Background: Malaria elimination strategies in the Republic of Korea (ROK) have decreased malaria incidence but face challenges due to delayed case detection and response. To improve this, machine learning models for predicting malaria, focusing on high-risk areas, have been developed. Methods: The study targeted the northern region of ROK, near the demilitarized zone, using a 1-km grid to identify areas for prediction. Grid cells without residential buildings were excluded, leaving 8,425 cells. The prediction was based on whether at least one malaria case was reported in each grid cell per month, using spatial data of patient locations. Four algorithms were used: gradient boosted (GBM), generalized linear (GLM), extreme gradient boosted (XGB), and ensemble models, incorporating environmental, sociodemographic, and meteorological data as predictors. The models were trained with data from May to October (2019–2021) and tested with data from May to October 2022. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results: The AUROC of the prediction models performed excellently (GBM = 0.9243, GLM = 0.9060, XGB = 0.9180, and ensemble model = 0.9301). Previous malaria risk, population size, and meteorological factors influenced the model most in GBM and XGB. Conclusion Machine-learning models with properly preprocessed malaria case data can provide reliable predictions. Additional predictors, such as mosquito density, should be included in future studies to improve the performance of models.

The use of thermogenic dietary supplements has led to growing concerns about potential adverse health effects. A 19-year-old female patient experienced recurrent blurred vision after consuming thermogenic dietary supplements for 5 weeks and eating only one meal per day. Initial brain imaging showed a lesion at the corpus callosum, which disappeared after a month, and her symptoms resolved within 2 days. The potential risks associated with using thermogenic dietary supplements in combination with intermittent fasting need to be carefully considered.

Objectives: This study aimed to analyze trends in the timely diagnosis of malaria cases over the past 10 years in relation to the utilization of different types of healthcare facilities. Methods: The study included 3,697 confirmed and suspected cases of malaria reported between January 1, 2013, and December 31, 2022, in the national integrative disease and healthcare management system. Some cases lacking a case report or with information missing from the case report were excluded from the analysis. A generalized linear model with a Poisson distribution was constructed to estimate rate ratios and 95% confidence intervals adjusted for other variables, such as distance. Results: When cases involving diagnosis >5 days after symptom onset in confirmed patients (5DD) were examined according to the type of healthcare facility, the rate ratio of 5DD cases was found to be higher for public health facilities than for tertiary hospitals. Specifically, the rate ratio was higher when the diagnosis was established at a tertiary hospital, even after a participant had visited primary or secondary hospitals. In an analysis adjusted for the distance to each participant’s healthcare facility, the results did not differ substantially from the results of the crude analysis. Conclusion It is imperative to improve the diagnostic capabilities of public facilities and raise awareness of malaria at primary healthcare facilities for effective prevention and control.

Objectives: Severe fever with thrombocytopenia syndrome (SFTS) has no vaccine or treatment and an extremely high fatality rate. We aimed to analyze and evaluate the risk factors for death associated with SFTS. Methods: Among reports from 2018 to 2022, we compared and analyzed 1,034 inpatients aged 18 years or older with laboratory-confirmed SFTS who underwent complete epidemiological investigations. Results: Most of the inpatients with SFTS were aged 50 years or older (average age, 67.6 years). The median time from symptom onset to death was 9 days, and the average case fatality rate was 18.5%. Risk factors for death included age of 70 years or older (odds ratio [OR], 4.82); agriculture-related occupation (OR, 2.01); underlying disease (OR, 7.20); delayed diagnosis (OR, 1.28 per day); decreased level of consciousness (OR, 5.53); fever/chills (OR, 20.52); prolonged activated partial thromboplastin time (OR, 4.19); and elevated levels of aspartate aminotransferase (OR, 2.91), blood urea nitrogen (OR, 2.62), and creatine (OR, 3.21). Conclusion The risk factors for death in patients with SFTS were old age; agriculture-related occupation; underlying disease; delayed clinical suspicion; fever/chills; decreased level of consciousness; and elevated activated partial thromboplastin time, aspartate aminotransferase, blood urea nitrogen, and creatine levels.

BACKGROUND/OBJECTIVES: The objective of this study was to describe the development process of the Korean Healthy Eating Index (KHEI) based on the Korea National Health and Nutrition Examination Survey (KNHANES). SUBJECTS/METHODS: The components of KHEI were selected based on Dietary Guidelines for Koreans, domestic and overseas dietary quality indices, and results of the analysis of association with chronic diseases. The standards for scoring of KHEI were selected based on the 2015 Dietary Reference Intakes for Koreans (KDRI). The KHEI scores of Korean adults were calculated using a 1-day 24-h recall data in the 2013–2015 KNHANES. RESULTS: The KHEI included eight adequacy components evaluating the proper intake of recommended foods such as fruit, vegetable, and milk and three moderation components evaluating the consumption of food that limit intake such as sodium and saturated fatty acid. In addition, three balance components assessing the balance of energy intake were included. The KHEI score was defined to range from the minimum of 0 point to the maximum of 100 points. Among Korean adults, the total KHEI score was 63.2 out of 100. Gender and age differences were found in the average of total KHEI scores. Women showed higher score than men (61.7 in men and 64.7 in women, respectively). By age group, 20s and 30s showed the lowest scores with 57.4 and 61.1 respectively, and the scores increased with age by peaking at 67.8 in ages 60–69 and slowed down again in ages 70 or over. CONCLUSIONS The KHEI can be useful for establishing and assessing national nutritional policies and in epidemiological studies to assess the relationship between overall dietary quality and chronic diseases. KHEI will need to be continuously updated to reflect changes in dietary guidelines and the KDRI.

To provide a nationwide representative dataset for the study on health impact of air pollution, we combined the data from the Korea National Health and Nutrition Examination Survey with the daily air quality and weather data by matching the date of examination and the residential address of the participants. The database of meteorological factors and air quality as sources of exposure data were estimated using the Community Multiscale Air Quality model. The linkage dataset was merged by three ways; administrative district, si-gun-gu (city, county, and district), and geocode (in latitude and longitude coordinate units) based on the participants’ residential address, respectively. During the study period, the exposure dataset of 85,018 individuals (38,306 men and 46,712 women) whose examination dates were recorded were obtained. According to the definition of exposure period, the dataset was combined with the data on short-term, mid-term, and long-term exposure to air pollutants and the meteorological indices. Calculation of the daily merged dataset’s average air pollution linked by si-gun-gu and geocode units showed similar results. This study generated a daily average of meteorological indices and air pollution exposure dataset for all regions including rural and remote areas in Korea for 11 years. It is expected to provide a platform for the researchers studying the health impact of air pollution and climate change on the representative population and area, which may facilitate the establishment of local health care plans by understanding the residents’ health status at the local as well as national level.

To provide a nationwide representative dataset for the study on health impact of air pollution, we combined the data from the Korea National Health and Nutrition Examination Survey with the daily air quality and weather data by matching the date of examination and the residential address of the participants. The database of meteorological factors and air quality as sources of exposure data were estimated using the Community Multiscale Air Quality model. The linkage dataset was merged by three ways; administrative district, si-gun-gu (city, county, and district), and geocode (in latitude and longitude coordinate units) based on the participants’ residential address, respectively. During the study period, the exposure dataset of 85,018 individuals (38,306 men and 46,712 women) whose examination dates were recorded were obtained. According to the definition of exposure period, the dataset was combined with the data on short-term, mid-term, and long-term exposure to air pollutants and the meteorological indices. Calculation of the daily merged dataset’s average air pollution linked by si-gun-gu and geocode units showed similar results. This study generated a daily average of meteorological indices and air pollution exposure dataset for all regions including rural and remote areas in Korea for 11 years. It is expected to provide a platform for the researchers studying the health impact of air pollution and climate change on the representative population and area, which may facilitate the establishment of local health care plans by understanding the residents’ health status at the local as well as national level.

BACKGROUND: Diverse microbiota exist in the lower respiratory tract. Although next generation sequencing (NGS) is the most widely used microbiome analysis technique, it is difficult to implement NGS in clinical microbiology laboratories. Therefore, we evaluated the performance of conventional culture methods together with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) in identifying microbiota in bronchoalveolar lavage (BAL) fluid. METHODS: BAL fluid samples (n=27) were obtained from patients undergoing diagnostic bronchoscopy for lung mass evaluation. Bacterial and fungal culture was performed with conventional media used in clinical microbiology laboratories. On an average, 20 isolated colonies were picked from each agar plate and identified by MALDI-TOF MS. Microbiome analysis using 16S rRNA NGS was conducted for comparison. RESULTS: Streptococcus spp. and Neisseria spp. were most frequently cultured from the BAL fluid samples. In two samples, Enterobacteriaceae grew predominantly on MacConkey agar. Actinomyces and Veillonella spp. were commonly identified anaerobes; gut bacteria, such as Lactobacillus, Bifidobacterium, and Clostridium, and fungi were also isolated. NGS revealed more diverse bacterial communities than culture, and Prevotella spp. were mainly identified solely by NGS. Some bacteria, such as Staphylococcus spp., Clostridium spp., and Bifidobacterium spp., were identified solely by culture, indicating that culture may be more sensitive for detecting certain bacteria. CONCLUSIONS: Culture and NGS of BAL fluid samples revealed common bacteria with some different microbial communities. Despite some limitations, culture combined with MALDI-TOF MS might play a complementary role in microbiome analysis using 16S rRNA NGS.
Actinomyces ; Agar ; Bacteria ; Bifidobacterium ; Bronchoalveolar Lavage Fluid* ; Bronchoalveolar Lavage* ; Bronchoscopy ; Clostridium ; Enterobacteriaceae ; Fungi ; Humans ; Lactobacillus ; Lung ; Mass Spectrometry ; Microbiota ; Neisseria ; Prevotella ; Respiratory System ; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization ; Staphylococcus ; Streptococcus ; Veillonella

Growth charts are curves or tables that facilitate the visualization of anthropometric parameters, and are widely used as an important indicator when evaluating the growth status of children and adolescents. The latest version of the Korean National Growth Charts released in 2007 has raised concerns regarding the inclusion of data from both breastfed and formula-fed infants, higher body mass index (BMI) values in boys, and smaller 3rd percentile values in height-for-age charts. Thus, new growth charts have been developed to improve the previous version. The 2006 World Health Organization Child Growth Standards, regarded as the standard for breastfed infants and children, were introduced for children aged 0–35 months. For children and adolescents aged 3–18 years, these new growth charts include height-for-age, weight-for-age, BMI-for-age, weight-for-height, and head circumference-for-age charts, and were developed using data obtained in 1997 and 2005. Data sets and exclusion criteria were applied differently for the development of the different growth charts. BMI-for-age charts were adjusted to decrease the 95th percentile values of BMI. Criteria for obesity were simplified and defined as a BMI of ≥95th percentile for age and sex. The 3rd percentile values for height-for-age charts were also increased. Additional percentile lines (1st and 99th) and growth charts with standard deviation lines were introduced. 2017 Korean National Growth Charts are recommended for the evaluation of body size and growth of Korean children and adolescents for use in clinics and the public health sector in Korea.
Adolescent* ; Body Mass Index ; Body Size ; Child* ; Dataset ; Growth Charts* ; Head ; Humans ; Infant ; Korea ; Obesity ; Public Health ; World Health Organization