Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition: Experiences from Nigeria

Abstract

Background: The burden of severe acute malnutrition (SAM) is estimated using unadjusted prevalence estimates. SAM is an acute condition and many children with SAM will either recover or die within a few weeks. Estimating SAM burden using unadjusted prevalence estimates results in significant underestimation. This has a negative impact on allocation of resources for the prevention and treatment of SAM. A simple method for adjusting prevalence estimates intended to improve the accuracy of burden estimates and caseload predictions has been proposed. This method employs an incidence correction factor. Application of this method using the globally recommended incidence correction factor has led to programs underestimating burden and caseload in some settings. Methods: A method for estimating a locally appropriate incidence correction factor from prevalence, population size, program caseload, and program coverage was developed and tested using data from the Nigerian national SAM treatment program. Results: Applying the developed method resulted in errors in caseload prediction of about 10%. This is a considerable improvement upon the current method, which resulted in a 79.5% underestimate. Methods for improving the precision of estimates are proposed. Conclusions: It is possible to considerably improve predictions of caseload by applying a simple model to data that are readily available to program managers. This implies that more accurate estimates of burden may also be made using the same methods and data.