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Non-Adherence Tree Analysis (NATA)-An adherence improvement framework: A COVID-19 case study.

Ernest Edem Edifor1, Regina Brown2, Paul Smith3

  • 1Operations, Technology, Events and Hospitality Management, Manchester Metropolitan University, Manchester, Lancashire, United Kingdom.

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|February 19, 2021
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Summary

Predicting medication non-adherence is crucial for treatment success. A new Non-Adherence Tree Analysis (NATA) method, using Fault Tree Analysis and Monte Carlo simulation, identifies key factors like side effects and forgetfulness to improve patient adherence.

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Area of Science:

  • Multidisciplinary study integrating systems engineering and clinical pharmacology.
  • Development of novel predictive modeling for medication adherence.

Background:

  • Poor medication adherence is a global challenge impacting clinical trial validity, financial outcomes, and patient mortality.
  • Current adherence measurement methods are primarily post-treatment, limiting proactive intervention.
  • Predicting non-adherence factors before or during treatment is essential for effective management.

Purpose of the Study:

  • To introduce and validate a novel technique, Non-Adherence Tree Analysis (NATA), for predicting medication non-adherence.
  • To apply NATA in the context of COVID-19 antiviral medication adherence.
  • To provide a framework for analyzing social and non-social adherence barriers.

Main Methods:

  • Developed Non-Adherence Tree Analysis (NATA) based on Fault Tree Analysis (FTA) and Monte Carlo simulation.
  • Translated non-adherence factors into a Non-Adherence Tree (NAT) model.
  • Utilized GoldSim software for dynamic system modeling and Monte Carlo simulation analysis.

Main Results:

  • NATA demonstrated a dynamic predictive model capable of learning from emerging datasets.
  • Identified therapy-related factors (medication side effects) as the primary contributor to COVID-19 antiviral non-adherence (32.44%).
  • Condition-related factors (asymptomatic disease) and patient-related factors (forgetfulness) were also significant contributors (22.67% and 18.22%, respectively).

Conclusions:

  • NATA offers a robust framework for proactively identifying and addressing medication non-adherence.
  • The predictive model highlights the critical impact of side effects, disease characteristics, and patient forgetfulness on adherence.
  • Findings enable targeted interventions and resource allocation to minimize non-adherence and improve patient outcomes.