Comparison of anthropometric data quality in children aged 6-23 and 24-59 months: lessons from

Oleg Bilukha1, Alexia Couture1, Kelly McCain2

  • 1Emergency Response and Recovery Branch, Division of Global Health Protection, Center for Global Health, Centers for Disease Control, 1600 Clifton Road, Atlanta, GA, 30329, USA.

BMC Nutrition
|December 9, 2020
PubMed

Insights

Anthropometry data quality is often lower for younger children (6-23 months) than older children (24-59 months) in humanitarian settings. Improved training for measurers focusing on young children is crucial for accurate malnutrition assessments.

Area of Science:

  • Public Health
  • Nutrition Science
  • Global Health

Background:

  • Accurate anthropometry data is vital for assessing child malnutrition in humanitarian and refugee settings.
  • Previous studies indicated potential data quality differences between younger and older preschool children.

Purpose of the Study:

  • To compare anthropometric data quality between children aged 6-23 months and 24-59 months in humanitarian settings.
  • To identify factors influencing data quality for key anthropometric indicators.

Main Methods:

  • Analysis of 712 population-representative surveys (2011-2018) in humanitarian/refugee settings.
  • Comparison of five anthropometric indicators (weight-for-height, weight-for-age, height-for-age, BMI-for-age, MUAC-for-age) between age groups.
  • Evaluation of standard deviation, outlier percentage, normality, missing data, and digit preference.

Main Results:

  • Younger children (6-23 months) showed significantly higher standard deviations and outlier percentages for all indicators compared to older children (24-59 months).
  • Digit preference was higher for weight, height, and MUAC in younger children, and for age in older children.
  • Survey-level data quality was positively correlated between the two age groups, suggesting consistent measurement practices.

Conclusions:

  • Enhanced training for survey measurers is essential, with a specific focus on improving anthropometric measurements in young children.
  • Standardization tests for measurers should include a minimum number of children under two years old to ensure proficiency.
Abstract

Related Concept Videos

Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
25.3K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.8K
Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

Body:The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
79