Statistical assessment of reliability of anthropometric measurements in the multi-site South African National Dietary Intake Survey 2022

Abstract

Abstract Background Anthropometric data quality in large multicentre nutrition surveys is seldom adequately assessed. In preparation for the South African National Dietary Intake Survey (NDIS-2022), this study assessed site leads’ and fieldworkers’ intra- and inter-rater reliability for measuring weight, length/height, mid-upper arm circumference (MUAC), waist circumference (WC) and calf circumference (CC). Methods Standardised training materials and measurement protocols were developed, and new anthropometric equipment was procured. Following two training rounds (12 site lead teams, 46 fieldworker teams), measurement reliability was assessed for both groups, using repeated measurements of volunteers similar to the survey target population. Reliability was statistically assessed using the technical error of measurement (TEM), relative TEM (%TEM), intra-class correlation coefficient (ICC) and coefficient of reliability (R). Agreement was visualised with Bland-Altman analysis. Results By %TEM, the best reliability was achieved for weight (%TEM = 0.260–0.923) and length/height (%TEM = 0.434–0.855), and the poorest for MUAC by fieldworkers (%TEM = 2.592–3.199) and WC (%TEM = 2.353–2.945). Whole-sample ICC and R were excellent ( > 0.90) for all parameters except site leads’ CC inter-rater reliability (ICC = 0.896, R = 0.889) and fieldworkers’ inter-rater reliability for MUAC in children under two (ICC = 0.851, R = 0.881). Bland-Altman analysis revealed no significant bias except in fieldworkers’ intra-rater reliability of length/height measurement in adolescents/adults ( + 0.220 (0.042, 0.400) cm). Reliability was higher for site leads vs. fieldworkers, for intra-rater vs. inter-rater assessment, and for weight and length/height vs. circumference measurements. Conclusion NDIS-2022 site leads and fieldworkers displayed acceptable reliability in performing anthropometric measurements, highlighting the importance of intensive training and standardised measurement protocols. Ongoing reliability assessment during data collection is recommended.