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/OBJECTIVES: To encourage schools to transform school meal programs to be more educational, it is necessary to evaluate the related environment using a whole school approach. We developed a school food culture evaluation tool to quantitatively evaluate school food culture in Gyeonggi Province, Korea. SUBJECTS/METHODS: Based on a literature review, a school food culture evaluation system consisting of areas, subareas, indicators, and questions (scored on a 5-point scale) was constructed. The validity of the tool was reviewed using focus group interviews, the Delphi technique, and a preliminary survey. Subsequently, evaluation tool was applied to elementary, middle, and high schools in Gyeonggi Province. Data from 115 schools were used for the final analysis. This included 64 elementary schools, 29 middle schools, and 22 high schools. At least one respondent from each group—school administrators, teachers, and nutrition teachers (or dietitians)—participated. The results were compared at the school level. RESULTS: The evaluation tool consisted of 66 questions in 5 areas (institutional environment, physical environment, educational environment, educational governance, and school meal quality). The total average score for school food culture was 3.83 points (elementary school 3.89 points, middle school 3.76 points, and high school 3.76 points) and did not differ significantly among school levels. Among the 5 evaluation areas, scores were highest for institutional environment (4.43 points) and lowest for physical environment (3.07 points).Scores for educational environment, educational governance, and school meal quality were 3.86, 3.85, and 3.97 points, respectively. CONCLUSION It is necessary to improve the physical environment to create a desirable school food culture in Gyeonggi Province. To effectively promote healthy eating, ongoing investment and interventions by local authorities at improving school food culture are needed, with an emphasis on particular factors, such as the eating environment and staff training.

BACKGROUND/OBJECTIVES: The diet is an important route of exposure to endocrinedisrupting chemicals (EDCs). However, few studies have investigated the association between dietary intake and EDC exposure levels among Koreans. In an earlier study, we showed that the bioactivity of serum aryl hydrocarbon receptor ligands (AhRLs) could be a surrogate biomarker to indicate exposure to EDCs and that they inhibit mitochondrial function. We also found that the mitochondria-inhibiting substances (MIS) in serum ascertained by intracellular adenosine triphosphate (MIS-ATP) and reactive oxygen species (MIS-ROS) levels could be biomarkers of exposure to EDCs, as they showed a strong correlation with AhRL and the levels of EDCs in the blood. Here, we investigated the association between the consumption of specific foods and surrogate serum biomarkers for EDCs, namely AhRL, MIS-ATP, and MIS-ROS, among middle-aged Korean adults. SUBJECTS/METHODS: A total of 1,466 participants aged 45–76 yrs from the Ansung cohort of the Korean Genome and Epidemiology Study were included. Food consumption, including that of meat, fish, vegetables, and fruits, was measured using a semiquantitative food frequency questionnaire. RESULTS: Fish intake was positively associated with AhRL (β = 0.0035, P = 0.0166), whereas cruciferous vegetable intake was negatively associated with AhRL (β = −0.0007, P = 0.0488).Cruciferous vegetable intake was positively associated with the MIS-ATP levels (β = 0.0051, P = 0.0420). A higher intake of fish was significantly associated with an increased risk of high AhRL (tertile: odds ratio [OR], 1.49; 95% confidence intervals (CIs), 1.08–2.06; P for trend = 0.0305). In addition, the second-highest tertile of cruciferous vegetable intake had lower odds of high AhRL than the lowest tertile (OR, 0.73; 95% CIs, 0.54–0.97), although no significant linear trend was observed. CONCLUSION Consumption of different types of foods may be differentially associated with EDC exposure in middle-aged Korean adults.