Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Background: Recent studies have reported that chronic mental health problems often emerge in young adulthood. This study elucidated the independent effects of smoking and drinking on depressed mood in young adults by sex. Methods: We used Data from the Korea National Health and Nutrition Examination Surveys conducted in 2014, 2016, and 2018. A total of 3,391 participants aged 19–35 years, without serious chronic diseases, were recruited for this study. Depression was evaluated using the Patient Health Questionnaire (PHQ-9). Results: Smoking behavior, current smoking, and number of days smoked were significantly associated with higher PHQ-9 scores in both men and women (all P<0.05). However, past and ever smoking were positively associated with PHQ-9 scores only in women (all P<0.001). Regarding alcohol consumption, the age at which drinking first began was negatively associated with PHQ-9 scores in both men and women (all P<0.001), but the amount of alcohol consumed at 1 time was positively associated with PHQ-9 scores only in women (P=0.013). Men who drank 2–4 times a month and women who had not drunk during the past year had the lowest PHQ-9 scores. Conclusion Smoking and alcohol consumption were independently associated with depressed mood in young Korean adults, which was more pronounced in women, and exhibited sex-specific characteristics.

OBJECTIVES: This study examined trends in the dietary intake and food sources of long-chain polyunsaturated fatty acids (LC-PUFAs) in Korean adults from 2007 to 2018. METHODS: In total, 46,307 adults (aged 19-64 years) were selected from the 2007-2018 Korea National Health and Nutrition Examination Surveys. Dietary data were obtained using 24-hour dietary recall. Intake levels and food sources of LC-PUFAs, including alpha-linolenic acid (ALA), eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and linoleic acid (LA), were evaluated across the survey years and assessed based on compliance with dietary recommendations over the 2007-2018 period. Linear trends in LC-PUFAs intake levels over time were examined through multiple linear regression analysis. RESULTS: From 2007 to 2018, ALA and LA consumption increased from 1.1 g (0.5% of energy) to 1.4 g (0.6% of energy) and from 8.6 g (3.9% of energy) to 10.0 g (4.5% of energy), respectively. EPA intake decreased from 0.14 g to 0.12 g, and EPA+DHA intake showed a decreasing trend. The proportion of individuals who did not meet the recommended intake of EPA+DHA (250 mg/day) increased (64.4 to 68.4%). Regarding changes in food sources of ALA and LA, the contributions from mayonnaise, eggs, and bread increased, while those from plant food sources decreased. Among food sources of EPA and DHA, anchovy, saury, and Atka mackerel showed lower contributions over time. CONCLUSIONS Our findings suggest that strategies to encourage the consumption of EPA and DHA from healthy food sources are necessary to improve cardiovascular health in the Korean population.

Purpose: This study examined the eating-out patterns of Korean infants and school-aged children and compared diet quality. Methods: Data were obtained from the 2016–2018 Korea National Health and Nutrition Examination Survey. The subjects were 306 children aged 3 to 11 years old that ate dinner at restaurants. Percentage energy intakes of 24 food groups were calculated, and cluster analysis was used to identify eating-out patterns. Diet quality was assessed by calculating percentage energy and nutrient intakes using one-third of the 2015 Dietary Reference Intakes for Korean (KDRIs), nutrient adequacy ratio (NAR), mean adequacy ratio (MAR), and index of nutritional quality (INQ). Results: Cluster analysis identified 2 eating-out patterns, that is, a ‘rice-centered’ (53%) and a ‘mixed diet’ (47%) pattern. For those with the mixed diet pattern, ratios of carbohydrates, protein, and fat to total calories were 48:20:31, whereas for the rice-centered pattern, ratios were 62:15:21 (p < 0.001). Intakes of energy and most nutrients in the mixed diet pattern were excessive, but the intakes of the most nutrients in the rice-centered pattern were much lower than their KDRIs. MARs were higher for the mixed diet pattern than the rice-centered pattern (0.74 vs. 0.66) (p < 0.001), and INQs for vitamin C (p = 0.007) and calcium (p = 0.018) were lower for the rice-centered pattern, whereas INQ for iron (p = 0.003) was lower for the mixed diet pattern. Conclusion The quality of meals for infants and school-aged children depended on eating-out patterns, but the rice-centered and mixed diet patterns both failed to provide an appropriately balanced meal pattern. The results of this study suggest that healthy menus need to be developed for children in restaurants.

Purpose: This study was done to investigate the experience of physical and emotional safety in nursing students during fundamentals of nursing practicum. Methods: A descriptive cross-sectional study was conducted from March to September 2019. A total of 553 nursing students, who had completed fundamental nursing laboratories, participated in this study. Data were collected using self-report questionnaires which included questions about general characteristics, physical and emotional safety during fundamental nursing laboratories along with an informed consent given prior to the practicum. Results: Of the students, 26 experienced physical safety accidents, and 18 reported emotional safety accidents. Students' mean stress score for physical safety was 1.95, and the mean score for emotional safety was 1.92. Of the students, more than 59.1% agreed to volunteer as practice models in certain nursing procedures’ training. Of the students, 55.8% were satisfied with using their bodies to train fundamental nursing skills. 61.8% of students reported that informed consent was obtained during the nursing laboratory, and 88.6% of students thought that informed consent needs to be obtained. Conclusion To prevent safety accidents during the fundamentals of nursing practicum and systematically manage accidents, standardized safety guidelines for nursing practicum should be developed. Establishing various training strategies using advanced models or simulators to increase education efficiency and satisfaction is necessary.

This study was undertaken to identify dietary patterns of Korean elderly people living alone, and to compare the quality of diet consumed. Data of 821 elderly people aged 65 years or older who were living alone, was obtained from the 2016∼2018 National Health and Nutrition Survey. The percentage energy intake from 24 food groups was calculated, and a cluster analysis was applied to identify dietary patterns. General characteristics, energy and nutrient intake, nutrient adequacy ratio (NAR), mean adequacy ratio (MAR), and index of nutritional quality (INQ) were analyzed. Dietary variety score (DVS) and dietary diversity score (DDS) were subsequently calculated. The rice-centered diet group and mixed diet group were characterized as consuming higher intake of rice and a wider variety of food groups, respectively. The mean age and percentage of participants living in rural areas were higher, whereas education level and monthly income were lower, in the rice-centered diet group (P＜0.001 for all). Intakes of energy and all other nutrients (except carbohydrate and thiamine) were lower in the rice-centered diet group (P＜0.001 for all). The NAR for all nutrients (except thiamine), MAR, and INQs (except thiamine and iron) were significantly lower in the rice-centered diet group (P＜0.001 for all). The rice-centered diet group had significantly lower DVS (P＜0.001) and DDS (P＜0.001), as compared with the mixed diet group (P＜0.001). Due to the insufficient energy and nutrient intake in the rice-centered diet group, it is necessary to develop nutritional monitoring and customized nutrition policies for these individuals.