A propensity score matched analysis of COVID-19 ongoing symptoms in primary medical staff members with different levels of stress in Jiangsu Province, China.

Objective: Ongoing symptoms which originated from coronavirus disease 2019 (COVID-19) infections threaten the health of a broad population of patients. With recent changes in COVID-19 control measures in China, medical staff members are currently experiencing a high level of stress. This study aimed to investigate the prevalence of ongoing symptomatic COVID-19 and explore the potential association between stress and ongoing COVID symptoms. Methods: From January 17th to February 2, 2023, primary medical staff members in Jiangsu Province were surveyed using a self-designed questionnaire. Univariate multinomial logistic analysis was used to illustrate the relationship between stress and ongoing symptoms after matching the low- and high-stress groups in a 1:1 ratio based on propensity scores. Results: Analysis revealed that 14.83 % (3785/25,516) of primary medical staff members infected with COVID-19 experienced ongoing symptoms, the most common of which included cough (9.51 %), dyspnea (9.51 %), sleep problems (4.40 %), anxiety (2.29 %), and reproductive system symptoms (1.89 %). In matched patients, higher stress levels were associated with a greater risk of ongoing symptoms than in patients without ongoing symptoms for 14 of the 15 reported symptoms in this study (odds ratios [ORs] > 1 and P < 0.05). Moreover, higher levels of stress were associated with a greater risk of more ongoing symptoms, and the overall ORs increased with the number of symptoms (ORs >1 and P < 0.05). Conclusion: To mitigate the possibility of experiencing ongoing symptoms, healthcare organizations and local authority agencies should institute helpful measures to decrease stress levels such as medical staff augmentation and enabling all staff to have a reasonable work-life balance.

Duration and severity of COVID-19 symptoms among primary healthcare workers: A cross-sectional survey.

AIMS: This study aims to investigate the epidemiological characteristics of COVID-19 infection among healthcare workers, including the severity, duration of infection, post-infection symptoms and related influencing factors. METHODS: A self-administered questionnaire was utilized to assess the post-infection status of primary healthcare workers in Jiangsu Province. The questionnaire collected information on demographic characteristics, lifestyle habits, post-infection clinical manifestations, work environment and recovery time of the respondents. Customized outcome events were selected as dependent variables and logistic regression models were employed to analyse the risk factors. Phi-coefficient was used to describe the relationship between post-infection symptoms. RESULTS: The analysis revealed that several factors, such as female, older age, obesity, previous medical history, exposure to high-risk environments and stress, were associated with a higher likelihood of experiencing more severe outcomes. On the other hand, vaccination and regular exercise were found to contribute to an earlier resolution of the infection. Among the post-infection symptoms, cough, malaise and muscle aches were the most frequently reported. Overall, there was a weak association among symptoms persisting beyond 14 days, with only cough and malaise, malaise and dizziness and headache showing a stronger correlation. CONCLUSION: The study findings indicate that the overall severity of the first wave of infection, following the complete lifting of restrictions in China, was low. The impact on primary healthcare workers was limited, and the post-infection symptoms exhibited similarity to those observed in other countries. It is important to highlight that these conclusions are specifically relevant to the population infected with the Omicron variant. IMPACTS: This study helps to grasp the impacts of the first wave of COVID-19 infections on healthcare workers in China after the national lockdown was lifted. PATIENTS: Primary healthcare workers in Jiangsu Province, including doctors, nurses, pharmacists and other personnel from primary healthcare units such as community health service centres and health centres.

Primary healthcare workers' COVID-19 infection status following implementation of adjusted epidemic prevention and control strategies: a cross-sectional study in Jiangsu, China.

Introduction: In times of epidemic outbreaks, healthcare workers (HCWs) emerge as a particularly vulnerable group. This cross-sectional study endeavors to assess the COVID-19 infection rate among the primary HCWs in Jiangsu Province subsequent to the implementation of adjusted epidemic prevention and control strategies. Methods: From January 17 to February 2, 2023, an extensive survey was conducted among primary HCWs in Jiangsu Province, employing a self-designed questionnaire. Logistic regression analysis was utilized to identify the factors associated with COVID-19 infection. Results: The overall infection rate among primary HCWs stood at 81.05%, with a 95% confidence interval (CI) of 80.61-81.48%. Among those afflicted, cough, fatigue, and fever emerged as the three most prevalent symptoms, each with an incidence rate exceeding 80%. In the context of multivariate logistic regression, an elevated risk of COVID-19 infection was observed in correlation with female gender (adjusted odds ratio [aOR] = 1.12, 95% CI: 1.04-1.21), possessing a bachelor's degree or higher (aOR = 1.32, 95% CI: 1.23-1.41), accumulating over 10 years of work experience (aOR = 1.28, 95% CI: 1.11-1.47), holding a middle-level cadre position (aOR = 1.22, 95% CI: 1.11-1.35), assuming the role of a unit leader (aOR = 1.30, 95% CI: 1.11-1.54), and working in a fever clinic for 1 to 10 days per month (aOR = 1.42, 95% CI: 1.29-1.57). Conversely, advanced age (aOR = 0.76, 95% CI: 0.70-0.82), being underweight (aOR = 0.78, 95% CI: 0.69-0.90), current smoking (aOR = 0.64, 95% CI: 0.57-0.71), receiving 4 doses of COVID-19 vaccine (aOR = 0.49, 95% CI: 0.37-0.66), and pregnancy or perinatal status (aOR = 0.85, 95% CI: 0.72-0.99) were associated with a diminished risk of infection. Conclusion: Following the implementation of adjusted policies, a substantial proportion of primary HCWs in Jiangsu province contracted COVID-19. Female gender and younger age emerged as risk factors for COVID-19 infection, while no discernible link was established between professions and COVID-19 susceptibility. The receipt of COVID-19 vaccines demonstrated efficacy in curtailing the infection rate, underscoring the significance of bolstering prevention knowledge and heightening self-protective awareness among primary HCWs.

An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma.

Methods: Patients (363 in total) with stomach adenocarcinoma from The Cancer Genome Atlas (TCGA) cohort were included. An autoencoder was constructed to integrate the RNA sequencing, miRNA sequencing, and methylation data. The features of the bottleneck layer were used to perform the k-means clustering algorithm to obtain different subgroups for evaluating the prognosis-related risk of stomach adenocarcinoma. The model's robustness was verified using a 10-fold cross-validation (CV). Survival was analyzed by the Kaplan-Meier method. Univariate and multivariate Cox regression was used to estimate hazard risk. The model was validated in three independent cohorts with different endpoints. Results: The patients were divided into low-risk and high-risk groups according to the k-means clustering algorithm. The high-risk group had a significantly higher risk of poor survival (log-rank P value = 2.80e - 06; adjusted hazard ratio = 2.386, 95% confidence interval: 1.607~3.543), a concordance index (C-index) of 0.714, and a Brier score of 0.184. The model performed well both in the 10-fold CV procedure and three independent cohorts from the Gene Expression Omnibus (GEO) repository. Conclusions: A robust and generalizable model based on the autoencoder was proposed to integrate multiomics data and predict the prognosis of patients with stomach adenocarcinoma. The model demonstrates better performance than two alternative approaches on prognosis prediction. The results might provide the grounds for further exploring the potential biomarkers to predict the prognosis of patients with stomach adenocarcinoma.