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: This study evaluated the practicability of the suicide rate reduction target set by the current national suicide prevention policy in Korea, the fifth Master Plan for Suicide Prevention (2023–2027). This policy aims to lower the suicide rate from 26/100,000 in 2021 to 18.2/100,000 by 2027. Methods: We utilized monthly suicide statistics data from 2011 onwards. Using Bayesian regression and Autoregressive Integrated Moving Average (ARIMA) models, we conducted interrupted time series analyses to estimate the effect of the previous policy, the National Action Plan for Suicide Prevention (2018–2022), on suicide rates. We assumed this as the additional suicide reduction expected from the current policy. We generated point predictions and simulations for suicide rates from 2023 to 2027 using Bayesian regression and ARIMA models. Results: The interrupted time series analyses did not reveal a significant reduction in suicides attributable to the previous policy. Point predictions from the two models indicated that the suicide rate would remain approximately 24/100,000 in 2027. Almost all of the simulations of the 2027 suicide rate did not meet the policy target of 18.2/100,000. Conclusion The findings suggest that the Korean government’s suicide rate reduction target for 2027 is likely unattainable based on current trends and the limited effectiveness of previous policies. The objectives of suicide prevention policies should be evidence-based, attainable, and accountable.

Background: This study evaluated the practicability of the suicide rate reduction target set by the current national suicide prevention policy in Korea, the fifth Master Plan for Suicide Prevention (2023–2027). This policy aims to lower the suicide rate from 26/100,000 in 2021 to 18.2/100,000 by 2027. Methods: We utilized monthly suicide statistics data from 2011 onwards. Using Bayesian regression and Autoregressive Integrated Moving Average (ARIMA) models, we conducted interrupted time series analyses to estimate the effect of the previous policy, the National Action Plan for Suicide Prevention (2018–2022), on suicide rates. We assumed this as the additional suicide reduction expected from the current policy. We generated point predictions and simulations for suicide rates from 2023 to 2027 using Bayesian regression and ARIMA models. Results: The interrupted time series analyses did not reveal a significant reduction in suicides attributable to the previous policy. Point predictions from the two models indicated that the suicide rate would remain approximately 24/100,000 in 2027. Almost all of the simulations of the 2027 suicide rate did not meet the policy target of 18.2/100,000. Conclusion The findings suggest that the Korean government’s suicide rate reduction target for 2027 is likely unattainable based on current trends and the limited effectiveness of previous policies. The objectives of suicide prevention policies should be evidence-based, attainable, and accountable.

Background: This study evaluated the practicability of the suicide rate reduction target set by the current national suicide prevention policy in Korea, the fifth Master Plan for Suicide Prevention (2023–2027). This policy aims to lower the suicide rate from 26/100,000 in 2021 to 18.2/100,000 by 2027. Methods: We utilized monthly suicide statistics data from 2011 onwards. Using Bayesian regression and Autoregressive Integrated Moving Average (ARIMA) models, we conducted interrupted time series analyses to estimate the effect of the previous policy, the National Action Plan for Suicide Prevention (2018–2022), on suicide rates. We assumed this as the additional suicide reduction expected from the current policy. We generated point predictions and simulations for suicide rates from 2023 to 2027 using Bayesian regression and ARIMA models. Results: The interrupted time series analyses did not reveal a significant reduction in suicides attributable to the previous policy. Point predictions from the two models indicated that the suicide rate would remain approximately 24/100,000 in 2027. Almost all of the simulations of the 2027 suicide rate did not meet the policy target of 18.2/100,000. Conclusion The findings suggest that the Korean government’s suicide rate reduction target for 2027 is likely unattainable based on current trends and the limited effectiveness of previous policies. The objectives of suicide prevention policies should be evidence-based, attainable, and accountable.