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Moo-Song Lee 2 Articles
Identifying Adverse Events Using International Classification of Diseases, Tenth Revision Y Codes in Korea: A Cross-sectional Study
Minsu Ock, Hwa Jung Kim, Bomin Jeon, Ye-Jee Kim, Hyun Mi Ryu, Moo-Song Lee
J Prev Med Public Health. 2018;51(1):15-22.   Published online January 4, 2018
DOI: https://doi.org/10.3961/jpmph.17.118
  • 6,940 View
  • 196 Download
  • 9 Crossref
AbstractAbstract PDFSupplementary Material
Objectives
The use of administrative data is an affordable alternative to conducting a difficult large-scale medical-record review to estimate the scale of adverse events. We identified adverse events from 2002 to 2013 on the national level in Korea, using International Classification of Diseases, tenth revision (ICD-10) Y codes.
Methods
We used data from the National Health Insurance Service-National Sample Cohort (NHIS-NSC). We relied on medical treatment databases to extract information on ICD-10 Y codes from each participant in the NHIS-NSC. We classified adverse events in the ICD-10 Y codes into 6 types: those related to drugs, transfusions, and fluids; those related to vaccines and immunoglobulin; those related to surgery and procedures; those related to infections; those related to devices; and others.
Results
Over 12 years, a total of 20 817 adverse events were identified using ICD-10 Y codes, and the estimated total adverse event rate was 0.20%. Between 2002 and 2013, the total number of such events increased by 131.3%, from 1366 in 2002 to 3159 in 2013. The total rate increased by 103.9%, from 0.17% in 2002 to 0.35% in 2013. Events related to drugs, transfusions, and fluids were the most common (19 446, 93.4%), followed by those related to surgery and procedures (1209, 5.8%) and those related to vaccines and immunoglobulin (72, 0.3%).
Conclusions
Based on a comparison with the results of other studies, the total adverse event rate in this study was significantly underestimated. Improving coding practices for ICD-10 Y codes is necessary to precisely monitor the scale of adverse events in Korea.
Summary

Citations

Citations to this article as recorded by  
  • Accuracy assessment of patient safety incident (PSI) codes and present-on-admission (POA) indicators: a cross-sectional analysis using the Patient Safety Incidents Inquiry (PSII) in Korea
    Jeehee Pyo, Eun Young Choi, Seung Gyeong Jang, Won Lee, Minsu Ock
    BMC Health Services Research.2024;[Epub]     CrossRef
  • Development of the Korean Patient Safety Incidents Code Classification System
    Eun Young Choi, Jeehee Pyo, Young-Kwon Park, Minsu Ock, Sukyeong Kim
    Journal of Patient Safety.2023; 19(1): 8.     CrossRef
  • Use of a hospital administrative database to identify and characterize community-acquired, hospital-acquired and drug-induced acute kidney injury
    Amayelle Rey, Valérie Gras-Champel, Thibaut Balcaen, Gabriel Choukroun, Kamel Masmoudi, Sophie Liabeuf
    Journal of Nephrology.2022; 35(3): 955.     CrossRef
  • Evaluation of Factors Associated with Adverse Drug Events in South Korea Using a Population-Based Database
    Eunkyeong Choi, Siin Kim, Hae Sun Suh
    Journal of Clinical Medicine.2022; 11(21): 6248.     CrossRef
  • Feasibility of Capturing Adverse Events From Insurance Claims Data Using International Classification of Diseases, Tenth Revision, Codes Coupled to Present on Admission Indicators
    Juyoung Kim, Eun Young Choi, Won Lee, Hae Mi Oh, Jeehee Pyo, Minsu Ock, So Yoon Kim, Sang-il Lee
    Journal of Patient Safety.2022; 18(5): 404.     CrossRef
  • The Korea National Patient Safety Incidents Inquiry Survey: Characteristics of Adverse Events Identified Through Medical Records Review in Regional Public Hospitals
    Min Ji Kim, Hee Jung Seo, Hong Mo Koo, Minsu Ock, Jee-In Hwang, Sang-Il Lee
    Journal of Patient Safety.2022; 18(5): 382.     CrossRef
  • Use of ICD‐10‐CM T codes in hospital claims data to identify adverse drug events in Taiwan
    Ya‐Fang Cheng, Chi‐Yuan Cheng, Szu‐Hsuan Wang, Yu‐Ting Lin, Tzu‐Cheng Tsai
    Journal of Clinical Pharmacy and Therapeutics.2021; 46(2): 476.     CrossRef
  • Perceptions of Hospital Health Information Managers Regarding Present on Admission Indicators in Korea: A Qualitative Study
    Jee-Hee Pyo, Eun-Young Choi, Hae-Mi Oh, Won Lee, Ju-Young Kim, Min-Su Ock, So-Yoon Kim, Sang-Il Lee
    Quality Improvement in Health Care.2020; 26(1): 23.     CrossRef
  • Variation between hospitals and reviewers in detection of adverse events identified through medical record review in Korea
    Sukyeong Kim, Ho Gyun Shin, A E Jeong Jo, Ari Min, Minsu Ock, Jee-In Hwang, Youngjin Jeong, Moon Sung Park, Jong Bouk Lee, Tae I K Chang, Eunhyang Song, Heungseon Kim, Sang-Il Lee
    International Journal for Quality in Health Care.2020; 32(8): 495.     CrossRef
Cardiovascular Health Metrics and All-cause and Cardiovascular Disease Mortality Among Middle-aged Men in Korea: The Seoul Male Cohort Study
Ji Young Kim, Young-Jin Ko, Chul Woo Rhee, Byung-Joo Park, Dong-Hyun Kim, Jong-Myon Bae, Myung-Hee Shin, Moo-Song Lee, Zhong Min Li, Yoon-Ok Ahn
J Prev Med Public Health. 2013;46(6):319-328.   Published online November 28, 2013
DOI: https://doi.org/10.3961/jpmph.2013.46.6.319
  • 15,099 View
  • 158 Download
  • 53 Crossref
AbstractAbstract PDF
Objectives

This study estimated the association of cardiovascular health behaviors with the risk of all-cause and cardiovascular disease (CVD) mortality in middle-aged men in Korea.

Methods

In total, 12 538 men aged 40 to 59 years were enrolled in 1993 and followed up through 2011. Cardiovascular health metrics defined the following lifestyle behaviors proposed by the American Heart Association: smoking, physical activity, body mass index, diet habit score, total cholesterol, blood pressure, and fasting blood glucose. The cardiovascular health metrics score was calculated as a single categorical variable, by assigning 1 point to each ideal healthy behavior. A Cox proportional hazards regression model was used to estimate the hazard ratio of cardiovascular health behavior. Population attributable risks (PARs) were calculated from the significant cardiovascular health metrics.

Results

There were 1054 total and 171 CVD deaths over 230 690 person-years of follow-up. The prevalence of meeting all 7 cardiovascular health metrics was 0.67%. Current smoking, elevated blood pressure, and high fasting blood glucose were significantly associated with all-cause and CVD mortality. The adjusted PARs for the 3 significant metrics combined were 35.2% (95% confidence interval [CI], 21.7 to 47.4) and 52.8% (95% CI, 22.0 to 74.0) for all-cause and CVD mortality, respectively. The adjusted hazard ratios of the groups with a 6-7 vs. 0-2 cardiovascular health metrics score were 0.42 (95% CI, 0.31 to 0.59) for all-cause mortality and 0.10 (95% CI, 0.03 to 0.29) for CVD mortality.

Conclusions

Among cardiovascular health behaviors, not smoking, normal blood pressure, and recommended fasting blood glucose levels were associated with reduced risks of all-cause and CVD mortality. Meeting a greater number of cardiovascular health metrics was associated with a lower risk of all-cause and CVD mortality.

Summary

Citations

Citations to this article as recorded by  
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JPMPH : Journal of Preventive Medicine and Public Health