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Intermittent preventive treatment for malaria in infants: a decision-support tool for sub-Saharan Africa

Ilona Carneiro1, Lucy Smith, Amanda Ross

  • 1Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, England. ilona.carneiro@lshtm.ac.uk

Bulletin of the World Health Organization
|November 16, 2010
PubMed

Insights

Intermittent preventive treatment in infants (IPTi) effectiveness for malaria control varies by local transmission intensity and seasonality. A new tool helps policymakers assess IPTi

Area of Science:

  • Malaria Control
  • Public Health Policy
  • Mathematical Modeling

Background:

  • Intermittent preventive treatment in infants (IPTi) is a strategy to combat malaria.
  • Assessing the local effectiveness of IPTi is crucial for policy decisions in sub-Saharan Africa.
  • Malaria epidemiology, including transmission intensity and seasonality, significantly influences treatment efficacy.

Purpose of the Study:

  • To develop a decision-support tool for policymakers in sub-Saharan Africa.
  • To evaluate the potential effectiveness of IPTi for local malaria control.

Main Methods:

  • Developed an algorithm combining two predictive approaches.
  • Analyzed age patterns of malaria cases, admissions, and deaths based on transmission intensity and seasonality.
  • Utilized a stochastic mathematical model to predict averted malaria cases under various epidemiological conditions.

Main Results:

  • The proportion of malaria cases targeted by IPTi increases with outcome severity and transmission intensity.
  • The decision-support tool provides context-specific estimates of targeted malaria outcomes (deaths, hospitalizations, clinical cases).
  • The tool quantifies the potential number of malaria cases that could be averted by IPTi implementation.

Conclusions:

  • IPTi effectiveness is contingent upon local malaria transmission intensity and seasonality.
  • Local malaria epidemiology must guide decisions on IPTi implementation.
  • An internet-based decision-support tool is available to predict IPTi effectiveness across diverse epidemiological settings.
Abstract

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