BACKGROUND/OBJECTIVES: This study examined the socio-demographic and dietary characteristics of the elderly in Seoul according to their food insecurity status and analyzed the characteristics associated with food insecurity. SUBJECTS/METHODS: The data from 513 elderly individuals aged 65 or older were analyzed using the raw data from the Seoul Food Survey 2023. The subjects were classified into food-secure or food-insecure groups. The socio-demographic and dietary characteristics of each group were analyzed. Logistic regression analysis was conducted to identify the characteristics associated with food insecurity among the elderly in Seoul. RESULTS: The proportion of the food-insecure group was 56.9% among the total elderly in Seoul. The main reasons for experiencing food insecurity were environmental (38.0%), followed by physical (37.5%) and financial (24.5%). The food-insecure group had a higher frequency of skipping breakfast and eating alone than the food-secure group while having lower dietary literacy and food environment satisfaction. Logistic regression analysis showed that the elderly living alone were approximately four times (odds ratio [OR], 3.59; 95% confidence interval [CI], 1.52–8.51) more likely to suffer food insecurity than those living with others. Higher digital food literacy (OR, 0.63; 95% CI, 0.41–0.97) and food environment satisfaction (OR, 0.38; 95% CI, 0.20–0.74) were associated with a lower likelihood of food insecurity. CONCLUSION These results suggest that policies aimed at improving food security for the elderly in Seoul should prioritize those living alone. Educational programs designed to enhance the ability to purchase online food, use self-service kiosks in restaurants, and use food delivery apps would effectively decrease food insecurity among the elderly in Seoul.Furthermore, policies aimed at improving the food environment may also help reduce food insecurity among this population.

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: This study examined the socio-demographic and dietary characteristics of the elderly in Seoul according to their food insecurity status and analyzed the characteristics associated with food insecurity. SUBJECTS/METHODS: The data from 513 elderly individuals aged 65 or older were analyzed using the raw data from the Seoul Food Survey 2023. The subjects were classified into food-secure or food-insecure groups. The socio-demographic and dietary characteristics of each group were analyzed. Logistic regression analysis was conducted to identify the characteristics associated with food insecurity among the elderly in Seoul. RESULTS: The proportion of the food-insecure group was 56.9% among the total elderly in Seoul. The main reasons for experiencing food insecurity were environmental (38.0%), followed by physical (37.5%) and financial (24.5%). The food-insecure group had a higher frequency of skipping breakfast and eating alone than the food-secure group while having lower dietary literacy and food environment satisfaction. Logistic regression analysis showed that the elderly living alone were approximately four times (odds ratio [OR], 3.59; 95% confidence interval [CI], 1.52–8.51) more likely to suffer food insecurity than those living with others. Higher digital food literacy (OR, 0.63; 95% CI, 0.41–0.97) and food environment satisfaction (OR, 0.38; 95% CI, 0.20–0.74) were associated with a lower likelihood of food insecurity. CONCLUSION These results suggest that policies aimed at improving food security for the elderly in Seoul should prioritize those living alone. Educational programs designed to enhance the ability to purchase online food, use self-service kiosks in restaurants, and use food delivery apps would effectively decrease food insecurity among the elderly in Seoul.Furthermore, policies aimed at improving the food environment may also help reduce food insecurity among this population.

BACKGROUND/OBJECTIVES: This study examined the socio-demographic and dietary characteristics of the elderly in Seoul according to their food insecurity status and analyzed the characteristics associated with food insecurity. SUBJECTS/METHODS: The data from 513 elderly individuals aged 65 or older were analyzed using the raw data from the Seoul Food Survey 2023. The subjects were classified into food-secure or food-insecure groups. The socio-demographic and dietary characteristics of each group were analyzed. Logistic regression analysis was conducted to identify the characteristics associated with food insecurity among the elderly in Seoul. RESULTS: The proportion of the food-insecure group was 56.9% among the total elderly in Seoul. The main reasons for experiencing food insecurity were environmental (38.0%), followed by physical (37.5%) and financial (24.5%). The food-insecure group had a higher frequency of skipping breakfast and eating alone than the food-secure group while having lower dietary literacy and food environment satisfaction. Logistic regression analysis showed that the elderly living alone were approximately four times (odds ratio [OR], 3.59; 95% confidence interval [CI], 1.52–8.51) more likely to suffer food insecurity than those living with others. Higher digital food literacy (OR, 0.63; 95% CI, 0.41–0.97) and food environment satisfaction (OR, 0.38; 95% CI, 0.20–0.74) were associated with a lower likelihood of food insecurity. CONCLUSION These results suggest that policies aimed at improving food security for the elderly in Seoul should prioritize those living alone. Educational programs designed to enhance the ability to purchase online food, use self-service kiosks in restaurants, and use food delivery apps would effectively decrease food insecurity among the elderly in Seoul.Furthermore, policies aimed at improving the food environment may also help reduce food insecurity among this population.

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: This study examined the socio-demographic and dietary characteristics of the elderly in Seoul according to their food insecurity status and analyzed the characteristics associated with food insecurity. SUBJECTS/METHODS: The data from 513 elderly individuals aged 65 or older were analyzed using the raw data from the Seoul Food Survey 2023. The subjects were classified into food-secure or food-insecure groups. The socio-demographic and dietary characteristics of each group were analyzed. Logistic regression analysis was conducted to identify the characteristics associated with food insecurity among the elderly in Seoul. RESULTS: The proportion of the food-insecure group was 56.9% among the total elderly in Seoul. The main reasons for experiencing food insecurity were environmental (38.0%), followed by physical (37.5%) and financial (24.5%). The food-insecure group had a higher frequency of skipping breakfast and eating alone than the food-secure group while having lower dietary literacy and food environment satisfaction. Logistic regression analysis showed that the elderly living alone were approximately four times (odds ratio [OR], 3.59; 95% confidence interval [CI], 1.52–8.51) more likely to suffer food insecurity than those living with others. Higher digital food literacy (OR, 0.63; 95% CI, 0.41–0.97) and food environment satisfaction (OR, 0.38; 95% CI, 0.20–0.74) were associated with a lower likelihood of food insecurity. CONCLUSION These results suggest that policies aimed at improving food security for the elderly in Seoul should prioritize those living alone. Educational programs designed to enhance the ability to purchase online food, use self-service kiosks in restaurants, and use food delivery apps would effectively decrease food insecurity among the elderly in Seoul.Furthermore, policies aimed at improving the food environment may also help reduce food insecurity among this population.

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.