descriptive cross-sectional design and utilized a questionnaire as the data collection instrument. Methods: Data was collected from 354 nurses working in a COVID-19 isolation ward between January 2020 and March 2021. Three questionnaires were administered to gather data: the Gastroesophageal Reflux Questionnaire, the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI), and the Symptom Checklist-90 (SCL-90). Binary regression analysis was conducted to determine the independent risk factors for vomiting syndrome among nurses working in COVID-19 isolation. Result: Of the 354 participants, 82 (23.16%) reported experiencing vomiting syndrome. The incidence of vomiting syndrome was higher among female participants (25.57%) compared to male participants (6.67%). Results of the study revealed that the scores for sleep quality, as assessed by the seven factors of the PSQI, were significantly higher in the vomiting syndrome group compared to the non-vomiting syndrome group (p < 0.05). The findings also indicated positive correlations between vomiting syndrome and several sleep-related factors, including sleep quality, sleep latency, sleep time, sleep disturbance, and sleep dysfunction (p < 0.05). In terms of self-symptom assessment, scores were found to be higher among participants in the vomiting syndrome group compared to the non-vomiting syndrome group (p < 0.05). Furthermore, positive correlations were observed between vomiting syndrome and somatization, obsessive-compulsive symptoms, phobic anxiety, and fear (p < 0.05), while a negative correlation was found between vomiting syndrome and paranoid ideation (p = 0.045). Binary regression analysis revealed that several independent risk factors for vomiting syndrome were identified, including gender (OR = 0.023, p = 0.001), personal protective equipment impact (OR = 3.647, p < 0.01), ICU work experience (OR = 0.003, p < 0.01), total SCL-90 score (OR = 1.148, p < 0.01), and total PSQI score (OR = 2.123, p < 0.01). Conclusions: The occurrence of vomiting syndrome among nurses working in COVID-19 isolation wards is substantial, yet has received limited attention in the literature. Further research is necessary to fully understand this phenomenon. The impact of the utilization of personal protective equipment on the incidence of vomiting syndrome warrants further investigation. Nurses with experience in intensive care units may be better equipped to handle the demands of working in isolation wards. Hospital administrators should be attentive to the issue of vomiting syndrome among nurses who are exposed to infectious diseases and wearing personal protective equipment, and should implement targeted measures in response to the specific characteristics of its occurrence as part of their health monitoring programs.

Research on decision-making optimization of emergency supply chain based on Markov chain and multi-objective model

In order to cope with the sudden disasters such as floods, COVID-19,etc.,a discrete time Markov chain and multi-objective programming model（DTMC-MOP） with the maximum supply satisfaction rate, the shortest supply time and the lowest supply cost was proposed to dynamically identify, analyze and respond to the emergency supply chain risk.The improved self-adaptive Non-dominated Sorting Genetic Algorithm-Ⅱ（NSGA-Ⅱ） was used to solve the optimization model, and the feasibility and effectiveness of the model were verified by testing and evaluation with standard test functions.Through the example analysis, the Pareto optimal front with higher precision and more uniform distribution was obtained.The results showed that the decision-maker could choose the appropriate emergency scheme based on the core objective of emergency management or different preferences.It provide a scientific method for the decision-making optimization of emergency supply chain, which has positive significance for ensuring the life safety of victims and maintaining the social harmony and stability.

Estimating the time interval between transmission generations and the presymptomatic period by contact tracing surveillance data from 31 provinces in the mainland of China

The global pandemic of 2019 coronavirus disease (COVID-19) is a great assault to public health. Presymptomatic transmission cannot be controlled with measures designed for symptomatic persons, such as isolation. This study aimed to estimate the interval of the transmission generation (TG) and the presymptomatic period of COVID-19, and compare the fitting effects of TG and serial interval (SI) based on the SEIHR model incorporating the surveillance data of 3453 cases in 31 provinces. These data were allocated into three distributions and the value of AIC presented that the Weibull distribution fitted well. The mean of TG was 5.2 days (95% CI: 4.6-5.8). The mean of the presymptomatic period was 2.4 days (95% CI: 1.5-3.2). The dynamic model using TG as the generation time performed well. Eight provinces exhibited a basic reproduction number from 2.16 to 3.14. Measures should be taken to control presymptomatic transmission in the COVID-19 pandemic.