A Prediction Model for Childhood Obesity in New Zealand

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aut.relation.articlenumber6380en_NZ
aut.relation.issue1en_NZ
aut.relation.journalScientific Reportsen_NZ
aut.relation.volume11en_NZ
aut.researcherDrabsch, Julie
dc.contributor.authorButler, ÉMen_NZ
dc.contributor.authorPillai, Aen_NZ
dc.contributor.authorMorton, SMBen_NZ
dc.contributor.authorSeers, BMen_NZ
dc.contributor.authorWalker, CGen_NZ
dc.contributor.authorLy, Ken_NZ
dc.contributor.authorTautolo, E-Sen_NZ
dc.contributor.authorGlover, Men_NZ
dc.contributor.authorTaylor, RWen_NZ
dc.contributor.authorCutfield, WSen_NZ
dc.contributor.authorDerraik, JGBen_NZ
dc.contributor.authorCOPABS Collaboratorsen_NZ
dc.date.accessioned2021-07-08T00:16:27Z
dc.date.available2021-07-08T00:16:27Z
dc.date.copyright2021en_NZ
dc.date.issued2021en_NZ
dc.description.abstractSeveral early childhood obesity prediction models have been developed, but none for New Zealand's diverse population. We aimed to develop and validate a model for predicting obesity in 4-5-year-old New Zealand children, using parental and infant data from the Growing Up in New Zealand (GUiNZ) cohort. Obesity was defined as body mass index (BMI) for age and sex ≥ 95th percentile. Data on GUiNZ children were used for derivation (n = 1731) and internal validation (n = 713). External validation was performed using data from the Prevention of Overweight in Infancy Study (POI, n = 383) and Pacific Islands Families Study (PIF, n = 135) cohorts. The final model included: birth weight, maternal smoking during pregnancy, maternal pre-pregnancy BMI, paternal BMI, and infant weight gain. Discrimination accuracy was adequate [AUROC = 0.74 (0.71-0.77)], remained so when validated internally [AUROC = 0.73 (0.68-0.78)] and externally on PIF [AUROC = 0.74 [0.66-0.82)] and POI [AUROC = 0.80 (0.71-0.90)]. Positive predictive values were variable but low across the risk threshold range (GUiNZ derivation 19-54%; GUiNZ validation 19-48%; and POI 8-24%), although more consistent in the PIF cohort (52-61%), all indicating high rates of false positives. Although this early childhood obesity prediction model could inform early obesity prevention, high rates of false positives might create unwarranted anxiety for families.en_NZ
dc.identifier.citationScientific Reports 11, 6380 (2021). https://doi.org/10.1038/s41598-021-85557-z
dc.identifier.doi10.1038/s41598-021-85557-zen_NZ
dc.identifier.issn2045-2322en_NZ
dc.identifier.issn2045-2322en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14355
dc.languageengen_NZ
dc.publisherNature Publishing Groupen_NZ
dc.relation.urihttps://www.nature.com/articles/s41598-021-85557-z
dc.rights© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.accessrightsOpenAccessen_NZ
dc.titleA Prediction Model for Childhood Obesity in New Zealanden_NZ
dc.typeJournal Article
pubs.elements-id399264
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Health & Environmental Science
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Public Health & Interdisciplinary Studies
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences/HY Public Health & Psychosocial Studies 2018 PBRF
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