198850 Cluster Designs to Assess the Prevalence of Acute Malnutrition by LQAS: A Validation by Computer Simulation

Wednesday, November 11, 2009: 10:30 AM

Casey Olives, SM , Biostatistics, Harvard School of Public Health, Boston, MA
Megan Deitchler, MPH , FANTA Project, Academy for Educational Development, Washington, DC
Bethany L. Hedt, PhD , Biostatistics, Harvard School of Public Health, Boston, MA
Joe Valadez, PhD, ScD, MPH , International Health, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
Marcello Pagano, PhD , Biostatistics, Harvard School of Public Health, Boston, MA
Background: The standard sampling design for assessing global acute malnutrition (GAM) is a 30 x 30 (30 clusters of thirty observations) cluster sample. Recently, Deitchler et al demonstrated that Lot Quality Assurance Sampling (LQAS) implemented in a cluster-sampling framework is a viable alternative to the standard methodology for assessing GAM prevalence. Yet, traditional LQAS methods require simple random sampling to guarantee valid results.

Methods: In this study, we simulate correlated binary outcomes to examine the classification error of two LQAS cluster designs, a 67 x 3 (67 clusters of three observations) and a 33 x 6 (33 clusters of six observations). Further, we explore the use of a 67 x 3 sequential sampling scheme for LQAS classification of GAM prevalence based on the Wald probability ratio test.

Results: Results indicate that, for independent clusters with moderate intracluster correlation for the GAM outcome, the three sampling designs maintain approximate validity for LQAS analysis. Moreover, sequential cluster sampling can substantially reduce the average sample size required for accurate classification. The presence of intercluster correlation, however, impacts dramatically the classification error that is associated with LQAS analysis.

Conclusion: These results suggest that LQAS cluster designs could be useful tools for assessing the prevalence of GAM, particularly in emergency situations where time is of critical importance. However, a certain degree of prior knowledge regarding the degree of homogeneity within and between clusters is a necessity when implementing such designs.

Learning Objectives:
Discuss the impact of cluster sampling on the expected misclassification error incurred using the LQAS classification procedure. Explore the potential benefit of sampling clusters sequentially. Assess the viability of using LQAS as a tool for classifying the prevalence of acute malnutrition.

Keywords: Statistics, Nutrition

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I worked as a short term consultant for the Academy of Educational Development's FANTA Project to assess the characteristics of cluster sampling designs, with the support of my advisor Marcello Pagano at the Harvard School of Public Health.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.