Household secondary attack rates and risk factors during periods of SARS-CoV-2 Delta and Omicron variant predominance in the Republic of Korea.

BACKGROUND: The household secondary attack rate (SAR) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an important indicator for community transmission. This study aimed to characterize transmission by comparing household SARs and identifying risk factors during the periods of Delta and Omicron variant predominance in Republic of Korea. METHODS: We defined the period of Delta variant predominance (Delta period) as July 25, 2021 to January 15, 2022, and the period of Omicron variant predominance (Omicron period) as February 7 to September 3, 2022. The number of index cases included was 214,229 for the Delta period and 5,521,393 for the Omicron period. To identify the household SARs and risk factors for each period, logistic regression was performed to determine the adjusted odds ratio (aOR). RESULTS: The SAR was 35.2% for the Delta period and 43.1% for the Omicron period. The aOR of infection was higher in 2 groups, those aged 0 to 18 years and ≥75 years, compared to those aged 19 to 49 years. Unvaccinated individuals (vs. vaccinated individuals) and individuals experiencing initial infection (vs. individuals experiencing a second or third infection) had an increased risk of infection with SARS-CoV-2. CONCLUSION: This study analyzed the household SARs and risk factors. We hope that the results can help develop age-specific immunization plans and responses to reduce the SAR in preparation for emerging infectious diseases or potential new variants of SARS-CoV-2.

Safety monitoring of COVID-19 vaccines: February 26, 2021, To June 4, 2022, Republic of Korea.

As of June 2022, 5 coronavirus disease 2019 (COVID-19) vaccine brands have been used in Korea's national immunization program. The Korea Disease Control and Prevention Agency has enhanced vaccine safety monitoring through a passive web-based reporting system and active text message-based monitoring. In this study, an enhanced safety monitoring system for COVID-19 vaccines is described and the frequencies and types of adverse events (AEs) associated with the 5 COVID-19 vaccine brands were analyzed. AE reports from the web-based COVID-19 Vaccination Management System and text message-based reports from recipients were analyzed. AEs were classified as nonserious or serious (e.g., death or anaphylaxis). The AE reporting rates were calculated based on the number of COVID-19 vaccine doses administered. A total of 125,107,883 doses were administered in Korea from February 26, 2021, to June 4, 2022. Among them, 471,068 AEs were reported, of which 96.1% were nonserious and 3.9% were serious. Among the 72,609 participants in the text message-based AE monitoring process, a higher AE rate of local and systemic reactions was reported for the 3rd versus 1st doses. A total of 874 cases of anaphylaxis (7.0 per 1,000,000 doses), 4 cases of thrombocytopenia syndrome (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. Six fatalities were causally associated with COVID-19 vaccination (1 of TTS and 5 of myocarditis). Young adult age and female sex were related with a higher AE rate for COVID-19 vaccines. Most reported AEs were nonserious and of mild intensity.

Effectiveness of A Fourth Dose of COVID-19 mRNA Vaccine in the Elderly Population During the Omicron BA.2 and BA.5 Circulation: A Nationwide Cohort Study in Korea (K-COVE).

We estimate the effectiveness of a fourth dose booster of coronavirus disease 2019 mRNA vaccine in individuals aged ≥60 years during Omicron BA.2 and BA.5 circulation in Korea. The effectiveness against critical infection was 67.7% (95% confidence interval, 50.7%-78.8%) at 31-60 days and 62.1% (95% confidence interval, 45.5%-73.7%) at 61-90 days.

Effectiveness of Second mRNA COVID-19 Booster Vaccine in Immunocompromised Persons and Long-Term Care Facility Residents.

We used a nationwide population registry in South Korea to estimate the effect of a second booster dose of mRNA COVID-19 vaccine on the risk for laboratory-confirmed SARS-CoV-2 infection, critical infection, and death in immunocompromised persons and long-term care facility (LTCF) residents. During February 16-May 7, 2022, among 972,449 eligible persons, 736,439 (75.7%) received a first booster and 236,010 (24.3%) persons received a second booster. Compared with the first booster group, at 30-53 days, the second booster recipients had vaccine effectiveness (VE) against all infections of 22.28% (95% CI 19.35%-25.11%), VE against critical infection of 56.95% (95% CI 29.99%-73.53%), and VE against death of 62.96% (95% CI 34.18%-79.15%). Our findings provide real-world evidence that a second booster dose of mRNA vaccine substantially increases protection against critical infection and death in these high-risk population groups.

Importation and Transmission of SARS-CoV-2 B.1.1.529 (Omicron) Variant of Concern in Korea, November 2021.

In November 2021, 14 international travel-related severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.529 (omicron) variant of concern (VOC) patients were detected in South Korea. Epidemiologic investigation revealed community transmission of the omicron VOC. A total of 80 SARS-CoV-2 omicron VOC-positive patients were identified until December 10, 2021 and 66 of them reported no relation to the international travel. There may be more transmissions with this VOC in Korea than reported.

Data resource profile: the Korea National Hospital Discharge In-depth Injury Survey.

The Korea National Hospital Discharge In-depth Injury Survey (KNHDIS), which was started in 2005, is a national probability survey of general hospitals in Korea with 100 or more beds conducted by the Korea Disease Control and Prevention Agency (KDCA). The KNHDIS captures approximately 9% of discharged cases from sampled hospitals using a 2-stage stratified cluster sampling scheme, among which 13% are injury related cases, defined as S00-T98 (injury, poisoning, and certain other consequences of external causes) using International Classification of Diseases, 10th revision codes. The KNHDIS collects information on characteristics of injury-related discharges in order to understand the scale of injuries, identify risk factors, and provide data supporting prevention policies and intervention strategies. The types of data captured include the hospitals' information, detailed clinical information, and injury-related codes such as the mechanism, activities undertaken when injured (sports, leisure activities, work, treatment, and education), external causes of the injury, and location of the occurrence of the injury based on the International Classification of External Causes of Injuries. Furthermore, the means of transportation, risk factors for suicide, and toxic substances are recoreded. Annual reports of the KNHDIS are publicly accessible to browse via the KDCA website (http://www.kdca.go.kr) and microdata are available free of charge upon request via email (kcdcinjury@korea.kr).

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.

[Assessment of applicability of standardized rates for health state comparison among areas: 2008 Community Health Survey].

OBJECTIVES: This study shows the issues that should be considered when applying standardized rates using Community Health Survey(CHS) data. METHODS: We analyzed 2008 CHS data. In order to obtain the reliability of standardized rates, we calculated z-score and rank correlation coefficients between direct standardized rate and indirect standardized rate for 31 major indices. Especially, we assessed the change of correlations according to population composition (age and sex), and characteristics of the index. We used Mantel-Haenszel chi-square to quantify the difference of population composition. RESULTS: Among 31 major indices, 29 indices' z-score and rank correlation coefficients were over 0.9. However, regions with larger differences in population composition showed lower reliability. Low reliability was also observed for the indices specific to subgroups with small denominator such as 'permanent lesion from stroke', and the index with large regional variations in age-related differences such as 'obtaining health examinations'. CONCLUSIONS: Standardized rates may have low reliability, if comparison is made between areas with extremely large differences in population composition, or for indicies with large regional variations in age-related differences. Therefore, the special features of standardized rates should be considered when health state are compared among areas.