COVID-19 vaccine safety profile in Republic of Korea, February 26, 2021 through April 30, 2022.

Background: As of June, 2022, five coronavirus disease 2019 (COVID-19) vaccine brands were used for the national immunization program. The Korea Diseases Control and Prevention Agency has enhanced vaccine safety monitoring through a passive, web-based reporting system and active, text message-based mornitoring. Purpose: This study described the enhanced safety monitoring system of COVID-19 vaccines, and analyzed the frequencies and types of AEs among five brands of COVID-19 vaccines. Methods: AEs reports through the web-based Adverse Events Reporting System in COVID-19 Vaccination Management System and the text message-based among recipients were analyzed. AEs were classified as non-serious AEs and serious AEs (e.g., death and anaphylaxis). AEs were classified as non-serious AEs and serious AEs (e.g., death and anaphylaxis). AE reporting rates were calculated based on the number of COVID-19 vaccine dose administered. Results: A total of 125,107,883 doses were administered in Korea from February 26, 2021 to June 4, 2022. Among them, a total of 471,068 AEs reported, of which 96.1% were non-serious AEs and 3.9% were serious AEs. Among the 72,609 participants in the text message-based AE monitoring, a higher AE rate was reported in the 3rd dose compare to primary doses in both local and systemic reactions. A total of 874 cases of anaphylaxis (7.0 per 1,000,000 doses), four cases of TTS, 511 cases of myocarditis (4.1 per 1,000,000 doses) and 210 cases of pericarditis (1.7 per 1,000,000 doses) were confirmed. A total of seven fatal cases were associated with COVID-19 vaccination (1 TTS case and 5 myocarditis cases). Conclusions: Young adult and female sex were related with a higher reported rate of AEs with COVID-19 vaccines, most of the reported AEs of COVID-19 vaccines was non-serious AEs of mild intensity.

Self-reported adverse events after 2 doses of COVID-19 vaccine in Korea.

OBJECTIVES: In Korea, a national coronavirus disease 2019 (COVID-19) vaccination program was implemented, including 4 vaccines against COVID-19. A text messaging-based survey, in addition to a passive adverse event reporting system, was launched to quickly report unusual symptoms post-vaccination. This study compared the frequency of adverse events after COVID-19 vaccination based on the vaccine type and the type of 2-dose regimen (homologous or heterologous). METHODS: Self-reported adverse events were collected through a text-message survey for 7 days after each vaccination. This study included 50,950 vaccine recipients who responded to the survey at least once. Informed consent to receive surveys via text was obtained from the vaccine recipients on the date of first vaccination. RESULTS: The recipients of mRNA vaccines reported local and systemic reactions 1.6 times to 2.8 times more frequently after dose 2 than after dose 1 (p<0.001), whereas ChAdOx1-S recipients reported significantly fewer local and systemic reactions after dose 2 than after dose 1 (p<0.001). Local and systemic reactions were approximately 2 times and 4 times more frequent for heterologous vaccination than for BNT162b2/BNT162b2 and ChAdOx1-S/ChAdOx1-S regimens, respectively. Young individuals, female, and those receiving heterologous vaccine regimens including ChAdOx1-S/BNT162b2 vaccines reported more adverse events than older participants, male, and those with homologous vaccine regimens. CONCLUSIONS: Although a heterologous regimen, youth, and female sex were associated with a higher risk of adverse reactions after COVID-19 vaccination, no critical issues were noted. Active consideration of heterologous schedules based on the evidence of efficacy and safety appears desirable.

Adverse events of the Pfizer-BioNTech COVID-19 vaccine in Korean children and adolescents aged 5 to 17 years.

OBJECTIVES: This study aimed to identify potential safety signals and adverse events following the primary Pfizer-BioNTech coronavirus disease 2019 (COVID-19) vaccination series among children and adolescents aged 5 to 17 years in the Republic of Korea. METHODS: Adverse events reported through the COVID-19 vaccination management system (CVMS, a web-based passive vaccine safety surveillance system) and adverse events and health conditions collected from a text message-based survey were analyzed. RESULTS: A total of 14,786 adverse events among 5 to 17-year-old children and adolescents were reported in the CVMS; 14,334 (96.9%) were non-serious and 452 (3.1%) were serious, including 125 suspected cases of acute cardiovascular injury and 101 suspected cases of anaphylaxis. The overall reporting rate was lower in 5 to 11-year-old children (64.5 per 100,000 doses) than in 12 to 17-year-old adolescents (300.5 per 100,000 doses). The text message survey identified that local and systemic adverse events after either dose were reported less frequently in 5 to 11-year-old children than in 12 to 17-year-old adolescents (p<0.001). The most commonly reported adverse events were pain at the injection site, myalgia, headache, and fatigue/tiredness. CONCLUSION: The overall results are consistent with the results of controlled trials; serious adverse events were extremely rare among 5 to 17-year-old children and adolescents following Pfizer-BioNTech COVID-19 vaccination. Adverse events were less frequent in children aged 5 to 11 years than in adolescents aged 12 to 17 years.

Effects of Air Pollution on Chemosensory Dysfunction in COVID-19 Patients.

BACKGROUND: In some patients, coronavirus disease 2019 (COVID-19) is accompanied by loss of smell and taste, and this has been reportedly associated with exposure to air pollutants. This study investigated the relationship between the occurrence of chemosensory dysfunction in COVID-19 patients and air pollutant concentrations in Korea. METHODS: Information on the clinical symptom of chemosensory dysfunction, the date of diagnosis, residential area, age, and sex of 60,194 confirmed COVID-19 cases reported to the Korea Disease Control and Prevention Agency from January 20 to December 31, 2020 was collected. In addition, the daily average concentration of air pollutants for a week in the patients' residential area was collected from the Ministry of Environment based on the date of diagnosis of COVID-19. A binomial logistic regression model, using age and gender, standardized smoking rate, number of outpatient visits, 24-hour mean temperature and relative humidity at the regional level as covariates, was used to determine the effect of air pollution on chemosensory dysfunction. RESULTS: Symptoms of chemosensory dysfunction were most frequent among patients in their 20s and 30s, and occurred more frequently in large cities. The logistic analysis showed that the concentration of particulate matter 10 (PM10) and 2.5 (PM2.5) up to 2 days before the diagnosis of COVID-19 and the concentration of sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) at least 7 days before the diagnosis of COVID-19 affected the development of chemosensory dysfunction. In the logistic regression model adjusted for age, sex, standardized smoking rate, number of outpatient visits, and daily average temperature and relative humidity, it was found that an increase in the interquartile range of PM10, PM2.5, SO2, NO2, and CO on the day of diagnosis increased the incidence of chemosensory dysfunction 1.10, 1.10, 1.17, 1.31, and 1.19-fold, respectively. In contrast, the O3 concentration had a negative association with chemosensory dysfunction. CONCLUSION: High concentrations of air pollutants such as PM10, PM2.5, SO2, NO2, and CO on the day of diagnosis increased the risk of developing chemosensory dysfunction from COVID-19 infection. This result underscores the need to actively prevent exposure to air pollution and prevent COVID-19 infection. In addition, policies that regulate activities and products that create high amounts of harmful environmental wastes may help in promoting better health for all during COVID-19 pandemic.

Safety monitoring of COVID-19 vaccination among adolescents aged 12 to 17 years old in the Republic of Korea.

OBJECTIVES: This study aimed to disseminate information on coronavirus disease 2019 (COVID-19) vaccine safety among adolescents aged 12 to 17 years in the Republic of Korea. METHODS: Two databases were used to assess COVID-19 vaccine safety in adolescents aged 12 to 17 years who completed the primary Pfizer-BioNTech vaccination series. Adverse events reported to the web-based COVID-19 vaccination management system (CVMS) and collected in the text message-based system were analyzed. RESULTS: From March 5, 2021 to February 13, 2022, 12,216 adverse events among 12- to 17-yearolds were reported to the CVMS, of which 97.1% were non-serious adverse events and 2.9% were serious adverse events, including 85 suspected cases of anaphylaxis, 74 suspected cases of myocarditis and/or pericarditis, and 2 deaths. From December 13, 2021 to January 26, 2022, 10,389 adolescents responded to a text message survey, and local/systemic adverse events were more common after dose 2 than after dose 1. The most commonly reported events following either vaccine dose were pain at the injection site, headache, fatigue/tiredness, and myalgia. CONCLUSION: The overall results are consistent with previous findings; the great majority of adverse events were non-serious, and serious adverse events were rare among adolescents aged 12 to 17 years following Pfizer-BioNTech COVID-19 vaccination.

COVID-19 vaccine safety monitoring in the Republic of Korea: February 26, 2021 to April 30, 2021.

OBJECTIVE: On February 26, 2021, coronavirus disease 2019 (COVID-19) vaccination was started for high-priority groups based on the recommendation of the Advisory Committee on Immunization Practices with 2 available COVID-19 vaccines (AstraZeneca and Pfizer-BioNTech) in Korea. This report provides a summary of adverse events following COVID-19 vaccination as of April 30, 2021. METHODS: Adverse events following immunization are notifiable by medical doctors to the Korea Immunization Management System (KIMS) under the national surveillance system. We analyzed all adverse events reports following COVID-19 vaccination to the KIMS from February 26 to April 30, 2021. RESULTS: In total, 16,196 adverse events following 3,586,814 administered doses of COVID-19 vaccines were reported in approximately 2 months (February 26 to April 30, 2021). Of these, 15,658 (96.7%) were non-serious adverse events, and 538 (3.3%) were serious adverse events, including 73 (0.5%) deaths. The majority of adverse events (n=13,063, 80.7%) were observed in women, and the most frequently reported adverse events were myalgia (52.2%), fever (44.9%), and headache (34.9%). Of the 73 deaths following the COVID-19 vaccination, none were related to the vaccines. CONCLUSION: By April 30, 3.6 million doses of the COVID 19 vaccine had been given in Korea, and the overwhelming majority of reports were for non-serious events. The Korea Disease Control and Prevention Agency continues to monitor the safety of COVID-19 vaccination.

COVID-19 vaccine safety monitoring in Republic of Korea from February 26, 2021 to October 31, 2021.

OBJECTIVE: This study aimed to present data on reported adverse events following coronavirus disease 2019 (COVID-19) vaccination in Republic of Korea from February 26 to October 31, 2021, and to determine whether any significant patterns emerged from an analysis of the characteristics of suspected adverse event cases for each type of vaccine. METHODS: Adverse events following COVID-19 vaccination reported by medical doctors and forensic pathologists were analyzed. Cases of suspected anaphylaxis were classified using the Brighton Collaboration definition. RESULTS: By October 31, 2021, a total of 353,535 (0.45%) adverse events were reported after 78,416,802 COVID-19 vaccine doses. Of the adverse events, 96.4% were non-serious and 3.6% were serious. The most frequently reported adverse events were headache, myalgia, and dizziness. Of the 835 reported deaths after COVID-19 vaccination, 2 vaccine-related deaths were confirmed. Suspected anaphylaxis was confirmed in 454 cases using the Brighton Collaboration definition. CONCLUSION: The commonly reported symptoms were similar to those described in clinical trials. Most reported adverse events were non-serious, and the reporting rate of adverse events following COVID-19 vaccination was higher in women than in men (581 vs. 315 per 100,000 vaccinations). Confirmed anaphylaxis was reported in 5.8 cases per 1,000,000 vaccinations.

Reports of anaphylaxis after coronavirus disease 2019 vaccination, South Korea, 26 February to 30 April 2021.

The South Korea mass vaccination programme administered 3.8 million doses of COVID-19 vaccinations between 26 February and 30 April 2021. After 173 suspected anaphylaxis reports to the nationwide monitoring system for adverse events following immunisation, 44 anaphylaxis cases were confirmed using Brighton Collaboration case definitions. The rates per million doses were 18.2 cases and 6.2 cases for Vaxzevria and Comirnaty, respectively. Median time of onset was 14 min after vaccination and most cases had recovered at the time of review.

Presenting characteristics and clinical outcome of patients with COVID-19 in South Korea: A nationwide retrospective observational study.

BACKGROUND: More than 13,000 cases were reported to be infected with COVID-19 by RT-PCR in South Korea. Most studies report clinical characteristics of hospitalized patients with COVID-19; the full spectrum of disease severity has thus not yet been well described. METHODS: Using retrospective observational methods, this study analyzed factors affecting early clinical symptoms, clinical progress, and severity of disease for COVID-19 positive patients released from quarantine to provide information on establishing optimized care for new patients. The medical data of 7803 laboratory-confirmed patients who had been discharged or died by April 30, 2020 were analyzed using multivariate logistic regression analysis. FINDINGS: On admission, 7383 (94•5%) patients were asymptomatic or showed mild illness, and 372 (4•8%) patients were severe illness. Also, 48 (0 0•6%) were hospitalized with critically ill when diagnosed. Most patients with asymptomatic or mild illness on admission remained mild until discharge, 253 (3•4%) progressed to severe illness, and 83 (1•1%) died in hospital. However, the case fatality were 29•8% and 62•5% in severe and critically ill patients, respectively. At admission, 73•0% of hospitalized patients had symptoms; most common were cough (42•5%), sputum (28•8%), and fever (20•1%). Only 35•2% of laboratory confirmed patients admitted to the temporary care facility complained of symptoms. Increasing odds of being critically ill was associated with older age (OR 28•93, 95% CI 13•34-62•75 for age >70y, vs. age <50 y; p<0•0001), being male (OR 2•15, 95% CI1•59-2•89; p<0•0001), fever (OR 2•52, 95% CI 1.84-3•45; p<0•0001), and shortness of breath (OR 7•40, 95% CI 5•37-10•19; p<0•0001). Comorbid illness significantly increased risk of critical illness or death. INTERPRETATION: Most cases were discharged as asymptomatic or recovered from mild illness, and only 9•7% developed severe disease requiring oxygen therapy or more. Case fatality rate was 2•9%, and markedly increased in those over age 50. Risk factors such as age, sex, fever, shortness of breath, and underlying disease can be useful in predicting future clinical severity. Additionally, the number of confirmed asymptomatic COVID-19 patients significantly contribute to continued spread. FUNDING: none.

Prediction of patients requiring intensive care for COVID-19: development and validation of an integer-based score using data from Centers for Disease Control and Prevention of South Korea.

BACKGROUND: Unavailability or saturation of the intensive care unit may be associated with the fatality of COVID-19. Prioritizing the patients for hospitalization and intensive care may be critical for reducing the fatality of COVID-19. This study aimed to develop and validate a new integer-based scoring system for predicting patients with COVID-19 requiring intensive care, using only the predictors available upon triage. METHODS: This is a retrospective study using cohort data from the Korean Centers for Disease Control and Prevention that included all admitted patients with COVID-19 between January 19 and June 3, 2020, in South Korea. The primary outcome was patients requiring intensive care defined as actual admission to the intensive care unit; at any time use of an extracorporeal life support device, mechanical ventilation, or vasopressors; and death. Patients admitted until March 20 were included for the training dataset to develop the prediction models and externally validated for the patients admitted afterward. Two logistic regression models were developed with different predictors and the predictive performance was compared: one with patient-provided variables and the other with added radiologic and laboratory variables. An integer-based scoring system was developed based on the developed logistic regression model. RESULTS: A total of 5193 patients were considered, with 4663 patients included after excluding patients with age under 18 or insufficient data. For the training dataset, 3238 patients were included. Of the included patients, 444 (9.5%) patients required intensive care. The model developed with only the clinical variables showed an area under the curve of 0.884 for the validation set. The performance did not differ when radiologic and laboratory variables were added. Seven variables were selected for developing an integer-based scoring system: age, sex, initial body temperature, dyspnea, hemoptysis, history of chronic kidney disease, and activities of daily living. The area under the curve of the scoring system was 0.880. CONCLUSIONS: An integer-based scoring system was developed for predicting patients with COVID-19 requiring intensive care, with high performance. This system may aid decision support for prioritizing the patient for hospitalization and intensive care, particularly in a situation with limited medical resources.

An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study.

BACKGROUND: Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not always easily available. OBJECTIVE: The purpose of this study was to develop a machine learning model that predicts the need for intensive care for patients with COVID-19 using easily obtainable characteristics-baseline demographics, comorbidities, and symptoms. METHODS: A retrospective study was performed using a nationwide cohort in South Korea. Patients admitted to 100 hospitals from January 25, 2020, to June 3, 2020, were included. Patient information was collected retrospectively by the attending physicians in each hospital and uploaded to an online case report form. Variables that could be easily provided were extracted. The variables were age, sex, smoking history, body temperature, comorbidities, activities of daily living, and symptoms. The primary outcome was the need for intensive care, defined as admission to the intensive care unit, use of extracorporeal life support, mechanical ventilation, vasopressors, or death within 30 days of hospitalization. Patients admitted until March 20, 2020, were included in the derivation group to develop prediction models using an automated machine learning technique. The models were externally validated in patients admitted after March 21, 2020. The machine learning model with the best discrimination performance was selected and compared against the CURB-65 (confusion, urea, respiratory rate, blood pressure, and 65 years of age or older) score using the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 4787 patients were included in the analysis, of which 3294 were assigned to the derivation group and 1493 to the validation group. Among the 4787 patients, 460 (9.6%) patients needed intensive care. Of the 55 machine learning models developed, the XGBoost model revealed the highest discrimination performance. The AUC of the XGBoost model was 0.897 (95% CI 0.877-0.917) for the derivation group and 0.885 (95% CI 0.855-0.915) for the validation group. Both the AUCs were superior to those of CURB-65, which were 0.836 (95% CI 0.825-0.847) and 0.843 (95% CI 0.829-0.857), respectively. CONCLUSIONS: We developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among patients with COVID-19.