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

Content Provider	Springer Nature : BioMed Central
Author	Bulti, Assaye Briend, André Dale, Nancy M. De Wagt, Arjan Chiwile, Faraja Chitekwe, Stanley Isokpunwu, Chris Myatt, Mark
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.
Related Links	https://archpublichealth.biomedcentral.com/counter/pdf/10.1186/s13690-017-0234-4.pdf
Ending Page	8
Page Count	8
Starting Page	1
File Format	HTM / HTML
ISSN	20493258
DOI	10.1186/s13690-017-0234-4
Journal	Archives of Public Health
Issue Number	1
Volume Number	75
Language	English
Publisher	BioMed Central
Publisher Date	2017-11-09
Access Restriction	Open
Subject Keyword	Public Health Medicine Health Policy Health Services Research Health Informatics Severe acute malnutrition Burden Caseload Prevalence Incidence Nigeria Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health
Journal Impact Factor	3.2/2023
5-Year Journal Impact Factor	3.3/2023

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