Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Sep 28;18(9):e0292046.
doi: 10.1371/journal.pone.0292046. eCollection 2023.

Impact of anthropometry training and feasibility of 3D imaging on anthropometry data quality among children under five years in a postmortem setting

Priya M Gupta  1 , Kasthuri Sivalogan  1 , Richard Oliech  2 , Eugene Alexander  3 , Jamie Klein  4 , O Yaw Addo  1   5 , Dickson Gethi  6 , Victor Akelo  6 , Dianna M Blau  5 , Parminder S Suchdev  1   4   5
Affiliations

Impact of anthropometry training and feasibility of 3D imaging on anthropometry data quality among children under five years in a postmortem setting

Priya M Gupta et al. PLoS One. .

Abstract

Background: The Child Health and Mortality Prevention Surveillance Network (CHAMPS) identifies causes of under-5 mortality in high mortality countries.

Objective: To address challenges in postmortem nutritional assessment, we evaluated the impact of anthropometry training and the feasibility of 3D imaging on data quality within the CHAMPS Kenya site.

Design: Staff were trained using World Health Organization (WHO)-recommended manual anthropometry equipment and novel 3D imaging methods to collect postmortem measurements. Following training, 76 deceased children were measured in duplicate and were compared to measurements of 75 pre-training deceased children. Outcomes included measures of data quality (standard deviations of anthropometric indices and digit preference scores (DPS)), precision (absolute and relative technical errors of measurement, TEMs or rTEMs), and accuracy (Bland-Altman plots). WHO growth standards were used to produce anthropometric indices. Post-training surveys and in-depth interviews collected qualitative feedback on measurer experience with performing manual anthropometry and ease of using 3D imaging software.

Results: Manual anthropometry data quality improved after training, as indicated by DPS. Standard deviations of anthropometric indices exceeded limits for high data quality when using the WHO growth standards. Reliability of measurements post-training was high as indicated by rTEMs below 1.5%. 3D imaging was highly correlated with manual measurements; however, on average 3D scans overestimated length and head circumference by 1.61 cm and 2.27 cm, respectively. Site staff preferred manual anthropometry to 3D imaging, as the imaging technology required adequate lighting and additional considerations when performing the measurements.

Conclusions: Manual anthropometry was feasible and reliable postmortem in the presence of rigor mortis. 3D imaging may be an accurate alternative to manual anthropometry, but technology adjustments are needed to ensure accuracy and usability.

PubMed Disclaimer

Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: Eugene Alexander holds an ownership position in Body Surface Translations and therefore has a financial interest in the success of the 3D testing device described in this study. Data were blinded and not shared with Mr. Alexander until completion of draft manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Additional disclosure: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Figures

Fig 1
Fig 1. Bland Altman Plots comparing manual anthropometry and 3D imaging, Manyatta, Kenya.
Y-axis: the difference between the scan measurements and manual measurements; X-axis: the average of the scan and manual measures; Dotted lines: represent the mean difference ± 3 standard deviations; Dashed lines: represent the mean difference ± 2 SD; Solid line: across the plot is the no difference line. Black points on the chart represent the 23 cases for which we had viable 3D scan data. Spearman correlation coefficients were examined to measure the strength of the relationship between scans and manual measures. AC: Arm Circumference, HC: Head Circumference.
See this image and copyright information in PMC

References

    1. Children: improving survival and well-being. 2020; https://www.who.int/news-room/fact-sheets/detail/children-reducing-morta....
    1. Pelletier D.L., et al., The effects of malnutrition on child mortality in developing countries. Bull World Health Organ, 1995. 73(4): p. 443–8. - PMC - PubMed
    1. Rice A.L., et al., Malnutrition as an underlying cause of childhood deaths associated with infectious diseases in developing countries. Bull World Health Organ, 2000. 78(10): p. 1207–21. - PMC - PubMed
    1. Schroeder D.G. and Brown K.H., Nutritional status as a predictor of child survival: summarizing the association and quantifying its global impact. Bull World Health Organ, 1994. 72(4): p. 569–79. - PMC - PubMed
    1. Salzberg N.T., et al., Mortality Surveillance Methods to Identify and Characterize Deaths in Child Health and Mortality Prevention Surveillance Network Sites. Clin Infect Dis, 2019. 69(Suppl 4): p. S262–s273. doi: 10.1093/cid/ciz599 - DOI - PMC - PubMed