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Minerva Pediatrics 2021 Apr 12

DOI: 10.23736/S2724-5276.21.06042-0


lingua: Inglese

Discriminant analysis and logistic regression on genetic history and environmental factors in children with asthma

Mohamed BRAHIM ERRAHMANI 1, 2 , Mériem AICHI 2, Mahdia MENAA 2

1 Department of Chemistry, Faculty of Science, Blida 1 University, Blida, Algeria; 2 Department of Cellular Biology, Faculty of Biological Sciences, Blida 1 University, Blida, Algeria


BACKGROUND: Asthma is known to be related to genetic and environmental factors, we aimed to identify the predictors discriminating between children with asthma and a control group in order to build typical profiles of these children.
METHODS: A multidimensional analysis covered children (58 with asthma and 217 as control group), under 17 years of age, involving environmental variables and medical history of these children and their families.
RESULTS: Chi-square tests highlighted significant links between variables as rhinitis and conjunctivitis (p<0.001). The results showed, in group of asthmatic children, significant high frequencies of allergies, mainly seasonal (p<0.001), rhinitis, family history more present in mothers (p=0.002) and in maternal aunts and uncles (p<0.02). Allergies were mostly present in mothers of asthmatic children (p=0.03). Children whose father, mother or both had asthma were significantly more numerous in asthmatic group (p=0.0007). A multiple correspondence analysis (MCA) identified two typical profiles of children, a first group of asthmatic children with positive modalities of family history, medical and environmental factors, a second, the control group), with essentially negative modalities of the variables. Logistic regression (LR) resulted in a final model which retained four significant predictors, rhinitis (p=0.01), atopic dermatitis (p=0.04), mother antecedents (p=0.03) and paternal uncle antecedents (p=0.008) with a globally appreciable predictive value (82%) of the Hosmer-Lemeshow (HL) test.
CONCLUSIONS: These results allowed the drafting of a typical profile quantifying through a function of a few predictors, the variation of the probability for a child to develop an asthma.

KEY WORDS: Allergies; Asthma; Logistic regression; Medical background; Multiple correspondence analysis; Rhinitis

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