PMID- 37286485
OWN - NLM
STAT- MEDLINE
DCOM- 20230609
LR  - 20230609
IS  - 1882-6482 (Electronic)
IS  - 0021-5082 (Linking)
VI  - 78
DP  - 2023
TI  - [New Methods of Evaluating Health Effects of Combined Exposures to Chemicals and 
      Their Problems to Be Solved].
LID - 10.1265/jjh.22009 [doi]
AB  - There are several basic prerequisites for the risk assessment of combined 
      exposures to pesticides and dioxins using human health effects as the endpoint. 
      First, all the target chemical substances exert the same toxicity to humans 
      through the same mechanisms. Second, there is a linear dose-response relationship 
      between the toxicity and effects of individual chemicals. With these two 
      prerequisites, the effects of combined exposures are estimated as the sum of the 
      toxicities of individual chemicals. For example, the toxicities of dioxins are 
      calculated using their toxic equivalent quantities (TEQ) by considering the 
      assigned toxic equivalent factor (TEF) of 2,3,7,8-tetrachlorodibenzo-p-dioxin 
      (2,3,7,8-TCDD) set individually from their isomers and homologs. In conventional 
      epidemiological studies, when the impact of each of multiple chemical substances 
      is examined, methods such as multiple regression analysis or using a generalized 
      linear model (GLM) have been used on the basis of the same prerequisites. 
      However, in practice, some of the chemicals exhibit collinearity in their effects 
      or do not show a linear dose-response relationship. In recent years, there have 
      been several methods developed in the field of machine learning being applied to 
      epidemiological research. Typical examples were methods using Bayesian kernel 
      machine regression (BKMR) and weighted quantile sum (WQS), and the shrinkage 
      method, i.e., using the least absolute shrinkage and selection operator (Lasso) 
      and elastic network model (ENM). In the future, while taking into account the 
      findings of experimental studies in biology, epidemiology, and other fields, it 
      is expected that various methods will be applied and selected.
FAU - Imai, Hideki
AU  - Imai H
AD  - Department of Health Sciences, Ishikawa Prefectural Nursing University.
FAU - Mizuno, Yuki
AU  - Mizuno Y
AD  - Department of Human Ecology, School of International Health, Graduate School of 
      Medicine, The University of Tokyo.
FAU - Aisyah, Cindy Rahman
AU  - Aisyah CR
AD  - Department of Human Ecology, School of International Health, Graduate School of 
      Medicine, The University of Tokyo.
FAU - Masuda, Momoka
AU  - Masuda M
AD  - Department of Human Ecology, School of International Health, Graduate School of 
      Medicine, The University of Tokyo.
FAU - Konishi, Shoko
AU  - Konishi S
AD  - Department of Human Ecology, School of International Health, Graduate School of 
      Medicine, The University of Tokyo.
LA  - jpn
PT  - English Abstract
PT  - Journal Article
PL  - Japan
TA  - Nihon Eiseigaku Zasshi
JT  - Nihon eiseigaku zasshi. Japanese journal of hygiene
JID - 0417457
RN  - 0 (Dioxins)
RN  - 0 (Polychlorinated Dibenzodioxins)
SB  - IM
MH  - Humans
MH  - *Dioxins
MH  - Bayes Theorem
MH  - *Polychlorinated Dibenzodioxins
MH  - Linear Models
OTO - NOTNLM
OT  - chemicals
OT  - combined exposures
OT  - epidemiology
OT  - health effects
OT  - statistical methods
EDAT- 2023/06/08 01:08
MHDA- 2023/06/09 06:41
CRDT- 2023/06/07 22:43
PHST- 2023/06/09 06:41 [medline]
PHST- 2023/06/08 01:08 [pubmed]
PHST- 2023/06/07 22:43 [entrez]
AID - 10.1265/jjh.22009 [doi]
PST - ppublish
SO  - Nihon Eiseigaku Zasshi. 2023;78. doi: 10.1265/jjh.22009.