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Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
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Published on: February 25, 2021

Composite indicators for famine early warning systems.

M M Khan1, N B Mock, W B Bertrand

  • 1International Health Academic Program, Tulane University, 1501 Canal Street, New Orleans, LA 70112 USA Tulane Center for International Health and Development, 1501 Canal Street, New Orleans, LA 70112 USA.

Disasters
|October 21, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces composite indicators for more accurate famine prediction. Child malnutrition rates effectively predict food crises three months in advance, improving early warning systems.

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Last Updated: Jun 7, 2026

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

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Published on: February 25, 2021

Area of Science:

  • Agricultural Science
  • Environmental Science
  • Public Health

Background:

  • Traditional famine early warning systems rely on numerous indicators, leading to subjective predictions due to limited understanding of signal sensitivity and specificity.
  • Existing methods for predicting food crises often lack the necessary credibility and effectiveness.

Purpose of the Study:

  • To identify a limited set of 'composite' indicators that effectively summarize critical food-related information for more accurate famine prediction.
  • To enhance the credibility and effectiveness of famine early warning systems through improved predictive indicators.

Main Methods:

  • Identified three composite indicators specific to different stages of the food production and consumption chain.
  • Utilized satellite data for Normalized Deviation of Vegetative Index (NDVI), major food grain prices, and child malnutrition rates as key indicators.
  • Empirically tested the predictive power of these composite indicators for food crisis probability.

Main Results:

  • Normalized Deviation of Vegetative Index (NDVI), food grain prices, and child malnutrition rates correlate with food production inputs and outcomes.
  • Child malnutrition rates demonstrated a high degree of specificity in predicting community-level food crises up to three months in advance.
  • Composite indicators inherently summarize information from various specific measures, improving forecast sensitivity and specificity.

Conclusions:

  • Composite indicators simplify data aggregation and enhance the sensitivity and specificity of food crisis forecasts.
  • Child malnutrition rates are a highly effective predictor of future food crises, offering a credible early warning signal.
  • The proposed composite indicator approach significantly improves the accuracy and reliability of famine early warning systems.