Diabetes is a global health priority, especially in low- and-middle-income countries, where over 50% of premature deaths are attributed to high blood glucose. Several studies have demonstrated the feasibility of using Community Health Worker (CHW) programs to provide affordable and culturally tailored solutions for early detection and management of diabetes. Yet, scalable models to design and implement CHW programs while accounting for screening, management, and patient enrollment decisions have not been proposed. We introduce an optimization framework to determine personalized CHW visits that maximize glycemic control at a community-level. Our framework explicitly models the trade-off between screening new patients and providing management visits to individuals who are already enrolled in treatment. We account for patients' motivational states, which affect their decisions to enroll or drop out of treatment and, therefore, the effectiveness of the intervention. We incorporate these decisions by modeling patients as utility-maximizing agents within a bi-level provider problem that we solve using approximate dynamic programming. By estimating patients' health and motivational states, our model builds visit plans that account for patients' tradeoffs when deciding to enroll in treatment, leading to reduced dropout rates and improved resource allocation. We apply our approach to generate CHW visit plans using operational data from a social enterprise serving low-income neighborhoods in urban areas of India. Through extensive simulation experiments, we find that our framework requires up to 73.4% less capacity than the best naive policy to achieve the same performance in terms of glycemic control. Our experiments also show that our solution algorithm can improve upon naive policies by up to 124.5% using the same CHW capacity.

翻译：糖尿病是一项全球性健康优先事项，尤其在低收入和中等收入国家，超过50%的过早死亡与高血糖相关。多项研究已证明，使用社区卫生工作者项目提供负担得起且文化适宜的解决方案进行糖尿病早期检测和管理的可行性。然而，尚未提出兼顾筛查、管理和患者登记决策的可扩展社区卫生工作者项目设计与实施模型。我们引入了一个优化框架，用于确定个性化社区卫生工作者家访计划，以最大化社区层面的血糖控制水平。该框架明确建模了筛查新患者与为已登记治疗患者提供管理家访之间的权衡关系。我们考虑了患者的动机状态，该状态会影响其登记或退出治疗的决定，进而影响干预效果。通过将患者建模为效用最大化主体并纳入双层提供者问题中，我们采用近似动态规划求解该问题。通过估计患者的健康状态和动机状态，我们的模型在制定家访计划时考虑了患者在决定登记治疗时的权衡取舍，从而降低退出率并改善资源配置。我们利用印度城市地区服务低收入社区的社会企业的运营数据，生成了社区卫生工作者家访计划。通过大量仿真实验，我们发现，在实现同等血糖控制效果时，该框架所需容量最多比最优朴素策略低73.4%。实验还表明，在相同社区卫生工作者容量下，我们的求解算法相比朴素策略可提升高达124.5%的性能。