Evaluation and prediction of individual growth trajectories
Background Conventional growth charts offer limited guidance to track individual growth. Aim To explore new approaches to improve the evaluation and prediction of individual growth trajectories. Subjects and methods We generalise the conditional SDS gain to multiple historical measurements, using th...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-01-01
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| Series: | Annals of Human Biology |
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| Online Access: | http://dx.doi.org/10.1080/03014460.2023.2190619 |
| Summary: | Background Conventional growth charts offer limited guidance to track individual growth. Aim To explore new approaches to improve the evaluation and prediction of individual growth trajectories. Subjects and methods We generalise the conditional SDS gain to multiple historical measurements, using the Cole correlation model to find correlations at exact ages, the sweep operator to find regression weights and a specified longitudinal reference. We explain the various steps of the methodology and validate and demonstrate the method using empirical data from the SMOCC study with 1985 children measured during ten visits at ages 0–2 years. Results The method performs according to statistical theory. We apply the method to estimate the referral rates for a given screening policy. We visualise the child’s trajectory as an adaptive growth chart featuring two new graphical elements: amplitude (for evaluation) and flag (for prediction). The relevant calculations take about 1 millisecond per child. Conclusion Longitudinal references capture the dynamic nature of child growth. The adaptive growth chart for individual monitoring works with exact ages, corrects for regression to the mean, has a known distribution at any pair of ages and is fast. We recommend the method for evaluating and predicting individual child growth. |
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| ISSN: | 0301-4460 1464-5033 |