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Background: Recent studies have highlighted the cardio-cerebrovascular manifestations of coronavirus disease 2019 (COVID-19). Objective: This study aimed to analyze the likelihood of cardiovascular and cerebrovascular manifestations among patients with COVID-19-positive individuals in South Korea. Methods: A cohort database for COVID-19 from the National Health Insurance Service was used which included patients diagnosed with COVID-19 between January 1 and June 4, 2020. Individuals who tested COVID-19 positive, notwithstanding the severity of the disease, were designated as cases. COVID-19- negative individuals were used as controls for the study. The exclusion criteria included people who had a history of cardiovascular and cerebrovascular diseases between 2015 and 2019. A new diagnosis of cardiovascular and cerebrovascular complications was considered the primary endpoint. The adjusted incidence rate ratio (IRR) of development of complications was estimated using log-link Poisson regression. The model was adjusted at two levels, the first one included age and sex while the second included age, sex, residence area, and level of income. The hazard ratio (HR) was estimated using Cox-proportional hazard regression analysis while adjusting for all demographic variables and covariates. Results: Significant results were obtained for acute conditions, such as ischemic heart disease and cerebral hemorrhage. The IRR of COVID-19- positive individuals compared with that of controls for the diagnosis of ischemic heart disease was 1.78 (1.57-2.02; 95% confidence interval [CI]) when adjusted for age and sex. HR was calculated as 3.02 (2.19-4.17; 95% CI) after adjusting for the covariates. In case of cerebral hemorrhage, the adjusted IRR was 2.06 (1.25-3.40; 95% CI) and the adjusted HR was 4.08 (0.90-19.19; 95% CI). Conclusion: The findings of our study suggest that COVID-19 infection can be a significant risk factor for acute cardiovascular complications, such as ischemic heart disease and acute cerebrovascular complications, such as cerebral infarction, after properly adjusting for covariates.
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OBJECTIVES: Effective infection control measures, based on a sound understanding of geographical community-specific health behavioral characteristics, should be implemented from the early stage of disease transmission. However, few studies have explored health behaviors as a possible contributor to COVID-19 infection in the spatial context. We investigated health behaviors as potential factors of COVID-19 incidence in the early phase of transmission in the spatial context. METHODS: We extracted COVID-19 cumulative case data as of February 25, 2021-one day prior to nationwide COVID-19 vaccination commencement-regarding health behaviors and covariates, including health condition and socio-economic factors, at the municipal level from publicly available datasets. The spatial autocorrelation of incidence was analyzed using Global Moran's I statistics. The associations between health behaviors and COVID-19 incidence were examined using Besag-York-Mollie models to deal with spatial autocorrelation of residuals. RESULTS: The COVID-19 incidence had positive spatial autocorrelation across South Korea (I = 0.584, p = 0.001). The results suggest that individuals vaccinated against influenza in the preceding year had a negative association with COVID-19 incidence (relative risk=0.913, 95 % Credible Interval=0.838-0.997), even after adjusting for covariates. CONCLUSIONS: Our ecological study suggests an association between COVID-19 infection and health behaviors, especially influenza vaccination, in the early stage of COVID-19 transmission at the municipal level.
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COVID-19 , Influenza, Human , Humans , COVID-19/epidemiology , Bayes Theorem , COVID-19 Vaccines , Spatial Analysis , Incidence , Health BehaviorABSTRACT
Background There have been approximately 158 million coronavirus disease 2019 (COVID-19) pandemic survivors worldwide by June 9, 2021. As a result, concerns about hair loss in COVID-19 patients have emerged among dermatologists. However, most of extant literature have limited implications by relying on cross-sectional studies with restricted study subjects without control group. Therefore, our study aims to investigate the risk of developing alopecia areata (AA) among COVID-19 patients in South Korea using adequate control based on national representative data. Methods We used the National Health Insurance Service (NHIS) COVID‐19 cohort database, comprising COVID‐19 patient and control group, all of whom were diagnosed from January 1, 2020 to June 4, 2020. Patients were defined as individuals who were confirmed as COVID‐19 positive, regardless of disease severity. Controls were defined as whom confirmed as COVID‐19 negative. People with a history of AA during the period 2015–2019 were excluded. The primary endpoint was a new diagnosis of AA (ICD-10-CM-Code: L63). Adjusted incidence rate ratio (IRR) of developing AA was estimated using log-link Poisson regression model based on incidence density of case and control group. The model adjusted for (1) age and sex (2) demographic variables (age, sex, place of residence, and income level). Statistical significance was set at p< 0.05. Results A total of 226,737 individuals (7,958 [3.5%] cases and 218,779 [96.5%] controls) were included in the final analysis. There were more females than males, both in test positives and negatives at 59.9% and 52.3%, respectively. The largest test positive population was those in age group 20 to 29 years (25.5%),. The test negatives had the largest population in age group 30 to 39 years (17.1%). The ratio of newly diagnosed AA was 18/7,958 (0.2%) in cases and 195/218,779 (0.1%) in controls. IRRs of COVID-19 patients having newly diagnosed AA compared to controls were 0.78 (0.48‒1.27) when age and sex were adjusted for, and 0.60 (0.35‒1.03) when all demographic variables were adjusted for. Flowchart of study subject selection Conclusion Diagnosis of COVID-19 was not significantly associated with development of AA even after appropriately adjusting for covariates. Disclosures All Authors: No reported disclosures
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Background Diabetes is emerging as one of the complications of coronavirus disease 2019 (COVID-19), but this is hard to be revealed with cross-sectional studies since it is also known as the major predisposing factor for high-risk COVID-19. Therefore, this study aimed to estimate the risk of new-onset diabetes after COVID-19 through a population follow-up study. Methods All COVID-19 confirmed cases in Korea from January 20 to June 4, 2020, were matched with national health insurance data and their health screening data, both provided by the National Health Insurance Service of Korea. Controls were selected as the people who received the PCR test for COVID-19 and showed negative results in the same period and followed up until July 19, 2020. We selected the outcome as the diagnosis of diabetes according to the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10, E10 – E14). People who were diagnosed with diabetes in the past five years were excluded from both groups. After performing a log-rank test between groups, adjusted incidence rate and hazard ratio were estimated using Cox proportional hazard modeling. Demographic characteristics (age, sex, region, family histories of hypertension/diabetes, and income) and underlying health conditions such as hypertension, dyslipidemia, heart disease, alcohol consumption, cigarette smoking, and BMI were adjusted. Proportional assumptions were tested by the zph test and the sensitivity analysis by excluding each factor in turn and comparing results. Results A total of 6,247 COVID-19 patients and 143,594 controls without diabetes in the past were included for the analysis. The number of new-onset diabetes were 759 (12.15%) in COVID-19 patients and 3,465 (2.41%) in controls (P < 0.01). The adjusted incidence of diabetes was 15.34 (95% confidence interval, CI: 14.10 – 16.66) and 11.18 (95% CI: 10.67 – 11.72) per 100 person-year, respectively, with the mean follow-up time as 46.31 (standard deviation: 16.37) days. The adjusted hazard ratio of diabetes in COVID-19 cases was 2.97 (95% CI: 2.44 – 3.63). Conclusion Since COVID-19 patients showed a higher incidence of new-onset diabetes in a short-time follow-up, we should consider diabetes as one of the possible complications of COVID-19. Disclosures All Authors: No reported disclosures
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Purpose: Revealing the clustering risks of COVID-19 and prediction is essential for effective quarantine policies, since clusters can lead to rapid transmission and high mortality in a short period. This study aimed to present which regional and social characteristics make COVID-19 cluster with high risk. Methods: By analyzing the data of all confirmed cases (14,423) in Korea between January 10 and August 3, 2020, provided by the Korea Disease Control and Prevention Agency, we manually linked each case and discovered clusters. After classifying the cases into clusters as nine types, we compared the duration and size of clusters by types to reveal high-risk cluster types. Also, we estimated odds for the risk factors for COVID-19 clustering by a spatial autoregressive model using the Bayesian approach. Results: Regarding the classified clusters (n = 539), the mean size was 19.21, and the mean duration was 9.24 days. The number of clusters was high in medical facilities, workplaces, and nursing homes. However, multilevel marketing, religious facilities, and restaurants/business-related clusters tended to be larger and longer when an outbreak occurred. According to the spatial analysis in COVID-19 clusters of more than 20 cases, the global Moran's I statistics value was 0.14 (p < 0.01). After adjusting for population size, the risks of COVID-19 clusters were related to male gender (OR = 1.29) and low influenza vaccination rate (OR = 0.87). After the spatial modeling, the predicted probability of forming clusters was visualized and compared with the actual incidence and local Moran's I statistics 2 months after the study period. Conclusions: COVID-19 makes different sizes of clusters in various contact settings; thus, precise epidemic control measures are needed. Also, when detecting and screening for COVID-19 clusters, regional risks such as vaccination rate should be considered for predicting risk to control the pandemic cost-effectively.
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Background: Concerns about alopecia areata (AA) in coronavirus disease 2019 (COVID-19) patients have emerged among dermatologists. However, most of the extant kinds of literature have limited implications by relying on cross-sectional studies with restricted study subjects without the control group. Objective: Our study aims to investigate the risk of developing AA among COVID-19 patients in South Korea using national representative data. Methods: We used the National Health Insurance Service COVID-19 cohort database, comprising COVID-19 patients and the control group, all of whom were diagnosed from January 1, 2020, to June 4, 2020. Patients were defined as individuals who were confirmed as COVID-19 positive, regardless of disease severity. Controls were defined as those who were confirmed as COVID-19 negatives. People with a history of AA during the period 2015-2019 were excluded. The primary endpoint was a new diagnosis of AA (ICD-10-Code: L63). The adjusted incidence rate ratio (IRR) of developing AA was estimated using a log-link Poisson regression model based on incidence density. The model adjusted for (1) age and sex and (2) demographic variables (age, sex, place of residence, and income level). Results: A total of 226,737 individuals (7,958 [3.5%] cases and 218,779 [96.5%] controls) were included in the final analysis. The ratio of newly diagnosed AA was 18/7,958 (0.2%) in cases and 195/218,779 (0.1%) in controls. IRRs of COVID-19 patients having newly diagnosed AA compared to controls were 0.78 (95% CI: 0.48-1.27) when age and sex were adjusted for and 0.60 (95% CI: 0.35-1.03) when all demographic variables were adjusted for. Conclusion: Diagnosis of COVID-19 was not significantly associated with the development of AA even after appropriately adjusting for covariates.
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Coronavirus disease (COVID-19) has been spreading all over the world; however, its incidence and case-fatality ratio differ greatly between countries and between continents. We investigated factors associated with international variation in COVID-19 incidence and case-fatality ratio (CFR) across 107 northern hemisphere countries, using publicly available COVID-19 outcome data as of 14 September 2020. We included country-specific geographic, demographic, socio-economic features, global health security index (GHSI), healthcare capacity, and major health behavior indexes in multivariate models to explain this variation. Multiple linear regression highlighted that incidence was associated with ethnic region (p < 0.05), global health security index 4 (GHSI4) (beta coefficient [ß] 0.50, 95% Confidence Interval [CI] 0.14-0.87), population density (ß 0.35, 95% CI 0.10-0.60), and water safety level (ß 0.51, 95% CI 0.19-0.84). The CFR was associated with ethnic region (p < 0.05), GHSI4 (ß 0.53, 95% CI 0.14-0.92), proportion of population over 65 (ß 0.71, 95% CI 0.19-1.24), international tourism receipt level (ß - 0.23, 95% CI - 0.43 to - 0.03), and the number of physicians (ß - 0.37, 95% CI - 0.69 to - 0.06). Ethnic region was the most influential factor for both COVID-19 incidence (partial [Formula: see text] = 0.545) and CFR (partial [Formula: see text] = 0.372), even after adjusting for various confounding factors.
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COVID-19/epidemiology , COVID-19/mortality , Mortality/trends , Global Health , Humans , Incidence , Population Density , Risk Factors , SARS-CoV-2/pathogenicityABSTRACT
INTRODUCTION: While the reduction in influenza cases in the Northern hemisphere in 2020 has been widely reported, the influenza transmission dynamics in the Southern hemisphere remain uncharacterized. METHODS: This study analysed the change in influenza-positive proportion (IPP) between 2010-2019 and 2020 in countries in the Southern hemisphere with ≤40% missing IPP data in FluNet to assess how coronavirus disease 2019 (COVID-19) relates to influenza activity. The analysis considered the incidence of COVID-19 reported by the World Health Organization and the implementation date of non-pharmaceutical interventions (NPIs) reported by the Oxford COVID-19 Government Response Tracker. RESULTS: In each of the seven included countries, the average IPP was lower in 2020 than in 2010-2019 (P < 0.01), with the largest difference being 31.1% (95% confidence interval 28.4-33.7%). In Argentina, Bolivia, Chile and South Africa, higher IPPs were observed during epidemiological weeks 4-16 in 2020 compared with the same weeks in 2010-2019. The IPP increased after NPIs were implemented in Argentina and South Africa, but started to decline in Bolivia, Chile, Madagascar and Paraguay before NPI implementation. CONCLUSIONS: Influenza burden and activity decreased in 2020 in the Southern hemisphere. The temporal decline in influenza activity varied between countries.
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COVID-19 , Influenza, Human , Humans , Incidence , Influenza, Human/epidemiology , Pandemics , SARS-CoV-2ABSTRACT
We compared weekly positivity rates of 8 respiratory viruses in South Korea during 2010-2019 and 2020. The overall mean positivity rate for these viruses decreased from 54.7% in 2010-2019 to 39.1% in 2020. Pandemic control measures might have reduced the incidence of many, but not all, viral respiratory infections.
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COVID-19/epidemiology , Pandemics , Respiratory Tract Infections/virology , Humans , Incidence , Population Surveillance , Republic of Korea/epidemiology , Respiratory Tract Infections/epidemiology , SARS-CoV-2ABSTRACT
Although coronavirus disease 2019 (COVID-19) is an ongoing pandemic, the mean serial interval was measured differently across nations. Through the Korean national COVID-19 contact tracing system, we were able to investigate personal contacts in all symptomatic cases in Korea from January 20 to August 3, 2020. The mean serial interval was calculated by the duration between the symptom onset of the infector and infectee, and became shorter after the case definition changed to include not-imported cases in Korea on February 20, 2020. The mean serial interval before and after this fifth case definition was 6.12 and 3.93 days based on the infectors' symptom onset date, respectively, and 4.02 days in total with the median of 3 days. Older age and women lead to longer serial intervals.