A prediction model for childhood obesity in New Zealand

Abstract Several 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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Éadaoin M. Butler, Avinesh Pillai, Susan M. B. Morton, Blake M. Seers, Caroline G. Walker, Kien Ly, El-Shadan Tautolo, Marewa Glover, Rachael W. Taylor, Wayne S. Cutfield, José G. B. Derraik, COPABS Collaborators
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2b2e43ec0dd945f7902cb27db5688c81
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2b2e43ec0dd945f7902cb27db5688c81
record_format	dspace
spelling	oai:doaj.org-article:2b2e43ec0dd945f7902cb27db5688c812021-12-02T17:05:46ZA prediction model for childhood obesity in New Zealand10.1038/s41598-021-85557-z2045-2322https://doaj.org/article/2b2e43ec0dd945f7902cb27db5688c812021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85557-zhttps://doaj.org/toc/2045-2322Abstract Several 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.Éadaoin M. ButlerAvinesh PillaiSusan M. B. MortonBlake M. SeersCaroline G. WalkerKien LyEl-Shadan TautoloMarewa GloverRachael W. TaylorWayne S. CutfieldJosé G. B. DerraikCOPABS CollaboratorsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Éadaoin M. Butler Avinesh Pillai Susan M. B. Morton Blake M. Seers Caroline G. Walker Kien Ly El-Shadan Tautolo Marewa Glover Rachael W. Taylor Wayne S. Cutfield José G. B. Derraik COPABS Collaborators A prediction model for childhood obesity in New Zealand
description	Abstract Several 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.
format	article
author	Éadaoin M. Butler Avinesh Pillai Susan M. B. Morton Blake M. Seers Caroline G. Walker Kien Ly El-Shadan Tautolo Marewa Glover Rachael W. Taylor Wayne S. Cutfield José G. B. Derraik COPABS Collaborators
author_facet	Éadaoin M. Butler Avinesh Pillai Susan M. B. Morton Blake M. Seers Caroline G. Walker Kien Ly El-Shadan Tautolo Marewa Glover Rachael W. Taylor Wayne S. Cutfield José G. B. Derraik COPABS Collaborators
author_sort	Éadaoin M. Butler
title	A prediction model for childhood obesity in New Zealand
title_short	A prediction model for childhood obesity in New Zealand
title_full	A prediction model for childhood obesity in New Zealand
title_fullStr	A prediction model for childhood obesity in New Zealand
title_full_unstemmed	A prediction model for childhood obesity in New Zealand
title_sort	prediction model for childhood obesity in new zealand
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2b2e43ec0dd945f7902cb27db5688c81
work_keys_str_mv	AT eadaoinmbutler apredictionmodelforchildhoodobesityinnewzealand AT avineshpillai apredictionmodelforchildhoodobesityinnewzealand AT susanmbmorton apredictionmodelforchildhoodobesityinnewzealand AT blakemseers apredictionmodelforchildhoodobesityinnewzealand AT carolinegwalker apredictionmodelforchildhoodobesityinnewzealand AT kienly apredictionmodelforchildhoodobesityinnewzealand AT elshadantautolo apredictionmodelforchildhoodobesityinnewzealand AT marewaglover apredictionmodelforchildhoodobesityinnewzealand AT rachaelwtaylor apredictionmodelforchildhoodobesityinnewzealand AT waynescutfield apredictionmodelforchildhoodobesityinnewzealand AT josegbderraik apredictionmodelforchildhoodobesityinnewzealand AT copabscollaborators apredictionmodelforchildhoodobesityinnewzealand AT eadaoinmbutler predictionmodelforchildhoodobesityinnewzealand AT avineshpillai predictionmodelforchildhoodobesityinnewzealand AT susanmbmorton predictionmodelforchildhoodobesityinnewzealand AT blakemseers predictionmodelforchildhoodobesityinnewzealand AT carolinegwalker predictionmodelforchildhoodobesityinnewzealand AT kienly predictionmodelforchildhoodobesityinnewzealand AT elshadantautolo predictionmodelforchildhoodobesityinnewzealand AT marewaglover predictionmodelforchildhoodobesityinnewzealand AT rachaelwtaylor predictionmodelforchildhoodobesityinnewzealand AT waynescutfield predictionmodelforchildhoodobesityinnewzealand AT josegbderraik predictionmodelforchildhoodobesityinnewzealand AT copabscollaborators predictionmodelforchildhoodobesityinnewzealand
_version_	1718381781260959744

A prediction model for childhood obesity in New Zealand

Ejemplares similares