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Dynamic prediction model to identify young children at high risk of future overweight: Development and internal validation in a cohort study
Author(s) -
Welten Marieke,
Wijga Alet H.,
Hamoen Marleen,
Gehring Ulrike,
Koppelman Gerard H.,
Twisk Jos W.R.,
Raat Hein,
Heymans Martijn W.,
Kroon Marlou L.A.
Publication year - 2020
Publication title -
pediatric obesity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.226
H-Index - 69
eISSN - 2047-6310
pISSN - 2047-6302
DOI - 10.1111/ijpo.12647
Subject(s) - overweight , medicine , body mass index , demography , population , cohort , obesity , pediatrics , environmental health , sociology
Summary Background Primary prevention of overweight is to be preferred above secondary prevention, which has shown moderate effectiveness. Objective To develop and internally validate a dynamic prediction model to identify young children in the general population, applicable at every age between birth and age 6, at high risk of future overweight (age 8). Methods Data were used from the Prevention and Incidence of Asthma and Mite Allergy birth cohort, born in 1996 to 1997, in the Netherlands. Participants for whom data on the outcome overweight at age 8 and at least three body mass index SD scores (BMI SDS) at the age of ≥3 months and ≤6 years were available, were included (N = 2265). The outcome of the prediction model is overweight (yes/no) at age 8 (range 7.4‐10.5 years), defined according to the sex‐ and age‐specific BMI cut‐offs of the International Obesity Task Force. Results After backward selection in a Generalized Estimating Equations analysis, the prediction model included the baseline predictors maternal BMI, paternal BMI, paternal education, birthweight, sex, ethnicity and indoor smoke exposure; and the longitudinal predictors BMI SDS, and the linear and quadratic terms of the growth curve describing a child's BMI SDS development over time, as well as the longitudinal predictors' interactions with age. The area under the curve of the model after internal validation was 0.845 and Nagelkerke R 2 was 0.351. Conclusions A dynamic prediction model for overweight was developed with a good predictive ability using easily obtainable predictor information. External validation is needed to confirm that the model has potential for use in practice.