• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.133-134
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• 2022

Recently, the criteria for assessing industrial accidents have been replaced by the mortality rate. It was found that the number of deaths from excavation work was the highest among construction machinery. The risk assessment is being conducted, however the industrial accident mortality rate has not decreased. Accordingly, this study aims to provide the basic for the create of a risk assessment model specialized in construction work at excavator. It provides absolute value from the risk model which is capable of delivery the probability of a disaster. In addition, we provide a relative risk model that compares the risk through scores between detailed works. The relative risk model is combined by likelihood and severity; the likelihood indicates the frequency of accidents and the severity indicates seriousness of fatal accidents. A variable that reflects the conditions of the construction site was added to the risk assessment model based on past disaster cases. And using the concepts of probability and average, the risk assessment process was quantified and used as an objective indicator. Therefore, the model is expected to reduce disasters by raising the awareness of disasters.

• Journal of Korean Academy of Nursing
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• v.41 no.1
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• pp.92-100
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• 2011

Purpose: Mortality Provability Model (MPM) II is a model for predicting mortality probability of patients admitted to ICU. This study was done to test the validity of MPM II for critically ill neurological patients and to determine applicability of MPM II in predicting mortality of neurological ICU patients. Methods: Data were collected from medical records of 187 neurological patients over 18 yr of age who were admitted to the ICU of C University Hospital during the period from January 2008 to May 2009. Collected data were analyzed through $X^2$ test, t-test, Mann-Whiteny test, goodness of fit test, and ROC curve. Results: As to mortality according to patients' general and clinically related characteristics, mortality was statistically significantly different for ICU stay, hospital stay, APACHE III score, APACHE predicted death rate, GCS, endotracheal intubation, and central venous catheter. Results of Hosmer-Lemeshow goodness-of-fit test were MPM $II_0$ ($X^2$=0.02, p=.989), MPM $II_24$ ($X^2$=0.99 p=.805), MPM $II_48$ ($X^2$=0.91, p=.822), and MPM $II_72$ ($X^2$=1.57, p=.457), and results of the discrimination test using the ROC curve were MPM $II_0$, .726 (p<.001), MPM $II_24$, .764 (p<.001), MPM $II_48$, .762 (p<.001), and MPM $II_72$, .809 (p<.001). Conclusion: MPM II was found to be a valid mortality prediction model for neurological ICU patients.

• Korean Journal of Adult Nursing
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• v.17 no.3
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• pp.464-473
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• 2005

Purpose: The purpose of this study was to evaluate and compare the predictive ability of three mortality scoring systems; Acute Physiology and Chronic Health Evaluation(APACHE) III, Simplified Acute Physiology Score(SAPS) II, and Mortality Probability Model(MPM) II in discriminating in-hospital mortality for intensive care unit(ICU) patients with spontaneous intracerebral hemorrhage. Methods: Eighty-nine patients admitted to the ICU at a university hospital in Daejeon Korea were recruited for this study. Medical records of the subject were reviewed by a researcher from January 1, 2003 to March 31, 2004, retrospectively. Data were analyzed using SAS 8.1. General characteristic of the subjects were analyzed for frequency and percentage. Results: The results of this study were summarized as follows. The values of the Hosmer-Lemeshow's goodness-of-fit test for the APACHE III, the SAPS II and the MPM II were chi-square H=4.3849 p=0.7345, chi-square H=15.4491 p=0.0307, and chi-square H=0.3356 p=0.8455, respectively. Thus, The calibration of the MPM II found to be the best scoring system, followed by APACHE III. For ROC curve analysis, the areas under the curves of APACHE III, SAPS II, and MPM II were 0.934, 0.918 and 0.813, respectively. Thus, the discrimination of three scoring systems were satisfactory. For two-by-two decision matrices with a decision criterion of 0.5, the correct classification of three scoring systems were good. Conclusion: Both the APACHE III and the MPM II had an excellent power of mortality prediction and discrimination for spontaneous intracerebral hemorrhage patients in ICU.

• Quality Improvement in Health Care
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• v.10 no.2
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• pp.216-231
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• 2003

Objectives: To develop a model that predicts a death probability of acute myocardial infarction(AMI) patient, and to evaluate a performance of hospital services using the developed model. Methods: Medical records of 861 AMI patients in 7 general hospitals during 1996 and 1997 were reviewed by two trained nurses. Variables studied were risk factors which were measured in terms of severity measures. A risk model was developed by using the logistic regression, and its performance was evaluated using cross-validation and bootstrap techniques. The statistical prediction capability of the model was assessed by using c-statistic, $R^2$ as well as Hosmer-Lemeshow statistic. The model performance was also evaluated using severity-adjusted mortalities of hospitals. Results: Variables included in the model building are age, sex, ejection fraction, systolic BP, congestive heart failure at admission, cardiac arrest, EKG ischemia, arrhythmia, left anterior descending artery occlusion, verbal response within 48 hours after admission, acute neurological change within 48 hours after admission, and 3 interaction terms. The c statistics and $R^2$ were 0.887 and 0.2676. The Hosmer-Lemeshow statistic was 6.3355 (p-value=0.6067). Among 7 hospitals evaluated by the model, two hospitals showed significantly higher mortality rates, while other two hospitals had significantly lower mortality rates, than the average mortality rate of all hospitals. The remaining hospitals did not show any significant difference. Conclusion: The comparison of the qualities of hospital service using risk-adjusted mortality rates indicated significant difference among them. We therefore conclude that risk-adjusted mortality rate of AMI patients can be used as an indicator for evaluating hospital performance in Korea.

• Korean Journal of Environmental Biology
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• v.30 no.4
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• pp.307-313
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• 2012

The Oriental White Stork (Ciconia boyciana) is a representative wetland species distributed across East Asia. The species has been declined to face the threat of species extinctions with estimation of at about 3000 individuals. In order to re-introduce the endangered storks in the field, we developed a baseline model using the program VORTEX, performed sensitivity test, and finally suggested an ideal model based on results of the sensitivity test. The baseline model predicted 12.5% extinction probability with mean time to first extinction of 82.0 year. Sensitivity test revealed that two demographic variables (first-year mortality and percent of adult female breeding) had the greatest impacts on population persistence. Thus, corrected model improved the population persistence, where the extinction probability decreased to 1.0% in 100 years by changing values of two variables within a range of applicable to the population. Our models for stork re-introduction suggest this population will be stable by improving first-year mortality and adult female fecundity.

• Health Policy and Management
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• v.24 no.4
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• pp.380-388
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• 2014

Background: The volume of surgery has been examined as a major source of variation in outcome after surgery. This study investigated the direct effect of surgery volume to in hospitals mortality and the moderating effect of structural complexity-the level of diversity and sophistication of technology a hospital applied in patient care-to the volume outcome relationship. Methods: Discharge summary data of 11,827 cancer patients who underwent surgery and were discharged during a month period in 2010 and 2011 were analyzed. The analytic model included the independent variables such as surgery volume of a hospital, structural complexity measured by the number of diagnosis a hospital examined, and their interaction term. This study used a hierarchical logistic regression model to test for an association between hospital complexity and mortality rates and to test for the moderating effect in the volume outcome relationship. Results: As structural complexity increased the probability of in-hospital mortality after cancer surgery reduced. The interaction term between surgery volume and structural complexity was also statistically significant. The interaction effect was the strongest among the patients group who had surgery in low volume hospitals. Conclusion: The structural complexity and volume of surgery should be considered simultaneously in studying volume outcome relationship and in developing policies that aim to reduce mortality after cancer surgery.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.769-789
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• 2011

The life table is a statistical model for life expectancy and reflects mortality experiences exposed to a particular group of people. The following three issues are prerequisite for constructing the life table : a selection of how to estimate the death probability from observed death rates, a graduation method to smooth irregularity of the death probabilities, and an extension method of the death probabilities for oldest-old ages. To construct the life table that is fittest to Korean mortality experiences, we examine five estimation methods such as Chiang's and Greville's for the death probability, three graduation techniques including Beer's and Greville's formulae, and twelve mathematical functions for the extension of death probabilities for oldest-old ages. We also propose a method to resolve the cross-over problem arising from construction the life table.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.115-125
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• 2011

By use of a mortality forecasting model and a life table, forecasting the average life expectancy is an effective way to evaluate the future mortality level. There are differences between the actual values of average life expectancy at present and the forecasted values of average life expectancy in population projection 2006 from Statistics Korea. The reason is that the average life expectancy forecasts did not reflect the increasing speed of the actual ones. The main causes of the problem may be errors from judgment for projection, from choice, or use of a mortality forecasting model. In this paper, we focus on the choice of the mortality forecasting model to inspect this problem. Statistics Korea should take a mortality forecasting model with considerable investigation to proceed population projection 2011 without the errors observed in population projection 2006. We compare the five mortality forecasting models that are the LC(Lee and Carter) model used widely and its variants, and the HP8(Heligman and Pollard 8 parameter) model for handling death probability. We make average life expectancy forecasts by sex using modeling results from 2010 to 2030 and compare with that of the population projection 2006 during the same period. The average life expectancy from all five models are forecasted higher than that of the population projection 2006. Therefore, we show that the new average life expectancy forecasts are relatively suitable to the future mortality level.

• Acute and Critical Care
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• v.33 no.4
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• pp.216-221
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• 2018

Background: In this study, we analyze the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE IV, Simplified Acute Physiology Score (SAPS) 3, and Mortality Probability Model $(MPM)_0$ III in order to determine which system best implements data related to the severity of medical intensive care unit (ICU) patients. Methods: The present study was a retrospective investigation analyzing the discrimination and calibration of APACHE II, APACHE IV, SAPS 3, and $MPM_0$ III when used to evaluate medical ICU patients. Data were collected for 788 patients admitted to the ICU from January 1, 2015 to December 31, 2015. All patients were aged 18 years or older with ICU stays of at least 24 hours. The discrimination abilities of the three systems were evaluated using c-statistics, while calibration was evaluated by the Hosmer-Lemeshow test. A severity correction model was created using logistics regression analysis. Results: For the APACHE IV, SAPS 3, $MPM_0$ III, and APACHE II systems, the area under the receiver operating characteristic curves was 0.745 for APACHE IV, resulting in the highest discrimination among all four scoring systems. The value was 0.729 for APACHE II, 0.700 for SAP 3, and 0.670 for $MPM_0$ III. All severity scoring systems showed good calibrations: APACHE II (chi-square, 12.540; P=0.129), APACHE IV (chi-square, 6.959; P=0.541), SAPS 3 (chi-square, 9.290; P=0.318), and $MPM_0$ III (chi-square, 11.128; P=0.133). Conclusions: APACHE IV provided the best discrimination and calibration abilities and was useful for quality assessment and predicting mortality in medical ICU patients.

• Journal of Preventive Medicine and Public Health
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• v.34 no.3
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• pp.244-252
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• 2001

Objective : Health insurers and policy makers are increasingly examining the hospital mortality rate as an indicator of hospital quality and performance. To be meaningful, a risk-adjustment of the death rates must be implemented. This study reviewed 5 severity measurement methods and applied them to the same data set to determine whether judgments regarding the severity-adjusted hospital mortality rates were sensitive to the specific severity measure. Methods : The medical records of 584 patients who underwent coronary artery bypass graft surgery in 6 general hospitals during 1996 and 1997 were reviewed by trained nurses. The MedisGroups, Disease Staging, Computerized Severity Index, APACHE III and KDRG were used to quantify severity of the patients. The predictive probability of death was calculated for each patient in the sample from a multivariate logistic regression model including the severity score, age and sex to evaluate the hospitals' performance, the ratio of the observed number of deaths to the expected number for each hospital was calculated. Results : The overall in-hospital mortality rate was 7.0%, ranging from 2.7% to 15.7% depending on the particular hospital. After the severity adjustment, the mortality rates for each hospital showed little difference according to the severity measure. The 5 severity measurement methods varied in their statistical performance. All had a higher c statistic and $R^2$ than the model containing only age and sex. There was a little difference in the relative hospital performance evaluation by the severity measure. Conclusion : These results suggest that judgments regarding a hospital's performance based on severity adjusted mortality can be sensitive to the severity measurement method. Although the 5 severity measures regarding hospital performance concurred, more often than would be expected by chance, the assessment of an individual hospital mortality rates varied by the different severity measurement method used.