Journal of Preventive Medicine and Public Health

Original Articles

Changes in the Hospital Standardized Mortality Ratio Before and During the COVID-19 Pandemic: A Disaggregated Analysis by Region and Hospital Type in Korea: EunKyo Kang, Won Mo Jang, Min Sun Shin, Hyejin Lee, Jin Yong Lee; J Prev Med Public Health. 2023;56(2):180-189. Published online March 20, 2023; DOI: https://doi.org/10.3961/jpmph.22.479

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Objectives
The coronavirus disease 2019 (COVID-19) pandemic has led to a global shortage of medical resources; therefore, we investigated whether COVID-19 impacted the quality of non-COVID-19 hospital care in Korea by comparing hospital standardized mortality rates (HSMRs) before and during the pandemic.
Methods
This retrospective cohort study analyzed Korean National Health Insurance discharge claim data obtained from January to June in 2017, 2018, 2019, and 2020. Patients’ in-hospital deaths were classified according to the most responsible diagnosis categories. The HSMR is calculated as the ratio of expected deaths to actual deaths. The time trend in the overall HSMR was analyzed by region and hospital type.
Results
The final analysis included 2 252 824 patients. In 2020, the HSMR increased nationwide (HSMR, 99.3; 95% confidence interval [CI], 97.7 to 101.0) in comparison to 2019 (HSMR, 97.3; 95% CI, 95.8 to 98.8). In the COVID-19 pandemic zone, the HSMR increased significantly in 2020 (HSMR, 112.7; 95% CI, 107.0 to 118.7) compared to 2019 (HSMR, 101.7; 95% CI, 96.9 to 106.6). The HSMR in all general hospitals increased significantly in 2020 (HSMR, 106.4; 95% CI, 104.3 to 108.5) compared to 2019 (HSMR, 100.3; 95% CI, 98.4 to 102.2). Hospitals participating in the COVID-19 response had a lower HSMR (HSMR, 95.6; 95% CI, 93.9 to 97.4) than hospitals not participating in the COVID-19 response (HSMR, 124.3; 95% CI, 119.3 to 129.4).
Conclusions
This study suggests that the COVID-19 pandemic may have negatively impacted the quality of care in hospitals, especially general hospitals with relatively few beds. In light of the COVID-19 pandemic, it is necessary to prevent excessive workloads in hospitals and to properly employ and coordinate the workforce.

Summary

Korean summary
코로나19 대유행 지역은 비감염 지역과 달리 2019년에 비해 2020년에 HSMR이 크게 증가했고, 상대적으로 병상 수가 적은 종합병원에서 HSMR이 증가했다. 코로나19 대응에 참여하는 병원은 병원 규모와 관계없이 HSMR이 낮은 경향을 보였다. 감염병 유행 시 병원의 과도한 업무량이 부여되지 않게하고 인력을 적절하게 고용하여 조정하는 것이 필요하다.

Citations

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National Expenditures on Anticancer and Immunomodulating Agents During 2013–2022 in Korea
Jieun Yun, Youngs Chang, Minsol Jo, Yerin Heo, Dong-Sook Kim
Journal of Korean Medical Science.2025;[Epub] CrossRef

Variations in the Hospital Standardized Mortality Ratios in Korea: Eun-Jung Lee, Soo-Hee Hwang, Jung-A Lee, Yoon Kim; J Prev Med Public Health. 2014;47(4):206-215. Published online July 31, 2014; DOI: https://doi.org/10.3961/jpmph.2014.47.4.206

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Abstract PDF

Objectives
The hospital standardized mortality ratio (HSMR) has been widely used because it allows for robust risk adjustment using administrative data and is important for improving the quality of patient care.
Methods
All inpatients discharged from hospitals with more than 700 beds (66 hospitals) in 2008 were eligible for inclusion. Using the claims data, 29 most responsible diagnosis (MRDx), accounting for 80% of all inpatient deaths among these hospitals, were identified, and inpatients with those MRDx were selected. The final study population included 703 571 inpatients including 27 718 (3.9% of all inpatients) in-hospital deaths. Using logistic regression, risk-adjusted models for predicting in-hospital mortality were created for each MRDx. The HSMR of individual hospitals was calculated for each MRDx using the model coefficients. The models included age, gender, income level, urgency of admission, diagnosis codes, disease-specific risk factors, and comorbidities. The Elixhauser comorbidity index was used to adjust for comorbidities.
Results
For 26 out of 29 MRDx, the c-statistics of these mortality prediction models were higher than 0.8 indicating excellent discriminative power. The HSMR greatly varied across hospitals and disease groups. The academic status of the hospital was the only factor significantly associated with the HSMR.
Conclusions
We found a large variation in HSMR among hospitals; therefore, efforts to reduce these variations including continuous monitoring and regular disclosure of the HSMR are required.

Summary

Citations

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Development and Application of New Risk-Adjustment Models to Improve the Current Model for Hospital Standardized Mortality Ratio in South Korea
Hyeki Park, Ji-Sook Choi, Min Sun Shin, Soomin Kim, Hyekyoung Kim, Nahyeong Im, Soon Joo Park, Donggyo Shin, Youngmi Song, Yunjung Cho, Hyunmi Joo, Hyeryeon Hong, Yong-Hwa Hwang, Choon-Seon Park
Yonsei Medical Journal.2025;[Epub] CrossRef
Differences in trends in discharge location in a cohort of hospitalized patients with cancer and non-cancer diagnoses receiving specialist palliative care: A retrospective cohort study
Michael Bonares, Kalli Stillos, Lise Huynh, Debbie Selby
Palliative Medicine.2023; 37(8): 1241. CrossRef
Functional training and timed nutrition intervention in infectious medical patients
M Holst, L N Søndergaard, M D Bendtsen, J Andreasen
European Journal of Clinical Nutrition.2016; 70(9): 1039. CrossRef

English Abstracts

A Comparative Study on Comorbidity Measurements with Lookback Period using Health Insurance Database: Focused on Patients Who Underwent Percutaneous Coronary Intervention.: Kyoung Hoon Kim, Lee Su Ahn; J Prev Med Public Health. 2009;42(4):267-273.; DOI: https://doi.org/10.3961/jpmph.2009.42.4.267

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Abstract PDF

OBJECTIVES
To compare the performance of three comorbidity measurements (Charlson comorbidity index, Elixhauser's comorbidity and comorbidity selection) with the effect of different comorbidity lookback periods when predicting in-hospital mortality for patients who underwent percutaneous coronary intervention. METHODS: This was a retrospective study on patients aged 40 years and older who underwent percutaneous coronary intervention. To distinguish comorbidity from complications, the records of diagnosis were drawn from the National Health Insurance Database excluding diagnosis that admitted to the hospital. C-statistic values were used as measures for in comparing the predictability of comorbidity measures with lookback period, and a bootstrapping procedure with 1,000 replications was done to determine approximate 95% confidence interval. RESULTS: Of the 61,815 patients included in this study, the mean age was 63.3 years (standard deviation: +/-10.2) and 64.8% of the population was male. Among them, 1,598 (2.6%) had died in hospital. While the predictive ability of the Elixhauser s comorbidity and comorbidity selection was better than that of the Charlson comorbidity index, there was no significant difference among the three comorbidity measurements. Although the prevalence of comorbidity increased in 3 years of lookback periods, there was no significant improvement compared to 1 year of a lookback period. CONCLUSIONS: In a health outcome study for patients who underwent percutaneous coronary intervention using National Health Insurance Database, the Charlson comorbidity index was easy to apply without significant difference in predictability compared to the other methods. The one year of observation period was adequate to adjust the comorbidity. Further work to select adequate comorbidity measurements and lookback periods on other diseases and procedures are needed.

Summary

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High‐Intensity Statin Reduces the Risk of Mortality Among Chronic Liver Disease Patients With Atherosclerotic Cardiovascular Disease: A Population‐Based Cohort Study
Sungho Bea, In‐Sun Oh, Ju Hwan Kim, Dong Hyun Sinn, Yoosoo Chang, Seungho Ryu, Ju‐Young Shin
Journal of the American Heart Association.2023;[Epub] CrossRef
Adjusting for Confounders in Outcome Studies Using the Korea National Health Insurance Claim Database: A Review of Methods and Applications
Seung Jin Han, Kyoung-Hoon Kim
Journal of Preventive Medicine and Public Health.2023;[Epub] CrossRef
Impact of comorbidity assessment methods to predict non-cancer mortality risk in cancer patients: a retrospective observational study using the National Health Insurance Service claims-based data in Korea
Sanghee Lee, Yoon Jung Chang, Hyunsoon Cho
BMC Medical Research Methodology.2021;[Epub] CrossRef
Evaluating the impact of covariate lookback times on performance of patient-level prediction models
Jill Hardin, Jenna M. Reps
BMC Medical Research Methodology.2021;[Epub] CrossRef
Comorbidity and cervical cancer survival of Indigenous and non-Indigenous Australian women: A semi-national registry-based cohort study (2003-2012)
Abbey Diaz, Peter D. Baade, Patricia C. Valery, Lisa J. Whop, Suzanne P. Moore, Joan Cunningham, Gail Garvey, Julia M. L. Brotherton, Dianne L. O’Connell, Karen Canfell, Diana Sarfati, David Roder, Elizabeth Buckley, John R. Condon, Stéphanie Filleur
PLOS ONE.2018; 13(5): e0196764. CrossRef
Comorbidity Adjustment in Health Insurance Claim Database
Kyoung Hoon Kim
Health Policy and Management.2016; 26(1): 71. CrossRef
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Lorenzo Azzalini, Kunle Tosin, Malorie Chabot-Blanchet, Robert Avram, Hung Q. Ly, Benoit Gaudet, Richard Gallo, Serge Doucet, Jean-François Tanguay, Réda Ibrahim, Jean C. Grégoire, Jacques Crépeau, Raoul Bonan, Pierre de Guise, Mohamed Nosair, Jean-Franço
JACC: Cardiovascular Interventions.2015; 8(14): 1854. CrossRef
A Convergence Study in the Severity-adjusted Mortality Ratio on inpatients with multiple chronic conditions
Young-Suk Seo, Sung-Hong Kang
Journal of Digital Convergence.2015; 13(12): 245. CrossRef
Development and validation of comorbidity index in South Korea
S.-R. Kil, S.-I. Lee, Y.-H. Khang, M.-S. Lee, H.-J. Kim, S.-O. Kim, M.-W. Jo
International Journal for Quality in Health Care.2012; 24(4): 391. CrossRef
Development of Mortality Model of Severity-Adjustment Method of AMI Patients
Ji-Hye Lim, Mun-Hee Nam
Journal of the Korea Academia-Industrial cooperation Society.2012; 13(6): 2672. CrossRef
Use of hospitalisation history (lookback) to determine prevalence of chronic diseases: impact on modelling of risk factors for haemorrhage in pregnancy
Jian Sheng Chen, Christine L Roberts, Judy M Simpson, Jane B Ford
BMC Medical Research Methodology.2011;[Epub] CrossRef
The Impact of Medicaid Expansion to include population with low income on the preventable hospitalizations
Hyun-Chul Shin, Se-Ra Kim
Korean Journal of Health Policy and Administration.2010; 20(1): 87. CrossRef
Comparative Study on Three Algorithms of the ICD-10 Charlson Comorbidity Index with Myocardial Infarction Patients
Kyoung Hoon Kim
Journal of Preventive Medicine and Public Health.2010; 43(1): 42. CrossRef

The Trend of Risk-adjusted Hospital Mortality Rates of Coronary Artery Bypass Graft Patients from 2001 to 2003.: Kwang Soo Lee; J Prev Med Public Health. 2007;40(1):29-35.; DOI: https://doi.org/10.3961/jpmph.2007.40.1.29

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Abstract PDF: OBJECTIVES
To assess whether the risk-adjusted inhospital mortality rates for non-emergent and isolated coronary artery bypass graft surgery (CABG) patients exhibited a consistent trend from 2001 to 2003. METHODS: The data used in this study came from CABG claims that were submitted to a Korean Health Insurance Review Agency (HIRA) in 2001, 2002, and 2003. Study datasets included data from 17 tertiary hospitals, which had at least 25 claims each year over 3 years. The interhospital differences in patients' risk-factors were identified and controlled in the risk-adjustment model. Actual and predicted mortality rates for each hospital were calculated in 2001, 2002, 2003, and 2001+2002, and were then examined to identify consistent rate patterns over time. Kappa analysis was applied to assess the agreements between rates. RESULTS: Hospitals with lower-than-expected inpatient mortality rates showed more consistent rates than those with higher-than-expected mortality rates. The mortality rates that were calculated based on data obtained over multiple years had less variation among hospitals than rates based on single year data. Based on the Kappa score, the highest agreement was found when the rates were compared between the 2-year combined data (2001+2002) and 2003. CONCLUSIONS: Consistent patterns over 3 years were most evident for hospitals which had lower-than expected mortality rates. Policy makers can use this information to identify the degree of outcomes in hospitals and help motivate or channel the behaviors of providers.; Summary

Original Articles

Severity Measurement Methods and Comparing Hospital Death Rates for Coronary Artery Bypass Graft Surgery.: Youngdae Kwon, Hyungsik Ahn, Youngsoo Shin; Korean J Prev Med. 2001;34(3):244-252.

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Abstract PDF: 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, APACHElll 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 R2 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.; Summary

Severity-Adjusted Mortality Rates: The Case of CABG Surgery.: Hyeung Keun Park, Hyeongsik Ahn, Young Dae Kwon, You Cheol Shin, Jin Seok Lee, Hae Joon Kim, Moon Jun Sohn; Korean J Prev Med. 2001;34(1):21-27.

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Abstract PDF: OBJECTIVES
To develop a model that will predict the mortality of patients undergoing Coronary Artery Bypass Graft (CABG) and evaluate the performance of hospitals. METHODS: Data from 564 CABGs performed 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 regression and validated with cross-validation. The statistical performance of the developed model was evaluated using c-statistic, R2, 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 R2 were 0.791 and 0.101, 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 not 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 be an indicator for evaluating hospital performance in Korea.; Summary

Relationship between structural characteristics and hospital mortality rates on tertiary referral hospitals in Korea.: Tae Yong Sohn, Seung Hum Yu; Korean J Prev Med. 1996;29(2):279-294.

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Abstract PDF: This study was to evaluate hospital characteristics as composition of manpower and facilities to the death rate of patient; and to earmark the factors affecting the overall hospital mortality rates. The data utilized were derived from survey material conducted by the Korean Hospital Association on 32 tertiary referral hospitals in Korea between 1986 and 1994. The findings are: 1. Those hospitals having the most capacity per bed had little difference to the mortality rates than the others. 2. Those hospitals having the most daily patients per specialist had significantly higher mortality rates than the others, but the number of daily patients per nurse had little effect on the mortality rates. 3. Those hospitals which had a relatively sufficient number of quality assurance activities revealed a lower mortality, and particularly in case where such effort was directed to the clinicians, the outcome was remarkable. we concluded that the major factor affecting the hospital mortality rates seems to be the number of specialists per number of beds, the degree of quality assurance assessment of the clinicians, the quality assurance activities of each hospital as a whole, and the number of daily patient per specialist. According to the findings of this study, the composition and quality of specialist and adequate quality assurance activities seemed to be the essential for the improvement of hospital care. Therefore, in this regard the proper implementation of policy and support is highly recommended. Due to lack of available research material, the personal characteristics of specialists haven't been considered in this study However, this longitudinal observation of 32 tertiary referral hospitals over a nine year period has significant merit alone.; Summary

English Abstract

Does a Higher Coronary Artery Bypass Graft Surgery Volume Always have a Low In-hospital Mortality Rate in Korea?.: Kwang Soo Lee, Sang Il Lee; J Prev Med Public Health. 2006;39(1):13-20.

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Abstract PDF: OBJECTIVES
To propose a risk-adjustment model with using insurance claims data and to analyze whether or not the outcomes of non-emergent and isolated coronary artery bypass graft surgery (CABG) differed between the low- and high-volume hospitals for the patients who are at different levels of surgical risk. METHODS: This is a cross-sectional study that used the 2002 data of the national health insurance claims. The study data set included the patient level data as well as all the ICD-10 diagnosis and procedure codes that were recorded in the claims. The patient's biological, admission and comorbidity information were used in the risk-adjustment model. The risk factors were adjusted with the logistic regression model. The subjects were classified into five groups based on the predicted surgical risk: minimal (<0.5%), low (0.5% to 2%), moderate (2% to 5%), high (5% to 20%), and severe (=20%). The differences between the low- and high-volume hospitals were assessed in each of the five risk groups. RESULTS: The final risk-adjustment model consisted of ten risk factors and these factors were found to have statistically significant effects on patient mortality. The C-statistic (0.83) and Hosmer-Lemeshow test (x2=6.92, p=0.55) showed that the model's performance was good. A total of 30 low-volume hospitals (971patients) and 4 high-volume hospitals (1,087patients) were identified. Significantdifferences for the in-hospital mortality were found between the low- and high-volume hospitals for the high (21.6% vs. 7.2%, p=0.00) and severe (44.4% vs. 11.8%, p=0.00) risk patient groups. CONCLUSIONS: Good model performance showed that insurance claims data can be used for comparing hospital mortality after adjusting for the patients' risk. Negative correlation was existed between surgery volume and in-hospital mortality. However, only patients in high and severe risk groups had such a relationship.; Summary

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