Publications Summary

Document Type
Working Papers
Publication Topic(s)
Anthropometry/Biomarkers, Nutrition
Guatemala, Nepal, Ethiopia, Chad, Egypt, Nigeria
Recommended Citation
Allen, Courtney K., Trevor N. Croft, Thomas W. Pullum, and Sorrel M. L. Namaste. Evaluation of Indicators to Monitor Quality of Anthropometry Data during Fieldwork. DHS Working Paper No. 162. Rockville, Maryland, USA: ICF.
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Publication Date
September 2019
Publication ID


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Background: High-quality data on stunting (height-for-age (HAZ) < -2SD) and wasting (weight-for-height (WHZ) < -2SD) are critical to inform country and global decision-making on nutrition policy and programming. Yet, anthropometry assessment in field settings remains a challenge, and tools are needed to improve data collection. We sought to evaluate the capability of anthropometric data quality indicators to assess survey teams’ performance during fieldwork. Methods: A total of 26 data quality indicators were identified for height, weight, and date of birth data. Two target levels for each indicator were established by taking the lowest 25th and median of 147 Demographic and Health Survey (DHS) surveys. We applied the indicators to six recent DHS surveys. Data quality indicators were summarized, and Pearson’s correlation coefficients were calculated between teams using cumulative data across the survey period. Patterns of performance over time were examined by calculating the quarterly HAZ implausible values and standard deviations by team. Principal component factor analysis (PCA) was used to generate a composite anthropometry data quality score in order to rank each team’s performance. Results: Thirteen of the 26 data quality indicators were retained, with those related to HAZ and WHZ z-scores being particularly useful. There was a wide range in the teams’ implausible HAZ and WHZ anthropometry z- scores (HAZ: range 0-17%, WHZ: 0-19%) and standard deviations (HAZ: range 0.97-2.54, WHZ: 0.94-2.35) across surveys. These indicators also tended to contribute the greatest to the PCA factor loading for the PCA using height and weight indicators in the four lowest-performing surveys (ranging from 0.28 to 0.49 for HAZ implausible, 0.35 to 0.47 for WHZ implausible, 0.25 to 0.43 for HAZ SD, and 0.13 to 0.37 for WHZ SD). The HAZ interval, used to capture date of birth quality, showed some teams achieving the expected near-zero HAZ z-score, but reached as high as a z-score of 1.86. There were inconsistent factor loadings across surveys for the PCA that used date of birth indicators. The targets we constructed based on the median performance of 147 surveys were 0.7% for HAZ implausible, 1.6%, for WHZ implausible, 1.59 for HAZ SD, 1.30 for WHZ SD, and 0.25 for HAZ interval. There was no clear pattern in improvements or degeneration as fieldwork progressed based on HAZ implausible values and HAZ SD. Conclusion: In the present study, we found anthropometry data quality indicators can be used to detect poorly performing teams during fieldwork. Further research should link data quality indicators to the outcomes of interest (stunting and wasting) to enhance monitoring practices.


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