Journal of Preventive Medicine and Public Health

Special Articles

The Humidifier Disinfectant Disaster in Korea: Implications for Preventive Medicine and Public Health: Sue K. Park, Woojin Lim, Dong-Wook Lee, Mina Ha, Chang Hyun Lee, Hae-Kwan Cheong; J Prev Med Public Health. 2026;59(4):337-342. Published online July 21, 2026; DOI: https://doi.org/10.3961/jpmph.26.248

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Abstract Summary PDF: The humidifier disinfectant disaster in Korea was one of the most severe environmental health disasters of recent decades. It showed that indoor household chemical products used in daily life can cause fatal health effects at the population level when inhalation risks are not adequately assessed before marketing. The disaster also reaffirmed the central role of epidemiology in detecting emerging environmental health threats and guiding public health responses. Fifteen years after the disaster was recognized, major challenges remain, including defining the full spectrum of health damage, providing compensation, and ensuring long-term support. The Special Act on Remedy for Damage Caused by Humidifier Disinfectants, fully amended in 2026, represents an important institutional advance. By explicitly defining the joint liability of the state and relevant companies, the amended Act reflects a shift toward a more public, state-administered system of compensation and health support. This disaster also highlights the roles of public health professionals in generating epidemiological evidence on delayed or newly recognized diseases and sequelae, applying that evidence in clinical and policy contexts, and establishing life-course–based lifelong health management programs. For the 2026 comprehensive amendment to produce meaningful progress, continued efforts are needed to clarify long-term health effects and refine health data–based surveillance systems so that scientific evidence can be translated effectively into policy.; Summary

Korean summary
가습기살균제 참사로 인한 지연성 및 다계통 건강영향을 규명하고, 역학적 근거를 배상과 장기 지원으로 연결하는 데에는 여전히 해결해야 할 과제가 남아 있다. 본 논문은 국가와 기업의 책임, 포용적 배상, 건강데이터 기반 감시체계, 생애주기별 건강관리를 강조하면서, 참사의 정책적 교훈과 2026년 「가습기살균제 피해구제를 위한 특별법」 전부개정의 함의를 검토한다. 이러한 체계의 강화는 예방의학과 공중보건이 사후적 재난 대응을 넘어 향후 환경보건 위기의 사전 예방으로 나아가는 데 기여할 수 있다.

Key Message
Unmet needs remain in identifying delayed and multisystem health effects from the humidifier disinfectant disaster and translating epidemiological evidence into compensation and long-term support. This article reviews the disaster’s policy lessons and the implications of the 2026 full amendment of the Special Act, emphasizing state–corporate responsibility, inclusive compensation, health-data–based surveillance, and life-course health management. Strengthening these systems can help preventive medicine and public health move from post-disaster response toward proactive prevention of future environmental health crises.

Application of Standardization for Causal Inference in Observational Studies: A Step-by-step Tutorial for Analysis Using R Software: Sangwon Lee, Woojoo Lee; J Prev Med Public Health. 2022;55(2):116-124. Published online February 11, 2022; DOI: https://doi.org/10.3961/jpmph.21.569

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5 Web of Science
5 Crossref

Abstract Summary PDF Supplementary Material

Epidemiological studies typically examine the causal effect of exposure on a health outcome. Standardization is one of the most straightforward methods for estimating causal estimands. However, compared to inverse probability weighting, there is a lack of user-centric explanations for implementing standardization to estimate causal estimands. This paper explains the standardization method using basic R functions only and how it is linked to the R package stdReg, which can be used to implement the same procedure. We provide a step-by-step tutorial for estimating causal risk differences, causal risk ratios, and causal odds ratios based on standardization. We also discuss how to carry out subgroup analysis in detail.

Summary

Korean summary
본 논문에서는 standardization 방법을 이용하여 risk difference, relative risk, risk ratio와 같은 인과성 효과를 R software을 이용하여 도출하는 튜토리얼을 제공합니다. 간암환자의 치료를 예시로, 합성 데이터를 이용한 치료제의 사망에 대한 인과적 효과를 탐색하는 튜토리얼을 제공합니다. 추가적으로, 인과성 관련 기본 이론을 집약적으로 설명하였고, standardization을 이용한 subgroup analysis 수행 방법이 제공됩니다.

Citations

Citations to this article as recorded by

A Latent Variable Approach for Causal Effect Estimation Under Misclassified Treatment Assignment
Yimeng Shang, Yu‐Han Chiu, Lan Kong
Statistics in Medicine.2026;[Epub] CrossRef
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Mingxuan Liu, Xinru Wang, Jin Wee Lee, Bibhas Chakraborty, Nan Liu, Victor Volovici
Acta Neurochirurgica.2025;[Epub] CrossRef
Improved Clinical Outcomes With Early Anti-Tumour Necrosis Factor Alpha Therapy in Children With Newly Diagnosed Crohn’s Disease: Real-world Data from the International Prospective PIBD-SETQuality Inception Cohort Study
Renz C W Klomberg, Hella C van der Wal, Martine A Aardoom, Polychronis Kemos, Dimitris Rizopoulos, Frank M Ruemmele, Mohammed Charrout, Hankje C Escher, Nicholas M Croft, Lissy de Ridder, Ivan D Milovanovich, James J Ashton, Paul Henderson, Oren Ledder, T
Journal of Crohn's and Colitis.2024; 18(5): 738. CrossRef
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Homologous and Heterologous Prime-Boost Vaccination: Impact on Clinical Severity of SARS-CoV-2 Omicron Infection among Hospitalized COVID-19 Patients in Belgium
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English Abstract

Strengthening Causal Inference in Studies using Non-experimental Data: An Application of Propensity Score and Instrumental Variable Methods.: Myoung Hee Kim, Young Kyung Do; J Prev Med Public Health. 2007;40(6):495-504.; DOI: https://doi.org/10.3961/jpmph.2007.40.6.495

7,174 View
103 Download
8 Crossref

Abstract PDF

OBJECTIVES
This study attempts to show how studies using non-experimental data can strengthen causal inferences by applying propensity score and instrumental variable methods based on the counterfactual framework. For illustrative purposes, we examine the effect of having private health insurance on the probability of experiencing at least one hospital admission in the previous year. METHODS: Using data from the 4th wave of the Korea Labor and Income Panel Study, we compared the results obtained using propensity score and instrumental variable methods with those from conventional logistic and linear regression models, respectively. RESULTS: While conventional multiple regression analyses fail to identify the effect, the results estimated using propensity score and instrumental variable methods suggest that having private health insurance has positive and statistically significant effects on hospital admission. CONCLUSIONS: This study demonstrates that propensity score and instrumental variable methods provide potentially useful alternatives to conventional regression approaches in making causal inferences using non-experimental data.

Summary

Citations

Citations to this article as recorded by

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