Related Experiment Video
Updated: Jan 14, 2026
05:35
Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
Published on: January 19, 2024
1.3K
Invited Commentary: Improving Estimates of Severe Acute Malnutrition Requires More Data
American Journal of Epidemiology
|November 19, 2016
Summary
Estimating severe acute malnutrition (SAM) requires an incidence correction factor. A meta-analysis found heterogeneity and data limitations undermine a single factor, recommending routine data collection for accurate child malnutrition estimates.
Area of Science:
- Epidemiology
- Global Health
- Pediatrics
Background:
- Estimating the number of children with severe acute malnutrition (SAM) relies on prevalence data from cross-sectional surveys.
- Calculating incidence, crucial for accurate SAM case counts, typically requires longitudinal data, which is rarely available.
- An incidence correction factor is often used as a proxy when longitudinal data is absent.
Purpose of the Study:
- To update the incidence correction factor for estimating SAM cases in children aged 6-59 months.
- To address the limitations of current methods for estimating SAM incidence.
Main Methods:
- Meta-analysis and pooling of longitudinal and community program data from Mali, Niger, and Burkina Faso (2009-2012).
- Evaluation of data heterogeneity and quality for SAM incidence estimation.
Main Results:
- Significant heterogeneity was observed across the pooled data.
- The study highlights the inadequacy of available data for a single, reliable incidence correction factor for SAM.
- Data limitations challenge the precision of current SAM estimation methods.
Conclusions:
- A single incidence correction factor is insufficient for accurate SAM estimation due to data heterogeneity and limitations.
- Routine, high-quality data collection is essential for improving child malnutrition surveillance.
- Findings align with World Health Organization recommendations for enhanced data collection in global health initiatives.
More Related Videos
Related Concept Videos
01:28Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration
188
Glomerular filtration rate (GFR) can be estimated from serum creatinine using the modification of diet in renal disease (MDRD) formula or the chronic kidney disease–epidemiology collaboration (CKD–EPI) equation. Both methods are widely used in clinical practice to assess kidney function and guide treatment decisions.The MDRD equation does not require weight or height measurements and is normalized to the body surface area of 1.73 m², considered the average adult surface area.
188
01:06What are Estimates?
8.1K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates.
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.1K
01:22Estimating Population Mean with Unknown Standard Deviation
8.8K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
William S. Gosset (1876–1937) of the...
8.8K
01:15Assumptions of Survival Analysis
391
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
391
01:16Estimating Population Mean with Known Standard Deviation
9.6K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.6K
01:07Midrange
4.2K
A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
4.2K