Understanding the Quality of Tuberculosis Care in Uganda

项目摘要/摘要 实现高质量的结核病护理仍然是结束结核病的最重要障碍之一 结核病在全球流行，是世界主要的传染病死亡原因。护理瀑布已经被用来展示 如何很好地实施从筛查到诊断和治疗的每个阶段的护理，并提供有用的 评估质量的多个维度的框架，包括效率、有效性和及时性。 然而，改善结核病病例发现、治疗启动和治愈的一个主要障碍是缺乏 卫生系统收集和利用常规数据，以确定高质量和低质量保健的决定因素 改进干预措施的目标。本培训方案解决了这些挑战并确定了 我要通过三个科学目标和三个培训目标来解决方法上的差距。首先，我们将 对照参考标准衡量乌干达常规结核病监测数据的准确性，并 确定聚合的常规数据或常规个别患者数据的高保真采样是否可以提供 乌干达结核病护理质量的最佳业务衡量标准。接下来，我们将使用最好的方法来评估 Xpert MTB/RIF--一种新的快速、超敏感诊断试验对病例发现和治疗的影响 入会仪式。最后，我们将开发不同质量改进的潜在影响的数学模型 在乌干达，对结核病护理的干预措施层出不穷。三个培训目标与这些科学目标非常匹配 并将提供课程作业和指导的研究经验，使我能够完善和发展掌握 高级生物统计学、数学建模，以及如何与政策制定者协作解决现实世界 分析问题。我们的发现将帮助项目决策者使用监控数据，不仅是为了监控 结核病，但也是为了解决高质量护理方面的主要差距。由此得出的推论将得出关于准确性的见解。 监测数据，衡量和改善护理质量，并将这些要素付诸实施 质量改进--超越乌干达，与广泛的高结核病负担、低结核病负担-- 收入设置。 该项目将利用乌干达结核病执行机构已建立的研究合作 耶鲁大学、Makerere大学的研究人员和 乌干达国家结核病和麻风病方案(NTLP)提供了获取现有数据的途径 这将在这项提案中使用。拟议的研究和协作的跨学科培训 耶鲁大学和U-TIRC的环境将允许申请者发展定量方法方面的专业知识 传染病流行病学。在完成奖学金后，申请者将处于有利地位，可以寻求 作为一名独立研究员的职业机会，在学术界、政府和 和其他公共卫生合作伙伴。

Project Summary/Abstract Achieving high-quality care for tuberculosis (TB) remains one of the most important obstacles to ending the global epidemic of TB, the world's leading infectious cause of death. Care cascades have been used to show how well each stage of care is implemented from screening to diagnosis and treatment, and to provide a useful framework for assessing multiple dimensions of quality, including efficiency, effectiveness, and timeliness. However, a major barrier to improving TB case finding, treatment initiation, and cure is a lack of capacity for health systems to collect and utilize routine data to identify determinants of high- and low-quality care and targets for improvement interventions. This training proposal addresses these challenges and identifies methodological gaps for me to address through three scientific aims and three training aims. First, we will measure the accuracy of routine TB surveillance data in Uganda as compared to a reference standard and determine if aggregated routine data or high-fidelity sampling of routine individual-patient data can provide the best operational measure of the quality of TB care in Uganda. Next, we will use the best approach to evaluate the impact of Xpert MTB/RIF, a novel rapid, ultra-sensitive diagnostic test, on case finding and treatment initiation. Finally, we will develop a mathematical model of the potential impact of different quality improvement interventions on the TB care cascade in Uganda. Three training aims are well-matched to these scientific aims and will provide coursework and mentored research experiences to allow me to refine and develop mastery in advanced biostatistics, mathematical modeling, and in how to collaborate with policymakers to solve real-world analytical problems. Our findings will help program decision makers use surveillance data not only to monitor TB, but also to address key gaps in quality care. The resulting inferences will yield insights about the accuracy of surveillance data, measuring and improving the quality of care, and operationalizing these elements for quality improvement – that go beyond Uganda and have relevance in a wide range of high TB burden, low- income settings. This project will draw upon established research collaborations at the Uganda Tuberculosis Implementation Research Consortium (U-TIRC) between investigators at Yale University, Makerere University, and the Uganda National Tuberculosis and Leprosy Programme (NTLP) who have provided access to the existing data that will be used in this proposal. The proposed research and the collaborative, interdisciplinary training environment at Yale and U-TIRC will allow the applicant to develop expertise in quantitative methods for infectious disease epidemiology. After completing the fellowship, the applicant will be well-positioned to seek career opportunities as an independent researcher working collaboratively between academic, government, and other public health partners.