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

Predicting the risk of childhood overweight and obesity at 4–5 years using pregnancy and early life healthcare data
Predicting the risk of childhood overweight and obesity at 4–5 years using pregnancy and early life healthcare data
Background: in England, 9.5% of children aged 4–5 years and 20.1% aged 10–11 years are obese, with the prevalence in the most deprived areas being more than twice as that in the least deprived. There is evidence illustrating the developmental origins of obesity, but it focuses on individual risk factors and comes mostly from research birth cohorts which are not necessarily representative of the wider population. There is no system-based early identification of childhood obesity risk at pregnancy stage and onwards.The aim was to develop and validate a risk identification system for childhood obesity using existing routinely collected maternal and early-life population-level healthcare data in Hampshire.

Methods: studying Lifecourse Obesity PrEdictors (SLOPE) study is an anonymised population-based linked cohort of maternal antenatal and delivery records for all births taking place at University Hospital Southampton 2003–2018, and child health records including information on postnatal growth, type of feeding and childhood body mass index (BMI) up to 14 years. Childhood age- and sex- adjusted BMI at 4–5 years was used to define the outcome of overweight and obesity in the models. 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. Predictive accuracy was evaluated by assessing model discrimination and calibration.

Results: childhood BMI was available for approximately 30000 children aged 4–5 years (9% obese). Models were developed in stages, incorporating data collected at first antenatal booking appointment, birth and early life predictors. The area under the curve (AUC) was lowest (0.64) for the model only incorporating maternal predictors from the booking appointment and highest for the model incorporating all factors up to weight at 2 years for predicting outcome at 4–5 years (0.82 for overweight and obesity and 0.89 for obesity excluding overweight). Maternal predictors included BMI, smoking status at first antenatal appointment, age and ethnicity. Early life predictors included birthweight, gender, breastfeeding and weight at 1 or 2 years of age. Although AUC was lower for the booking 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.

Conclusion: this prediction modelling can be used to identify and 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.
0143-005X
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
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
Alwan, Nisreen
0d37b320-f325-4ed3-ba51-0fe2866d5382

Ziauddeen, Nida, Wilding, Sam, Roderick, Paul, Macklon, Nick and Alwan, Nisreen (2019) Predicting the risk of childhood overweight and obesity at 4–5 years using pregnancy and early life healthcare data. Journal of Epidemiology & Community Health, 73, [OP38]. (doi:10.1136/jech-2019-SSMabstracts.38).

Record type: Meeting abstract

Abstract

Background: in England, 9.5% of children aged 4–5 years and 20.1% aged 10–11 years are obese, with the prevalence in the most deprived areas being more than twice as that in the least deprived. There is evidence illustrating the developmental origins of obesity, but it focuses on individual risk factors and comes mostly from research birth cohorts which are not necessarily representative of the wider population. There is no system-based early identification of childhood obesity risk at pregnancy stage and onwards.The aim was to develop and validate a risk identification system for childhood obesity using existing routinely collected maternal and early-life population-level healthcare data in Hampshire.

Methods: studying Lifecourse Obesity PrEdictors (SLOPE) study is an anonymised population-based linked cohort of maternal antenatal and delivery records for all births taking place at University Hospital Southampton 2003–2018, and child health records including information on postnatal growth, type of feeding and childhood body mass index (BMI) up to 14 years. Childhood age- and sex- adjusted BMI at 4–5 years was used to define the outcome of overweight and obesity in the models. 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. Predictive accuracy was evaluated by assessing model discrimination and calibration.

Results: childhood BMI was available for approximately 30000 children aged 4–5 years (9% obese). Models were developed in stages, incorporating data collected at first antenatal booking appointment, birth and early life predictors. The area under the curve (AUC) was lowest (0.64) for the model only incorporating maternal predictors from the booking appointment and highest for the model incorporating all factors up to weight at 2 years for predicting outcome at 4–5 years (0.82 for overweight and obesity and 0.89 for obesity excluding overweight). Maternal predictors included BMI, smoking status at first antenatal appointment, age and ethnicity. Early life predictors included birthweight, gender, breastfeeding and weight at 1 or 2 years of age. Although AUC was lower for the booking 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.

Conclusion: this prediction modelling can be used to identify and 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.

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Published date: 4 September 2019

Identifiers

Local EPrints ID: 434046
URI: http://eprints.soton.ac.uk/id/eprint/434046
ISSN: 0143-005X
PURE UUID: 330cd876-ff5e-47df-b389-59953fc2a5aa
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 Nisreen Alwan: ORCID iD orcid.org/0000-0002-4134-8463

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Date deposited: 11 Sep 2019 16:30
Last modified: 17 Mar 2024 02:41

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Contributors

Author: Nida Ziauddeen
Author: Sam Wilding ORCID iD
Author: Paul Roderick ORCID iD
Author: Nick Macklon
Author: Nisreen Alwan ORCID iD

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