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Potential for misclassification of infants' growth increments by using existing reference data
E G Piwoz1, J M Peerson, K H Brown
1Department of International Health, Johns Hopkins University, Baltimore.
Insights
Curve-fitting methods underestimate infant growth variance, leading to misclassification of normal infant growth trajectories. This impacts accurate assessment of pediatric growth and development.
Area of Science:
- Pediatric Growth Monitoring
- Biostatistics
- Infant Development
Background:
- Accurate assessment of infant growth is crucial for identifying potential health issues.
- Existing reference data and statistical methods are used to evaluate infant weight and length gains.
- Curve-fitting methods are commonly applied to growth data for prediction and analysis.
Purpose of the Study:
- To compare observed variances in infant growth with predicted variances from reference curve-fitting methods.
- To quantify the underestimation of variance by curve-fitting techniques.
- To assess the impact of underestimation on the misclassification of infant growth patterns.
Main Methods:
- Analysis of monthly weight and length gains in 96 Peruvian infants.
- Application of reference curve-fitting methods to Peruvian infant data.
- Comparison of observed variances with predicted variances from curve-fitting and interpolation.
Main Results:
- Predicted variance estimates were significantly lower than observed variances from 2 to 12 months of age (P < 0.0001).
- Underestimation of total variance ranged from 59% to 94% due to ignoring random infant deviations.
- Approximately 24-67% of infants were misclassified as abnormal gainers in evaluated intervals.
Conclusions:
- Standard curve-fitting methods significantly underestimate growth variance in infants.
- This underestimation leads to a high rate of misclassification, potentially misidentifying normal growth as abnormal.
- Refined statistical approaches are needed to accurately assess infant growth trajectories and avoid misclassification.
Abstract:
The observed variances in monthly weight and length gains of 96 Peruvian infants were compared with predicted variances obtained by applying the reference curve-fitting methods to the same Peruvian data. Predicted variance estimates were significantly less (P less than 0.0001) than the observed variances from 2 to 12 mo of age. The extent of underestimation in total variance that occurred when 1-mo growth-increment reference data were generated by curve-fitting and interpolation was approximated. This underestimation, caused by not taking into account infants' random deviations from their own growth trajectories, ranged from 59% to 94% and resulted in misclassification of approximately 24-67% of infants as abnormal gainers (below the 5th percentile or above the 95th percentile with respect to existing reference data) in the intervals evaluated.