Nutrition survey planning relies on several key guidelines to ensure accurate, reliable, and representative data.
Stand-alone SMART surveys focus specifically on assessing nutrition status and mortality with clear, standardized methods.
However, in many operational contexts, resource constraints, access limitations, or competing assessment priorities make stand-alone nutrition surveys difficult to implement. To address this, anthropometric measurements can be integrated into broader food security assessments. The integrated JANSFA guidance provides the preferred approach for doing so, enabling the collection of nutrition data within food security surveys while maintaining the methodological rigor required for nutrition indicators in line with SMART standards.
Building on this approach, recent joint guidance developed by the Nutrition and VAM teams outlines three options for integrating anthropometry into food security assessments in English and French:
Representative sample (preferred option): Design the assessment to generate representative nutrition estimates for the survey domains. This approach is aligned with the principles of integrated assessments such as JANSFA and can support nutrition analyses, programme planning, and decision-making.
Pooling or stratification (second-best option): Combine neighbouring areas or survey strata when individual domains do not meet the sample size requirements needed for representative nutrition estimates.
MUAC screening component (minimum acceptable option): Where representative estimation is not feasible, include a MUAC screening component to provide risk signalling and support situational awareness.
These approaches complement other nutrition information sources recognised by the IPC Acute Malnutrition (IPC AMN) framework, including nutrition surveys, sentinel site surveillance, and screening data, which together contribute to nutrition situation analysis and monitoring.
Regardless of the data collection method, it is important to have dedicated and trained nutrition enumerator collecting anthropometric data for each team, helping to minimize measurement bias. During the training phase, a standardisation test of all nutrition enumerators is needed to assess, measure, and address every enumerator’s strength and weakness.
Checking the quality of the collected data is equally important. The quality checks are unpacked in the guidance for all the options above.