Prevalence and influencing factors of sleep disorders in medical students after the COVID-19 pandemic (preprint)

Background The prevalence of sleep disorders among medical students was high during the COVID-19 pandemic. However, there are fewer studies of sleep disorders in medical students after the COVID-19 pandemic. This study aimed to investigate the prevalence and factors influencing sleep disorders among Chinese medical students after COVID-19.Methods We enrolled 1194 medical students. The Self-administered scale was used to collect the demographic characteristics. The Self-rating Depression Scale (SDS), the Self-rating Anxiety Scale (SAS), and the Pittsburgh Sleep Quality Index (PSQI) were used to assess subjects' depression, anxiety, and sleep disorders, respectively. The chi-square test and binary logistic regression were used to identify factors that influence sleep disorders. The receiver operating characteristic (ROC) curve was used to assess the predictive value of relevant variables for sleep disorders.Results We found that the prevalence of sleep disorders among medical students after COVID-19 was 82.3%. According to logistic regression results, medical students with depression were 1.151 times more likely to have sleep disorders than those without depression (OR = 1.151, 95% CI 1.114 to 1.188). Doctoral students were 1.908 times more likely to have sleep disorders than graduate and undergraduate students (OR = 1.908, 95% CI 1.264 to 2.880). In addition, the area under the ROC curve for depression is 0.689.Conclusion The prevalence of sleep disorders among medical students is high after COVID-19. In addition, high academic level and depression are risk factors for sleep disorders. Therefore, medical colleges and administrators should pay more attention to sleep disorders in medical students after the COVID-19 pandemic. Regular assessment of sleep disorders and depression is extremely necessary.

Factors influencing COVID-19 vaccination decision-making among African American and Hispanic pregnant and postpartum women in Deep South (preprint)

Background COVID-19 vaccination is vital for ending the pandemic but concerns about its safety among pregnant and postpartum women, especially among African American (AA) and Hispanic women, persist. This study aims to explore factors that influence vaccination decision-making among AA and Hispanic pregnant and postpartum women through womens experiences and maternal care providers (MCPs) observations. Methods From January and August 2022, we conducted semi-structured interviews with AA and Hispanic women and MCPs. Participants were recruited from obstetric and pediatric clinics in South Carolina, and all births took place after March 2020. Thematic analysis was employed for data analysis. Results The study involved 19 AA and 20 Hispanic women, along with 9 MCPs, and revealed both barriers and facilitators to COVID-19 vaccination. The factors that influence pregnant and postpartum womens decision about COVID-19 vaccine uptake included: 1) awareness of health threats associated with COVID-19 vaccines, 2) vaccine availability and accessibility, 3) vaccine-related knowledge and exposure to misinformation, 4) concerns regarding pre-existing health conditions and potential side effects of COVID-19 vaccines, 5) emotional factors associated with vaccination decision-making processes, 6) concerns about the well-being of infants, 7) cultural perspectives, and 8) encouragement by trusted supporters. Conclusion Findings suggest that reliable information, social support, and trusted doctors advice can motivate COVID-19 vaccination. However, barriers such as misinformation, mistrust in the health care system, and fears related to potential side effects impede vaccination uptake among AA and Hispanic pregnant and postpartum women. Future interventions should target these barriers, along with health disparities, involve trusted doctors in outreach, and initiate vaccine conversations to promote vaccination among this population.

Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets (preprint)

Deep learning faces a significant challenge wherein the trained models often underperform when used with external test data sets. This issue has been attributed to spurious correlations between irrelevant features in the input data and corresponding labels. This study uses the classification of COVID-19 from chest x-ray radiographs as an example to demonstrate that the image contrast and sharpness, which are characteristics of a chest radiograph dependent on data acquisition systems and imaging parameters, can be intrinsic shortcuts that impair the model’s generalizability. The study proposes training certified shortcut detective models that meet a set of qualification criteria which can then identify these intrinsic shortcuts in a curated data set.

A study on level of mental health and influencing factors of medical staff in the makeshift hospital during COVID-19 in Shanghai

Objective　To analyze the mental health status of medical staff in the Fourth Branch of National Convention and Exhibition Center Makeshift Hospital during the COVID-19 epidemic in Shanghai to lay a theoretical foundation for the mental health and psychological intervention of medical staff in COVID-19 and other public health emergencies. Methods　An online questionnaire survey was conducted with the generalized anxiety disorder scale (GAD-7), patient health questionnaire (PHQ-9), and Athens insomnia scale (AIS) before medical staff entering the makeshift hospital and one month later. Results　The detection rates of anxiety, depression and insomnia were 18.4%, 22.1% and 27.0% respectively before entering the makeshift hospital, and 28.8%, 59.3% and 64.2% respectively during the follow-up period one month later. The GAD-7, PHQ-9 and AIS scores of medical staff after working in the makeshift hospital for one month increased significantly compared with those at the baseline period (P<0.01). Female and previous history of using sedative and hypnotic drugs were risk factors for increased depression level among medical staff in the makeshift hospital. Conclusions　The anxiety, depression and insomnia levels of the medical staff in Shanghai increased after working in the makeshift hospital for one month. It is of great significance for the front-line support work to identify the medical staff with serious psychological problems and carry out psychological intervention in the early stage.

Substance use, psychiatric symptoms, personal mastery, and social support among COVID-19 long haulers: A compensatory model (preprint)

Background: Substance use has become a critical health concern during the COVID-19 pandemic, and emerging attention has been paid to people with the persistent symptoms of COVID-19 (COVID-19 long haulers) due to their high vulnerability. However, scant research has investigated their substance use and relevant psychosocial factors. The current study was to (1) examine substance use behaviors (i.e., legal drug use, illicit drug use, and non-medical use of prescription drugs); and (2) assessed their associations with psychiatric symptoms (i.e., depression, anxiety, and post-traumatic stress disorder) and psychosocial factors (i.e., personal mastery and social support) among COVID-19 long haulers. Methods: In January to March 2022, 460 COVID-19 long haulers (50% female), with an average age of 32, completed online surveys regarding their demographics, substance use, psychiatric symptoms, and psychosocial factors. Results: In the past three months, the most commonly used or non-medically used substances were tobacco (82%) for legal drugs, cocaine (53%) for illicit drugs, and prescription opioids (67%) for prescription drugs. Structural equation modeling suggested that psychiatric symptoms were positively associated with substance use behaviors (bs = .38 to .68, ps < .001), while psychosocial factors were negatively associated with substance use behaviors (bs = -.61 to -.43, ps < .001). Conclusion: Substance use is common in COVID-19 long haulers and psychiatric symptoms are the risk factors. Personal mastery and social support appear to offer protection offsetting the psychiatric influences. Substance use prevention and mental health services for COVID-19 long haulers should attend to personal mastery and social support.

A Generalizable Artificial Intelligence Model for COVID-19 Classification Task Using Chest X-ray Radiographs: Evaluated Over Four Clinical Datasets with 15,097 Patients (preprint)

Purpose: To answer the long-standing question of whether a model trained from a single clinical site can be generalized to external sites. Materials and Methods: 17,537 chest x-ray radiographs (CXRs) from 3,264 COVID-19-positive patients and 4,802 COVID-19-negative patients were collected from a single site for AI model development. The generalizability of the trained model was retrospectively evaluated using four different real-world clinical datasets with a total of 26,633 CXRs from 15,097 patients (3,277 COVID-19-positive patients). The area under the receiver operating characteristic curve (AUC) was used to assess diagnostic performance. Results: The AI model trained using a single-source clinical dataset achieved an AUC of 0.82 (95% CI: 0.80, 0.84) when applied to the internal temporal test set. When applied to datasets from two external clinical sites, an AUC of 0.81 (95% CI: 0.80, 0.82) and 0.82 (95% CI: 0.80, 0.84) were achieved. An AUC of 0.79 (95% CI: 0.77, 0.81) was achieved when applied to a multi-institutional COVID-19 dataset collected by the Medical Imaging and Data Resource Center (MIDRC). A power-law dependence, N^(k )(k is empirically found to be -0.21 to -0.25), indicates a relatively weak performance dependence on the training data sizes. Conclusion: COVID-19 classification AI model trained using well-curated data from a single clinical site is generalizable to external clinical sites without a significant drop in performance.

Where the truth really lies: Listening to voices from African American communities in the Southern States about COVID-19 vaccine information and communication (preprint)

Background: High uptake of COVID-19 vaccine is one of the most promising measures to control the pandemic. However, some African American (AA) communities exhibit vaccination hesitancy due to mis- or dis-information. It is important to understand the challenges in accessing reliable COVID-19 vaccine information and to develop feasible health communication interventions based on voices from AA communities. Methods: We conducted two focus group discussions (FGDs) among 18 community leaders recruited from three counties in South Carolina on October 8 and October 29, 2021. The FGDs were conducted online via Zoom meetings. The FGD data were managed and thematically analyzed using QSR NVivo 12 software. Results: Participants (73% female and 61% between the ages of 18 and 30) worked primarily in colleges (55.5%), churches (39%), and health agencies (5.5%). We found that challenges of accessing reliable COVID-19 vaccine information in AA communities primarily included structural barriers, information barriers, and lack of trust. Community leaders recommended recruiting trusted messengers, using homecoming events, football games, and other social events to reach target populations and conducting health communication campaigns through open dialogue among stakeholders. Conclusion: Health communication interventions on COVID-19 vaccine uptake should be grounded in ongoing community engagement, trust-building activities, and transparent communication about vaccine development. Tailoring health communication interventions to different groups may help reduce misinformation spread and thus promote vaccination in AA communities in the Southern States.

Impact of COVID-19 symptoms on social aspects of life of female long haulers: A qualitative study (preprint)

Introduction: Persistent COVID-19 symptoms (long COVID) may bring numerous challenges to long haulers' social lives. Women may have to endure more profound impacts given their social roles and existing structural inequality. This study aims to explore the impacts of long COVID on various aspects of social life among female long haulers. Methods: We conducted 15 semi-structured interviews with female long haulers in the United States purposely recruited from Facebook groups, Slack group, and organization websites. The interviews were audio recorded after appropriate consent and transcribed verbatim. Inductive approach was applied in thematic analysis, which consists of six stages: becoming familiar with data, developing initial codes, extracting themes, refining themes, labeling themes, and reporting. The MAXQDA software was used in data analysis. Results: Participants reported persistent symptoms that negatively affected their social lives in many ways. The main impacts included physical limitation, financial hardship, social relationship, conflict of social roles, and social stigma. Negative effects of long COVID hindered female long haulers' recovery process. Social isolation, COVID-19 associated stigma, and conflicts of social roles cause tremendous stress. Employers' support and social media usage may play positive role in their coping with impacts of long COVID on their social life. Conclusion: Existing policies and intervention programs need to be adapted to address the challenges and barriers that long haulers face in returning to normal social life, especially for females. Tailored social life-related recommendations and social support are needed for female long haulers.

Impact of COVID-19 symptoms on social aspects of life among female long haulers: A qualitative study (preprint)

Introduction: Persistent COVID-19 symptoms (long COVID) may bring numerous challenges to long haulers' social lives. Women may have to endure more profound impacts given their social roles and existing structural inequality. This study aims to explore the impacts of long COVID on various aspects of social life among female long haulers.Methods: We conducted 15 semi-structured interviews with female long haulers in the United States purposely recruited from Facebook groups, Slack group, and organization websites. The interviews were audio recorded after appropriate consent and transcribed verbatim. Inductive approach was applied in thematic analysis, which consists of six stages: becoming familiar with data, developing initial codes, extracting themes, refining themes, labeling themes, and reporting. The MAXQDA software was used in data analysis.Results: Participants reported persistent symptoms that negatively affected their social lives in many ways. The main impacts included physical limitation, financial hardship, social relationship, conflict of social roles, and social stigma. Negative effects of long COVID hindered female long haulers’ recovery process. Social isolation, COVID-19 associated stigma, and conflicts of social roles cause tremendous stress. Employers’ support and social media usage may play positive role in their coping with impacts of long COVID on their social life.Conclusion: Existing policies and intervention programs need to be adapted to address the challenges and barriers that long haulers face in returning to normal social life, especially for females. Tailored social life-related recommendations and social support are needed for female long haulers. 

Modeling and reviewing analysis of the COVID-19 epidemic in Algeria with diagnostic shadow (preprint)

In this paper, we formulate a special epidemic dynamic model to describe the transmission of COVID-19 in Algeria. We derive the threshold parameter control reproduction number (R0c ), and present the effective control reproduction number (Rc(t)) as a real-time index for evaluating the epidemic under different control strategies. Due to the limitation of the reported data, we redefine the number of accumulative confirmed cases with diagnostic shadow and then use the processed data to do the optimal numerical simulations. According to the control measures, we divide the whole research period into six stages. And then the corresponding medical resource estimations and the average effective control reproduction numbers for each stage are given. Meanwhile, we use the parameter values which are obtained from the optimal numerical simulations to forecast the whole epidemic tendency under different control strategies.

Fast detection of harmful trace elements in glycyrrhiza using standard addition and internal standard method - laser-induced breakdown spectroscopy (SAIS-LIBS)

Glycyrrhiza is traditional Chinese medicine, whose active compounds have great potential in treating COVID-19. Detecting harmful trace elements of glycyrrhiza has become essential. However, it is not easy to detect trace elements due to the complex matrix of nonstandard glycyrrhiza. Calibration-free laser-induced breakdown spectroscopy (CF-LIBS) can be used in quantitive of nonstandard, but its stability and accuracy are low. To detect trace elements of glycyrrhiza quickly and accurately, this work introduced the standard addition method and internal standard method into LIBS, namely SAIS-LIBS. SAIS-LIBS was applied to determine trace copper and manganese in glycyrrhiza. The results showed that SAIS-LIBS had higher efficiency (<0.3 h), and could be up to 3-25 times more accuracy and stability than CF-LIBS. Furthermore, SAIS-LIBS results and inductively coupled plasma-optical emission spectroscopy (ICP-OES) were very similar (p-values > 0.05). This research provided a foundation for the rapid and accurate detection of harmful trace elements in glycyrrhiza.

Application of Latent Class Analysis in Assessing the Mental Health of Medical Students During the COVID-19 Epidemic (preprint)

Background: The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. This study aims to identify subgroups of medical students based on mental health status and explore the influencing factors during the COVID-19 epidemic in China. Methods: A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Latent class analysis of the mental health of medical students was performed using M-plus software to identify subtypes of medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. Results: In this study, three distinct subgroups were identified, namely, the high-risk group, the low-risk group and the normal group. Therefore, medical students can be divided into three latent classes, and the number of students in each class is 4325, 9321 and 16,017. The multinomial logistic regression results showed that compared with the normal group, the factors influencing mental health in the high-risk group were insomnia, perceived stress, family psychiatric disorders, fear of being infected, drinking, individual psychiatric disorders, sex, educational level and knowledge of COVID-19, according to the intensity of influence from high to low. Conclusions: Our findings suggested that latent class analysis can be used to categorize different medical students according to their mental health subgroup during the outbreak of COVID-19. The main factors influencing the high-risk group and low-risk group are basic demographic characteristics, disease history, COVID-19 related factors and behavioral lifestyle, among which insomnia and perceived stress have the greatest impact. School administrative departments could utilize more specific measures on the basis of different subgroups, and provide targeted measures.

Household Secondary Attack Rate of COVID-19 and Associated Determinants (preprint)

Background: As of April 2, 2020, the global reported number of COVID-19 cases has crossed over 1 million with more than 55,000 deaths. The household transmissibility of SARS-CoV-2, the causative pathogen, remains elusive. Methods: Based on a comprehensive contact-tracing dataset from Guangzhou, we estimated both the population-level effective reproductive number and individual-level secondary attack rate (SAR) in the household setting. We assessed age effects on transmissibility and the infectivity of COVID-19 cases during their incubation period. Results: A total of 195 unrelated clusters with 212 primary cases, 137 nonprimary (secondary or tertiary) cases and 1938 uninfected close contacts were traced. We estimated the household SAR to be 13.8% (95% CI: 11.1-17.0%) if household contacts are defined as all close relatives and 19.3% (95% CI: 15.5-23.9%) if household contacts only include those at the same residential address as the cases, assuming a mean incubation period of 4 days and a maximum infectious period of 13 days. The odds of infection among children (<20 years old) was only 0.26 (95% CI: 0.13-0.54) times of that among the elderly ([≥]60 years old). There was no gender difference in the risk of infection. COVID-19 cases were at least as infectious during their incubation period as during their illness. On average, a COVID-19 case infected 0.48 (95% CI: 0.39-0.58) close contacts. Had isolation not been implemented, this number increases to 0.62 (95% CI: 0.51-0.75). The effective reproductive number in Guangzhou dropped from above 1 to below 0.5 in about 1 week. Conclusion: SARS-CoV-2 is more transmissible in households than SARS-CoV and MERS-CoV, and the elderly [≥]60 years old are the most vulnerable to household transmission. Case finding and isolation alone may be inadequate to contain the pandemic and need to be used in conjunction with heightened restriction of human movement as implemented in Guangzhou.

The impact of multilateral imported cases of COVID-19 on the epidemic control in China (preprint)

Nowadays, the epidemic of COVID-19 in China is under control. However, the epidemic are developing rapidly around the world. Due to the normal migration of population, China is facing high risk from imported cases. The potential specific medicine and vaccine is still in the process of clinical trials. Currently, controlling the impact of imported cases is the key to prevent new outbreak of COVID-19 in China. In this paper, we propose two impulsive systems to describe the impact of multilateral imported cases of COVID-19. Based on the published data, we simulate and discussed the epidemic trends under different control strategies. We compare four different scenarios and show the corresponding medical burden. The results help to design appropriate control strategy for imported cases in practice.

Modeling the Control of COVID-19: Impact of Policy Interventions and Meteorological Factors (preprint)

In this paper, we propose a dynamical model to describe the transmission of COVID-19, which is spreading in China and many other countries. To avoid a larger outbreak in the worldwide, Chinese government carried out a series of strong strategies to prevent the situation from deteriorating. Home quarantine is the most important one to prevent the spread of COVID-19. In order to estimate the effect of population quarantine, we divide the population into seven categories for simulation. Based on a Least-Squares procedure and officially published data, the estimation of parameters for the proposed model is given. Numerical simulations show that the proposed model can describe the transmission of COVID-19 accurately, the corresponding prediction of the trend of the disease is given. The home quarantine strategy plays an important role in controlling the disease spread and speeding up the decline of COVID-19. The control reproduction number of most provinces in China are analyzed and discussed adequately. We should pay attention to that, though the epidemic is in decline in China, the disease still has high risk of human-to-human transmission continuously. Once the control strategy is removed, COVID-19 may become a normal epidemic disease just like flu. Further control for the disease is still necessary, we focus on the relationship between the spread rate of the virus and the meteorological conditions. A comprehensive meteorological index is introduced to represent the impact of meteorological factors on both high and low migration groups. As the progress on the new vaccine, we design detail vaccination strategies for COVID-19 in different control phases and show the effectiveness of efficient vaccination. Once the vaccine comes into use, the numerical simulation provide a promptly prospective research.