Use of Percentiles and Z-Scores in Anthropometry
Percentiles and Z-scores are often used to assess anthropometric measures to help evaluate children’s growth and nutritional status. In this chapter, we first compare the concepts and applications of percentiles and Z-scores and their strengths and limitations. Compared to percentiles, Z-scores have a number of advantages: first, they are calculated based on the distribution of the reference population (mean and standard deviation), and thus reflect the reference distribution; second, as standardized quantities, they are comparable across ages, sexes, and anthropometric measures; third, Z-scores can be analyzed as a continuous variable in studies. In addition, they can quantify extreme growth status at both ends of the distribution. However, Z-scores are not straightforward to explain to the public and are hard to use in clinical settings. In recent years, there has been growing support to the use of percentiles in some growth and obesity references. We also discuss the issues related to cut point selections and outline the fitting/smoothing techniques for developing reference curves. Finally, several important growth references and standards including the previous and new WHO growth reference/standards and the US 2000 CDC Growth Charts, are presented and compared. They have been developed based on different principles and data sets and have provided different cut points for the same anthropometric measures; they could, thus, provide different results. This chapter will guide readers to understand and use percentiles and Z-scores based on recent growth references and standards.
KeywordsGrowth Standard Growth Chart Growth Reference International Obesity Task Force Percentile Curve
Body mass index
Centers for Disease Control and Prevention
International Obesity Task Force
Height- or length-for-age Z-score
Multicentre Growth Reference Study
National Center for Health Statistics
National Health and Nutrition Examination Survey
World Health Organization
This work was supported in part by research grants from the NIH/NIDDK (R01DK81335-01A1, 1R03HD058077-01A1, R03HD058077-01A1S1) and the Nestle Foundation. We also thank Irwin Shorr for his comments on an earlier draft of this chapter.
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