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Systematic Review
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
J Prev Med Public Health. 2024;57(1):1-7.   Published online November 16, 2023
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  • 150 Download
AbstractAbstract AbstractSummary PDF
Adjusting for potential confounders is crucial for producing valuable evidence in outcome studies. Although numerous studies have been published using the Korea National Health Insurance Claim Database, no study has critically reviewed the methods used to adjust for confounders. This study aimed to review these studies and suggest methods and applications to adjust for confounders.
We conducted a literature search of electronic databases, including PubMed and Embase, from January 1, 2021 to December 31, 2022. In total, 278 studies were retrieved. Eligibility criteria were published in English and outcome studies. A literature search and article screening were independently performed by 2 authors and finally, 173 of 278 studies were included.
Thirty-nine studies used matching at the study design stage, and 171 adjusted for confounders using regression analysis or propensity scores at the analysis stage. Of these, 125 conducted regression analyses based on the study questions. Propensity score matching was the most common method involving propensity scores. A total of 171 studies included age and/or sex as confounders. Comorbidities and healthcare utilization, including medications and procedures, were used as confounders in 146 and 82 studies, respectively.
This is the first review to address the methods and applications used to adjust for confounders in recently published studies. Our results indicate that all studies adjusted for confounders with appropriate study designs and statistical methodologies; however, a thorough understanding and careful application of confounding variables are required to avoid erroneous results.
Korean summary
건강보험청구자료를 사용한 성과연구에서는 교란요인 통제가 중요하다. 최근 발표된 연구들은 연구설계와 통계 분석 과정에 적절하게 교란요인을 통제하였다. 연구의 질을 높이기 위해서는 건강보험청구자료에서 수집 가능한 교란 요인에 대한 이해와 방법론적 가이드라인이 요구된다.
Key Message
All recently published studies using Health Insurance Claims Database adjusted for confounders with appropriate study designs and statistical methodologies. The review suggests the need for careful application of confounding variables and the methodological guidance to improve the quality of outcome studies.
English Abstract
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.
  • 5,196 View
  • 107 Download
  • 13 Crossref
AbstractAbstract PDF
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.


Citations to this article as recorded by  
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    Journal of Preventive Medicine and Public Health.2024; 57(1): 1.     CrossRef
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  • 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
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    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
Original Articles
Inter-hospital Comparison of Cesarean Section Rates after Risk Adjustment.
Sang Il Lee, Young Ho Khang, Beom Man Ha, Moo Song Lee, Weechang Kang, Hee Jo Koo, Chang Yup Kim
Korean J Prev Med. 2001;34(4):337-346.
  • 2,050 View
  • 26 Download
AbstractAbstract PDF
To determine the clinical risk factors associated with the mode of delivery decision and to compare cesarean section rates after adjusting for risk factors identified among Korean hospitals. METHODS: Data were collected from 9 general hospitals in two provincial regions by medical record abstraction during February 2000. A total of 3,467 cases were enrolled and analyzed by stepwise logistic regression. Performance of the risk-adjustment model (discrimination and calibration) was evaluated by the C statistic and the Hosmer-Lemeshow test. Crude rates, predicted rates with 95% confidence intervals, and adjusted rates of cesarean section were calculated and compared among the hospitals. RESULTS: The average crude cesarean section rate was 53.2%, ranging from 39.4% to 65.7%. Several risk factors such as maternal age, previous history of cesarean section, placenta previa, placental abruption, malpresentation, amniotic fluid abnormality, gestational anemia, infant body weight, pregnancy-induced hypertension, and chorioamnionitis were found to have statistically significant effects on the mode of delivery. It was confirmed that information about most of these risk factors was able to be collected through the national health insurance claims database in Korea. Performance of the risk-adjustment model was good (c statistic=0.815, Hosmer-Lemeshow test=0.0621). Risk factor adjustment did lead to some change in the rank of hospital cesarean section rates. The crude rates of three hospitals were beyond 95% confidence intervals of the predicted rates. CONCLUSIONS: Considering that cesarean section rates in Korean hospitals are too high, it is apparent that some policy interventions need to be introduced. The concept and methodology of risk adjustment should be used in the process of health policy development to lower the cesarean section rate in Korea.
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.
  • 1,850 View
  • 23 Download
AbstractAbstract PDF
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.
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.
  • 1,870 View
  • 24 Download
AbstractAbstract PDF
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.
Relationship between Percutaneous Transluminal Coronary Angioplasty Volume and Associated Immediate Outcome.
Young Ho Khang, Yong Ik Kim, Chang Yup Kim, Young Sung Lee, Sunmean Kim, Jin Seok Lee, Byung Hee Oh
Korean J Prev Med. 2001;34(1):9-20.
  • 1,888 View
  • 23 Download
AbstractAbstract PDF
To explore the relationship between Percutaneous Transluminal Coronary Angioplasty (PTCA) volume and the associated immediate outcome. METHODS: A total of 1,379 PTCAs were performed in 25 hospitals in Korea between October 1 and December 31 in 1997. Data from 1,317 PTCAs (95.5%) were collected through medical record abstraction. Inter-observer reliability of the data was examined using the Kappa statistic on a subsample of 110 PTCA procedures from five hospitals. Intra-observer reliability of the data was also examined. PTCA success and immediate adverse outcomes were selected as the outcome variables. A successful PTCA was defined as a case that shows less than 50% diameter stenosis and more than 20% reduction of diameter stenosis. Immediate adverse outcomes included deaths during the same hospitalization, emergency coronary artery bypass graft (CABG) within 24 hours after PTCA, and acute myocardial infarction within 24 hours after PTCA. The numbers of PTCAs performed in 1997 per hospital were used as the volume variables. RESULTS: Without adjusting for patient risk factors that may affect outcomes, procedures at high volume hospitals (200 cases per year) had a greater success rate (P=0.001) than low volume hospitals. There was a marginally significant difference (P=0.070) in major adverse outcome rates between high and low volume hospitals. After adjusting for risk factors, there were significant differences in procedural failure and major adverse outcome rates between high and low volume hospitals. CONCLUSIONS: After adjusting for patient clinical risk factors, the hospital volume of PTCA was associated with immediate outcomes. It is recommended that a PTCA volume per year be established in order to improve the immediate outcome of this procedure in Korea.
English Abstracts
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.
  • 2,048 View
  • 29 Download
AbstractAbstract PDF
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.
Impact of Risk Adjustment with Insurance Claims Data on Cesarean Delivery Rates of Healthcare Organizations in Korea.
Kwang Soo Lee, Sang Il Lee, Kyung Seo, Young Mi Do
J Prev Med Public Health. 2005;38(2):132-140.
  • 1,896 View
  • 30 Download
AbstractAbstract PDF
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 riskadjustment model, and were found to have statistically significant effects on the mode of delivery. The c-statistic (0.78) and Hosmer-Lemeshow test (chi2=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 chi2. 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.

JPMPH : Journal of Preventive Medicine and Public Health