National Trends in Sadness, Suicidality, and COVID-19 Pandemic-Related Risk Factors Among South Korean Adolescents From 2005 to 2021.

Importance: Despite the COVID-19 pandemic's effect on daily life, limited research exists on the prevalence and risk factors of suicidality and sadness among South Korean adolescents. Objectives: To examine whether the observed sadness and suicidality in the early to middle periods of the COVID-19 pandemic differed from the expected level and to investigate changes in risk factors for sadness and suicidality. Design, Setting, and Participants: This nationwide serial cross-sectional survey study used data on 1 109 776 Korean adolescents aged 13 to 18 years from the Korea Youth Risk Behavior Web-based Survey from 2005 to 2021. Exposure: The COVID-19 pandemic. Main Outcomes and Measures: The pattern of changes in the percentage or proportion of sadness or suicidality, as well as the risk factors for sadness or suicidality. The transitional effect of the COVID-19 pandemic was assessed using weighted odds ratios (wORs) or weighted beta coefficients with 95% CIs. Results: Between 2005 and 2021, 1 109 776 adolescents (mean [SD] age, 15.0 [1.7] years; 51.5% male adolescents; and 51.7% in grades 7-9 and 48.3% in grades 10-12) were included in the Korea Youth Risk Behavior Web-based Survey. The slope of the long-term trends in sadness and suicidality decreased in the prepandemic period (sadness: from 37.8% [95% CI, 37.4%-38.2%] in 2005-2007 to 26.1% [95% CI, 25.9%-26.4%] in 2016-2019; suicidality: from 23.0% [95% CI, 22.7%-23.3%] in 2005-2007 to 12.3% [95% CI, 12.1%-12.5%] in 2016-2019), whereas the slope increased during the COVID-19 pandemic (sadness: from 25.0% [95% CI, 24.5%-25.6%] in 2020 to 26.6% [95% CI, 26.1%-27.1%] in 2021; trend difference in ß, 0.249 [95% CI, 0.236-0.262]; suicidality: from 10.7% [95% CI, 10.3%-11.1%] in 2020 to 12.5% [95% CI, 12.1%-12.9%] in 2021; trend difference in ß, 0.328 [95% CI, 0.312-0.344]). The trends presented a similar tendency in the subgroups according to sex, school grade, residential area, smoking status, and current alcohol use. Compared with the prepandemic period, the risk factors associated with sadness during the pandemic were younger age (wOR, 0.907; 95% CI, 0.881-0.933), female sex (wOR, 1.031; 95% CI, 1.001-1.062), urban residence (wOR, 1.120; 95% CI, 1.087-1.153), current smoking status (wOR, 1.134; 95% CI, 1.059-1.216), and current alcohol use (wOR, 1.051; 95% CI, 1.002-1.102). Female sex (wOR, 1.064; 95% CI, 1.021-1.109), urban residence (wOR, 1.117; 95% CI, 1.074-1.162), and low economic status (wOR, 1.286; 95% CI, 1.180-1.403) were the risk factors significantly associated with suicidality after the COVID-19 pandemic began. Conclusions and Relevance: In this nationwide serial cross-sectional survey study of South Korean adolescents, the slope of the prevalence of sadness and suicidality increased during the COVID-19 pandemic after a decrease prior to the pandemic. The findings suggest that public health measures are needed to recognize vulnerable groups with risk factors and to prevent an increase in sadness and suicidality among adolescents during the COVID-19 pandemic.

Vertigo and Dizziness After Coronavirus Disease-2019 Vaccination: A Nationwide Analysis.

BACKGROUND: Side effects occurring after COVID-19 vaccination can include vertigo and dizziness. Despite its high incidence, few studies to date have assessed dizziness/vertigo after vaccination. The present study investigated the incidence of dizziness/vertigo after COVID-19 vaccination in South Korea. METHODS: Adverse reactions to COVID-19 vaccination reported to the Korea Disease Control and Prevention Agency from February 26, 2021, to July 31, 2022 (week 74) were analyzed. The incidence rates of dizziness/vertigo in subjects vaccinated with 5 COVID-19 vaccines, AZD1222 (AstraZeneca), BNT162b2 (Pfizer-BioNTech), JNJ-78436735 (Janssen), mRNA-1273 (Moderna), and NVX-CoV2373 (Novavax), were determined. RESULTS: A total of 126 725 952 doses of COVID-19 vaccine were administered, with 473 755 suspected adverse reactions (374 per 100 000 vaccinations) reported. Vertigo/dizziness was reported after the administration of 68 759 doses, or 54.3 per 100 000 vaccinations, making it the third most common adverse reaction after headache and muscle pain. CONCLUSION: Dizziness/vertigo was generally a mild adverse reaction after COVID-19 vaccination, but it was the third most common adverse reaction in Korea. Studies are necessary to clarify the causal relationship between vaccination and dizziness/vertigo and to prepare subjects for this possible adverse reaction.

National trends in alcohol and substance use among adolescents from 2005 to 2021: a Korean serial cross-sectional study of one million adolescents.

BACKGROUND: Although previous studies have provided data on early pandemic periods of alcohol and substance use in adolescents, more adequate studies are needed to predict the trends of alcohol and substance use during recent periods, including the mid-pandemic period. This study investigated the changes in alcohol and substance use, except tobacco use, throughout the pre-, early-, and mid-pandemic periods in adolescents using a nationwide serial cross-sectional survey from South Korea. METHODS: Data on 1,109,776 Korean adolescents aged 13-18 years from 2005 to 2021 were obtained in a survey operated by the Korea Disease Control and Prevention Agency. We evaluated adolescents' alcohol and substance consumption prevalence and compared the slope of alcohol and substance prevalence before and during the COVID-19 pandemic to see the trend changes. We define the pre-COVID-19 period as consisting of four groups of consecutive years (2005-2008, 2009-2012, 2013-2015, and 2016-2019). The COVID-19 pandemic period is composed of 2020 (early-pandemic era) and 2021 (mid-pandemic era). RESULTS: More than a million adolescents successfully met the inclusion criteria. The weighted prevalence of current alcohol use was 26.8% [95% confidence interval (CI) 26.4-27.1] from 2005 to 2008 and 10.5% (95% CI 10.1-11.0) in 2020 and 2021. The weighted prevalence of substance use was 1.1% (95% CI 1.1-1.2) from 2005 to 2008 and 0.7% (95% CI 0.6-0.7) between 2020 and 2021. From 2005 to 2021, the overall trend of use of both alcohol and drugs was found to decrease, but the decline has slowed since COVID-19 epidemic (current alcohol use: ßdiff 0.167; 95% CI 0.150-0.184; substance use: ßdiff 0.152; 95% CI 0.110-0.194). The changes in the slope of current alcohol and substance use showed a consistent slowdown with regard to sex, grade, residence area, and smoking status from 2005 to 2021. CONCLUSION: The overall prevalence of alcohol consumption and substance use among over one million Korean adolescents from the early and mid-stage (2020-2021) of the COVID-19 pandemic showed a slower decline than expected given the increase during the prepandemic period (2005-2019).

Comparing the Clinical Manifestations of Bell's Palsy between Pre-COVID-19 Pandemic and COVID-19 Pandemic Periods.

BACKGROUND: COVID-19 has been shown to affect the onset and severity of various diseases. We examined whether the clinical characteristics of Bell's palsy differed between before and during the COVID-19 pandemic. METHODS: From January 2005 to December 2021, 1839 patients were diagnosed and treated for Bell's palsy at Kyung Hee University Hospital. These patients were divided into a pre-COVID period group and COVID-19 period group, and the clinical characteristics of the two groups were compared. RESULTS: There were 1719 patients in the pre-COVID period group and 120 patients in the COVID-19 period group. There were no between-group differences in sex (p = 0.103) or in the presence of underlying hypertension (p = 0.632) or diabetes (p = 0.807). Regarding symptoms, there were no significant between-group differences in otalgia, dizziness, tinnitus, hyperacusis, or hearing loss (p = 0.304, p = 0.59, p = 0.351, p = 0.605, and p = 0.949). There were also no significant between-group differences in electroneurography results (p = 0.398), electromyography results (p = 0.331), House-Brackmann Grade at visit (p = 0.634), or recovery rate after treatment (p = 0.525). CONCLUSIONS: Contrary to our expectation that Bell's palsy cases during the COVID-19 pandemic would show different clinical features than those occurring before COVID-19, the present study found no differences in clinical features or prognosis.

Estimated prevalence and trends in smoking among adolescents in South Korea, 2005-2021: a nationwide serial study.

BACKGROUND: Although smoking is classified as a risk factor for severe COVID-19 outcomes, there is a scarcity of studies on prevalence of smoking during the COVID-19 pandemic. Thus, this study aims to analyze the trends of prevalence of smoking in adolescents over the COVID-19 pandemic period. METHODS: The present study used data from middle to high school adolescents between 2005 and 2021 who participated in the Korea Youth Risk Behavior Web-based Survey (KYRBS). We evaluated the smoking prevalence (ever or daily) by year groups and estimated the slope in smoking prevalence before and during the pandemic. RESULTS: A total of 1,137,823 adolescents participated in the study [mean age, 15.04 years [95% confidence interval (CI) 15.03-15.06]; and male, 52.4% (95% CI 51.7-53.1)]. The prevalence of ever smokers was 27.7% (95% CI 27.3-28.1) between 2005 and 2008 but decreased to 9.8% (95% CI 9.3-10.3) in 2021. A consistent trend was found in daily smokers, as the estimates decreased from 5.4% (95% CI 5.2-5.6) between 2005 and 2008 to 2.3% (95% CI 2.1-2.5) in 2021. However, the downward slope in the overall prevalence of ever smokers and daily smokers became less pronounced in the COVID-19 pandemic period than in the pre-pandemic period. In the subgroup with substance use, the decreasing slope in daily smokers was significantly more pronounced during the pandemic than during the pre-pandemic period. CONCLUSIONS: The proportion of ever smokers and daily smokers showed a less pronounced decreasing trend during the pandemic. The findings of our study provide an overall understanding of the pandemic's impact on smoking prevalence in adolescents. Supplementary file2 (MP4 64897 KB).

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms.

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns HR variability patterns in presymptom by tracking relationships in sequential HR data. In the cross-validation (CV) results from the COVID-19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and area under the receiver operating characteristics (AUROC) of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID-19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID-19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the CV: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID-19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID-19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared with the vaccinated patients. The last finding is that the model trained in a certain period of time may provide degraded diagnosis performances as the virus continues to mutate.

National trends in physical activity among adolescents in South Korea before and during the COVID-19 pandemic, 2009-2021.

With the ongoing COVID-19 pandemic, several previous studies from different countries showed that physical activity (PA) decreased during the COVID-19 outbreak. However, few studies have examined the recent tendency of PA in the adolescent population. Thus, we aimed to investigate the long-term trend of PA in Korean youth and the prevalence changes between before and during the COVID-19 pandemic. Data from Korea Youth Risk Behavior Web-Based Survey (KYRBS) was collected for consecutive years between 2009 and 2021. The period was separated into prepandemic (2009-2019), early-pandemic (2020), and mid-pandemic (2021). Self-reported amount of PA was categorized into four groups (insufficient, aerobic, muscle strengthening, and both physical activities) according to World Health Organization (WHO) PA guidelines. A total of 840 488 adolescents aged 12-18 who fully responded to the survey were selected (response rate: 95.2%). The 13-year trends in the proportion of adolescents who reported aerobic and muscle-strengthening activities met or exceeded 2020 WHO exercise guidelines for adolescents plateaued (11.9% from 2009 to 2011, 14.2% from 2018 to 2019, 14.4% from 2020, and 14.0% from 2021); however, the slope decreased during the pandemic (ßdiff , -0.076; 95% confidence interval [CI], -0.123 to -0.029). Proportion of sufficient aerobic exercise among adolescents sharply decreased midst the pandemic (28.0% from 2009 to 2011, 29.4% from 2018 to 2019, and 23.8% from 2020; ßdiff , -0.266; 95% CI, -0.306 to -0.226) but increased again in 2021 (26.0% from mid-COVID 19; 95% CI, 25.4-26.7). Similar patterns were observed in Metabolic Equivalent Task (MET) score (MET-min/week; 804.1 from 2018 to 2019, 720.9 from 2020, and 779.6 from 2021). The mean difference in MET score between pre-COVID and post-COVID was -55.4 MET-min/week (95% CI, -70.5 to -40.3). Through a nationwide representative study, there was no significant difference with regard to the number of Korean adolescents who achieved the PA guidelines (pre and postpandemic); however, the prevalence of recommended levels of PA needs to increase more based on the trend before the COVID-19 outbreak. The findings of this study suggest reinforcement of the importance of public health policies for Korean youths to be more physically active, especially during and after the pandemic.

The Impact of the COVID-19 Pandemic on Otitis Media.

Otitis media is one of the most common diseases in children, with 80% of children experiencing it by the age of three years. Therefore, the resulting social burden is enormous. In addition, many countries still suffer from complications due to otitis media. Meanwhile, COVID-19 has affected many diseases, with otitis media being one of the most strongly affected. This review aims to find out how COVID-19 has affected otitis media and its significance. A series of measures brought about by COVID-19, including emphasis on personal hygiene and social distancing, had many unexpected positive effects on otitis media. These can be broadly classified into four categories: first, the incidence of otitis media was drastically reduced. Second, antibiotic prescriptions for otitis media decreased. Third, the incidence of complications of otitis media was reduced. Fourth, the number of patients visiting the emergency room due to otitis media decreased. The quarantine measures put in place due to COVID-19 suppressed the onset and exacerbation of otitis media. This has great implications for the treatment and prevention of otitis media.

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models.

Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19. This method can miss patients and cause more complications. Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. In this system, classification undergoes some modification before applying the three CT image techniques to determine normal and COVID-19 cases. A large-scale and challenging CT image dataset was used in the training process of the employed deep learning model and reporting their final performance. Experimental outcomes show that the highest accuracy rate was achieved using the CNN model with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity of 87.97%. Furthermore, the proposed system has outperformed the current existing state-of-the-art models in detecting the COVID-19 virus using CT images.

Gender Bias in Artificial Intelligence: Severity Prediction at an Early Stage of COVID-19.

Artificial intelligence (AI) technologies have been applied in various medical domains to predict patient outcomes with high accuracy. As AI becomes more widely adopted, the problem of model bias is increasingly apparent. In this study, we investigate the model bias that can occur when training a model using datasets for only one particular gender and aim to present new insights into the bias issue. For the investigation, we considered an AI model that predicts severity at an early stage based on the medical records of coronavirus disease (COVID-19) patients. For 5,601 confirmed COVID-19 patients, we used 37 medical records, namely, basic patient information, physical index, initial examination findings, clinical findings, comorbidity diseases, and general blood test results at an early stage. To investigate the gender-based AI model bias, we trained and evaluated two separate models-one that was trained using only the male group, and the other using only the female group. When the model trained by the male-group data was applied to the female testing data, the overall accuracy decreased-sensitivity from 0.93 to 0.86, specificity from 0.92 to 0.86, accuracy from 0.92 to 0.86, balanced accuracy from 0.93 to 0.86, and area under the curve (AUC) from 0.97 to 0.94. Similarly, when the model trained by the female-group data was applied to the male testing data, once again, the overall accuracy decreased-sensitivity from 0.97 to 0.90, specificity from 0.96 to 0.91, accuracy from 0.96 to 0.91, balanced accuracy from 0.96 to 0.90, and AUC from 0.97 to 0.95. Furthermore, when we evaluated each gender-dependent model with the test data from the same gender used for training, the resultant accuracy was also lower than that from the unbiased model.

Social isolation-related depression accelerates ethanol intake via microglia-derived neuroinflammation.

Social isolation is common in modern society and is a contributor to depressive disorders. People with depression are highly vulnerable to alcohol use, and abusive alcohol consumption is a well-known obstacle to treating depressive disorders. Using a mouse model involving isolation stress (IS) and/or ethanol intake, we investigated the mutual influence between IS-derived depressive and ethanol-seeking behaviors along with the underlying mechanisms. IS increased ethanol craving, which robustly exacerbated depressive-like behaviors. Ethanol intake activated the mesolimbic dopaminergic system, as evidenced by dopamine/tyrosine hydroxylase double-positive signals in the ventral tegmental area and c-Fos activity in the nucleus accumbens. IS-induced ethanol intake also reduced serotonergic activity, via microglial hyperactivation in raphe nuclei, that was notably attenuated by a microglial inhibitor (minocycline). Our study demonstrated that microglial activation is a key mediator in the vicious cycle between depression and alcohol consumption. We also propose that dopaminergic reward might be involved in this pathogenicity.

Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation.

BACKGROUND: The number of deaths from COVID-19 continues to surge worldwide. In particular, if a patient's condition is sufficiently severe to require invasive ventilation, it is more likely to lead to death than to recovery. OBJECTIVE: The goal of our study was to analyze the factors related to COVID-19 severity in patients and to develop an artificial intelligence (AI) model to predict the severity of COVID-19 at an early stage. METHODS: We developed an AI model that predicts severity based on data from 5601 COVID-19 patients from all national and regional hospitals across South Korea as of April 2020. The clinical severity of COVID-19 was divided into two categories: low and high severity. The condition of patients in the low-severity group corresponded to no limit of activity, oxygen support with nasal prong or facial mask, and noninvasive ventilation. The condition of patients in the high-severity group corresponded to invasive ventilation, multi-organ failure with extracorporeal membrane oxygenation required, and death. For the AI model input, we used 37 variables from the medical records, including basic patient information, a physical index, initial examination findings, clinical findings, comorbid diseases, and general blood test results at an early stage. Feature importance analysis was performed with AdaBoost, random forest, and eXtreme Gradient Boosting (XGBoost); the AI model for predicting COVID-19 severity among patients was developed with a 5-layer deep neural network (DNN) with the 20 most important features, which were selected based on ranked feature importance analysis of 37 features from the comprehensive data set. The selection procedure was performed using sensitivity, specificity, accuracy, balanced accuracy, and area under the curve (AUC). RESULTS: We found that age was the most important factor for predicting disease severity, followed by lymphocyte level, platelet count, and shortness of breath or dyspnea. Our proposed 5-layer DNN with the 20 most important features provided high sensitivity (90.2%), specificity (90.4%), accuracy (90.4%), balanced accuracy (90.3%), and AUC (0.96). CONCLUSIONS: Our proposed AI model with the selected features was able to predict the severity of COVID-19 accurately. We also made a web application so that anyone can access the model. We believe that sharing the AI model with the public will be helpful in validating and improving its performance.

An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model.

BACKGROUND: COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. OBJECTIVE: To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. METHODS: We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. RESULTS: In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. CONCLUSIONS: Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients' outcomes.

COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation.

BACKGROUND: Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE: We aimed to rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non-COVID-19 pneumonia and nonpneumonia diseases. METHODS: A simple 2D deep learning framework, named the fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning using one of four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS: Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS: FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.