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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11296

Title: Determinants of Serum PCBs in Adolescents and Adults: Regression Tree Analysis and Linear Regression Analysis
Authors: GOVARTS, Eva
Den Hond, Elly
Schoeters, Greet
BRUCKERS, Liesbeth
Issue Date: 2010
Publisher: TAYLOR & FRANCIS INC
Citation: HUMAN AND ECOLOGICAL RISK ASSESSMENT, 16(5). p. 1115-1132
Abstract: Regression tree analysis, a non-parametric method, was undertaken to identify predictors of the serum concentration of polychlorinated biphenyls (sum of marker PCB1 138, 153, and 180) in humans. This method was applied on biomonitoring data of the Flemish Environment and Health study (2002-2006) and included 1679 adolescents and 1583 adults. Potential predictor variables were collected via a self-administered questionnaire, assessing information on lifestyle, food intake, use of tobacco and alcohol, residence history, health, education, hobbies, and occupation. Relevant predictors of human PCB exposure were identified with regression tree analysis using ln-transformed sum of PCBs, separately in adolescents and adults. The obtained results were compared with those from a standard linear regression approach. The results of the non-parametric analysis confirm the selection of the covariates in the multiple regression models. In both analyses, blood fat, gender, age, body-mass index (BMI) or change in bodyweight, former breast-feeding, and a number of nutritional factors were identified as statistically significant predictors in the serum PCB concentration, either in adolescents, in adults or in both. Regression trees can be used as an explorative analysis in combination with multiple linear regression models, where relationships between the determinants and the biomarkers can be quantified.
Notes: [Govarts, Eva; Den Hond, Elly; Schoeters, Greet] Flemish Inst Technol Res VITO, B-2400 Mol, Belgium. [Schoeters, Greet] Univ Antwerp, Dept Biomed Sci, B-2020 Antwerp, Belgium. [Bruckers, Liesbeth] Univ Hasselt, Interuniv Inst Biostat & Stat Bioinformat, Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/11296
DOI: 10.1080/10807039.2010.512256
ISI #: 000282810500010
ISSN: 1080-7039
Category: A1
Type: Journal Contribution
Validation: ecoom, 2011
Appears in Collections: Research publications

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