Sex differences in the association between sleep duration and frailty in older adults: evidence from the KNHANES study.

BACKGROUND: Frailty is a pervasive clinical syndrome among the older population. It is associated with an increased risk of diverse adverse health outcomes including death. The association between sleep duration and frailty remains unclear. Therefore, the aim of this study was to investigate the relationship between sleep duration and frailty in community-dwelling Korean older adults and to determine whether this relationship is sex-dependent. METHODS: Data on 3,953 older adults aged ≥ 65 years were obtained from the 7th (2016-2018) Korea National Health and Nutrition Examination Survey (KNHANES). Frailty was defined using the Fried phenotype with criteria customized for the KNHANES dataset. Self-reported sleep duration was classified as short sleep duration (≤ 6 h), middle sleep duration (6.1-8.9 h), and long sleep duration (≥ 9 h). Complex samples multivariate logistic regression analysis was conducted to estimate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: The percentage of male participants with short, middle, and long sleep durations was 34.9%, 62.1%, and 16.8%, respectively, while that of female participants was 26.1%, 59.2%, and 14.7%. The prevalence of frailty in the middle sleep duration group was lower than that in the short and long sleep duration groups in both men (short, 14.7%; middle, 14.2%; long, 24.5%; p < 0.001) and women (short, 42.9%; middle, 27.6%; long, 48.6%; p < 0.001). Both short (OR = 2.61, 95% CI = 1.91 - 4.83) and long (OR = 2.57, 95% CI = 1.36 - 3.88) sleep duration groups had a significantly higher OR for frailty than the middle sleep duration group even after adjusting for confounding variables among women, but not among men. CONCLUSION: Short and long sleep durations were independently associated with frailty in community-dwelling Korean older adult women. Managing sleep problems among women should be prioritized, and effective interventions to prevent frailty should be developed accordingly.

Effectiveness of single-dose administration of inactivated hepatitis A virus vaccination in the Republic of Korea armed forces, 2013-2016.

The Republic of Korea Armed Forces has implemented the hepatitis A virus (HAV) vaccination programme with a single-dose administration schedule in new recruits since 2013. A single-dose administration was selected for economic feasibility. We analysed the effectiveness of the single-dose HAV vaccination in a young and healthy population. To measure the effectiveness of the programme, we observed the incidence of HAV between the vaccinated and unvaccinated groups. A comparison between the two groups during the vaccine introduction period (2013-2016) revealed a lower incidence rate of infection in the vaccinated group (3 cases/603 550 person-years) than in the unvaccinated group (21 cases/1 020 450 person-years). The effectiveness of single-dose HAV vaccination was found to be 75.85%.

COVID-19 Outcome Prediction and Monitoring Solution for Military Hospitals in South Korea: Development and Evaluation of an Application.

BACKGROUND: COVID-19 has officially been declared as a pandemic, and the spread of the virus is placing sustained demands on public health systems. There are speculations that the COVID-19 mortality differences between regions are due to the disparities in the availability of medical resources. Therefore, the selection of patients for diagnosis and treatment is essential in this situation. Military personnel are especially at risk for infectious diseases; thus, patient selection with an evidence-based prognostic model is critical for them. OBJECTIVE: This study aims to assess the usability of a novel platform used in the military hospitals in Korea to gather data and deploy patient selection solutions for COVID-19. METHODS: The platform's structure was developed to provide users with prediction results and to use the data to enhance the prediction models. Two applications were developed: a patient's application and a physician's application. The primary outcome was requiring an oxygen supplement. The outcome prediction model was developed with patients from four centers. A Cox proportional hazards model was developed. The outcome of the model for the patient's application was the length of time from the date of hospitalization to the date of the first oxygen supplement use. The demographic characteristics, past history, patient symptoms, social history, and body temperature were considered as risk factors. A usability study with the Post-Study System Usability Questionnaire (PSSUQ) was conducted on the physician's application on 50 physicians. RESULTS: The patient's application and physician's application were deployed on the web for wider availability. A total of 246 patients from four centers were used to develop the outcome prediction model. A small percentage (n=18, 7.32%) of the patients needed professional care. The variables included in the developed prediction model were age; body temperature; predisease physical status; history of cardiovascular disease; hypertension; visit to a region with an outbreak; and symptoms of chills, feverishness, dyspnea, and lethargy. The overall C statistic was 0.963 (95% CI 0.936-0.99), and the time-dependent area under the receiver operating characteristic curve ranged from 0.976 at day 3 to 0.979 at day 9. The usability of the physician's application was good, with an overall average of the responses to the PSSUQ being 2.2 (SD 1.1). CONCLUSIONS: The platform introduced in this study enables evidence-based patient selection in an effortless and timely manner, which is critical in the military. With a well-designed user experience and an accurate prediction model, this platform may help save lives and contain the spread of the novel virus, COVID-19.

A Patient Self-Checkup App for COVID-19: Development and Usage Pattern Analysis.

BACKGROUND: Clear guidelines for a patient with suspected COVID-19 infection are unavailable. Many countries rely on assessments through a national hotline or telecommunications, but this only adds to the burden of an already overwhelmed health care system. In this study, we developed an algorithm and a web application to help patients get screened. OBJECTIVE: This study aims to aid the general public by developing a web-based application that helps patients decide when to seek medical care during a novel disease outbreak. METHODS: The algorithm was developed via consultations with 6 physicians who directly screened, diagnosed, and/or treated patients with COVID-19. The algorithm mainly focused on when to test a patient in order to allocate limited resources more efficiently. The application was designed to be mobile-friendly and deployed on the web. We collected the application usage pattern data from March 1 to March 27, 2020. We evaluated the association between the usage pattern and the numbers of COVID-19 confirmed, screened, and mortality cases by access location and digital literacy by age group. RESULTS: The algorithm used epidemiological factors, presence of fever, and other symptoms. In total, 83,460 users accessed the application 105,508 times. Despite the lack of advertisement, almost half of the users accessed the application from outside of Korea. Even though the digital literacy of the 60+ years age group is half of that of individuals in their 50s, the number of users in both groups was similar for our application. CONCLUSIONS: We developed an expert-opinion-based algorithm and web-based application for screening patients. This innovation can be helpful in circumstances where information on a novel disease is insufficient and may facilitate efficient medical resource allocation.

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.

Current Status of Cold Injuries in the South Korean Military Over the Past 5 Years: Analysis and Assessment Based on the 2023-2024 Surveillance System.

INTRODUCTION: This study aimed to examine the prevalence of cold injuries and review the cold injury monitoring system in the South Korean military. MATERIALS AND METHODS: This study conducted a retrospective and cross-sectional analysis using data from the Defense Medical Statistic Information System (DMSIS) on cold injuries among military personnel from 2018 to 2023. It incorporated findings from the Korean Armed Forces Medical Command's cold injury surveillance system and analyzed cases from the Armed Forces Capital Hospital, covering all branches of the military by status and rank. RESULTS: An analysis of cold injury in the South Korean military over the past 5 years revealed varying numbers of cases treated, from 799 in 2018-2019 to 467 in 2022-2023. According to the surveillance system in 2023-2024, the Army experienced the majority of these cases, with 94.5% of the total. The incidence per 1,000 personnel was the highest in the Army at 0.98 compared to the Air Force and Navy/Marine Corps. Rank-based analysis indicated the most affected were private first-class soldiers. Frostbite was the most frequent condition, alongside chilblains, hypothermia, and immersion injuries, with no marked difference in the type of illness across branches. CONCLUSIONS: The study underscores the need for focused prevention and treatment, particularly in the Army. By analyzing data from a newly implemented surveillance system, it revealed a higher incidence of frostbite and chilblains among lower ranks. The findings highlight the importance of targeted educational measures and enhanced response strategies to protect personnel against cold injuries.

Impact of Korean Military Service on the Prevalence of Steatotic Liver Disease: A Longitudinal Study of Pre-enlistment and In-Service Health Check-Ups.

Background/Aims: Young Korean men are obligated to serve in the military for 18 to 21 months. We investigated the effects of military service on steatotic liver disease (SLD) and other metabolic parameters. Methods: Pre-enlistment health check-up performed from 2019 to 2022 and in-service health check-up performed from 2020 to 2022 were merged as paired data. SLD was defined as a hepatic steatosis index of 36 or higher. Hypertension (HTN) and hypertriglyceridemia were also included in the analysis. Results: A total of 503,136 paired cases were included in the analysis. Comparing pre-enlistment and in-service health check-ups, the prevalence of SLD (22.2% vs 17.6%, p<0.001), HTN (7.6% vs 4.3%, p<0.001), and hypertriglyceridemia (8.1% vs 2.9%, p<0.001) decreased during military service. In terms of body mass index, the proportion of underweight (8.2% vs 1.4%, p<0.001) and severely obese (6.1% vs 4.9%, p<0.001) individuals decreased over time. Regarding factors associated with SLD development and resolution, age was positively associated with SLD development (odds ratio, 1.146; p<0.001) and a health check-up interval of <450 days was a protective factor for SLD development (odds ratio, 0.746; p<0.001). Those serving in the marines were less likely to develop SLD, whereas those serving in the navy were more likely to develop SLD. Serving in the army or the navy was negatively associated with SLD resolution, whereas serving in the air force was positively associated with SLD resolution. Conclusions: The prevalence of SLD, HTN, and hypertriglyceridemia decreased substantially during Korean military service.

Effects of Nonpharmacological Interventions on Sleep Quality and Insomnia in Perimenopausal and Postmenopausal Women: A Meta-Analysis.

This study aimed to analyze the effects of nonpharmacological interventions on perimenopausal and postmenopausal women with sleep problems. Eight databases (MEDLINE/PubMed, Cochrane Library, EMBASE, CINAHL, and four Korean databases) were searched, from their inception through to 30 November 2021, for randomized controlled trials (RCTs) evaluating the effects of nonpharmacological interventions versus control conditions on sleep quality and insomnia in perimenopausal and postmenopausal women. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), and the severity of insomnia was assessed using the Insomnia Severity Index (ISI). In the meta-analysis, corrected standardized mean differences (SMDs; Hedges' g) and 95% confidence intervals (CIs) were calculated as effect measures by applying the random effects model and inverse variant method. Fifteen trials met our inclusion criteria. Nonpharmacological interventions were found to have positive effects on sleep quality, measured with the PSQI (SMD = -1.32; 95% CI = -1.78 to -0.86; p < 0.001), and on the severity of insomnia, measured using the ISI (SMD = -1.11; 95% CI = -1.82 to -0.41; p = 0.002), compared with the control groups. Among perimenopausal and postmenopausal women with sleep problems, nonpharmacological interventions improved sleep quality and reduced the severity of insomnia.

Prevalence of common skin diseases and their associated factors among military personnel in Korea: a cross-sectional study.

This study was conducted to clarify the prevalence of common skin diseases and their associated factors among military personnel in Korea. Four dermatologists visited adjacent military units and examined soldiers. A structured questionnaire that included questions about known skin diseases, demographic information, and questions for the Perceived Stress Index was completed for each participant. The soldiers that had been diagnosed with a skin disease answered one additional questionnaire (Skindex-29) which assess the influence of an individual's skin disease on daily life. Of 1,321 soldiers examined, 798 (60.4%) had one or more skin diseases. The three most common skin problems were acne (35.6%), tinea pedis (15.2%) and atopic dermatitis (5.1%). The diseases closely related to the period of military service were acne, tinea pedis, viral warts and corns. The diseases related to the amount of stress were atopic dermatitis, seborrheic dermatitis, and acne. The most troublesome skin diseases were atopic dermatitis, tinea cruris, and seborrheic dermatitis. These results demonstrated that the prevalence of skin disease among military personnel in Korea is very high, and that some of the skin disorders may have a significant influence on their daily lives.

Long-term exposure to air pollution and the blood lipid levels of healthy young men.

BACKGROUND: There is insufficient evidence of an association between long-term exposure to air pollution and changes in blood lipid levels, and assessments may be influenced by residual confounding factors, such as socioeconomic status. OBJECTIVES: To investigate the associations between long-term exposure to air pollution and blood lipid profiles while controlling for the risk of residual confounding factors. METHODS: We conducted a study involving conscripted Korean soldiers to assess the associations between air pollution and blood lipid levels. The soldiers, who were randomly distributed among military units throughout the country, led homogenous lives and were subjected to health checkups 8-12 months post-enlistment. We analyzed data pertaining to those who enlisted and underwent health checkups in 2019 (n = 12,778) using linear mixed models. Additionally, we evaluated quantile-specific associations using quantile regression models. We also assessed interactions based on body mass index (BMI) at the time of enlistment (≥25.0 vs. < 25.0 kg/m2). RESULTS: The linear mixed models revealed that a 10-µg/m3 increase in fine particulate matter ≤ 2.5 µm (PM2.5) decreased high-density lipoprotein cholesterol (HDL-C) levels by -0.66% (95% confidence interval [CI]: -1.21, -0.10), and a 10-ppb increase in nitrogen dioxide (NO2) increased total cholesterol (TC) levels by 1.04% (95% CI: 0.24, 1.84). In the quantile regression models, associations were also found at specific deciles. PM2.5 exposure contributed to higher TC, NO2 resulted in higher triglycerides and lower HDL-C, and ozone (O3) led to lower HDL-C. The association between O3 and TC differed according to BMI (p-value for interaction = 0.03); among those with a BMI ≥ 25.0 kg/m2, a 10-ppb increase in O3 increased TC by 1.09% (95% CI: 0.20, 1.09). DISCUSSION: These results shed new light on the importance of controlling air pollution, which can contribute to abnormal blood lipid levels, an independent risk factor for cardiovascular disease.

COVID-19 Outbreak in a Military Unit in Korea.

OBJECTIVES: This study presents the response of a military unit to the COVID-19 outbreak in Gyeonggi Province. As soon as two soldiers were identified as index cases, the infectious disease investigators of the Gyeonggi Provincial Government, Korea Disease Control and Prevention Agency and the Armed Forces Epidemiologic Investigation Center, discussed the investigation and response plan for an imminent massive outbreak. METHODS: The joint immediate response team (IRT) conducted interviews with confirmed patients with COVID-19, reviewed medical records, performed contact tracing using global positioning system (GPS), and undertook a field investigation. For risk assessment, the joint IRT visited all eight sites of the military units and the army chaplain's church to evaluate the transmission risk of each site. The evaluation items included the size of the site, the use of air conditioning, whether windows were opened, and whether masks were worn. A pooled testing was used for a low-risk population to quickly detect the spread of COVID-19 in the military base. RESULTS: A day before the symptom onset of the index case, the lecturer and >50% of the attendees were infected with COVID-19 while attending a lecture that lasted 2 h and 30 min. Attendees were not wearing masks and were in a poorly ventilated room. CONCLUSION: Since the disease can be spread before symptom onset, contact tracing must be performed to investigate potential exposures prior to symptom onset and manage any exposed persons.

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.

A framework for monitoring the malaria eradication programme in Korea.

The World Health Organisation established the Roll Back Malaria (RBM) strategy to reduce the global burden of malaria in 1998. A set of RBM indicators will not be suitable to the Republic of Korea due to the differences of the epidemiological and socio-cultural situation. Therefore, we have developed a framework and indicators for monitoring the outcomes and impact of the national malaria eradication programme that are appropriate for the situation in Korea. We reviewed the existing data in the national malaria eradication programme. We adopted general principles in developing a monitoring framework and indicators for monitoring RBM. Monitoring areas were recombined components and content that included the project plan, training health personnel, health education and public information, supply for manpower, equipment and materials, disease control, information system, supervision, community participation, intersectoral collaboration within practical guidelines for the national malaria eradication programme along with malaria control and RBM strategy of WHO. Twenty one monitoring areas were selected that identified critical areas for the national malaria eradication programme. Fifteen indicators of ten categories are proposed for use by the national malaria eradication programme in Korea.