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Sample-size implications for population-based cluster surveys of nutritional status
1Department of International Health, School of Hygiene and Public Health, Johns Hopkins University, Baltimore, MD 21205-2103.
The American Journal of Clinical Nutrition
|January 1, 1995
Summary
This study on childhood malnutrition in four countries found that nutrition surveys using clusters of 30 children can reduce sample sizes. This optimization is possible when basic prevalence and cluster data are known before sampling.
Area of Science:
- Public Health
- Epidemiology
- Nutrition Science
Background:
- Childhood malnutrition, including wasting and stunting, remains a significant global health issue.
- Population-based surveys are crucial for estimating malnutrition prevalence and understanding its distribution.
- Village-level clustering of malnutrition impacts survey design and efficiency.
Purpose of the Study:
- To analyze design effects in population-based surveys estimating childhood malnutrition prevalence.
- To quantify the clustering of different malnutrition types within villages.
- To inform optimal sample size and cluster selection strategies for nutrition surveys.
Main Methods:
- Conducted a study on design effects for malnutrition surveys in Malawi, Zambia, Indonesia, and Nepal.
- Measured weight, height, and mid-upper arm circumference in children within randomly selected villages.
- Analyzed the magnitude of malnutrition clustering at the village level.
Main Results:
- Design effects varied significantly across countries and malnutrition indicators, ranging from 0.53 to 6.12.
- Simulated design effects for clusters of 30 children ranged from 0.44 to 2.59.
- Malnutrition, particularly wasting, demonstrated significant clustering within villages; stunting clustered less.
Conclusions:
- Nutrition surveys utilizing clusters of 30 children may require fewer clusters than currently recommended.
- Availability of prior prevalence and cluster data can enhance the efficiency of nutrition survey sampling.
- Optimized survey designs can improve the cost-effectiveness of monitoring childhood malnutrition.
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