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Study comparing the management decisions by IMNCI algorithm and pediatricians in a teaching hospital for the young

Agnihotri Bhattacharyya1, Sanjay Kumar Saha, Pramit Ghosh

  • 1Department of Community Medicine, Bankura Sammilani Medical College, India. b.agnihotri@yahoo.com

Indian Journal of Public Health
|February 3, 2012
PubMed

Insights

The Integrated Management of Neonatal and Childhood Illness (IMNCI) algorithm showed moderate agreement with pediatrician decisions in India, with significant over-diagnosis of severe illness in infants with multiple symptoms.

Area of Science:

  • Pediatrics
  • Public Health
  • Global Health

Background:

  • The Integrated Management of Neonatal and Childhood Illness (IMNCI) strategy is widely implemented in India.
  • Limited studies have validated IMNCI's accuracy against expert pediatrician decisions within the Indian context.

Purpose of the Study:

  • To compare IMNCI diagnostic decisions with those of pediatricians for sick young infants.
  • To evaluate the agreement, over-diagnosis, and under-diagnosis rates of the IMNCI algorithm.
  • To assess the impact of multiple presenting symptoms on IMNCI classifications.

Main Methods:

  • A comparative study involving sick young infants presenting to a pediatric department.
  • Analysis of IMNCI algorithm decisions versus pediatrician assessments.
  • Statistical evaluation using percent agreement, Kappa, and weighted Kappa.

Main Results:

  • Overall diagnostic agreement between IMNCI and pediatricians was 55.56% (Kappa=0.32).
  • The IMNCI algorithm demonstrated a 33.33% over-diagnosis and 11.11% under-diagnosis rate.
  • IMNCI significantly over-classified infants with multiple symptoms as 'red' (71.88%) compared to single symptoms (40%).

Conclusions:

  • The IMNCI algorithm shows moderate diagnostic agreement with pediatricians for sick young infants in India.
  • Over-diagnosis, particularly in cases with multiple symptoms, is a notable finding.
  • Further refinement of the IMNCI algorithm may be needed to improve accuracy and reduce unnecessary referrals.

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