• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.1171-1180
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• 2021

This study aims to investigate the impact of the multimarket contract on risk-adjusted profitability. Risk-adjusted profitability is measured in terms of risk-adjusted return on assets. This study employs dynamic panel data of 27 commercial banks in Vietnam using the GMM estimator to test the multimarket contact hypothesis in the Vietnamese banking sector. The results show that there is a negative impact of multimarket contact on the profitability of banks. Multimarket contact, deposit to asset ratio, non-interest income to total income, GDP growth rate, Worldwide Governance Indicator (WGI), and operating cost to assets are the major determinants of risk-adjusted profitability of commercial banks. Our main findings show that Vietnamese banks' focus to increase the multimarket contact may lead to lower profitability and there is evidence that supports theory predictions, since the average number of contacts among banks, bank size, and capitalization are positively related to risk-adjusted profitability. The study has policy implications for commercial banks in that they should not only focus on interest as a source of income and diversify their income source from non-interest income as well since it helps to improve risk-adjusted profitability for them.

• Journal of Preventive Medicine and Public Health
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• v.38 no.2
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• pp.132-140
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• 2005

Objectives: To propose a risk-adjustment model from insurance claims data, and analyze the changes in cesarean section rates of healthcare organizations after adjusting for risk distribution. Methods: The study sample included delivery claims data from January to September, 2003. A risk-adjustment model was built using the 1st quarter data, and the 2nd and 3rd quarter data were used for a validation test. Patients' risk factors were adjusted using a logistic regression analysis. The c-statistic and Hosmer-Lemeshow test were used to evaluate the performance of the risk-adjustment model. Crude, predicted and risk-adjusted rates were calculated, and compared to analyze the effects of the adjustment. Results: Nine risk factors (malpresentation, eclampsia, malignancy, multiple pregnancies, problems in the placenta, previous Cesarean section, older mothers, bleeding and diabetes) were included in the final risk-adjustment model, and were found to have statistically significant effects on the mode of delivery. The c-statistic (0.78) and Hosmer-Lemeshow test ($x^2$=0.60, p=0.439) indicated a good model performance. After applying the 2nd and 3rd quarter data to the model, there were no differences in the c-statistic and Hosmer-Lemeshow $x^2$. Also, risk factor adjustment led to changes in the ranking of hospital Cesarean section rates, especially in tertiary and general hospitals. Conclusion: This study showed a model performance, using medical record abstracted data, was comparable to the results of previous studies. Insurance claims data can be used for identifying areas where risk factors should be adjusted. The changes in the ranking of hospital Cesarean section rates implied that crude rates can mislead people and therefore, the risk should be adjusted before the rates are released to the public. The proposed risk-adjustment model can be applied for the fair comparisons of the rates between hospitals.

• Quality Improvement in Health Care
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• v.17 no.1
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• pp.69-78
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• 2011

Objectives : The purpose of this study was to determine whether the published AMI report card could reduce in-patient mortality, 7-day after discharge mortality, and length of stay (LOS). Methods : Interrupted time-series intervention analysis was used to evaluate the impact of the report card for AMI care quality in November 2005 in terms of risk-adjusted in-patient mortality, risk-adjusted 7-day after discharge mortality, and DRGs case-mix LOS using the claim data of Health Insurance Review and Assessment Service. Results : Public disclosure of AMI care quality decreased risk-adjusted in-patient mortality and DRGs case-mix LOS by 0.00050% per month and 0.042 days per month respectively, however there was no effect on risk-adjusted 7-day after discharge mortality. Patterns of effect of public disclosure on AMI outcomes were a fluctuating pattern on risk-adjusted mortalities and a pulse impact for 1 month on DRGs case-mix LOS. Conclusions : We found the public disclosure of AMI care quality had decreasing effects on risk-adjusted in-patient mortality and DRGs case-mix LOS, but the size of the effect was marginal.

• Journal of Preventive Medicine and Public Health
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• v.46 no.2
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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.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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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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• v.34 no.1
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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.

• Health Policy and Management
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• v.17 no.3
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• pp.87-105
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• 2007

The purpose of this study was to analyze whether nonemergency, isolated coronary artery bypass graft (CABG) surgery for high- or low-risk patients biases the assessment of the risk-adjusted mortality rates of hospitals. This study used 2002 National Health Insurance claims data for tertiary hospitals in Korea. The study sample consisted of 1,959 patients from 23 tertiary hospitals. The risk-adjustment model used the patients' biological, admission, and comorbidity data identified in the claims. The subjects were classified into high- and low-risk groups based on predicted surgical risk. The crude mortality rates and risk-adjusted mortality rates for low-risk, high-risk, and all patients in a hospital were compared based on the rank and the four intervals defined by quartile. Also, the crude mortality rates of the three groups were compared with their 95% confidence intervals of predicted mortality rates. The C-statistic (0.83) and Hosmer-Lemeshow test ($X^2$=11.47, p=0.18) indicated that the risk-adjustment model performed well. Presenting crude mortality rates with their 95% confidence intervals of predicted rates showed higher agreements among the three groups than using the rank or intervals of mortality rates defined by quartile in the hospital performance assessment. The crude mortality rates for the low-risk patients in 21 of the 23 hospitals were located on the same side of their 95% confidence intervals compared to that for all patients. High-risk patients and all patients differed at only one hospital. In conclusion, the impact of risk selection by hospital on the assessment results was the smallest when comparing the crude inpatient mortality rates of CABG patients with the 95% confidence intervals of predicted mortality rates. Given the increasing importance of quality improvements in Korean health policy, it will be necessary to use the appropriate method of releasing the hospital performance data to the public to minimize any unwanted impact such as risk-based hospital selection.

• Journal of the Economic Geographical Society of Korea
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• v.12 no.4
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• pp.665-680
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• 2009

This study aims at analyzing the performance of Real Estate Investment Trusts(REITs) by Risk Adjustment Model. The main results are as follows. Firstly, most property types of REITs gain positive(+) excess overall returns at first and second period. On the contrary, most property types of REITs gain negative(-) excess overall returns and their standard deviations are larger at financial crisis period. Secondly, lodging, regional mall and commercial mortgage show lower risk-lower return, and freestanding, apartment and specialty show higher risk-higher return than average REITs, according to the CAPM results of . Moreover CAPM results of show the characteristics of REITs as investment commodities changes into higher risk-higher return for financial crisis period. Lastly, risk adjusted demanded returns of REITs are affected positively(+) by systemic risks and negatively(-) by unsystemic risks, according to the Risk Adjustment Model results of both and . Comparing risk adjusted demanded returns of REITs with their realized returns, healthcare reveals the largest outperformance.

• Health Policy and Management
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• v.25 no.3
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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.