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Background Portable systems using three-dimensional (3D) scan data to calculate young child anthropometry measurements in population-based surveys and surveillance systems lack acceptability data from field workers and caregivers. Objective : Assess acceptability and experiences with 3D scans measuring child aged 0-59 months anthropometry in population-based surveys and surveillance systems in Guatemala, Kenya, and China (0-23 months only) among field teams and caregivers of young children as secondary objectives of an external effectiveness evaluation. Methods Manual data were collected twice and 12 images captured per child by anthropometrist/expert and assistant (AEA) field teams (individuals/country, N = 15/Guatemala, N = 8/Kenya, N = 6/China). Caregivers were interviewed after observing their child's manual and scan data collection. Mixed methods included an administered caregiver interview (Guatemala N = 465, Kenya N = 496, China N = 297) and self-administered AEA questionnaire both with closed- and open-ended questions, and 6 field team focus group discussions (FGD, Guatemala N = 2, Kenya N = 3, China N = 1). Qualitative data were coded by two authors and quantitative data produced descriptive statistics. Mixed method results were compared and triangulated. Results Most AEA were female with ≥secondary education. About 80-90% of caregivers were the child's mother. To collect all anthropometry data, 62.1% of the 29 AEA preferred scan, while 31% preferred manual methods. In FGD, a key barrier for manual and scan methods was lack of child cooperation. Across countries, around 30% to almost 50% of caregivers said their child was bothered by each manual and scan method, while ≥95% of caregivers were willing to have their child measured by scans in the future. Conclusions Use of 3D scans to calculate anthropometry measurements was generally at least as acceptable as manual anthropometry measurement among AEA field workers and caregivers of young children aged <60 months, and in some cases preferred.

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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.

Background Practice-based experiences documenting development and implementation of nutrition and health surveillance systems are needed. Objectives Described processes, methods, and lessons learned from developing and implementing a population-based household nutrition and health surveillance system in Guatemala. Methods Described the phases and methods for the design and implementation of the surveillance system. Described efforts to institutionalize the system in government institutions, provided illustrative examples describing different data uses, and lessons learned. Results After initial assessments of data needs and consultations with officials in government institutions and partners in country, a population-based nutrition surveillance system prototype with complex sampling was designed and tested in five Guatemalan Highland departments in 2011. After dissemination of the prototype, government and partners expanded the content and multi-topic nutrition and health surveillance cycles were collected in 2013, 2015, 2016, 2017/18, and 2018/19 providing nationally representative data for households, women of reproductive age 15–49 years, and children 0–59 months. Each cycle, data were to be collected from 100 clusters, 30 households in each, and one woman and one child per household. Content covered around 25 health and nutrition topics, including coverage of all large-scale nutrition-specific interventions; the micronutrient content of fortifiable sugar, salt, and bread samples; anthropometry; and biomarkers to assess annually, or at least once, around 25 indicators of micronutrient status and chronic disease. Data were collected by 3–5 highly trained field teams. The design was flexible and revised each cycle allowing potential changes to questionnaires, population groups, biomarkers, survey design, or other changes. Data were used to change national guidelines for vitamin A and B12 interventions, among others, and evaluate interventions. Barriers included frequent changes of high-level government officials and heavy dependence on USA funding. Conclusions This system provides high quality data and fills critical data gaps and may serve as a useful model for others.