Identifying a risk score for childhood obesity based on predictors identified          in pregnant women and 1-year-old infants: An analysis of the data of the Hokkaido Study on          Environment and Children’s Health

Yasuaki Saijo; Yoshiya Ito; Eiji Yoshioka; Yukihiro Sato; Machiko Minatoya; Atsuko Araki; Chihiro Miyashita; Reiko Kishi

doi:10.1297/cpe.28.81

Original Article

Identifying a risk score for childhood obesity based on predictors identified in pregnant women and 1-year-old infants: An analysis of the data of the Hokkaido Study on Environment and Children’s Health

Yasuaki Saijo, Yoshiya Ito, Eiji Yoshioka, Yukihiro Sato, Machiko Minatoya, Atsuko Araki, Chihiro Miyashita, Reiko Kishi

Author information

Yasuaki Saijo
Department of Social Medicine, Asahikawa Medical University, Sapporo, Japan
Yoshiya Ito
Faculty of Nursing, Japanese Red Cross Hokkaido College of Nursing, Sapporo, Japan
Eiji Yoshioka
Department of Social Medicine, Asahikawa Medical University, Sapporo, Japan
Yukihiro Sato
Department of Social Medicine, Asahikawa Medical University, Sapporo, Japan
Machiko Minatoya
Center for Environmental and Health Sciences, Hokkaido University, Sapporo, Japan
Faculty of Health Sciences, Hokkaido University, Sapporo, Japan
Atsuko Araki
Center for Environmental and Health Sciences, Hokkaido University, Sapporo, Japan
Chihiro Miyashita
Center for Environmental and Health Sciences, Hokkaido University, Sapporo, Japan
Reiko Kishi
Center for Environmental and Health Sciences, Hokkaido University, Sapporo, Japan

Keywords: childhood, obesity, risk score, pregnancy, body mass index

JOURNAL OPEN ACCESS
Supplementary material

2019 Volume 28 Issue 3 Pages 81-89

DOI https://doi.org/10.1297/cpe.28.81

Details

Abstract

This study aimed to construct a childhood obesity risk index based on predictors identified in pregnant women and 1-yr-old infants. The primary outcome was an identified obesity index of > 20% at 6–8 yr of age. Of a total sample size of 6,846 mother-child pairs, 80% and 20% were randomly allocated to the derivation and validation cohorts, respectively. For the derivation cohort, univariate and multivariate logistic regression analyses of data were conducted to identify the final predictors to determine the childhood obesity risk score algorithm. These included pre-pregnancy body mass index (BMI), child’s gender, smoking during pregnancy, education, and obesity index at one yr of age. The β coefficients for categories of predictor variables were each divided by the smallest value among them. The quotient was rounded off to the integer and assigned to the risk score, and a value of zero was assigned to reference categories. A total risk score was calculated for each individual. A cutoff point ≥ 16 had 22.2% and 21.8% positive predictive values in the derivation and validation cohorts, respectively. In conclusion, the childhood obesity risk score algorithm was constructed based on generic predictors that can be easily obtained from maternal and child health handbooks.

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