Why can one survey miss children who need treatment?
Severe wasting is a form of acute malnutrition in which a child is dangerously thin for their height. It increases the risk of illness and death and can affect development. In 2020, the United Nations estimated that 45.4 million children under five were affected by wasting worldwide, including 13.6 million with severe wasting.
Planning treatment requires an estimate of how many children are likely to need care over an entire year. But many nutrition surveys measure prevalence: the proportion of children who have wasting at one point in time. A single survey can miss children who develop severe wasting, recover or relapse between survey dates.
Why does the 1.6 multiplier matter for planning?
To turn a one-time prevalence estimate into an annual caseload, planners have often multiplied it by 1.6. The multiplier is intended to account for new cases that appear during the year but are not visible in a single survey.
Using one value everywhere assumes that the duration of wasting, recovery, relapse and access to treatment are broadly similar across settings. If the resulting annual estimate is too low, programmes may procure too little therapeutic food, plan too little treatment capacity or request insufficient funding. If it is too high, limited resources may be allocated away from other needs.
The analytical question therefore matters for later decisions about procurement, staffing and budgets, even though the model itself focuses on estimating caseload rather than prescribing those decisions directly.
How did the team test a local alternative?
Mayukh Ghosh and Chintan Amrit at Amsterdam Business School, researchers from Northeastern University and the World Food Programme developed an approach that combines several sources of information to estimate a correction factor for a specific setting.
Instead of starting with 1.6, the method uses local data to reflect how severe wasting changes over time in that context. The approach was tested with the World Food Programme’s Regional Bureau in Dakar, which coordinates food security and nutrition work across West Africa.
What did the analysis show?
The researchers found that the standard 1.6 multiplier tends to underestimate the burden of severe wasting in many locations. In those settings, some children who are likely to need treatment during the year may be absent from the caseload estimate used for planning. A context-specific estimate gives programme teams a stronger basis for deciding how much therapeutic food to procure, how much treatment capacity to plan and how much funding to request.
What can this mean for future nutrition planning?
The wider shift is from applying one global multiplier to testing what available data indicates in each setting. That makes a major planning assumption visible and adjustable rather than fixed.
Future applications could compare context-specific estimates with observed treatment demand and refine them as more local data becomes available. For nutrition programmes operating with limited resources, better annual caseload estimates can make procurement, staffing and funding plans more closely reflect the number of children expected to need treatment.
