Scientific rationale and applicability of dose-response models for environmental carcinogens

환경성 발암물질의 용량-반응모델의 이론적 근거와 응용에 관한 연구 - 음용수 중 chloroform을 중심으로

  • Shin, Dong-Chun (Department of Preventive Medicine and Institute for Environmental Research, Yonsei University College of Medicine) ;
  • Chung, Yong (Department of Preventive Medicine and Institute for Environmental Research, Yonsei University College of Medicine) ;
  • Kim, Jong-Man (Department of Preventive Medicine and Institute for Environmental Research, Yonsei University College of Medicine) ;
  • Lee, Seong-Im (Department of Preventive Medicine and Institute for Environmental Research, Yonsei University College of Medicine) ;
  • Hwang, Man-Sik (Department of Preventive Medicine and Institute for Environmental Research, Yonsei University College of Medicine)
  • 신동천 (연세대학교 의과대학 예방의학교실 및 환경공해연구소) ;
  • 정용 (연세대학교 의과대학 예방의학교실 및 환경공해연구소) ;
  • 김종만 (연세대학교 의과대학 예방의학교실 및 환경공해연구소) ;
  • 이성임 (연세대학교 의과대학 예방의학교실 및 환경공해연구소) ;
  • 황만식 (연세대학교 의과대학 예방의학교실 및 환경공해연구소)
  • Published : 1996.03.01

Abstract

This study described methods to predict human health risk associated with exposure to environmental carcinogens using animal bioassay data. Also, biological assumption for various dose-response models were reviewed. To illustrate the process of risk estimate using relevant dose-response models such as Log-normal, Mantel-Bryan, Weibull and Multistage model, we used four animal carcinogenesis bioassy data of chloroform and chloroform concentrations of tap water measured in large cities of Korea from 1987 to 1995. As a result, in the case of using average concentration in exposure data and 95% upper boud unit risk of Multistge model, excess cancer risk(RISK I) was about $1.9\times10^{-6}$, in the case of using probability distribution of cumulative exposure data and unit risks, those risks(RISK II) which were simulated by Monte-Carlo analysis were about $2.4\times10^{-6}\;and\;7.9\times10^{-5}$ at 50 and 95 percentile, respectively. Therefore risk estimated by Monte-Carlo analysis using probability distribution of input variables may be more conservative.

Keywords