PMID- 36705578
OWN - NLM
STAT- MEDLINE
DCOM- 20230208
LR  - 20240210
IS  - 1520-5851 (Electronic)
IS  - 0013-936X (Print)
IS  - 0013-936X (Linking)
VI  - 57
IP  - 5
DP  - 2023 Feb 7
TI  - Distinguishing Exposure to Secondhand and Thirdhand Tobacco Smoke among U.S. 
      Children Using Machine Learning: NHANES 2013-2016.
PG  - 2042-2053
LID - 10.1021/acs.est.2c08121 [doi]
AB  - While the thirdhand smoke (THS) residue from tobacco smoke has been recognized as 
      a distinct public health hazard, there are currently no gold standard biomarkers 
      to differentiate THS from secondhand smoke (SHS) exposure. This study used 
      machine learning algorithms to assess which combinations of biomarkers and 
      reported tobacco smoke exposure measures best differentiate children into three 
      groups: no/minimal tobacco smoke exposure (NEG); predominant THS exposure (TEG); 
      and mixed SHS and THS exposure (MEG). Participants were 4485 nonsmoking 
      3-17-year-olds from the National Health and Nutrition Examination Survey 
      2013-2016. We fitted and tested random forest models, and the majority (76%) of 
      children were classified in NEG, 16% were classified in TEG, and 8% were 
      classified in MEG. The final classification model based on reported exposure, 
      biomarker, and biomarker ratio variables had a prediction accuracy of 95%. This 
      final model had prediction accuracies of 100% for NEG, 88% for TEG, followed by 
      71% for MEG. The most important predictors were the reported number of household 
      smokers, serum cotinine, serum hydroxycotinine, and urinary 
      4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL). In the absence of validated 
      biomarkers specific to THS, comprehensive biomarker and questionnaire data for 
      tobacco smoke exposure can distinguish children exposed to SHS and THS with high 
      accuracy.
FAU - Merianos, Ashley L
AU  - Merianos AL
AUID- ORCID: 0000-0002-5640-7227
AD  - School of Human Services, University of Cincinnati, P.O. Box 210068, Cincinnati, 
      Ohio 45221, United States.
FAU - Mahabee-Gittens, E Melinda
AU  - Mahabee-Gittens EM
AUID- ORCID: 0000-0002-3893-885X
AD  - Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center, 
      University of Cincinnati College of Medicine, 3333 Burnet Avenue, Cincinnati, 
      Ohio 45229, United States.
FAU - Stone, Timothy M
AU  - Stone TM
AD  - Division of Biostatistics and Bioinformatics, Department of Environmental and 
      Public Health Sciences, College of Medicine, University of Cincinnati, Kettering 
      Lab Building, 160 Panzeca Way, Cincinnati, Ohio 45267-0056, United States.
FAU - Jandarov, Roman A
AU  - Jandarov RA
AD  - Division of Biostatistics and Bioinformatics, Department of Environmental and 
      Public Health Sciences, College of Medicine, University of Cincinnati, Kettering 
      Lab Building, 160 Panzeca Way, Cincinnati, Ohio 45267-0056, United States.
FAU - Wang, Lanqing
AU  - Wang L
AD  - Tobacco and Volatiles Branch, Division of Laboratory Sciences, National Center 
      for Environmental Health, Centers for Disease Control and Prevention, 4770 Buford 
      Hwy NE, Atlanta, Georgia 30341, United States.
FAU - Bhandari, Deepak
AU  - Bhandari D
AUID- ORCID: 0000-0001-6059-2739
AD  - Tobacco and Volatiles Branch, Division of Laboratory Sciences, National Center 
      for Environmental Health, Centers for Disease Control and Prevention, 4770 Buford 
      Hwy NE, Atlanta, Georgia 30341, United States.
FAU - Blount, Benjamin C
AU  - Blount BC
AD  - Tobacco and Volatiles Branch, Division of Laboratory Sciences, National Center 
      for Environmental Health, Centers for Disease Control and Prevention, 4770 Buford 
      Hwy NE, Atlanta, Georgia 30341, United States.
FAU - Matt, Georg E
AU  - Matt GE
AD  - Department of Psychology, College of Sciences, San Diego State University, 9245 
      Sky Park Court, Suite 225, San Diego, California 92123, United States.
LA  - eng
GR  - K01 DA044313/DA/NIDA NIH HHS/United States
GR  - R01 ES027815/ES/NIEHS NIH HHS/United States
GR  - R01 ES030743/ES/NIEHS NIH HHS/United States
GR  - R21 ES032161/ES/NIEHS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20230127
PL  - United States
TA  - Environ Sci Technol
JT  - Environmental science & technology
JID - 0213155
RN  - 0 (Tobacco Smoke Pollution)
RN  - K5161X06LL (Cotinine)
RN  - 0 (Biomarkers)
RN  - 8PJ61P6TS3 (1-Butanol)
SB  - IM
MH  - Humans
MH  - Child
MH  - *Tobacco Smoke Pollution/analysis
MH  - Nutrition Surveys
MH  - Cotinine
MH  - Biomarkers
MH  - 1-Butanol
MH  - Algorithms
MH  - Nicotiana/chemistry
PMC - PMC10230650
MID - NIHMS1894782
OTO - NOTNLM
OT  - National Health and Nutrition Examination Survey
OT  - biomarkers
OT  - child
OT  - machine learning
OT  - thirdhand smoke
OT  - tobacco smoke exposure
COIS- The authors declare no competing financial interest.
EDAT- 2023/01/28 06:00
MHDA- 2023/02/09 06:00
PMCR- 2024/02/07
CRDT- 2023/01/27 10:06
PHST- 2023/01/28 06:00 [pubmed]
PHST- 2023/02/09 06:00 [medline]
PHST- 2023/01/27 10:06 [entrez]
PHST- 2024/02/07 00:00 [pmc-release]
AID - 10.1021/acs.est.2c08121 [doi]
PST - ppublish
SO  - Environ Sci Technol. 2023 Feb 7;57(5):2042-2053. doi: 10.1021/acs.est.2c08121. 
      Epub 2023 Jan 27.