Design of Incentive Programs for Optimal Medication Adherence in the Presence of Observable Consumption

Premature cessation of antibiotic therapy (nonadherence) is common in long treatment regimens and can severely compromise health outcomes. In this work, we investigate the problem of designing a schedule of incentive payments to induce socially optimal treatment adherence levels in a setting in which treatment adherence can be observed (e.g., through directly observed therapy for tuberculosis), but patient preferences for treatment adherence are heterogeneous and unobservable to a health provider. The novel elements of this problem stem from its institutional features: there is a single incentive schedule applied to all patients, incentive payments must be increasing in patients’ adherence, and patients cannot be a priori prohibited from any given levels of adherence. We develop models to design optimal incentives incorporating these features, and they are also applicable in other problem contexts that share the same features. We also conduct a numerical study using representative data in the context of the tuberculosis epidemic in India. Our study shows that our optimally designed incentive schedules are generally cost-effective compared with a linear incentive benchmark.