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Child 3D anthropometry evaluation datasets

Citation

Bougma, Karim et al. (2022), Child 3D anthropometry evaluation datasets, Dryad, Dataset, https://doi.org/10.5061/dryad.fbg79cnxc

Abstract

Background: An efficacy evaluation of the AutoAnthro system to measure child (0–59 months) anthropometry in USA found three-dimensional imaging performed as well as gold-standard manual measurements for biological plausibility and precision.

Objectives: Conduct an effectiveness evaluation of the accuracy of the AutoAnthro system to measure 0–59 months child anthropometry in population-based surveys and surveillance systems in households in Guatemala and Kenya, and in hospitals in China.

Methods: The evaluation was done using health or nutrition surveillance system platforms among 600 children 0–59 months (Guatemala, Kenya) and 300 children 0-23 months (China).  Field team anthropometrists and their assistants collected from each child manual and scan anthropometric measurements including length/height, mid-upper arm circumference (MUAC), and head circumference (HC, China only).  An anthropometry expert and assistant later collected both manual and scan anthropometric measurements on the same child.  The expert manual measurements were considered the standard compared to field team scans.

Results: Overall, in Guatemala, Kenya and China, respectively, for inter-rater accuracy, average bias for length/height was -0.3 cm, -1.9 cm, -6.2 cm; for MUAC was 0.9 cm, 1.2 cm, -0.8 cm; for HC was 2.4 cm; the inter-technical error of measurement (TEM) for length/height was 2.8 cm, 3.4 cm, 5.5 cm; for MUAC was 1.1 cm, 1.5 cm, 1.0 cm; for HC was 2.8 cm.  For intra-rater precision, absolute mean difference and intra-TEM were 0.1 cm for all countries for all manual measurements. For scan, overall, absolute mean difference ranged for length/height 0.4-0.6 cm; MUAC 0.1-0.1 cm; HC was 0.4 cm. For intra-TEM, length/height was 0.5 cm in Guatemala and China, 0.7 cm in Kenya, and other measurements were <0.3 cm.

Conclusions: Understanding the factors that cause the many poor scan results and how to correct them will be needed prior to using this instrument in routine population-based survey and surveillance systems.

Methods

Details of the methods are provided in the article.

Usage Notes

A detailled codebook is included in the dataset.

Funding

Bill and Melinda Gates Foundation, Award: OPP1179307