Dynamics of individual adherence to mass drug administration in a conditional probability model

We present a comprehensive framework which describes the systematic (binary) choice of individuals to either take treatment, or not for any reason, over the course of multiple rounds of mass drug administration (MDA) - which we here here refer to as ‘adherence’ and ‘non-adherence’. This methodology can be fitted to (or informed by) program data as well as manipulated to reproduce the same adherence behaviours of past analyses, and can go beyond past analyses to describe new behaviours that have yet to be considered in the literature. Our model also has a straightforward interpretation and implementation in simulations of mass drug trials for disease transmission studies and forecasts for control through MDA. We demonstrate how our analysis may be implemented to statistically infer adherence behaviour from a dataset by applying our approach to the recent adherence data from the TUMIKIA project, a recent trial of deworming strategies in Kenya. We stratify our analysis according to age and sex, though the framework which we introduce here may be readily adapted to accommodate other categories. Our findings include the detection of past behaviour dependent non-adherence in all age groups to varying degrees of severity and particularly strong non-adherent behaviour of men of ages 30+. We then demonstrate the use of our model in stochastic individual-based simulations by running two example forecasts for elimination in TUMIKIA with the learned adherence behaviour implemented. Our results demonstrate the impact and utility of including non-adherence from real world datasets in simulations.