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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Improved care and survival in severe malnutrition through eLearning.

Sunhea Choi1, Ho Ming Yuen2, Reginald Annan3

  • 1Human Development and Health, University of Southampton, Southampton, UK.

Archives of Disease in Childhood
|August 1, 2019
PubMed
Summary

An interactive eLearning course significantly improved the identification, diagnosis, and management of severe acute malnutrition (SAM) in children. This intervention led to a substantial reduction in child mortality rates, demonstrating its effectiveness in resource-limited settings.

Keywords:
WHO ten stepscapacity buildingeLearningsevere acute malnutrition

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Area of Science:

  • Global Health
  • Pediatrics
  • Medical Education

Background:

  • Scaling up management of severe acute malnutrition (SAM) is crucial for reducing child mortality.
  • Improved operational capacity in healthcare facilities is essential for effective SAM management.
  • Resource-poor countries face challenges in providing adequate care for children with SAM.

Purpose of the Study:

  • To evaluate the effectiveness of a scalable eLearning course for managing SAM in resource-limited settings.
  • To determine if the eLearning intervention improves diagnosis, clinical management, and survival rates of children with SAM.
  • To assess the impact of eLearning on the operational capacity of healthcare professionals in managing SAM.

Main Methods:

  • A 2-year pre- and post-intervention study was conducted across eleven healthcare facilities in Ghana, Guatemala, and El Salvador.
  • The intervention involved a scenario-based eLearning course on caring for infants and young children with severe malnutrition.
  • Data collection included medical record reviews, ward observations, and interviews with hospital personnel.

Main Results:

  • Post-intervention, there was a significant improvement in SAM identification, with more children having anthropometric data (34.9% vs 15.9%) and correct diagnoses (58.5% vs 47.1%).
  • Improvements were noted across most aspects of the WHO 'Ten Steps' for case management.
  • The case-fatality rate for SAM decreased significantly from 5.8% to 1.9%.

Conclusions:

  • High-quality, interactive eLearning is an effective strategy for capacity building in SAM management.
  • eLearning interventions can successfully scale up the management of severe acute malnutrition.
  • This approach leads to improved clinical outcomes and reduced child mortality in resource-limited settings.