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Updated: Nov 26, 2025

Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
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.
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.
Background:
Ensuring the quality of anthropometry data is paramount for getting accurate estimates of malnutrition prevalence among children aged 6-59 months in humanitarian and refugee settings. Previous reports based on data from Demographic and Health Surveys suggested systematic differences in anthropometric data quality between the younger and older groups of preschool children.
Methods:
We analyzed 712 anthropometric population-representative field surveys from humanitarian and refugee settings conducted during 2011-2018. We examined and compared the quality of five anthropometric indicators in children aged 6-23 months and children aged 24-59 months: weight for height, weight for age, height for age, body mass index for age and mid-upper arm circumference (MUAC) for age. Using the z-score distribution of each indicator, we calculated the following parameters: standard deviation (SD), percentage of outliers, and measures of distribution normality. We also examined and compared the quality of height, weight, MUAC and age measurements using missing data and rounding criteria.
Results:
Both SD and percentage of flags were significantly smaller on average in older than in younger age group for all five anthropometric indicators. Differences in SD between age groups did not change meaningfully depending on overall survey quality or on the quality of age ascertainment. Over 50% of surveys overall did not deviate significantly from normality. The percentage of non-normal surveys was higher in older than in the younger age groups. Digit preference score for weight, height and MUAC was slightly higher in younger age group, and for age slightly higher in the older age group. Children with reported exact date of birth (DOB) had much lower digit preference for age than those without exact DOB. SD, percentage flags and digit preference scores were positively correlated between the two age groups at the survey level, such as those surveys showing higher anthropometry data quality in younger age group also tended to show higher quality in older age group.
Conclusions:
There should be an emphasis on increased rigor of training survey measurers in taking anthropometric measurements in the youngest children. Standardization test, a mandatory component of the pre-survey measurer training and evaluation, of 10 children should include at least 4-5 children below 2 years of age.
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