Challenges with healthcare workers' protection during the COVID-19 pandemic in Iran

Risk Factors for SARS-CoV-2 Among a Cohort of Healthcare Workers in Lebanon

BACKGROUND

Healthcare workers (HCWs) faced substantial risk of infection during the COVID-19 outbreak. This study aims to determine the prevalence of anti-SARS-CoV-2 antibodies in a cross-sectional sample of HCWs as well as risk factors associated with exposure to SARS-CoV-2.

METHODS

The study was conducted between March and May 2021 at the American University of Beirut Medical Center (AUBMC), a tertiary hospital located in Lebanon. Socio-demographic and clinical data, as well as data on exposure, PCR results, PPE adherence, and vaccination status, were collected using an online questionnaire. Sera were also collected to determine seropositivity using commercially available enzyme-linked immunoassay (ELISA) targeting the spike (S) and the nucleocapsid proteins (NCP) of SARS-CoV-2.

FINDINGS

Among 92 recruited HCWs, 72.3% received PPE training, more than 70% were adherent to using appropriate PPEs, and around 80% were vaccinated. Nurses in this study population were at higher risk of exposure compared to medical doctors, technicians, and other HCWs. Among the HCWs who performed a PCR test, 28.6% were infected with SARs-CoV-2 with workplace exposure not associated with COVID-19 infection. All vaccinated HCWs were seropositive for anti-S IgG with high titer (≥384 BAU/mL), with a significantly higher median anti-S IgG titer compared to unvaccinated HCWs with previous infection (384 vs. 140.1 BAU/mL; p = .0043).

CONCLUSIONS

Our study highlights the importance of implementing strict infection control policies among HCWs and deploying an effective COVID-19 vaccination strategy. More studies are needed in Lebanon to assess risk factors of SARS-CoV-2 exposure in the workplace.

Psychosocial status and risk perception among Iranian healthcare workers during the fifth wave of the COVID-19 pandemic

BACKGROUND

Healthcare workers (HCWs) are essential resources, and their health and wellbeing are key not only for offering constant and useful care facilities to clients, but also for maintaining the safety of the workforce and patients. The risk of severe mental health problems among HCWs may have increased during large outbreaks of COVID-19. To evaluate the psychosocial status and risk perception of HCWs who participated in treating COVID-19 patients in Northern Iran, we performed a web-based cross-sectional study.

METHODS

The web-based cross-sectional design was applied between June 27 and September 2, 2021. Using convenience sampling, 637 HCWs were recruited from hospitals in Northern Iran (Mazandaran). The HCWs completed self-report questionnaires that included a sociodemographic information form, the 12-item General Health Questionnaire, Impact of the Event Scale-Revised, Risk Perception Questionnaire, and Anxiety Stress Scale-21. The data were analyzed via descriptive and inferential statistics and univariate/multivariate logistic regression to assess the risk factors linked to each psychosocial consequence.

RESULTS

The results reveal that the COVID-19 pandemic had an adverse psychosocial influence on HCWs, which was already apparent 1.5 years after the crisis began. Based on the results, 71.6%, 55.6%, and 32.3% of HCWs reported having anxiety, depression, and stress symptoms, respectively, since the outbreak of this disease. The logistic regression models displayed that marital status, having children, and working hours with patients were all risk factors of psychosocial impairment.

CONCLUSIONS

The outbreak of COVID-19 can be considered an important experience of a bio-disaster resulting in a significant rate of psychiatric problems in HCWs. There is a need for designing and promoting supportive programs to help HCWs cope and to improve their psychosocial state, and the present study has detected for whom psychosocial support may be effective and practical 1.5 years after the primary outbreak. Moreover, detecting and managing concerns and reducing infection-related embarrassment/stigma are essential for improving HCWs' mental health.

Predicting COVID-19 Cases Among Nurses Using Artificial Neural Network Approach

We designed a forecasting model to determine which frontline health workers are most likely to be infected by COVID-19 among 220 nurses. We used multivariate regression analysis and different classification algorithms to assess the effect of several covariates, including exposure to COVID-19 patients, access to personal protective equipment, proper use of personal protective equipment, adherence to hand hygiene principles, stressfulness, and training on the risk of a nurse being infected. Access to personal protective equipment and training were associated with a 0.19- and 1.66-point lower score in being infected by COVID-19. Exposure to COVID-19 cases and being stressed of COVID-19 infection were associated with a 0.016- and 9.3-point higher probability of being infected by COVID-19. Furthermore, an artificial neural network with 75.8% (95% confidence interval, 72.1-78.9) validation accuracy and 76.6% (95% confidence interval, 73.1-78.6) overall accuracy could classify normal and infected nurses. The neural network can help managers and policymakers determine which frontline health workers are most likely to be infected by COVID-19.