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A data-driven typology of asthma medication adherence using cluster analysis.

Holly Tibble1,2, Amy Chan3,4, Edwin A Mitchell5

  • 1Usher Institute, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Doorway 1, Old Medical School, Teviot Place, Edinburgh, EH8 9AG, UK. Holly.Tibble@ed.ac.uk.

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Understanding medication non-adherence in pediatric asthma is crucial for better control. This study developed a multi-dimensional approach to categorize asthma medication-taking behaviors, revealing diverse patient patterns.

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Area of Science:

  • Pediatric Pulmonology
  • Behavioral Science
  • Data Science in Healthcare

Background:

  • Non-adherence to asthma preventer medication is a significant factor in poor asthma control.
  • Existing adherence measures may oversimplify complex medication-taking behaviors.

Purpose of the Study:

  • To develop a data-driven, multi-dimensional typology of medication non-adherence in children with asthma.
  • To enhance understanding of diverse patient medication-taking patterns.

Main Methods:

  • Analysis of electronic inhaler monitoring data from 211 children with asthma.
  • Extraction of five adherence measures: dose percentage, zero-dose days, full-dose days, intermission frequency, and intermission duration.
  • Application of principal component analysis and k-means clustering for typology development.

Main Results:

  • A three-group categorization of medication non-adherence was established, reflecting varied patient behaviors.
  • Decision trees identified the percentage of prescribed doses taken as the most accurate predictor of cluster assignment (84% accuracy).

Conclusions:

  • A multi-dimensional approach provides a more nuanced understanding of asthma medication non-adherence than one-dimensional measures.
  • This typology can improve clinical utility and inform targeted interventions for pediatric asthma management.