Researchers are a step closer to developing a prediction model for childhood obesity in New Zealand.

Childhood obesity is a serious public health challenge, and identifying high-risk individuals for early intervention is a priority.

Worldwide, an estimated 40.6 million children aged 5 years and under are overweight or have obesity. New Zealand is no exception, with 1 in 3 children already above a healthy weight by the time they start school. High body mass index (BMI) in infancy usually continues into childhood and adolescence, and from there into adulthood – making it crucial to identify those children at higher risk, to allow for an earlier and more targeted approach to preventative healthcare.

Internationally, several childhood obesity prediction models have been developed, but none of these caters to New Zealand’s ethnically-diverse population.

So, A Better Start researchers attempted to develop one.

Using data from the Growing Up in New Zealand study, they developed a statistical method to calculate the likelihood of obesity at 4-5 years old based on a combination of clinical and demographic factors – such as the mother’s pre-pregnancy BMI, maternal smoking during pregnancy, the father’s BMI, and infant weight gain.

One of the study’s lead authors, A Better Start Director Professor Wayne Cutfield, says the results of New Zealand’s childhood obesity prediction model (reported in a paper just released) are encouraging, but more work needs to be done to increase accuracy.

“Providing health professionals with a tool that enables accurate prediction of an infant’s risk of obesity could serve to increase the effectiveness of early childhood obesity interventions, through enabling timely intervention,” he says.

Importantly, though, any such model needs to be “accurate enough to warrant telling families what could be worrying information for them.”

Professor Cutfield says the performance of their childhood obesity prediction model is comparable with previous international ones, however, in the NZ study, infancy weight gain data were obtained using only two time points – birth and anywhere between 6-12 months.

“It is possible that data collected at more regular intervals would have improved our model’s discrimination.

“The observed high rates of false positives in our study mean that, in New Zealand’s multi-ethnic population, a large number of children would be incorrectly classified as at risk of childhood obesity,” says Professor Cutfield.

“There is a large social stigma associated with childhood obesity, and a high number of false positives (ie children being wrongly labelled as at risk of developing obesity) could create considerable unwarranted anxiety for many families.

“The overall Growing Up in New Zealand cohort was representative of New Zealand’s population, but the exclusion of participants with missing data (in particular missing parental data) meant that a greater proportion of children from lower socio-economic backgrounds and/or with mothers of Māori or Pacific ethnicity were excluded.”

While the prediction model is not ready to be used in clinical practice, it takes researchers a step closer to creating a tool that health professionals could use to help prevent the ongoing health and psychological impacts of obesity.

“Health professionals report they lack the knowledge required to identify obesity risk in young children with confidence. More work is needed to give them this valuable knowledge.”

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