Non-adherence to therapy in chronic conditions (initially tuberculosis): a quantitative, methodological approach.

The severity and spread of an infectious disease can be seen as the culmination of three elements: its global prevalence, its lethality and the difficulty in eradicating it. This severity of this summation should be mitigated by the existence of an effective cure. However, difficulties in ensuring adherence to treatment prevents the success of reducing the burden of many preventable diseases. This project will use tuberculosis (TB) as an example of a chronic disease.

TB continues to be one of the most widespread infectious diseases and is responsible for 1.5 million deaths a year. This estimate is set to rise because of the disruption to supply chains of medication and health care systems caused by SARS-CoV-2. TB is one of the top ten causes of death and it the most lethal single infectious agent in humans.

The pathogens that cause the majority of TB transmission are Mycobacterium tuberculosis and the closely related Mycobacterium africanum. The Mycobacterium genus is notoriously difficult to eliminate from the body and requires a long course of several drugs which can have serious side-effects that can be difficult to manage. Adherence to these drugs is important for curing the disease. The extent of adherence to therapy required for a positive outcome is unknown.

Aims: To investigate how adherence patterns should influence our approach to treatment to maximise favourable outcomes, starting with tuberculosis as a model disease:

Objectives

1) Describe non-adherence patterns across the duration of treatment in tuberculosis, using pre-existing and newly collected data sources.

2) Determine how different adherence patterns impact treatment outcomes?

3) Ascertain if patient characteristics are associated with being at risk of the most detrimental adherence patterns.

4) Working with modelling colleagues, examine how such patterns could influence optimal dosing.