Challenges of Estimating the Annual Caseload of Severe Acute Malnutrition: The Case of Niger.
BACKGROUND
Reliable prospective estimates of annual severe acute malnutrition (SAM) caseloads for treatment are needed for policy decisions and planning of quality services in the context of competing public health priorities and limited resources. This paper compares the reliability of SAM caseloads of children 6-59 months of age in Niger estimated from prevalence at the start of the year and counted from incidence at the end of the year.


METHODS
Secondary data from two health districts for 2012 and the country overall for 2013 were used to calculate annual caseload of SAM. Prevalence and coverage were extracted from survey reports, and incidence from weekly surveillance systems.


RESULTS
The prospective caseload estimate derived from prevalence and duration of illness underestimated the true burden. Similar incidence was derived from two weekly surveillance systems, but differed from that obtained from the monthly system. Incidence conversion factors were two to five times higher than recommended.


CONCLUSIONS
Obtaining reliable prospective caseloads was challenging because prevalence is unsuitable for estimating incidence of SAM. Different SAM indicators identified different SAM populations, and duration of illness, expected contact coverage and population figures were inaccurate. The quality of primary data measurement, recording and reporting affected incidence numbers from surveillance. Coverage estimated in population surveys was rarely available, and coverage obtained by comparing admissions with prospective caseload estimates was unrealistic or impractical.


CONCLUSIONS
Caseload estimates derived from prevalence are unreliable and should be used with caution. Policy and service decisions that depend on these numbers may weaken performance of service delivery. Niger may improve SAM surveillance by simplifying and improving primary data collection and methods using innovative information technologies for single data entry at the first contact with the health system. Lessons may be relevant for countries with a high burden of SAM, including for targeted emergency responses.
Institut de recherche santé et société, Université catholique de Louvain, Brussels, Belgium.
Hedwig
http://orcid.org/0000-0003-1552-2402
H
Deconinck
École de médecine, Université catholique de Louvain, Brussels, Belgium.
Anaïs
A
Pesonen
École de santé publique, Université libre de Bruxelles, Brussels, Belgium.
Mahaman
M
Hallarou
Save the Children, Niamey, Niger.
Jean-Christophe
JC
Gérard
Tampere Centre for Child Health Research, University of Tampere and Tampere University Hospital, Tampere, Finland.|Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Frederiksberg, Denmark.
André
A
Briend
École de santé publique, Université libre de Bruxelles, Brussels, Belgium.
Philippe
P
Donnen
Institut de recherche santé et société, Université catholique de Louvain, Brussels, Belgium.
Jean
J
Macq
D002675:Child, Preschool; D006801:Humans; D015994:Incidence; D007223:Infant; D009548:Niger / Q000453:epidemiology; D000067011:Severe Acute Malnutrition / Q000453:epidemiology*