School of Medicine
Biostatistics | Epidemiology | Health Services Administration | Health Services Research | Infectious Disease | Statistical Models | Virus Diseases
Poor medication adherence is a global phenomenon that has received a significant amount of research attention yet remains largely unsolved. Medication non-adherence can blur drug efficacy results in clinical trials, lead to substantial financial losses, increase the risk of relapse and hospitalisation, or lead to death. The most common methods of measuring adherence are post-treatment measures; that is, adherence is usually measured after the treatment has begun. What the authors are proposing in this multidisciplinary study is a new technique for predicting the factors that are likely to cause non-adherence before or during medication treatment, illustrated in the context of potential non-adherence to COVID-19 antiviral medication. Fault Tree Analysis (FTA), allows system analysts to determine how combinations of simple faults of a system can propagate to cause a total system failure. Monte Carlo simulation is a mathematical algorithm that depends heavily on repeated random sampling to predict the behaviour of a system. In this study, the authors propose a new technique called Non-Adherence Tree Analysis (NATA), based on the FTA and Monte Carlo simulation techniques, to improve adherence. Firstly, the non-adherence factors of a medication treatment lifecycle are translated into what is referred to as a Non-Adherence Tree (NAT). Secondly, the NAT is coded into a format that is translated into the GoldSim software for performing dynamic system modelling and analysis using Monte Carlo. Finally, the GoldSim model is simulated and analysed to predict the behaviour of the NAT. NATA is dynamic and able to learn from emerging datasets to improve the accuracy of future predictions. It produces a framework for improving adherence by analysing social and non-social adherence barriers. Novel terminologies and mathematical expressions have been developed and applied to real-world scenarios. The results of the application of NATA using data from six previous studies in relation to antiviral medication demonstrate a predictive model which suggests that the biggest factor that could contribute to non-adherence to a COVID-19 antiviral treatment is a therapy-related factor (the side effects of the medication). This is closely followed by a condition-related factor (asymptomatic nature of the disease) then patient-related factors (forgetfulness and other causes). From the results, it appears that side effects, asymptomatic factors and forgetfulness contribute 32.44%, 22.67% and 18.22% respectively to discontinuation of medication treatment of COVID-19 antiviral medication treatment. With this information, clinicians can implement relevant interventions and measures and allocate resources appropriately to minimise non-adherence.
COVID 19, Antiviral therapy, Drug therapy, Measurement, Monte Carlo method, Computer software, Clinical trials, Machine learning algorithms, medication adherence
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Copyright: © 2021 Edifor et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DOI of Published Version
Edifor EE, Brown R, Smith P, Kossik R. Non-Adherence Tree Analysis (NATA)-An adherence improvement framework: A COVID-19 case study. PLoS One. 2021 Feb 19;16(2):e0247109. doi: 10.1371/journal.pone.0247109. PMID: 33606789; PMCID: PMC7895356. Link to article on publisher's site
Edifor EE, Brown R, Smith P, Kossik R. (2021). Non-Adherence Tree Analysis (NATA)-An adherence improvement framework: A COVID-19 case study. COVID-19 Publications by UMMS Authors. https://doi.org/10.1371/journal.pone.0247109. Retrieved from https://escholarship.umassmed.edu/covid19/196
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