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.

Poor adherence to medication 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 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 technique for analysing the factors that can cause non-adherence before or during medication treatment. 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 the use of 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. This study produces a framework for improving adherence by analysing social and non-social adherence barriers. The results reveal that the biggest factor that could contribute to non-adherence to a COVID-19 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). With this information, clinicians can implement relevant measures and allocate resources appropriately to minimise non-adherence.

OBJECTIVES: Unplanned pregnancy is a public health problem in the United States, including in rural areas. Primary care physicians are the main providers of health care to women in rural areas and are uniquely positioned to help reduce unplanned pregnancy in rural women. This study documents provision of contraception by rural primary care physicians, focusing on the most effective, long acting methods, intrauterine devices (IUDs) and contraceptive implants. METHODS: We surveyed all primary care physicians practicing in rural areas of Illinois and Wisconsin. Bivariate analysis was performed using chi squared and Fisher's exact test, and multivariable analysis was performed with logistic regression to determine factors associated with provision. RESULTS: The response rate was 862 out of 2312 physicians (37%). Nine percent of respondents place implants and 35% place IUDs. Eighty-seven percent of physicians had not had training in implant placement, and 41% had not had training in IUD placement. In multivariable analysis, factors associated with placement of long acting contraception include provision of maternity care, and female gender of the physician. The most common reasons for not providing the methods were lack of training and perceived low demand from patients. CONCLUSIONS: Many rural primary care providers do not place long acting contraceptive devices due to lack of training. Female physicians and those providing maternity care are the most likely to place these devices. Increased training for primary care physicians both during and after residency would help increase access to these options for women in rural areas.