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Updated: Mar 24, 2026
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Predicting future weight status from measurements made in early childhood: a novel longitudinal approach applied to
E Mead1, A M Batterham1, G Atkinson1
1Health and Social Care Institute, Teesside University, Middlesbrough, UK.
Insights
Childhood obesity tracking into adulthood is concerning. A novel ordinal logistic regression model predicts weight status changes, offering better insights into childhood obesity risks and transitions.
Area of Science:
- Public Health
- Pediatrics
- Biostatistics
Background:
- Childhood obesity is a growing concern with implications for long-term health.
- Traditional tracking statistics may not fully capture individual risk or be easily applicable.
- A novel analytical method is needed to better predict weight status transitions.
Purpose of the Study:
- To utilize ordinal logistic regression to predict the weight status of 11-year-old children based on measurements at age 5.
- To quantify the probability of children transitioning between weight categories (underweight, normal, overweight, obese).
Main Methods:
- Utilized UK 1990 growth references for weight status categorization.
- Analyzed data from 12,076 children in the Millennium Cohort Study.
- Employed ordinal logistic regression to derive predicted probabilities of weight status at age 11 from age 5 status.
Main Results:
- Overweight 5-year-olds had a 32.3% chance of being obese at age 11; obese 5-year-olds had a 68.1% chance of remaining obese.
- Severely obese 5-year-olds had a 50.3% chance of remaining severely obese at age 11.
- Deprivation status influenced obesity tracking differently in boys versus girls.
Conclusions:
- Ordinal logistic regression provides an interpretable method for predicting children's weight status changes.
- This approach can effectively quantify the likelihood of transitioning into or out of unhealthy weight categories.
- The method is adaptable for use with various longitudinal datasets featuring ordinal outcomes.
Background/Objective:
There are reports that childhood obesity tracks into later life. Nevertheless, some tracking statistics such as correlations do not quantify individual agreement, whereas others such as diagnostic test statistics can be difficult to translate into practice. We aimed to employ a novel analytic approach, based on ordinal logistic regression, to predict weight status of 11-year-old children from measurements at age 5 years.
Subjects/Methods:
The UK 1990 growth references were used to generate clinical weight status categories of 12 076 children enrolled in the Millennium Cohort Study. Using ordinal regression, we derived the predicted probability (percent chances) of 11-year-old children becoming underweight, normal weight, overweight, obese and severely obese from their weight status category at age 5 years.
Results:
The chances of becoming obese (including severely obese) at age 11 years were 5.7% (95% confidence interval: 5.2 to 6.2%) for a normal-weight 5-year-old child and 32.3% (29.8 to 34.8%) for an overweight 5-year-old child. An obese 5-year-old child had a 68.1% (63.8 to 72.5%) chance of remaining obese at 11 years. Severely obese 5-year-old children had a 50.3% (43.1 to 57.4%) chance of remaining severely obese. There were no substantial differences between sexes. Nondeprived obese 5-year-old boys had a lower probability of remaining obese than deprived obese boys: -21.8% (-40.4 to -3.2%). This association was not observed in obese 5-year-old girls, in whom the nondeprived group had a probability of remaining obese 7% higher (-15.2 to 29.2%). The sex difference in this interaction of deprivation and baseline weight status was therefore -28.8% (-59.3 to 1.6%).
Conclusions:
We have demonstrated that ordinal logistic regression can be an informative approach to predict the chances of a child changing to, or from, an unhealthy weight status. This approach is easy to interpret and could be applied to any longitudinal data set with an ordinal outcome.
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