  Long-term adherence to medication is a critical factor in preventing chronic
diseases, such as cardiovascular disease. To address poor adherence, physicians
may recommend adherence-improving interventions; however, such interventions
are costly and limited in their availability. Knowing which patients will stop
adhering helps distribute the available resources more effectively. We
developed a binary integer program (BIP) model to select patients for
adherence-improving intervention under budget constraints. We further studied a
long-term adherence prediction model using dynamic logistic regression (DLR)
model that uses patients' claim data, medical health factors, demographics, and
monitoring frequencies to predict the risk of future non-adherence. We trained
and tested our predictive model to longitudinal data for cardiovascular disease
in a large cohort of patients taking medication for cholesterol control seen in
the national Veterans Affairs health system. Our study shows the importance of
including past adherence to increase prediction accuracy. Finally, we assess
the potential benefits of using the prediction model by proposing an algorithm
that combines the DLR and BIP models to decrease the number of CVD events in a
population.
