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1.
Sci Total Environ ; 832: 154981, 2022 Apr 02.
Article in English | MEDLINE | ID: covidwho-1768530

ABSTRACT

BACKGROUND: The rapid spread of COVID-19 has caused an emergency situation worldwide. Investigating the association between environmental characteristics and COVID-19 incidence can be of the occurrence and transmission. The objective of this study was to evaluate the association between greenness exposure and COVID-19 cases at the district levels in South Korea. We also explored this association by considering several environmental indicators. METHODS: District-level data from across South Korea were used to model the cumulative count of COVID-19 cases per 100,000 persons between January 20, 2020, and February 25, 2021. Greenness exposure data were derived from the Environmental Geographic Information Service of the Korean Ministry of Environment. A negative binomial mixed model evaluated the association between greenness exposure and COVID-19 incidence rate at the district level. Furthermore, we assessed this association between demographic, socioeconomic, environmental statuses, and COVID-19 incidence. RESULTS: Data from 239 of 250 districts (95.6%) were included in the analyses, resulting in 127.89 COVID-19 cases per 100,000 persons between January 20, 2020 and February 25, 2021. Several demographic and socioeconomic variables, districts with a higher rate of natural greenness exposure, were significantly associated with lower COVID-19 incidence rates (incidence rate ratio (IRR), 0.70; 95% confidence interval (CI), 0.54-0.90; P-value = 0.008) after adjusting covariates, but no evidence for the association between built greenness and COVID-19 incidence rates was found. CONCLUSION: In this ecological study of South Korea, we found that higher rates of exposure to natural greenness were associated with lower rates of COVID-19 cases.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-305541

ABSTRACT

The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7,772 (75.9%) recovered, and 2,237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality ( p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities >90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer;for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.

3.
Epidemiol Health ; 43: e2021061, 2021.
Article in English | MEDLINE | ID: covidwho-1534391

ABSTRACT

OBJECTIVES: During the coronavirus disease 2019 (COVID-19) pandemic, crude incidence and mortality rates have been widely reported; however, age-standardized rates are more suitable for comparisons. In this study, we estimated and compared the age-standardized incidence, mortality, and case fatality rates (CFRs) among countries and investigated the relationship between these rates and factors associated with healthcare resources: gross domestic product per capita, number of hospital beds per population, and number of doctors per population. METHODS: The incidence, mortality, and CFRs of 79 countries were age-standardized using the World Health Organization standard population. The rates for persons 60 years or older were also calculated. The relationships among the rates were analysed using trend lines and coefficients of determination (R2). Pearson correlation coefficients between the rates and the healthcare resource-related factors were calculated. RESULTS: The countries with the highest age-standardized incidence, mortality, and CFRs were Czechia (14,253 cases/100,000), Mexico (182 deaths/100,000), and Mexico (6.7%), respectively. The R2 between the incidence and mortality rates was 0.852 for all ages and 0.945 for those 60 years or older. The healthcare resources-related factors were associated positively with incidence rates and negatively with CFRs, with weaker correlations among the elderly. CONCLUSIONS: Compared to age-standardized rates, crude rates showed greater variation among countries. Medical resources may be important in preventing COVID-19-related deaths; however, considering the small variation in fatality among the elderly, preventive measures such as vaccination are more important, especially for the elderly population, to minimize the mortality rates.


Subject(s)
COVID-19 , Aged , Cross-Sectional Studies , Humans , Incidence , Infant , Mortality , Pandemics , SARS-CoV-2
4.
Clin Infect Dis ; 73(7): e1855-e1862, 2021 10 05.
Article in English | MEDLINE | ID: covidwho-1455257

ABSTRACT

BACKGROUND: Increased body mass index (BMI) has been associated with a higher risk of severe coronavirus disease 2019 (COVID-19) infections. However, whether obesity is a risk factor for contracting COVID-19 has hardly been investigated so far. METHODS: We examined the association between BMI level and the risk of COVID-19 infection in a nationwide case-control study comprised of 3788 case patients confirmed to have COVID-19 between 24 January and 9 April 2020 and 15 152 controls matched by age and sex, who were aged 20 years or more and underwent National Health Insurance Service (NHIS) health examinations between 2015-2017, using data from the Korean NHIS with linkage to the Korea Centers for Disease Control and Prevention data. Our primary exposure of interest was BMI level, categorized into 4 groups: <18.5 (underweight), 18.5-22.9 (normal weight), 23-24.9 (overweight), and ≥25 kg/m2 (obese). RESULTS: Of the entire 18 940 study participants, 11 755 (62.1%) were women, and the mean age of the study participants was 53.7 years (standard deviation, 13.8). In multivariable logistic regression models adjusted for sociodemographic, comorbidity, laboratory, and medication data, there was a graded association between higher BMI levels and higher risk of COVID-19 infection. Compared to normal-weight individuals, the adjusted odds ratios in the overweight and obese individuals were 1.13 (95% confidence interval [CI], 1.03-1.25) and 1.26 (95% CI, 1.15-1.39), respectively. This association was robust across age and sex subgroups. CONCLUSIONS: Higher BMI levels were associated with a higher risk of contracting COVID-19.


Subject(s)
COVID-19 , Adult , Body Mass Index , Case-Control Studies , Female , Humans , Middle Aged , Republic of Korea/epidemiology , Risk Factors , SARS-CoV-2 , Young Adult
5.
Int J Infect Dis ; 106: 363-369, 2021 May.
Article in English | MEDLINE | ID: covidwho-1279590

ABSTRACT

BACKGROUND: The complete contact tracing of coronavirus disease-19 (COVID-19) cases in South Korea allows a unique opportunity to investigate cluster characteristics. This study aimed to investigate all reported COVID-19 clusters in the Seoul metropolitan area from January 23 to September 24, 2020. METHODS: Publicly available COVID-19 data was collected from the Seoul Metropolitan City and Gyeonggi Province. Community clusters with ≥5 cases were characterized by size and duration, categorized using K-means clustering, and the correlation between the types of clusters and the level of social distancing investigated. RESULTS: A total of 134 clusters comprised of 4033 cases were identified. The clusters were categorized into small (type I and II), medium (type III), and large (type IV) clusters. A comparable number of daily reported cases in different time periods were composed of different types of clusters. Increased social distancing was related to a shift from large to small-sized clusters. CONCLUSIONS: Classification of clusters may provide opportunities to understand the pattern of COVID-19 outbreaks better and implement more effective suppression strategies. Social distancing administered by the government may effectively suppress large clusters but may not effectively control small and sporadic clusters.


Subject(s)
COVID-19/epidemiology , Disease Outbreaks , Physical Distancing , COVID-19/prevention & control , Contact Tracing , Humans , Seoul/epidemiology
6.
Nicotine Tob Res ; 23(10): 1787-1792, 2021 08 29.
Article in English | MEDLINE | ID: covidwho-1199496

ABSTRACT

INTRODUCTION: It is unclear whether smokers are more vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. This study aimed to evaluate the association between smoking and the risk of SARS-CoV-2 infection. METHODS: A matched case-control study was conducted using a large nationwide database. The case group included patients with SARS-CoV-2 infection confirmed by the Korea Centers for Disease Control and Prevention, and the control group was randomly sampled from the general Korean population in the National Health Insurance Service database by matching sex, age, and region of residence. Conditional logistic regression models were used to investigate whether the risk of infection with SARS-CoV-2 was affected by smoking status. RESULTS: A total of 4167 patients with SARS-CoV-2 infection and 20 937 matched controls were enrolled. The proportion of ex-smokers and current smokers was 26.6% of the total participants. In multivariate analysis, smoking was not associated with an increased risk of SARS-CoV-2 infection (odds ratio [OR] = 0.56, confidence interval [CI] = 0.50-0.62). When ex-smokers and current smokers were analyzed separately, similar results were obtained (current smoker OR = 0.33, CI = 0.28-0.38; ex-smoker OR = 0.81, CI = 0.72-0.91). CONCLUSIONS: This study showed that smoking may not be associated with an increased risk of SARS-CoV-2 infection. Smoking tends to lower the risk of SARS-CoV-2 infection; however, these findings should be interpreted with caution. IMPLICATIONS: It is unclear whether smokers are more vulnerable to coronavirus disease 2019. In this large nationwide study in South Korea, smoking tended to lower the risk of infection with severe acute respiratory syndrome coronavirus 2. However, these findings should be interpreted with caution, and further confirmatory studies are required.


Subject(s)
COVID-19 , SARS-CoV-2 , Smoking , COVID-19/epidemiology , Case-Control Studies , Humans , Korea/epidemiology , Logistic Models , Risk Factors , Smoking/adverse effects , Smoking/epidemiology
7.
Nano Today ; 38: 101149, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1171534

ABSTRACT

In response to the coronavirus disease-19 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), global efforts are focused on the development of new therapeutic interventions. For the treatment of COVID-19, selective lung-localizing strategies hold tremendous potential, as SARS-CoV-2 invades the lung via ACE2 receptors and causes severe pneumonia. Similarly, recent reports have shown the association of COVID-19 with decreased 25-hydroxycholesterol (25-HC) and increased cytokine levels. This mechanism, which involves the activation of inflammatory NF-κB- and SREBP2-mediated inflammasome signaling pathways, is believed to play a crucial role in COVID-19 pathogenesis, inducing acute respiratory distress syndrome (ARDS) and sepsis. To resolve those clinical conditions observed in severe SARS-CoV-2 patients, we report 25-HC and didodecyldimethylammonium bromide (DDAB) nanovesicles (25-HC@DDAB) as a COVID-19 drug candidate for the restoration of intracellular cholesterol level and suppression of cytokine storm. Our data demonstrate that 25-HC@DDAB can selectively accumulate the lung tissues and effectively downregulate NF-κB and SREBP2 signaling pathways in COVID-19 patient-derived PBMCs, reducing inflammatory cytokine levels. Altogether, our findings suggest that 25-HC@DDAB is a promising candidate for the treatment of symptoms associated with severe COVID-19 patients, such as decreased cholesterol level and cytokine storm.

8.
BMC Infect Dis ; 20(1): 901, 2020 Nov 30.
Article in English | MEDLINE | ID: covidwho-1005880

ABSTRACT

BACKGROUND: Staphylococcus aureus bacteremia (SAB) presents heterogeneously, owing to the differences in underlying host conditions and immune responses. Although Toll-like receptor 2 (TLR2) is important in recognizing S. aureus, its function during S. aureus infection remains controversial. We aimed to examine the association of TLR2 expression and associated cytokine responses with clinical SAB outcomes. METHODS: Patients from a prospective SAB cohort at two tertiary-care medical centers were enrolled. Blood was sampled at several timepoints (≤5 d, 6-9 d, 10-13 d, 14-19 d, and ≥ 20 d) after SAB onset. TLR2 mRNA levels were determined via real-time PCR and serum tumor necrosis factor [TNF]-α, interleukin [IL]-6, and IL-10 levels were analyzed with multiplex-high-sensitivity electrochemiluminescent ELISA. RESULTS: TLR2 levels varied among 59 SAB patients. On days 2-5, TLR2 levels were significantly higher in SAB survivors than in healthy controls (p = 0.040) and slightly but not significantly higher than non-survivors (p = 0.120), and SAB patients dying within 7 d had lower TLR2 levels than survivors (P = 0.077) although statistically insignificant. IL-6 and IL-10 levels were significantly higher in non-survivors than in survivors on days 2-5 post-bacteremia (P = 0.010 and P = 0.021, respectively), and those dying within 7 d of SAB (n = 3) displayed significantly higher IL-10/TNF-α ratios than the survivors did (P = 0.007). CONCLUSION: TLR2 downregulation and IL-6 and IL-10 concentrations suggestive of immune dysregulation during early bacteremia may be associated with mortality from SAB. TLR2 expression levels and associated cytokine reactions during early-phase SAB may be potential prognostic factors in SAB, although larger studies are warranted.


Subject(s)
Bacteremia/metabolism , Bacteremia/mortality , Cytokines/metabolism , Down-Regulation/genetics , Staphylococcal Infections/metabolism , Staphylococcal Infections/mortality , Staphylococcus aureus/isolation & purification , Toll-Like Receptor 2/genetics , Adult , Aged , Aged, 80 and over , Cytokines/analysis , Female , Humans , Male , Middle Aged , Prognosis , Prospective Studies , RNA, Messenger/genetics , Real-Time Polymerase Chain Reaction , Staphylococcal Infections/microbiology , Staphylococcus aureus/metabolism , Survivors , Tertiary Care Centers
9.
Diabetes Metab J ; 44(6): 897-907, 2020 12.
Article in English | MEDLINE | ID: covidwho-1005674

ABSTRACT

BACKGROUND: This study aimed to determine the infection risk of coronavirus disease 2019 (COVID-19) in patients with diabetes (according to treatment method). METHODS: Claimed subjects to the Korean National Health Insurance claims database diagnosed with COVID-19 were included. Ten thousand sixty-nine patients with COVID-19 between January 28 and April 5, 2020, were included. Stratified random sampling of 1:5 was used to select the control group of COVID-19 patients. In total 50,587 subjects were selected as the control group. After deleting the missing values, 60,656 subjects were included. RESULTS: Adjusted odds ratio (OR) indicated that diabetic insulin users had a higher risk of COVID-19 than subjects without diabetes (OR, 1.25; 95% confidence interval [CI], 1.03 to 1.53; P=0.0278). In the subgroup analysis, infection risk was higher among diabetes male insulin users (OR, 1.42; 95% CI, 1.07 to 1.89), those between 40 and 59 years (OR, 1.66; 95% CI, 1.13 to 2.44). The infection risk was higher in diabetic insulin users with 2 to 4 years of morbidity (OR, 1.744; 95% CI, 1.003 to 3.044). CONCLUSION: Some diabetic patients with certain conditions would be associated with a higher risk of acquiring COVID-19, highlighting their need for special attention. Efforts are warranted to ensure that diabetic patients have minimal exposure to the virus. It is important to establish proactive care and screening tests for diabetic patients suspected with COVID-19 for timely disease diagnosis and management.


Subject(s)
COVID-19/economics , COVID-19/epidemiology , Diabetes Mellitus/economics , Diabetes Mellitus/epidemiology , Population Surveillance , Social Class , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/diagnosis , Databases, Factual/trends , Diabetes Mellitus/diagnosis , Female , Humans , Male , Middle Aged , National Health Programs/trends , Republic of Korea/epidemiology , Risk Factors , Young Adult
10.
Sci Rep ; 10(1): 18716, 2020 10 30.
Article in English | MEDLINE | ID: covidwho-894420

ABSTRACT

The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7772 (75.9%) recovered, and 2237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities > 90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.


Subject(s)
Coronavirus Infections/mortality , Machine Learning , Pneumonia, Viral/mortality , Adult , Aged , Aged, 80 and over , COVID-19 , Female , Humans , Male , Middle Aged , Models, Statistical , Mortality/trends , Pandemics , Republic of Korea
11.
Int J Infect Dis ; 99: 266-268, 2020 Oct.
Article in English | MEDLINE | ID: covidwho-695925

ABSTRACT

OBJECTIVES: To delineate clinical characteristics of asymptomatic and symptomatic patients confirmed with COVID-19 in South Korea. METHODS: Data were obtained from the Korean National Health Insurance Service database linked to the Korea Centers for Disease Control and Prevention data. RESULTS: Among 10,237 patients (mean [SD] age, 45.0 [19.8] years; 60.1% female) who met the eligibility criteria for the study, 6,350 (62.0%) patients were asymptomatic, and 3,887(38.0%) patients were symptomatic. The mean and median age were similar between asymptomatic and symptomatic patients. Notably, we observed a U-shaped association between age group and the proportion of asymptomatic patients, with the nadir at 57.3% in the 40-49 age group. This U-shaped distribution was largely similar between men and women. The overall prevalence of asymptomatic individuals was higher, regardless of sex, residential area, income levels, and comorbid conditions. CONCLUSIONS: In this national cohort of over 10,000 patients with COVID-19, more than 60% of all cases in South Korea reported no symptoms at the time of diagnosis. Expanding criteria for contact tracing and testing to capture potential transmission before symptom onset should be urgently considered to inform control strategies for COVID-19.


Subject(s)
Asymptomatic Infections , Betacoronavirus , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Pneumonia, Viral/diagnosis , Pneumonia, Viral/epidemiology , Adult , COVID-19 , Cohort Studies , Female , Humans , Male , Middle Aged , Pandemics , Republic of Korea/epidemiology , SARS-CoV-2 , Young Adult
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