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.