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

TY - JOUR

T1 - Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition

T2 - experiences from Nigeria

AU - Bulti, Assaye

AU - Briend, André

AU - Dale, Nancy M

AU - De Wagt, Arjan

AU - Chiwile, Faraja

AU - Chitekwe, Stanley

AU - Isokpunwu, Chris

AU - Myatt, Mark

N1 - CURIS 2017 NEXS 334

PY - 2017/11/9

Y1 - 2017/11/9

N2 - 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.

AB - 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.

KW - Severe acute malnutrition

KW - Burden

KW - Caseload

KW - Prevalence

KW - Incidence

KW - Nigeria

U2 - 10.1186/s13690-017-0234-4

DO - 10.1186/s13690-017-0234-4

M3 - Journal article

C2 - 29152260

SN - 0778-7367

VL - 75

JO - Archives of Public Health

JF - Archives of Public Health

M1 - 66

ER -