Related Experiment Video
Updated: Feb 18, 2026

Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Misclassification of child body mass index from cut-points defined by rounded percentiles instead of Z-scores
Laura N Anderson1,2, Sarah Carsley3,4, Gerald Lebovic5,4
1Department of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, ON, L8N 3Z5, Canada. LN.Anderson@mcmaster.ca.
Insights
Using rounded percentiles instead of Z-scores for body mass index (BMI) misclassifies child growth. This impacts accurate identification of children who are wasted or obese, affecting study comparability.
Area of Science:
- Pediatric Nutrition
- Growth Monitoring
- Biostatistics
Background:
- Accurate assessment of child growth is crucial for identifying nutritional status.
- Body Mass Index (BMI) is a common metric, but its interpretation in early childhood requires careful consideration of standardized references.
- The use of rounded percentiles versus Z-scores for BMI cut-points can lead to discrepancies in classification.
Purpose of the Study:
- To evaluate the extent of misclassification in early childhood growth assessment.
- To compare the diagnostic accuracy of BMI cut-points defined by rounded percentiles versus Z-scores.
- To highlight the implications of using different classification methods on study comparability.
Main Methods:
- A cross-sectional study was conducted using data from 5836 children under 6 years old.
- Body Mass Index for Age Z-scores were calculated using World Health Organization growth standards.
- Misclassification rates were determined by comparing BMI categories derived from rounded percentiles against those derived from Z-score cut-points.
Main Results:
- The use of rounded percentiles resulted in different proportions of children classified as wasted, at risk of overweight, overweight, and obese compared to Z-scores.
- Overall, 2% of children were misclassified, with significant misclassification rates observed for wasted (13%) and obese (14%) categories.
- Discrepancies in classification were noted, particularly at the extremes of the BMI distribution.
Conclusions:
- The use of rounded percentiles for BMI cut-points in early childhood leads to significant misclassification of growth status.
- This misclassification can hinder accurate identification of children's nutritional status and limit the comparability of research findings.
- Standardized use of Z-scores is recommended for consistent and accurate child growth assessment.
Objective:
To evaluate the misclassification resulting from the use of body mass index (BMI) cut-points defined by rounded percentiles instead of Z-scores in early childhood. Using data from the TARGet Kids primary care network we conducted a cross-sectional study among 5836 children < 6 years of age. The World Health Organization growth standards were used to calculate BMI-for-age Z-scores. BMI Z-score cut-points of < - 2.0, > 1.0, > 2.0, > 3.0 are recommended to define wasted, at risk of overweight, overweight and obese. However, rounded percentiles of the 3rd, 85th, 97th, and 99.9th are commonly used. Misclassification was calculated comparing the frequency distributions for BMI categories defined by rounded percentiles and Z-score cut-points.
Results:
Using rounded percentiles, the proportion of children who were wasted, at risk of overweight, overweight, and obese was 4.2, 12.5, 4.3 and 0.8%, whereas the distribution using Z-scores was: 3.6, 13.8, 3.4 and 1.0%, respectively. Overall, 117 (2%) children were misclassified when using percentiles instead of Z-scores; however, 13% (33/245) of children who were wasted and 14% (8/57) of children who were obese were misclassified. Misclassification of child growth results from the use of cut-points defined by rounded percentiles instead of Z-scores and limits comparability between studies. Trial registration Clinicaltrials.gov NCT01869530 June 5, 2013.
Related Concept Videos
z Scores and Area Under the Curve
Introduction to z Scores
z scores...
Introduction to z Scores
z scores...
z Scores and Unusual Values
This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data...
Percentile
Obesity

