Premature cessation of antibiotic therapy (non-adherence) 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 the presence of (a) budget constraints, (b) heterogeneous patient preferences for treatment adherence, which are furthermore (c) unobservable to the social planner. Although similar incentive design problems have been studied in the field of contract theory, a unique challenge in this problem is that any prior commitment that a patient makes to a given level of treatment adherence typically cannot be enforced and contracted upon in practice. Incorporating non-contractibility into the model renders standard contract-theoretic models and analyses inapplicable. Consequently, we had to develop new approaches to handle this problem feature. In an extension, we consider how an additional constraint can be put on the shape of the incentive payment schedule: this constraint is motivated by pragmatic issues of implementing such incentive payments in resource-poor clinics serving a primarily low-income population. We show that the optimal payment schedule can be constructed through the solution of a single convex optimization problem in the base case and a sequence of convex optimization problems in the extension. In our numerical analysis, we conduct a simulation study based on representative data in the context of the tuberculosis epidemic in India. Our simulation shows that using either the base case or extension incentive schedules to encourage treatment adherence would be very cost-effective, although costs may vary widely across incentive schedule functional forms.

Dynamic resource allocation for prevention, screening, and treatment interventions in population disease management has received much attention in recent years due to excessive healthcare costs. In this paper, our goal is to design a model and an efficient algorithm to optimize sequential intervention policies under resource constraints to improve population health outcomes. We consider a discrete-time finite-horizon budget allocation problem with disease progression within a closed birth-cohort population. To address the computational challenges associated with large-state and multiple-period dynamic decision-making problems, we propose a low-fidelity approximation that preserves the population dynamics under a stationary policy. To improve the healthcare interventions in terms of population health outcomes, we then embed the low-fidelity approximation into a high-fidelity optimization model to efficiently identify a good non-stationary sequential intervention policy. Our approach is illustrated by a numerical example of screening and treatment policy implementation for chronic hepatitis C virus (HCV) infection over a budget planning period. We numerically compare our Multi-Fidelity Rollout Algorithm (MF-RA) to a grid search approach and demonstrate the similarity of sequential policy trends and closeness of overall health outcomes measured by quality-adjusted life-years (QALYs) and the total number of individuals that undergo screening and treatment for different annual budgets and birth-cohorts. We also show how our approach scales well to problems with high dimensionality due to many decision periods by studying time to elimination of HCV.