Journal of Preventive Medicine and Public Health

The Korean Society for Preventive Medicine (대한예방의학회)
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Updating Korean Disability Weights for Causes of Disease: Adopting an Add-on Study Method

  • Dasom Im (Department of Preventive Medicine, Ulsan University Hospital, University of Ulsan College of Medicine) ;
  • Noor Afif Mahmudah (Department of Preventive Medicine, Ulsan University Hospital, University of Ulsan College of Medicine) ;
  • Seok-Jun Yoon (Department of Preventive Medicine, Korea University College of Medicine) ;
  • Young-Eun Kim (Big Data Department, National Health Insurance Service) ;
  • Don-Hyung Lee (Research & Statistics Team, Korean Health Promotion Institute) ;
  • Yeon-hee Kim (Research & Statistics Team, Korean Health Promotion Institute) ;
  • Yoon-Sun Jung (Artificial Intelligence and Big-Data Convergence Center, Gil Medical Center, Gachon University College of Medicine) ;
  • Minsu Ock (Department of Preventive Medicine, Ulsan University Hospital, University of Ulsan College of Medicine)
  • Received : 2023.04.19
  • Accepted : 2023.06.13
  • Published : 2023.07.31
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Abstract

Objectives: Disability weights require regular updates, as they are influenced by both diseases and societal perceptions. Consequently, it is necessary to develop an up-to-date list of the causes of diseases and establish a survey panel for estimating disability weights. Accordingly, this study was conducted to calculate, assess, modify, and validate disability weights suitable for Korea, accounting for its cultural and social characteristics. Methods: The 380 causes of disease used in the survey were derived from the 2019 Global Burden of Disease Collaborative Network and from 2019 and 2020 Korean studies on disability weights for causes of disease. Disability weights were reanalyzed by integrating the findings of an earlier survey on disability weights in Korea with those of the additional survey conducted in this study. The responses were transformed into paired comparisons and analyzed using probit regression analysis. Coefficients for the causes of disease were converted into predicted probabilities, and disability weights in 2 models (model 1 and 2) were rescaled using a normal distribution and the natural logarithm, respectively. Results: The mean values for the 380 causes of disease in models 1 and 2 were 0.488 and 0.369, respectively. Both models exhibited the same order of disability weights. The disability weights for the 300 causes of disease present in both the current and 2019 studies demonstrated a Pearson correlation coefficient of 0.994 (p=0.001 for both models). This study presents a detailed add-on approach for calculating disability weights. Conclusions: This method can be employed in other countries to obtain timely disability weight estimations.

Keywords

Acknowledgement

The authors would like to thank the survey respondents.

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