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1.
J Korean Med Sci ; 37(45): e325, 2022 Nov 21.
Article in English | MEDLINE | ID: covidwho-2141687

ABSTRACT

As most individuals acquire immunity to severe acute respiratory syndrome coronavirus 2, South Korea declared a return to normalcy a few months ago. However, epidemic waves continue because of endlessly emerging variants and waning immunity. Health authorities are focusing on those at high risk of severe coronavirus disease 2019 to minimize damage to public health and the economy. In this regard, we investigated the vaccination rates in patients with various chronic medical conditions by examining the national health insurance claims data and the national immunization registry. We found that patients with chronic medical conditions, especially those of higher severity, such as malignancy, had vaccination rates approximately 10-20% lower than those of the general population. Public health authorities and healthcare providers should try to vaccinate these patients to avoid preventable morbidity and mortality.


Subject(s)
COVID-19 Vaccines , COVID-19 , Humans , Cross-Sectional Studies , COVID-19/prevention & control , Vaccination , Immunization , Chronic Disease
2.
J Med Internet Res ; 23(2): e26257, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1063292

ABSTRACT

BACKGROUND: As the COVID-19 pandemic continues, an initial risk-adapted allocation is crucial for managing medical resources and providing intensive care. OBJECTIVE: In this study, we aimed to identify factors that predict the overall survival rate for COVID-19 cases and develop a COVID-19 prognosis score (COPS) system based on these factors. In addition, disease severity and the length of hospital stay for patients with COVID-19 were analyzed. METHODS: We retrospectively analyzed a nationwide cohort of laboratory-confirmed COVID-19 cases between January and April 2020 in Korea. The cohort was split randomly into a development cohort and a validation cohort with a 2:1 ratio. In the development cohort (n=3729), we tried to identify factors associated with overall survival and develop a scoring system to predict the overall survival rate by using parameters identified by the Cox proportional hazard regression model with bootstrapping methods. In the validation cohort (n=1865), we evaluated the prediction accuracy using the area under the receiver operating characteristic curve. The score of each variable in the COPS system was rounded off following the log-scaled conversion of the adjusted hazard ratio. RESULTS: Among the 5594 patients included in this analysis, 234 (4.2%) died after receiving a COVID-19 diagnosis. In the development cohort, six parameters were significantly related to poor overall survival: older age, dementia, chronic renal failure, dyspnea, mental disturbance, and absolute lymphocyte count <1000/mm3. The following risk groups were formed: low-risk (score 0-2), intermediate-risk (score 3), high-risk (score 4), and very high-risk (score 5-7) groups. The COPS system yielded an area under the curve value of 0.918 for predicting the 14-day survival rate and 0.896 for predicting the 28-day survival rate in the validation cohort. Using the COPS system, 28-day survival rates were discriminatively estimated at 99.8%, 95.4%, 82.3%, and 55.1% in the low-risk, intermediate-risk, high-risk, and very high-risk groups, respectively, of the total cohort (P<.001). The length of hospital stay and disease severity were directly associated with overall survival (P<.001), and the hospital stay duration was significantly longer among survivors (mean 26.1, SD 10.7 days) than among nonsurvivors (mean 15.6, SD 13.3 days). CONCLUSIONS: The newly developed predictive COPS system may assist in making risk-adapted decisions for the allocation of medical resources, including intensive care, during the COVID-19 pandemic.


Subject(s)
COVID-19/diagnosis , COVID-19/mortality , Age Factors , Aged , Critical Care/statistics & numerical data , Dementia/epidemiology , Female , Humans , Kidney Failure, Chronic/epidemiology , Length of Stay/statistics & numerical data , Male , Middle Aged , Pandemics , Prognosis , Proportional Hazards Models , ROC Curve , Republic of Korea/epidemiology , Retrospective Studies , Risk Factors , Severity of Illness Index , Survival Rate
3.
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
4.
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
5.
J Korean Med Sci ; 35(26): e243, 2020 Jul 06.
Article in English | MEDLINE | ID: covidwho-633958

ABSTRACT

BACKGROUND: Mortality of coronavirus disease 2019 (COVID-19) is a major concern for quarantine departments in all countries. This is because the mortality of infectious diseases determines the basic policy stance of measures to prevent infectious diseases. Early screening of high-risk groups and taking action are the basics of disease management. This study examined the correlation of comorbidities on the mortality of patients with COVID-19. METHODS: We constructed epidemiologic characteristics and medical history database based on the Korean National Health Insurance Service Big Data and linked COVID-19 registry data of Korea Centers for Disease Control & Prevention (KCDC) for this emergent observational cohort study. A total of 9,148 patients with confirmed COVID-19 were included. Mortalities by sex, age, district, income level and all range of comorbidities classified by International Classification of Diseases-10 based 298 categories were estimated. RESULTS: There were 3,556 male confirmed cases, 67 deaths, and crude death rate (CDR) of 1.88%. There were 5,592 females, 63 deaths, and CDR of 1.13%. The most confirmed cases were 1,352 patients between the ages of 20 to 24, followed by 25 to 29. As a result of multivariate logistic regression analysis that adjusted epidemiologic factors to view the risk of death, the odds ratio of death would be hemorrhagic conditions and other diseases of blood and blood-forming organs 3.88-fold (95% confidence interval [CI], 1.52-9.88), heart failure 3.17-fold (95% CI, 1.88-5.34), renal failure 3.07-fold (95% CI, 1.43-6.61), prostate malignant neoplasm 2.88-fold (95% CI, 1.01-8.22), acute myocardial infarction 2.38-fold (95% CI, 1.03-5.49), diabetes was 1.82-fold (95% CI, 1.25-2.67), and other ischemic heart disease 1.71-fold (95% CI, 1.09-2.66). CONCLUSION: We hope that this study could provide information on high risk groups for preemptive interventions. In the future, if a vaccine for COVID-19 is developed, it is expected that this study will be the basic data for recommending immunization by selecting those with chronic disease that had high risk of death, as recommended target diseases for vaccination.


Subject(s)
Comorbidity , Coronavirus Infections/epidemiology , Coronavirus Infections/mortality , Pneumonia, Viral/epidemiology , Pneumonia, Viral/mortality , Adult , Aged , Betacoronavirus , Big Data , COVID-19 , Chronic Disease/epidemiology , Chronic Disease/mortality , Coronavirus Infections/therapy , Female , Humans , Male , Middle Aged , National Health Programs , Pandemics , Pneumonia, Viral/therapy , Republic of Korea/epidemiology , Risk Factors , SARS-CoV-2 , Young Adult
6.
J Korean Med Sci ; 35(25): e232, 2020 Jun 29.
Article in English | MEDLINE | ID: covidwho-619779

ABSTRACT

BACKGROUND: There is a controversy whether it is safe to continue renin-angiotensin system blockers in patients with coronavirus disease 2019 (COVID-19). We analyzed big data to investigate whether angiotensin-converting enzyme inhibitors and/or angiotensin II receptor blockers have any significant effect on the risk of COVID-19. Population-based cohort study was conducted based on the prescription data from nationwide health insurance records. METHODS: We investigated the 1,374,381 residents aged ≥ 40 years living in Daegu, the epicenter of the COVID-19 outbreak, between February and March 2020. Prescriptions of antihypertensive medication during the year before the outbreak were extracted from the National Health Insurance Service registry. Medications were categorized by types and stratified by the medication possession ratios (MPRs) of antihypertensive medications after controlling for the potential confounders. The risk of COVID-19 was estimated using a difference in difference analysis. RESULTS: Females, older individuals, low-income earners, and recently hospitalized patients had a higher risk of infection. Patients with higher MPRs of antihypertensive medications had a consistently lower risk of COVID-19 than those with lower MPRs of antihypertensive medications and non-users. Among patients who showed complete compliance, there was a significantly lower risk of COVID-19 for those prescribed angiotensin II receptor blockers (relative risk [RR], 0.751; 95% confidence interval [CI], 0.587-0.960) or calcium channel blockers (RR, 0.768; 95% CI, 0.601-0.980). CONCLUSION: Renin-angiotensin system blockers or other antihypertensive medications do not increase the risk of COVID-19. Patients should not stop antihypertensive medications, including renin-angiotensin system blockers, because of concerns of COVID-19.


Subject(s)
Angiotensin Receptor Antagonists/therapeutic use , Angiotensin-Converting Enzyme Inhibitors/therapeutic use , Antihypertensive Agents/therapeutic use , Calcium Channel Blockers/therapeutic use , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Adult , Aged , Aged, 80 and over , Angiotensin Receptor Antagonists/adverse effects , Angiotensin-Converting Enzyme 2 , Angiotensin-Converting Enzyme Inhibitors/adverse effects , Antihypertensive Agents/adverse effects , Betacoronavirus/drug effects , COVID-19 , Calcium Channel Blockers/adverse effects , Female , Humans , Hypertension/drug therapy , Male , Middle Aged , Pandemics , Peptidyl-Dipeptidase A/metabolism , Renin-Angiotensin System/drug effects , Republic of Korea/epidemiology , SARS-CoV-2
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