Epidemiology and Health

Special Article

Identification of acute myocardial infarction and stroke events using the National Health Insurance Service database in Korea: Minsung Cho, Hyeok-Hee Lee, Jang-Hyun Baek, Kyu Sun Yum, Min Kim, Jang-Whan Bae, Seung-Jun Lee, Byeong-Keuk Kim, Young Ah Kim, JiHyun Yang, Dong Wook Kim, Young Dae Kim, Haeyong Pak, Kyung Won Kim, Sohee Park, Seng Chan You, Hokyou Lee, Hyeon Chang Kim; Epidemiol Health. 2024;46:e2024001. Published online December 26, 2023; DOI: https://doi.org/10.4178/epih.e2024001

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Abstract

OBJECTIVES
The escalating burden of cardiovascular disease (CVD) is a critical public health issue worldwide. CVD, especially acute myocardial infarction (AMI) and stroke, is the leading contributor to morbidity and mortality in Korea. We aimed to develop algorithms for identifying AMI and stroke events from the National Health Insurance Service (NHIS) database and validate these algorithms through medical record review.

METHODS

We first established a concept and definition of “hospitalization episode,” taking into account the unique features of health claims-based NHIS database. We then developed first and recurrent event identification algorithms, separately for AMI and stroke, to determine whether each hospitalization episode represents a true incident case of AMI or stroke. Finally, we assessed our algorithms’ accuracy by calculating their positive predictive values (PPVs) based on medical records of algorithm-identified events.

RESULTS

We developed identification algorithms for both AMI and stroke. To validate them, we conducted retrospective review of medical records for 3,140 algorithm-identified events (1,399 AMI and 1,741 stroke events) across 24 hospitals throughout Korea. The overall PPVs for the first and recurrent AMI events were around 92% and 78%, respectively, while those for the first and recurrent stroke events were around 88% and 81%, respectively.

CONCLUSIONS

We successfully developed algorithms for identifying AMI and stroke events. The algorithms demonstrated high accuracy, with PPVs of approximately 90% for first events and 80% for recurrent events. These findings indicate that our algorithms hold promise as an instrumental tool for the consistent and reliable production of national CVD statistics in Korea.

Summary

Key Message
In this study, we developed algorithms to identify acute myocardial infarction (AMI) and stroke events from the Korean National Health insurance Service database. To validate them, we conducted retrospective review of medical records across 24 hospitals throughout Korea. The overall positive predictive values for the first and recurrent AMI events were around 92% and 78%, respectively, while those for the first and recurrent stroke events were around 88% and 81%, respectively.

Citations

Citations to this article as recorded by

Incidence and case fatality rates of stroke in Korea, 2011-2020
Jenny Moon, Yeeun Seo, Hyeok-Hee Lee, Hokyou Lee, Fumie Kaneko, Sojung Shin, Eunji Kim, Kyu Sun Yum, Young Dae Kim, Jang-Hyun Baek, Hyeon Chang Kim
Epidemiology and Health.2023; : e2024003. CrossRef

Methods

The unrealized potential: cohort effects and age-period-cohort analysis: Jongho Heo, Sun-Young Jeon, Chang-Mo Oh, Jongnam Hwang, Juhwan Oh, Youngtae Cho; Epidemiol Health. 2017;39:e2017056. Published online December 5, 2017; DOI: https://doi.org/10.4178/epih.e2017056

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Abstract Summary PDF Supplementary Material

Abstract

This study aims to provide a systematical introduction of age-period-cohort (APC) analysis to South Korean readers who are unfamiliar with this method (we provide an extended version of this study in Korean). As health data in South Korea has substantially accumulated, population-level studies that explore long-term trends of health status and health inequalities and identify macrosocial determinants of the trends are needed. Analyzing long-term trends requires to discern independent effects of age, period, and cohort using APC analysis. Most existing health and aging literature have used cross-sectional or short-term available panel data to identify age or period effects ignoring cohort effects. This under-use of APC analysis may be attributed to the identification (ID) problem caused by the perfect linear dependency across age, period, and cohort. This study explores recently developed three APC models to address the ID problem and adequately estimate the effects of A-P-C: intrinsic estimator-APC models for tabular age by period data; hierarchical cross-classified random effects models for repeated cross-sectional data; and hierarchical APC-growth curve models for accelerated longitudinal panel data. An analytic exemplar for each model was provided. APC analysis may contribute to identifying biological, historical, and socioeconomic determinants in long-term trends of health status and health inequalities as well as examining Korean’s aging trajectories and temporal trends of period and cohort effects. For designing effective health policies that improve Korean population’s health and reduce health inequalities, it is essential to understand independent effects of the three temporal factors by using the innovative APC models.

Summary

Korean summary
-건강수준 및 건강불평등의 장기적인 추이에 미치는 연령, 기간, 출생 코호트의 독립적인 영향을 분해하는 방법인 연령-기간-코호트 분석법(Age-Period-Cohort analysis)을 국내 보건의료 연구자들에게 체계적으로 소개함 -APC 분석법은 건강수준 및 건강행태, 건강불평등의 추세 분석 및 고령화, 만성질환, 생애주기 연구 등에 있어서 널리 활용될 수 있음 -APC 연구 결과를 바탕으로 향후 보건의료 정책에 있어서도 기간 또는 연령에 따른 정책뿐 아니라 코호트에 특정한 정책들도 고려되어야 할 필요가 있음

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