title: Reliability of a decision-tree model in predicting occupational lead poisoning in a group of highly exposed workers.
abstract: This study aimed to provide the toxicological profile of some lead-exposed workers and obtain a predictive model for lead poisoning.
  
  
  
  Data regarding external and absorbed exposure were collected from 585 subjects employed in ten metallurgical production departments. Airborne lead concentration, blood lead level (BLL), cumulative blood lead index (CBLI), urine delta-aminolevulinic acid (DALA), age, workplace/section, exposure period, and whether reported lead poisoning as occupational disease were examined using ANOVA, and, post-ANOVA, Pearson correlation matrix, PCA (principal component analysis), decision-tree modeling, and logistic modeling.
  
  
  
  BLL was less sensitive than CBLI in predicting poisoning. Decision-tree modeling highlighted the importance of CBLI ≥1,041 µg.years/dl and air lead concentration ≥0.3 mg/m(3) in the occurrence of occupational poisoning. Age ≥48 years and DALA ≥19.3 mg/L were also factors.
  
  
  
  Workers were at risk of poisoning as a result of their long term unacceptable exposure. Decision-tree modeling is potentially useful for risk management. Am. J. Ind. Med. 59:575-582, 2016. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Faculty of Medicine, Lucian Blaga University of Sibiu, Sibiu, Romania.
authors.forename: Mihaela
authors.initials: M
authors.lastname: Stoia
authors.affiliation: Faculty of Food Technology and Biotechnology, University of Zagreb, Zagreb, Croatia.
authors.forename: Zelimir
authors.initials: Z
authors.lastname: Kurtanjek
authors.affiliation: Department of Agricultural Sciences and Food Engineering, Lucian Blaga University of Sibiu, Sibiu, Romania.
authors.forename: Simona
authors.initials: S
authors.lastname: Oancea
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D003663:Decision Trees; D006801:Humans; D007854:Lead / Q000032:analysis* / Q000097:blood; D007855:Lead Poisoning / Q000175:diagnosis / Q000209:etiology*; D016015:Logistic Models; D008669:Metallurgy; D009784:Occupational Diseases / Q000175:diagnosis / Q000209:etiology*; D016273:Occupational Exposure / Q000009:adverse effects / Q000032:analysis / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods
chemical_list: D007854:Lead
keywords: biomarker; cumulative exposure; metallurgy; occupational disease; prioritization