Non-adherence to treatment: a methodological approach to compensating for its impact in chronic infections, using tuberculosis as a model disease.

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Imperfectly designed treatment regimens for chronic (i.e. long-lasting) diseases result in continued, and possibly worsened, symptoms. When a disease can be passed from person-to-person we additionally risk the development and spread of drug resistance, which is when a bacterium or virus becomes untreatable by normal medicines as it has genetically changed. This endangers both individual- and population-level health. If someone incompletely 'adheres' to their treatment (does not take all their medicine as recommended) this can result in the same problems, but the impact of this depends on the drugs being used, due to their differing properties. Poor adherence to treatment costs hundreds of billions of dollars globally each year.For many chronic diseases, we do not know how much medicine it is safe for patients not to take and still be cured. Taking at least eight of every 10 doses across the entire length of treatment is often said to be important, but this lacks good evidence and is highly simplistic, especially given how much drugs vary from disease to disease.This study aims to use different methodologies and real-life, detailed, adherence and treatment outcome data to decide how normal patient behaviour should influence our approach to treatment. Tuberculosis (TB) has been chosen as a model disease due to its global importance (10.4 million new patients, 1.4 million related deaths in 2015) and the need for better treatments. The Objectives are:1) Describe how, when and why poor adherence occurs during the six months of daily treatment, and how adherence is related to the outcome of treatment across different places.2) Determine how adherence patterns influence the development of drug resistance over time.3) Adapt a theoretical model to examine the impact of adherence on the best way to give treatment (e.g. slow release tablets).4) Draw together these strands of evidence to determine how adherence should alter our approach to treatment and how new regimens should be designed.This work will not only guide the best way of treating TB globally, but the methods used will also help to interpret how common adherence patterns should influence the use of drugs for other conditions in the real world. The best ways of dosing TB drugs can be included in new clinical trials to improve health services for patients and value for money. The way in which drug trial data is interpreted as reflecting real life by the people writing clinical guidance will be refined. The findings can also inform trials of tools to promote adherence.