Cluster
Designs to Assess the Prevalence of Acute Malnutrition by Lot Quality
Assurance Sampling: A Validation Study by Computer Simulation
To assess acute malnutrition with a population based survey a large
sample size is generally required. This is true even when lot quality
assurance sampling (LQAS), an otherwise time and cost efficient
method, is used. Cluster sampling, or sampling observations in batches,
offers an alternative to the large simple random sample size that
would typically be needed for LQAS analysis.
The study "Cluster Designs to Assess the Prevalence of Acute Malnutrition
by Lot Quality Assurance Sampling: A Validation Study by Computer
Simulation," examines the classification error of three cluster
designs, a 67X3, a 33X6, and a sequential sampling scheme, to assess
the prevalence of acute malnutrition with LQAS. The study concludes
that for independent clusters with moderate intracluster correlation,
the three sampling designs maintain approximate validity for LQAS
analysis of acute malnutrition prevalence.
Although the 30x30 cluster design is currently the most common
sampling method used to assess the prevalence of acute malnutrition
in emergency settings, the 67x3, 33x6 and sequential sampling designs
provide an alternative, well-tested approach to the collection and
analysis of acute malnutrition data. Comparative field studies in
Ethiopia and Sudan have shown the alternative sampling designs to
provide reliable and reasonably precise results and to require less
time and resources in comparison to a 30x30 cluster design.
The study was funded by USAID's Bureau for Global Health's Office
of Health, Infectious Disease and Nutrition and grants provided
to the Harvard School of Public Health from the US National Institutes
of Health.
The
article is available at no cost from the Journal of the Royal Statistical
Society Series A website.
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