The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

Predicting the risk of childhood overweight and obesity at 4-5 years using population-level pregnancy and early-life healthcare data

Predicting the risk of childhood overweight and obesity at 4-5 years using population-level pregnancy and early-life healthcare data
Predicting the risk of childhood overweight and obesity at 4-5 years using population-level pregnancy and early-life healthcare data
Background: nearly a third of children in the UK are overweight, with the prevalence in the most deprived areas more than twice that in the least deprived. The aim was to develop a risk identification model for childhood overweight/obesity applied during pregnancy and early life using routinely collected population-level healthcare data.

Methods: a population-based anonymised linked cohort of maternal antenatal records (January 2003 to September 2013) and birth/early-life data for their children with linked body mass index (BMI) measurements at 4-5 years (n=29060 children) in Hampshire, UK. Childhood age- and sex- adjusted BMI at 4-5 years, measured between September 2007 and November 2018, using a clinical cut-off of ≥91st centile for overweight/obesity. Logistic regression models together with multivariable fractional polynomials were used to select model predictors and to identify transformations of continuous predictors that best predict the outcome.

Results: fifteen percent of children had a BMI≥91st centile. Models were developed in stages, incorporating data collected at first antenatal booking appointment, later pregnancy/birth and early life predictors (1 and 2 years). The area under the curve (AUC) was lowest (0.64) for the model only incorporating maternal predictors from early pregnancy and highest for the model incorporating all factors up to weight at 2 years for predicting outcome at 4-5 years (0.83). The models were well calibrated. The prediction models identify 21% (at booking) to 24% (at ~2 years) of children as being at high risk of overweight or obese by the age of 4-5 years (as defined by a ≥20% risk score). Early pregnancy predictors included maternal BMI, smoking status, maternal age and ethnicity. Early life predictors included birthweight, baby’s sex and weight at 1 or 2 years of age.

Conclusions: although predictive ability was lower for the early pregnancy models, maternal predictors remained consistent across the models, thus high-risk groups could be identified at an early stage with more precise estimation as the child grows. A tool based on these models can be used to quantify clustering of risk for childhood obesity as early as the first trimester of pregnancy, and can strengthen the long-term preventive element of antenatal and early-years care.



Early life, Obesity, Overweight, Prediction, Pregnancy
1741-7015
Ziauddeen, Nida
3ad67dd8-26ba-498a-af0a-b1174298995b
Wilding, Sam
a026cae1-cc72-49b5-a52b-ec1d931d72e1
Roderick, Paul
dbb3cd11-4c51-4844-982b-0eb30ad5085a
Macklon, Nick
d08e4844-96cf-4333-aa84-aec9b8febb42
Smith, Dianna
e859097c-f9f5-4fd0-8b07-59218648e726
Chase, Debbie
d4f47e11-d0cd-4de7-afeb-9ebae1222fc9
Alwan, Nisreen
0d37b320-f325-4ed3-ba51-0fe2866d5382
Ziauddeen, Nida
3ad67dd8-26ba-498a-af0a-b1174298995b
Wilding, Sam
a026cae1-cc72-49b5-a52b-ec1d931d72e1
Roderick, Paul
dbb3cd11-4c51-4844-982b-0eb30ad5085a
Macklon, Nick
d08e4844-96cf-4333-aa84-aec9b8febb42
Smith, Dianna
e859097c-f9f5-4fd0-8b07-59218648e726
Chase, Debbie
d4f47e11-d0cd-4de7-afeb-9ebae1222fc9
Alwan, Nisreen
0d37b320-f325-4ed3-ba51-0fe2866d5382

Ziauddeen, Nida, Wilding, Sam, Roderick, Paul, Macklon, Nick, Smith, Dianna, Chase, Debbie and Alwan, Nisreen (2020) Predicting the risk of childhood overweight and obesity at 4-5 years using population-level pregnancy and early-life healthcare data. BMC Medicine, 18 (1), [105]. (doi:10.1186/s12916-020-01568-z).

Record type: Article

Abstract

Background: nearly a third of children in the UK are overweight, with the prevalence in the most deprived areas more than twice that in the least deprived. The aim was to develop a risk identification model for childhood overweight/obesity applied during pregnancy and early life using routinely collected population-level healthcare data.

Methods: a population-based anonymised linked cohort of maternal antenatal records (January 2003 to September 2013) and birth/early-life data for their children with linked body mass index (BMI) measurements at 4-5 years (n=29060 children) in Hampshire, UK. Childhood age- and sex- adjusted BMI at 4-5 years, measured between September 2007 and November 2018, using a clinical cut-off of ≥91st centile for overweight/obesity. Logistic regression models together with multivariable fractional polynomials were used to select model predictors and to identify transformations of continuous predictors that best predict the outcome.

Results: fifteen percent of children had a BMI≥91st centile. Models were developed in stages, incorporating data collected at first antenatal booking appointment, later pregnancy/birth and early life predictors (1 and 2 years). The area under the curve (AUC) was lowest (0.64) for the model only incorporating maternal predictors from early pregnancy and highest for the model incorporating all factors up to weight at 2 years for predicting outcome at 4-5 years (0.83). The models were well calibrated. The prediction models identify 21% (at booking) to 24% (at ~2 years) of children as being at high risk of overweight or obese by the age of 4-5 years (as defined by a ≥20% risk score). Early pregnancy predictors included maternal BMI, smoking status, maternal age and ethnicity. Early life predictors included birthweight, baby’s sex and weight at 1 or 2 years of age.

Conclusions: although predictive ability was lower for the early pregnancy models, maternal predictors remained consistent across the models, thus high-risk groups could be identified at an early stage with more precise estimation as the child grows. A tool based on these models can be used to quantify clustering of risk for childhood obesity as early as the first trimester of pregnancy, and can strengthen the long-term preventive element of antenatal and early-years care.



Text
Prediction_YrR_r2_v1_clean - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (151kB)

More information

Accepted/In Press date: 19 March 2020
e-pub ahead of print date: 11 May 2020
Published date: 11 May 2020
Keywords: Early life, Obesity, Overweight, Prediction, Pregnancy

Identifiers

Local EPrints ID: 439386
URI: http://eprints.soton.ac.uk/id/eprint/439386
ISSN: 1741-7015
PURE UUID: 83d15a7c-91fe-415e-8f99-b6faddb8a4f9
ORCID for Sam Wilding: ORCID iD orcid.org/0000-0003-4184-2821
ORCID for Paul Roderick: ORCID iD orcid.org/0000-0001-9475-6850
ORCID for Dianna Smith: ORCID iD orcid.org/0000-0002-0650-6606
ORCID for Nisreen Alwan: ORCID iD orcid.org/0000-0002-4134-8463

Catalogue record

Date deposited: 21 Apr 2020 16:30
Last modified: 10 Jan 2022 03:12

Export record

Altmetrics

Contributors

Author: Nida Ziauddeen
Author: Sam Wilding ORCID iD
Author: Paul Roderick ORCID iD
Author: Nick Macklon
Author: Dianna Smith ORCID iD
Author: Debbie Chase
Author: Nisreen Alwan ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×