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Prediction Models for Early Childhood Obesity: Applicability and Existing Issues
Author(s) -
Éadaoin M. Butler,
José G. B. Derraik,
Rachael W. Taylor,
Wayne S. Cutfield
Publication year - 2018
Publication title -
hormone research in paediatrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.816
H-Index - 89
eISSN - 1663-2826
pISSN - 1663-2818
DOI - 10.1159/000496563
Subject(s) - childhood obesity , predictive modelling , psychological intervention , medicine , overweight , set (abstract data type) , obesity , intervention (counseling) , computer science , machine learning , pathology , psychiatry , programming language
Statistical models have been developed for the prediction or diagnosis of a wide range of outcomes. However, to our knowledge, only 7 published studies have reported models to specifically predict overweight and/or obesity in early childhood. These models were developed using known risk factors and vary greatly in terms of their discrimination and predictive capacities. There are currently no established guidelines on what constitutes an acceptable level of risk (i.e., risk threshold) for childhood obesity prediction models, but these should be set following consideration of the consequences of false-positive and false-negative predictions, as well as any relevant clinical guidelines. To date, no studies have examined the impact of using early childhood obesity prediction models as intervention tools. While these are potentially valuable to inform targeted interventions, the heterogeneity of the existing models and the lack of consensus on adequate thresholds limit their usefulness in practice.

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