• Journal of Preventive Medicine and Public Health
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• 제34권3호
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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.

• 한국의료질향상학회지
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• 제10권2호
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• pp.164-175
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• 2003

Objectives: To evaluate the performance of models to predict AMI patients death using severity adjustment measures in Korea. Methods: Medical records of 861 patients treated by AMI in 7 general hospitals during 1996 and 1997 were reviewed by trained nurses. We measured the severity of patients by APACHE III, MedisGroups, CSI and DS. Using each severity method a predictive mortality for each patient was calculated from a logistic regression model including the severity score. The statistical performance of each severity method model was evaluated by using c-statistics and R2. For each hospital, z scores compared actual and expected mortality rates. Results: The overall in-hospital mortality was 14.5%, ranged from 10.0% to 22.2%. The distributions of severity scores for each method was significantly different by hospitals. The four severity-adjusted models to predict AMI patients death varied in their statistical performance for discrimination power of patients death. Order of Severity-adjusted mortality rates and z scores by four severity measures was different. Conclusion: Severity-adjusted mortality rates of AMI patients might be applied as an indicator for hospital performance evaluation in Korea. Because different severity methods frequently produce different impressions about relative hospital performance, more studies has to be done to use it as quality indicator and more attention should be paid to select appropriate severity measures.

• Journal of the Korean Data and Information Science Society
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• 제26권1호
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• pp.217-227
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• 2015

In observational study, handling confounders is a primary issue in measuring treatment effect of interest. Historically, a regression with covariate adjustment (covariate-adjusted regression) has been the typical approach to estimate treatment effect incorporating potential confounders into model. However, ever since the introduction of the propensity score, covariate-adjusted regression has been gradually replaced in medical literatures with various balancing methods based on propensity score. On the other hand, there is only a paucity of researches assessing propensity score methods compared with the covariate-adjusted regression. This paper examined the performance of propensity score methods in estimating risk difference and compare their performance with the covariate-adjusted regression by a Monte Carlo study. The study demonstrated in general the covariate-adjusted regression with variable selection procedure outperformed propensity-score-based methods in terms both of bias and MSE, suggesting that the classical regression method needs to be considered, rather than the propensity score methods, if a performance is a primary concern.

• Journal of Preventive Medicine and Public Health
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• 제46권2호
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• pp.74-81
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• 2013

Objectives: The objective of this study was to evaluate the performance of risk-adjusted mortality models for colorectal cancer surgery. Methods: We investigated patients (n=652) who had undergone colorectal cancer surgery (colectomy, colectomy of the rectum and sigmoid colon, total colectomy, total proctectomy) at five teaching hospitals during 2008. Mortality was defined as 30-day or in-hospital surgical mortality. Risk-adjusted mortality models were constructed using claims data (basic model) with the addition of TNM staging (TNM model), physiological data (physiological model), surgical data (surgical model), or all clinical data (composite model). Multiple logistic regression analysis was performed to develop the risk-adjustment models. To compare the performance of the models, both c-statistics using Hanley-McNeil pair-wise testing and the ratio of the observed to the expected mortality within quartiles of mortality risk were evaluated to assess the abilities of discrimination and calibration. Results: The physiological model (c=0.92), surgical model (c=0.92), and composite model (c=0.93) displayed a similar improvement in discrimination, whereas the TNM model (c=0.87) displayed little improvement over the basic model (c=0.86). The discriminatory power of the models did not differ by the Hanley-McNeil test (p>0.05). Within each quartile of mortality, the composite and surgical models displayed an expected mortality ratio close to 1. Conclusions: The addition of clinical data to claims data efficiently enhances the performance of the risk-adjusted postoperative mortality models in colorectal cancer surgery. We recommended that the performance of models should be evaluated through both discrimination and calibration.

• 한국의료질향상학회지
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• 제7권2호
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• pp.218-228
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• 2000

Background : Among 'structure', 'process' and 'outcome' approaches, outcome evaluation is considered as the most direct and best approach to assess the quality of health care providers. Risk-adjustment is an essential method to compare outcome across providers. This study has aims to judge performance of hospitals by severity adjusted mortality rates of coronary artery bypass graft (CABG) surgery. Methods : Medical records of 584 patients who got the CABG surgery in 6 general hospitals during 1996 and 1997 were reviewed by trained nurses. The MedisGroups was used to quantify severity of 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. For evaluation of hospital performance, we calculated ratio of observed number to expected number of deaths and z score [(observed number of deaths - expected number of deaths)/square root of the variance in the number of deaths], and compared observed mortality rate with confidence interval of adjusted mortality rate for each hospital. Results : The overall in-hospital mortality was 7.0%, ranged from 2.7% to 15.7% by hospital. After severity adjustment the mortality by hospital was from 2.7% to 10.7%. One hospital with poor performance was distinctly divided from others with good performance. Conclusion : In conclusion, severity-adjusted mortality rate of CABG surgery might be applied as an indicator for hospital performance evaluation in Korea. But more pilot studies and improvement of methodologies has to be done to use it as quality indicator.

• 한국의료질향상학회지
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• 제10권2호
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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.

• 대기
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• 제34권2호
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• pp.107-121
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• 2024

In this study, we empirically analyzed the impact of physical risks due to climate change on the soundness and operational performance of the financial industry by combining economics and climatology. Particularly, unlike previous studies, we employed the Seasonal-Trend decomposition using LOESS (STL) method to extract trends of climate-related risk variables and economic-financial variables, conducting a two-stage empirical analysis. In the first stage estimation, we found that the delinquency rate and the Bank for International Settlement (BIS) ratio of commercial banks have significant negative effects on the damage caused by natural disasters, frequency of heavy rainfall, average temperature, and number of typhoons. On the other hand, for insurance companies, the damage from natural disasters, frequency of heavy rainfall, frequency of heavy snowfall, and annual average temperature have significant negative effects on return on assets (ROA) and the risk-based capital ratio (RBC). In the second stage estimation, based on the first stage results, we predicted the soundness and operational performance indicators of commercial banks and insurance companies until 2035. According to the forecast results, the delinquency rate of commercial banks is expected to increase steadily until 2035 under assumption that recent years' trend continues until 2035. It indicates that banks' managerial risk can be seriously worsened from climate change. Also the BIS ratio is expected to decrease which also indicates weakening safety buffer against climate risks over time. Additionally, the ROA of insurance companies is expected to decrease, followed by an increase in the RBC, and then a subsequent decrease.

• 보건행정학회지
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• 제25권3호
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• pp.197-206
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• 2015

Background: The purpose of this study was to propose a method for developing a measure of hospital-wide all-cause risk-standardized readmissions using administrative claims data in Korea and to discuss further considerations in the refinement and implementation of the readmission measure. Methods: By adapting the methodology of the United States Center for Medicare & Medicaid Services for creating a 30-day readmission measure, we developed a 6-step approach for generating a comparable measure using Korean datasets. Using the 2010 Korean National Health Insurance (NHI) claims data as the development dataset, hierarchical regression models were fitted to calculate a hospital-wide all-cause risk-standardized readmission measure. Six regression models were fitted to calculate the readmission rates of six clinical condition groups, respectively and a single, weighted, overall readmission rate was calculated from the readmission rates of these subgroups. Lastly, the case mix differences among hospitals were risk-adjusted using patient-level comorbidity variables. The model was validated using the 2009 NHI claims data as the validation dataset. Results: The unadjusted, hospital-wide all-cause readmission rate was 13.37%, and the adjusted risk-standardized rate was 10.90%, varying by hospital type. The highest risk-standardized readmission rate was in hospitals (11.43%), followed by general hospitals (9.40%) and tertiary hospitals (7.04%). Conclusion: The newly developed, hospital-wide all-cause readmission measure can be used in quality and performance evaluations of hospitals in Korea. Needed are further methodological refinements of the readmission measures and also strategies to implement the measure as a hospital performance indicator.

• Journal of Preventive Medicine and Public Health
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• 제34권1호
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• pp.21-27
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• 2001

Objectives : To develop a model that will predict the mortality of patients undergoing Coronary Artery Bypass Graft (CABG) and evaluate the perfermance of hospitals. Methods : Data from 564 CABGs peformed in six general hospitals were collected through medical record abstraction by registered nurses. Variables studied involved risk factors determined by severity measures. Risk modeling was performed through logistic repression and validated with cross-validation. The statistical performance of the developed model was evaluated using c-statistic, $R^2$, and Hosmer-Lemeshow statistic. Hospital performance was assessed by severity-adjusted mortalities. Results : The developed model included age, sex, BUN, EKG rhythm, Congestive Heart Failure at admission. acute mental change within 24 hours, and previous angina pectoris history. The c-statistic and $R^2$ were 0.791 and 0.001, respectively. Hosmer-Lemeshow statistic was 10.3(p value=0.2415). One hospital had a significantly higher mortality rate than the average mortality rate, while others were net significantly different. Conclusion : Comparing the quality of service by severity adjusted mortality rates, there were significant differences in hospital performance. The severity adjusted mortality rate of CABG surgery may He an indicator for evaluating hospital performance in Korea.

• Communications for Statistical Applications and Methods
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• 제24권3호
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• pp.211-226
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• 2017

The chain-ladder method, for which run-off data is employed is popularly used in the rate-adjustment and loss-reserving practices of non-life-insurance and health-insurance companies. The method is applicable when the underlying assumption of a consistent development pattern is in regards to a cumulative loss payment after the occurrence of an insurance event. In this study, a modified chain-ladder algorithm is proposed for when the assumption is considered to be only partially appropriate for the given run-off data. The concept of a structural-change point in the run-off data and its reflection in the estimation of unpaid loss amounts are discussed with numerical illustrations. Experience data from private health insurance coverage in Korea were analyzed based on the suggested method. The performance in estimation of loss reserve was also compared with traditional approaches. We present evidence in this paper that shows that a reflection of a structural-change point in the chain-ladder method can improve the risk management of the relevant insurance products. The suggested method is expected to be utilized easily in actuarial practice as the algorithm is straightforward.