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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Rapid Nutrition Surveys: How Many Clusters are Enough?
N Binkin1, K Sullivan, N Staehling
1Division of Nutrition, Centers for Disease Control, 1600 Clifton Road, Atlanta, GA 30333, USA.
Disasters
|October 21, 2010
Summary
For accurate malnutrition prevalence estimates, around 30 clusters are recommended. Using fewer clusters significantly reduces precision and validity, while more offer minimal gains.
Area of Science:
- Public Health Nutrition
- Epidemiology
Background:
- Population-based nutrition surveys are crucial for assessing malnutrition prevalence.
- Theoretical considerations suggest 30 clusters of 30 children offer valid estimates with 95% confidence.
- Logistical challenges in famine-stricken areas necessitate evaluating fewer than 30 clusters for rapid nutritional assessment.
Purpose of the Study:
- To evaluate the impact of using fewer than 30 clusters on the validity and precision of undernutrition prevalence estimates.
- To determine the optimal number of clusters for reliable rapid nutritional assessments in resource-limited settings.
Main Methods:
- Analysis of data from the 1983 Swaziland National Nutrition Survey.
- Utilizing data from rapid nutrition surveys conducted in Burkina Faso, Guinea, and Niger in 1984 and 1985.
- Comparing prevalence estimates derived from varying numbers of clusters against established benchmarks.
Main Results:
- Using fewer than 30 clusters can lead to point prevalence estimates that significantly deviate from true values.
- Reduced cluster numbers generally result in less precise estimates of undernutrition.
- Collecting data from more than 30 clusters yields diminishing returns in terms of precision and validity.
Conclusions:
- Approximately 30 clusters provide a balance of validity and precision for estimating undernutrition prevalence.
- Efforts should focus on securing logistical support to survey around 30 clusters for reliable nutritional assessments.
- The study underscores the importance of adequate sample size in epidemiological surveys for public health decision-making.
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