Growth charts compared

Ekhard E Ziegler1, Steven E Nelson

  • 1Fomon Infant Nutrition Unit, Department of Pediatrics, University of Iowa, Iowa City, IA, USA.

Insights

New World Health Organization (WHO) growth charts serve as global standards for optimal child development. Differences between WHO standards and traditional growth references are substantial when assessing groups, but less so for individuals.

Area of Science:

  • Pediatric endocrinology and growth monitoring
  • Public health and child development assessment

Background:

  • Child growth assessment relies on comparing measurements to normative references, typically growth charts.
  • Traditional growth charts (references) are geographically specific, reflecting 'normal' growth in a defined area.
  • The World Health Organization (WHO) has developed new growth charts as global standards.

Purpose of the Study:

  • To compare existing national growth references with new multinational growth standards.
  • To investigate the reasons for differences between various growth charts.
  • To assess the impact of these differences on individual and group child health assessments.

Main Methods:

  • Comparative analysis of five different growth charts: UK, Netherlands, USA (national references), Euro-Growth (multinational reference), and WHO (multinational standard).
  • Examination of methodological differences, including prescriptive approaches and data truncation.
  • Evaluation of the clinical significance of chart discrepancies in different assessment contexts.

Main Results:

  • Significant, largely unexplained differences exist between the WHO growth standard and traditional national growth references.
  • Differences are partially attributable to the WHO's prescriptive approach and data handling methods.
  • Discrepancies have minimal impact on monitoring individual child growth but are substantial for group health assessments.

Conclusions:

  • The new WHO growth charts represent a global standard for optimal child growth under ideal conditions.
  • While differences between growth charts may be trivial for individual monitoring, they significantly affect population-level health assessments.
  • Further research is needed to fully understand the unexplained variations between different growth assessment tools.

Related Concept Videos

Bar Graph01:07

Bar Graph

A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
Relative Frequency Histogram01:14

Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
Multiple Bar Graph01:07

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Ogive Graph01:07

Ogive Graph

An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this type...