Abstract
Cluster surveys are commonly used in humanitarian emergencies to measure health and nutrition indicators. Deitchler et al. have proposed to use Lot Quality Assurance Sampling (LQAS) hypothesis testing in cluster surveys to classify the prevalence of global acute malnutrition as exceeding or not exceeding the pre-established thresholds. Field practitioners and decision-makers must clearly understand the meaning and implications of using this test in interpreting survey results to make programmatic decisions. We demonstrate that the LQAS test–as proposed by Deitchler et al. – is prone to producing false-positive results and thus is likely to suggest interventions in situations where interventions may not be needed. As an alternative, to provide more useful information for decision-making, we suggest reporting the probability of an indicator's exceeding the threshold as a direct measure of "risk". Such probability can be easily determined in field settings by using a simple spreadsheet calculator. The "risk" of exceeding the threshold can then be considered in the context of other aggravating and protective factors to make informed programmatic decisions.
Highlights
Cluster surveys are often used in humanitarian emergencies to measure important nutrition and health indicators
The Lot Quality Assurance Sampling (LQAS) hypothesis test is performed by counting the number of global acute malnutrition (GAM) cases in the survey sample and comparing this count to a pre-established decision rule number [8]
In the LQAS decision-making algorithm advocated by Deitchler and colleagues, GAM is classified as being above the threshold when the statistical probability of the true population value of GAM exceeding the threshold is 10% or higher
Summary
Cluster surveys are often used in humanitarian emergencies to measure important nutrition and health indicators. Deitchler and colleagues [5,6] recently proposed using decision rules based on the lot quality assurance (LQAS) method to classify the prevalence of GAM in cluster emergency nutrition surveys vis-à-vis pre-established thresholds. Emerging Themes in Epidemiology 2008, 5:25 http://www.ete-online.com/content/5/1/25 ters by 30 children) design. The implications of these proposed designs on precision, validity and resources required to complete the survey have been discussed in detail in a recent paper [7]. Since the LQAS method has not been routinely used to analyze nutrition cluster survey data, we consider it important to provide a simple explanation to field practitioners of how this test is conducted, what it means, and why there may be apparent discrepancies between the results of the LQAS decision rule method and the observed prevalence of GAM. It is important to consider how this proposed method for decision-making compares to existing practices and to explore the issue of whether there are better statistical options available to compare survey prevalence estimates to preset thresholds
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