Tracking the COVID-19 outbreak in India through Twitter: Opportunities for social media based global pandemic surveillance.

We investigated the utility of Twitter for conducting multi-faceted geolocation-centric pandemic surveillance, using India as an example. We collected over 4 million COVID19-related tweets related to the Indian outbreak between January and July 2021. We geolocated the tweets, applied natural language processing to characterize the tweets (eg., identifying symptoms and emotions), and compared tweet volumes with the numbers of confirmed COVID-19 cases. Tweet numbers closely mirrored the outbreak, with the 7-day average strongly correlated with confirmed COVID-19 cases nationally (Spearman r=0.944; p=0.001), and also at the state level (Spearman r=0.84, p=0.0003). Fatigue, Dyspnea and Cough were the top symptoms detected, while there was a significant increase in the proportion of tweets expressing negative emotions (eg., fear and sadness). The surge in COVID-19 tweets was followed by increased number of posts expressing concern about black fungus and oxygen supply. Our study illustrates the potential of social media for multi-faceted pandemic surveillance.

Characteristics of Intimate Partner Violence and Survivor's Needs During the COVID-19 Pandemic: Insights From Subreddits Related to Intimate Partner Violence.

Intimate partner violence (IPV) increased during the COVID-19 pandemic. Collecting actionable IPV-related data from conventional sources (e.g., medical records) was challenging during the pandemic, generating a need to obtain relevant data from non-conventional sources, such as social media. Social media, like Reddit, is a preferred medium of communication for IPV survivors to share their experiences and seek support with protected anonymity. Nevertheless, the scope of available IPV-related data on social media is rarely documented. Thus, we examined the availability of IPV-related information on Reddit and the characteristics of the reported IPV during the pandemic. Using natural language processing, we collected publicly available Reddit data from four IPV-related subreddits between January 1, 2020 and March 31, 2021. Of 4,000 collected posts, we randomly sampled 300 posts for analysis. Three individuals on the research team independently coded the data and resolved the coding discrepancies through discussions. We adopted quantitative content analysis and calculated the frequency of the identified codes. 36% of the posts (n = 108) constituted self-reported IPV by survivors, of which 40% regarded current/ongoing IPV, and 14% contained help-seeking messages. A majority of the survivors' posts reflected psychological aggression, followed by physical violence. Notably, 61.4% of the psychological aggression involved expressive aggression, followed by gaslighting (54.3%) and coercive control (44.3%). Survivors' top three needs during the pandemic were hearing similar experiences, legal advice, and validating their feelings/reactions/thoughts/actions. Albeit limited, data from bystanders (survivors' friends, family, or neighbors) were also available. Rich data reflecting IPV survivors' lived experiences were available on Reddit. Such information will be useful for IPV surveillance, prevention, and intervention.

The Early Detection of Fraudulent COVID-19 Products From Twitter Chatter: Data Set and Baseline Approach Using Anomaly Detection.

Background: Social media has served as a lucrative platform for spreading misinformation and for promoting fraudulent products for the treatment, testing, and prevention of COVID-19. This has resulted in the issuance of many warning letters by the US Food and Drug Administration (FDA). While social media continues to serve as the primary platform for the promotion of such fraudulent products, it also presents the opportunity to identify these products early by using effective social media mining methods. Objective: Our objectives were to (1) create a data set of fraudulent COVID-19 products that can be used for future research and (2) propose a method using data from Twitter for automatically detecting heavily promoted COVID-19 products early. Methods: We created a data set from FDA-issued warnings during the early months of the COVID-19 pandemic. We used natural language processing and time-series anomaly detection methods for automatically detecting fraudulent COVID-19 products early from Twitter. Our approach is based on the intuition that increases in the popularity of fraudulent products lead to corresponding anomalous increases in the volume of chatter regarding them. We compared the anomaly signal generation date for each product with the corresponding FDA letter issuance date. We also performed a brief manual analysis of chatter associated with 2 products to characterize their contents. Results: FDA warning issue dates ranged from March 6, 2020, to June 22, 2021, and 44 key phrases representing fraudulent products were included. From 577,872,350 posts made between February 19 and December 31, 2020, which are all publicly available, our unsupervised approach detected 34 out of 44 (77.3%) signals about fraudulent products earlier than the FDA letter issuance dates, and an additional 6 (13.6%) within a week following the corresponding FDA letters. Content analysis revealed misinformation, information, political, and conspiracy theories to be prominent topics. Conclusions: Our proposed method is simple, effective, easy to deploy, and does not require high-performance computing machinery unlike deep neural network-based methods. The method can be easily extended to other types of signal detection from social media data. The data set may be used for future research and the development of more advanced methods.

Effects of a online brief modified mindfulness-based stress reduction therapy for anxiety among Chinese adults: A randomized clinical trial

The COVID-19 pandemic has exacerbated anxiety and related symptoms among the general population. In order to cope with the mental health burden, we developed an online brief modified mindfulness-based stress reduction (mMBSR) therapy. We performed a parallel-group randomized controlled trial to evaluate the efficacy of the mMBSR for adult anxiety with cognitive-behavioral therapy (CBT) as an active control. Participants were randomized to mMBSR, CBT or waitlist group. Those in the intervention arms performed each therapy for 6 sections in 3 weeks. Measurements were conducted at baseline, post-treatment and 6 months post-treatment by Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, Patient Health Questionnaire-15, reverse scored Cohen Perceived Stress scale, Insomnia Severity Index, and Snaith-Hamilton Pleasure Scale. 150 participants with anxiety symptoms were randomized to mMBSR, CBT or waitlist group. Post intervention assessments showed that mMBSR improved the scores of all the six mental problem dimensions (anxiety, depression, somatization, stress, insomnia, and the experience of pleasure) significantly compared to the waitlist group. During 6-month post treatment assessment, the scores of all six mental problem dimensions in the mMBSR group still showed improvement compared to baseline and showed no significant difference with the CBT group. Our results provide positive evidence for the efficacy and feasibility of an online brief modified MBSR program to alleviate anxiety and related symptoms of individuals from the general population, and the therapeutic benefits of mMBSR persisted for up to six months. This low resource-consuming intervention could facilitate the challenges of supplying psychological health therapy to large scale of population.

SuperMini-seg: An ultra lightweight network for COVID-19 lung infection segmentation from CT images.

The automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images is helpful in establishing a quantitative model to diagnose and treat COVID-19. To this end, this study proposes a lightweight segmentation network called the SuperMini-Seg. We propose a new module called the transformer parallel convolution module (TPCB), which introduces both transformer and convolution operations in one module. SuperMini-seg adopts the structure of a double-branch parallel to downsample the image and designs a gated attention mechanism in the middle of the two parallel branches. At the same time, the attentive hierarchical spatial pyramid (AHSP) module and criss-cross attention module are adopted, and more than 100K parameters are present in the model. At the same time, the model is scalable, and the parameter quantity of SuperMini-seg-V2 reaches more than 70K. Compared with other advanced methods, the segmentation accuracy was almost reached the state-of-art method. The calculation efficiency was high, which is convenient for practical deployment.

Effects of a online brief modified mindfulness-based stress reduction therapy for anxiety among Chinese adults: A randomized clinical trial.

The COVID-19 pandemic has exacerbated anxiety and related symptoms among the general population. In order to cope with the mental health burden, we developed an online brief modified mindfulness-based stress reduction (mMBSR) therapy. We performed a parallel-group randomized controlled trial to evaluate the efficacy of the mMBSR for adult anxiety with cognitive-behavioral therapy (CBT) as an active control. Participants were randomized to mMBSR, CBT or waitlist group. Those in the intervention arms performed each therapy for 6 sections in 3 weeks. Measurements were conducted at baseline, post-treatment and 6 months post-treatment by Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, Patient Health Questionnaire-15, reverse scored Cohen Perceived Stress scale, Insomnia Severity Index, and Snaith-Hamilton Pleasure Scale. 150 participants with anxiety symptoms were randomized to mMBSR, CBT or waitlist group. Post intervention assessments showed that mMBSR improved the scores of all the six mental problem dimensions (anxiety, depression, somatization, stress, insomnia, and the experience of pleasure) significantly compared to the waitlist group. During 6-month post treatment assessment, the scores of all six mental problem dimensions in the mMBSR group still showed improvement compared to baseline and showed no significant difference with the CBT group. Our results provide positive evidence for the efficacy and feasibility of an online brief modified MBSR program to alleviate anxiety and related symptoms of individuals from the general population, and the therapeutic benefits of mMBSR persisted for up to six months. This low resource-consuming intervention could facilitate the challenges of supplying psychological health therapy to large scale of population.

MMViT-Seg: A lightweight transformer and CNN fusion network for COVID-19 segmentation.

BACKGROUND AND OBJECTIVE: COVID-19 is a serious threat to human health. Traditional convolutional neural networks (CNNs) can realize medical image segmentation, whilst transformers can be used to perform machine vision tasks, because they have a better ability to capture long-range relationships than CNNs. The combination of CNN and transformers to complete the task of semantic segmentation has attracted intense research. Currently, it is challenging to segment medical images on limited data sets like that on COVID-19. METHODS: This study proposes a lightweight transformer+CNN model, in which the encoder sub-network is a two-path design that enables both the global dependence of image features and the low layer spatial details to be effectively captured. Using CNN and MobileViT to jointly extract image features reduces the amount of computation and complexity of the model as well as improves the segmentation performance. So this model is titled Mini-MobileViT-Seg (MMViT-Seg). In addition, a multi query attention (MQA) module is proposed to fuse the multi-scale features from different levels of decoder sub-network, further improving the performance of the model. MQA can simultaneously fuse multi-input, multi-scale low-level feature maps and high-level feature maps as well as conduct end-to-end supervised learning guided by ground truth. RESULTS: The two-class infection labeling experiments were conducted based on three datasets. The final results show that the proposed model has the best performance and the minimum number of parameters among five popular semantic segmentation algorithms. In multi-class infection labeling results, the proposed model also achieved competitive performance. CONCLUSIONS: The proposed MMViT-Seg is tested on three COVID-19 segmentation datasets, with results showing that this model has better performance than other models. In addition, the proposed MQA module, which can effectively fuse multi-scale features of different levels further improves the segmentation accuracy.

Anxiety, depression, psychological stress and coping style in medical postgraduates in southeastern China when restricted to commuting between the campus and hospital during the COVID-19 pandemic.

Background: As the COVID-19 epidemic was gradually brought under control, a new autumn semester began in 2020. How was the mental health of postgraduates as they experienced quarantine at home, only commuting between the school and hospital? Methods: The research was conducted in a cross-sectional online survey in October 2020. The data were collected from 1,645 medical postgraduates (master's and doctoral students) by using the demographic information questionnaire, the Self-rating Depression Scale (SDS), the Self-rating Anxiety Scale (SAS), the Questionnaire on Psychological Stressors of Postgraduates (QPSP), the Simplified Coping Style Questionnaire (SCSQ) and the Social Support Rate Scale (SSRS). One-way ANOVA and Pearson correlation were used to explore the relationships among anxiety, depression, psychological stressors, social support and coping style. Structural equation modeling (SEM) was conducted to assess the mediation model. Results: Among the total of 1,645 medical postgraduates, 21.6% (n = 356) had self-rated depression symptoms, and 9.4% (n = 155) had self-rated anxiety symptoms. The main disturbances they experienced were employment, academic and interpersonal pressure. The master of third grade students had the highest employment pressure, and the master of second grade students had the highest academic and interpersonal pressure. Negative coping played a negative mediating role and social support played a positive mediating role in the relationships between perceived stress and anxiety (ß = 0.027, P < 0.01; ß = 0.124, P < 0.01) and depression (ß = 0.016, P < 0.01; ß = 0.193, P < 0.01). Conclusion: Medical postgraduates in China restricted to studies on campus and in the hospital experienced psychological distress. Our results suggest that providing employment and learning guidance, while strengthening social support and guiding positive coping may be effective at improving the mental health of the medical graduate students, mediating their perceived stress and negative emotions.

Anxiety, depression, psychological stress and coping style in medical postgraduates in southeastern China when restricted to commuting between the campus and hospital during the COVID-19 pandemic

Background As the COVID-19 epidemic was gradually brought under control, a new autumn semester began in 2020. How was the mental health of postgraduates as they experienced quarantine at home, only commuting between the school and hospital? Methods The research was conducted in a cross-sectional online survey in October 2020. The data were collected from 1,645 medical postgraduates (master's and doctoral students) by using the demographic information questionnaire, the Self-rating Depression Scale (SDS), the Self-rating Anxiety Scale (SAS), the Questionnaire on Psychological Stressors of Postgraduates (QPSP), the Simplified Coping Style Questionnaire (SCSQ) and the Social Support Rate Scale (SSRS). One-way ANOVA and Pearson correlation were used to explore the relationships among anxiety, depression, psychological stressors, social support and coping style. Structural equation modeling (SEM) was conducted to assess the mediation model. Results Among the total of 1,645 medical postgraduates, 21.6% (n = 356) had self-rated depression symptoms, and 9.4% (n = 155) had self-rated anxiety symptoms. The main disturbances they experienced were employment, academic and interpersonal pressure. The master of third grade students had the highest employment pressure, and the master of second grade students had the highest academic and interpersonal pressure. Negative coping played a negative mediating role and social support played a positive mediating role in the relationships between perceived stress and anxiety (β = 0.027, P < 0.01;β = 0.124, P < 0.01) and depression (β = 0.016, P < 0.01;β = 0.193, P < 0.01). Conclusion Medical postgraduates in China restricted to studies on campus and in the hospital experienced psychological distress. Our results suggest that providing employment and learning guidance, while strengthening social support and guiding positive coping may be effective at improving the mental health of the medical graduate students, mediating their perceived stress and negative emotions.

Automatic Detection of Twitter Users Who Express Chronic Stress Experiences via Supervised Machine Learning and Natural Language Processing.

Americans bear a high chronic stress burden, particularly during the COVID-19 pandemic. Although social media have many strengths to complement the weaknesses of conventional stress measures, including surveys, they have been rarely utilized to detect individuals self-reporting chronic stress. Thus, this study aimed to develop and evaluate an automatic system on Twitter to identify users who have self-reported chronic stress experiences. Using the Twitter public streaming application programming interface, we collected tweets containing certain stress-related keywords (eg, "chronic," "constant," "stress") and then filtered the data using pre-defined text patterns. We manually annotated tweets with (without) self-report of chronic stress as positive (negative). We trained multiple classifiers and tested them via accuracy and F1 score. We annotated 4195 tweets (1560 positives, 2635 negatives), achieving an inter-annotator agreement of 0.83 (Cohen's kappa). The classifier based on Bidirectional Encoder Representation from Transformers performed the best (accuracy of 83.6% [81.0-86.1]), outperforming the second best-performing classifier (support vector machines: 76.4% [73.5-79.3]). The past tweets from the authors of positive tweets contained useful information, including sources and health impacts of chronic stress. Our study demonstrates that users' self-reported chronic stress experiences can be automatically identified on Twitter, which has a high potential for surveillance and large-scale intervention.

Caregiver Willingness to Vaccinate Children with Pneumococcal Vaccines and to Pay in a Low-Resource Setting in China: A Cross-Sectional Study.

To determine the vaccine hesitancy of pneumococcal conjugate vaccines (PCVs) in a low-resource setting in China and to identify associated factors, a face-to-face questionnaire survey was conducted in the city of Guilin, China, from December 2021 to March 2022, which comprised sociodemographic information, attitudes toward vaccines and pneumonia, and PCV13 vaccination willingness and willingness to pay (WTP). Stepwise logistic regression and Tobit regression models were fitted to identify factors associated with PCV13 vaccination willingness and WTP, respectively. In total, 1254 questionnaires were included, of which 899, 254, and 101 participants showed acceptance, hesitancy, and refusal to vaccinate their children with PCV13, respectively. Only 39.07% of participants knew about PCV13 before this survey. A total of 558 (48.40%) participants accepted the full payment of vaccination, and 477 (41.37%) other participants accepted the partial payment, with a median cost of CNY 920.00. Demographics, social and psychological context, and attitudes toward vaccines were all associated with PCV13 vaccination but varied for hesitators and refusers. There is a substantial local demand for vaccinating children with PCV13 and partial payment is widely accepted. More publicity and educational efforts and a socially supportive environment are required to alleviate vaccine hesitancy.

The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges.

The COVID-19 pandemic is the most devastating public health crisis in at least a century and has affected the lives of billions of people worldwide in unprecedented ways. Compared to pandemics of this scale in the past, societies are now equipped with advanced technologies that can mitigate the impacts of pandemics if utilized appropriately. However, opportunities are currently not fully utilized, particularly at the intersection of data science and health. Health-related big data and technological advances have the potential to significantly aid the fight against such pandemics, including the current pandemic's ongoing and long-term impacts. Specifically, the field of natural language processing (NLP) has enormous potential at a time when vast amounts of text-based data are continuously generated from a multitude of sources, such as health/hospital systems, published medical literature, and social media. Effectively mitigating the impacts of the pandemic requires tackling challenges associated with the application and deployment of NLP systems. In this paper, we review the applications of NLP to address diverse aspects of the COVID-19 pandemic. We outline key NLP-related advances on a chosen set of topics reported in the literature and discuss the opportunities and challenges associated with applying NLP during the current pandemic and future ones. These opportunities and challenges can guide future research aimed at improving the current health and social response systems and pandemic preparedness.

Decreased influenza vaccination coverage among Chinese healthcare workers during the COVID-19 pandemic.

BACKGROUND: Healthcare workers (HCWs) were the priority group for influenza vaccination, in China during the 2020/2021 and 2021/2022 influenza seasons. However, vaccination rates in HCWs have always been low. This study investigated influenza vaccination status among Chinese HCWs and analyzed the factors driving vaccination. METHODS: We provided electronic questionnaires to HCWs from January 27, 2022 to February 21, 2022, using the WeChat platform "Breath Circles". HCWs who received the link could also forward it to their colleagues. Binary logistic regression models were used to analyze vaccination-associated factors among HCWs. RESULTS: Among the 1697 HCWs surveyed, vaccination coverage was 43.7% (741/1697) during the 2020/2021 influenza season, and 35.4% (600/1697) during the 2021/2022 influenza season, as of February 21, 2022. Additionally, 22.7% (385/1697) and 22.1% (358/1697) of HCWs reported that their workplaces implemented a free vaccination policy for all employees during the 2020/2021 and 2021/2022 influenza seasons. HCWs who were required to be vaccinated according to hospital regulations, and whose hospitals implemented the free influenza vaccine policy were more likely to be vaccinated (2020/2021 and 2021/2022; P < 0.05). In addition, the economic level of the HCWs' province (2021/2022, P < 0.05) and the HCWs' knowledge about vaccination and willingness to get vaccinated, such as active learning about vaccines (2020/2021, P < 0.05), supportive attitude toward vaccination for all HCWs (2020/2021 and 2021/2022; P < 0.05), also had an impact on vaccine coverage. CONCLUSIONS: A free influenza vaccination policy and workplace required vaccination are effective in improving influenza vaccination coverage among HCWs. Influenza vaccination coverage of Chinese HCWs remained low and showed a downward trend after the COVID-19 outbreak. Further effective measures, such as advocacy campaigns, free vaccine policies, and on-site vaccination could be implemented to improve influenza vaccination coverage.

DBF-Net: a semi-supervised dual-task balanced fusion network for segmenting infected regions from lung CT images

Accurate segmentation of infected regions in lung computed tomography (CT) images is essential to improve the timeliness and effectiveness of treatment for coronavirus disease 2019 (COVID-19). However, the main difficulties in developing of lung lesion segmentation in COVID-19 are still the fuzzy boundary of the lung-infected region, the low contrast between the infected region and the normal trend region, and the difficulty in obtaining labeled data. To this end, we propose a novel dual-task consistent network framework that uses multiple inputs to continuously learn and extract lung infection region features, which is used to generate reliable label images (pseudo-labels) and expand the dataset. Specifically, we periodically feed multiple sets of raw and data-enhanced images into two trunk branches of the network;the characteristics of the lung infection region are extracted by a lightweight double convolution (LDC) module and fusiform equilibrium fusion pyramid (FEFP) convolution in the backbone. According to the learned features, the infected regions are segmented, and pseudo-labels are made based on the semi-supervised learning strategy, which effectively alleviates the semi-supervised problem of unlabeled data. Our proposed semi-supervised dual-task balanced fusion network (DBF-Net) creates pseudo-labels on the COVID-SemiSeg dataset and the COVID-19 CT segmentation dataset. Furthermore, we perform lung infection segmentation on the DBF-Net model, with a segmentation sensitivity of 70.6% and specificity of 92.8%. The results of the investigation indicate that the proposed network greatly enhances the segmentation ability of COVID-19 infection.

Impact of COVID-19 pandemic on Respiratory Viral Pathogens with Acute Respiratory Infection in Shanghai, China (preprint)

Objective This study aimed to evaluate the impact of the nonpharmaceutical interventions (NPIs) taken to combat the Coronavirus Disease 2019(COVID-19) pandemic on the prevalence of respiratory viruses (RVs) of acute respiratory infections (ARIs) in Shanghai.Methods Samples from patients with ARI were collected and screened for 17 respiratory viral pathogens using TagMan low-density microfluidic chip technology in Shanghai from January 2019 to December 2020. The data of pathogens were analyzed to evaluate the changes of acute respiratory infections between 2019 and 2020.Results Here, a total of 2,744 patients were enrolled, including 1,710 patients and 1,034 patients in 2019 and 2020, respectively. The total detection rate of RVs was decreased significantly in 2020. However, the detection rate of RSVB, HPIV3, HPIV4 and HCoV229E were increased in 2020. The increased positive rates of RSVB, HPIV3 resulted in more outpatients in 2020 than in 2019. The detection rates of IFVs were decreased dramatically in different gender, age groups, and seasons in 2020.Conclusion The NPIs taken to eliminate COVID-19 had an impact on the prevalence of respiratory viral pathogens, especially the IFVs in the early phases of the pandemic. Partial respiratory viruses resurged with the lifting of NPIs, leading to an increase in ARIs infection.

Tracking the COVID-19 outbreak in India through Twitter: Opportunities for social media based global pandemic surveillance

We investigated the utility of Twitter for conducting multi-faceted geolocation-centric pandemic surveillance, using India as an example. We collected over 4 million COVID19-related tweets related to the Indian outbreak between January and July 2021. We geolocated the tweets, applied natural language processing to characterize the tweets (eg., identifying symptoms and emotions), and compared tweet volumes with the numbers of confirmed COVID-19 cases. Tweet numbers closely mirrored the outbreak, with the 7-day average strongly correlated with confirmed COVID-19 cases nationally (Spearman r=0.944;p=0.001), and also at the state level (Spearman r=0.84, p=0.0003). Fatigue, Dyspnea and Cough were the top symptoms detected, while there was a significant increase in the proportion of tweets expressing negative emotions (eg., fear and sadness). The surge in COVID-19 tweets was followed by increased number of posts expressing concern about black fungus and oxygen supply. Our study illustrates the potential of social media for multi-faceted pandemic surveillance.

Seroprevalence and dynamics of anti-SARS-CoV-2 antibodies: a longitudinal study based on patients with underlying diseases in Wuhan.

BACKGROUND: Assessing the humoral immunity of patients with underlying diseases after being infected with SARS-CoV-2 is essential for adopting effective prevention and control strategies. The purpose of this study is to analyze the seroprevalence of people with underlying diseases and the dynamic change features of anti-SARS-CoV-2 antibodies. METHODS: We selected 100 communities in Wuhan using the probability-proportional-to-size sampling method. From these 100 communities, we randomly selected households according to a list provided by the local government. Individuals who have lived in Wuhan for at least 14 days since December 2019 and were ≥ 40 years old were included. From April 9-13, 2020, community staff invited all selected individuals to the community healthcare center in batches by going door-to-door or telephone. All participants completed a standardized electronic questionnaire simultaneously. Finally, 5 ml of venous blood was collected from all participants. Blood samples were tested for the presence of pan-immunoglobulins, IgM, IgA, and IgG antibodies against SARS-CoV-2 nucleocapsid protein and neutralising antibodies were assessed. During the period June 11-13, 2020 and October 9-December 5, 2020, all family members of a positive family and matched negative families were followed up twice. RESULTS: The seroprevalence of anti-SARS-CoV-2 antibodies in people with underlying diseases was 6.30% (95% CI [5.09-7.52]), and that of people without underlying diseases was 6.12% (95% CI [5.33-6.91]). A total of 313 people were positive for total antibodies at baseline, of which 97 had underlying disease. At the first follow-up, a total of 212 people were positive for total antibodies, of which 66 had underlying disease. At the second follow-up, a total of 238 people were positive for total antibodies, of which 68 had underlying disease. A total of 219 participants had three consecutive serum samples with positive total antibodies at baseline. The IgG titers decreased significantly with or without underlying diseases (P < 0.05) within the 9 months at least, while the neutralizing antibody titer remained stable. The titer of asymptomatic patients was lower than that of symptomatic patients (baseline, P = 0.032, second follow-up, P = 0.018) in the underlying diseases group. CONCLUSION: Our research focused on the serological changes of people with and without underlying diseases in a state of single natural infection. Regardless of the underlying diseases, the IgG titer decreased significantly over time, while there was no significant difference in the decline rate of IgG between with and without underlying diseases. Moreover, the neutralizing antibody titer remained relatively stable within the 9 months at least.

The Impact of World Trade Center Related Medical Conditions on the Severity of COVID-19 Disease and Its Long-Term Sequelae.

The individuals who served our country in the aftermath of the attacks on the World Trade Center (WTC) following the attacks of 11 September 2001 have, since then, been diagnosed with a number of conditions as a result of their exposures. In the present study, we sought to determine whether these conditions were risk factors for increased COVID-19 disease severity within a cohort of N = 1280 WTC responders with complete information on health outcomes prior to and following COVID-19 infection. We collected data on responders diagnosed with COVID-19, or had evidence of receiving positive SARS-CoV-2 polymerase chain reaction or antigen testing, or were asymptomatic but had IgG positive antibody testing. The presence of post-acute COVID-19 sequelae was measured using self-reported symptom severity scales. Analyses revealed that COVID-19 severity was associated with age, Black race, obstructive airway disease (OAD), as well as with worse self-reported depressive symptoms. Similarly, post-acute COVID-19 sequelae was associated with initial analysis for COVID-19 severity, upper respiratory disease (URD), gastroesophageal reflux disease (GERD), OAD, heart disease, and higher depressive symptoms. We conclude that increased COVID-19 illness severity and the presence of post-acute COVID-19 sequelae may be more common in WTC responders with chronic diseases than in those responders without chronic disease processes resulting from exposures at the WTC disaster.

Willingness toward COVID-19 vaccination, coadministration with other vaccines and receive a COVID-19 vaccine booster: a cross-sectional study on the guardians of children in China.

This study aimed to investigate the changes in the willingness of guardians to administer the COVID-19 vaccine to their children, allow the coadministration of other vaccines, and administer the COVID-19 vaccine booster dose. This was a follow-up study conducted 6 months after a similar previous study. The self-administered questionnaire was distributed through the "Xiao Dou Miao" app and 9424 guardians with access to this app participated in the survey that was conducted from September 15 to October 8, 2021. Of all the participating guardians, 86.68% were willing to vaccinate their children with the COVID-19 vaccine, which was approximately 16% more than those in our previous study. Guardians aged ≥40 years, healthcare workers, and those with children aged ≥3 years were more willing to vaccinate their children. Approximately 77% of the guardians were willing toward the coadministration of COVID-19 and influenza vaccines. Approximately 64% of the guardians were willing toward the coadministration of other nonimmunization program vaccines with the COVID-19 vaccine for their children. The primary reasons for reluctance toward the coadministration of vaccines were concerns about vaccine safety and effectiveness. If necessary, 92% of the guardians were willing to receive a COVID-19 vaccine booster and 82% were willing to vaccinate their children with a COVID-19 vaccine booster. We hope that this research will facilitate the formulation of successful strategies for the implementation of COVID-19 vaccinations, covaccinations, and COVID-19 booster doses, particularly for children aged <6 years.