Interpreting results of cluster surveys in emergency settings: is the LQAS test the best option?

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

Old and new cluster designs in emergency field surveys: in search of a one-fits-all solution

<p>Abstract</p> <p>Introduction</p> <p>Cluster surveys are frequently used to measure key nutrition and health indicators in humanitarian emergencies. The survey design of 30 clusters of 7 children (30 × 7) was initially proposed by the World Health Organization for measuring vaccination coverage, and later a design of 30 clusters of 30 children (30 × 30) was introduced to measure acute malnutrition in emergency settings. Recently, designs of 33 clusters of 6 children (33 × 6) and 67 clusters of 3 children (67 × 3) have been proposed as alternatives that enable measurement of several key indicators with sufficient precision, while offering substantial savings in time. This paper explores expected effects of using 67 × 3, 33 × 6, or 30 × 7 designs instead of a "standard" 30 × 30 design on precision and accuracy of estimates, and on time required to complete the survey.</p> <p>Analysis</p> <p>The 67 × 3, 33 × 6, and 30 × 7 designs are expected to be more statistically efficient for measuring outcomes having high design effects (e.g., vaccination coverage, vitamin A distribution coverage, or access to safe water sources), and less efficient for measuring outcomes with more within-cluster variability, such as global acute malnutrition or anemia. Because of small sample sizes, these designs may not provide sufficient levels of precision to measure crude mortality rates. Given the small number (3 to 7) of survey subjects per cluster, it may be hard to select representative samples of subjects within clusters.</p> <p>The smaller sample size in these designs will likely result in substantial time savings. The magnitude of the savings will depend on several factors, including the average travel time between clusters. The 67 × 3 design will provide the least time savings. The 33 × 6 and 30 × 7 designs perform similarly to each other, both in terms of statistical efficiency and in terms of time required to complete the survey.</p> <p>Conclusion</p> <p>Cluster designs discussed in this paper may offer substantial time and cost savings compared to the traditional 30 × 30 design, and may provide acceptable levels of precision when measuring outcomes that have high intracluster homogeneity. Further investigation is required to determine whether these designs can consistently provide accurate point estimates for key outcomes of interest. Organizations conducting cluster surveys in emergency settings need to build their technical capacity in survey design to be able to calculate context-specific sample sizes individually for each planned survey.</p

Methods for health surveys in difficult settings: charting progress, moving forward

Abstract Health surveys are a very important component of the epidemiology toolbox, and play a critical role in gauging population health, especially in developing countries. Research on health survey methods, however, is sparse. In particular, current sampling methods are not well adapted for certain 'difficult' settings, such as emergencies, remote regions without easily available sampling frames, hidden and vulnerable population groups, urban slums and populations living under strong political pressure. This special issue of Emerging Themes in Epidemiology is entirely devoted to survey methods in such settings, and builds upon a successful conference in London highlighting problems with current approaches and possible ways forward. Greater investment in research on health survey methods is needed and will have beneficial effects for populations in need.</jats:p

Parameters associated with design effect of child anthropometry indicators in small-scale field surveys

Abstract Background Cluster surveys provide rapid but representative estimates of key nutrition indicators in humanitarian crises. For these surveys, an accurate estimate of the design effect is critical to calculate a sample size that achieves adequate precision with the minimum number of sampling units. This paper describes the variability in design effect for three key nutrition indicators measured in small-scale surveys and models the association of design effect with parameters hypothesized to explain this variability. Methods 380 small-scale surveys from 28 countries conducted between 2006 and 2013 were analyzed. We calculated prevalence and design effect of wasting, underweight, and stunting for each survey as well as standard deviations of the underlying continuous Z-score distribution. Mean cluster size, survey location and year were recorded. To describe design effects, median and interquartile ranges were examined. Generalized linear regression models were run to identify potential predictors of design effect. Results Median design effect was under 2.00 for all three indicators; for wasting, the median was 1.35, the lowest among the indicators. Multivariable linear regression models suggest significant, positive associations of design effect and mean cluster size for all three indicators, and with prevalence of wasting and underweight, but not stunting. Standard deviation was positively associated with design effect for wasting but negatively associated for stunting. Survey region was significant in all three models. Conclusions This study supports the current field survey guidance recommending the use of 1.5 as a benchmark for design effect of wasting, but suggests this value may not be large enough for surveys with a primary objective of measuring stunting or underweight. The strong relationship between design effect and region in the models underscores the continued need to consider country- and locality-specific estimates when designing surveys. These models also provide empirical evidence of a positive relationship between design effect and both mean cluster size and prevalence, and introduces standard deviation of the underlying continuous variable (Z-scores) as a previously unexplored factor significantly associated with design effect. The magnitude and directionality of this association differed by indicator, underscoring the need for further investigation into the relationship between standard deviation and design effect