Task Shifting Model of Depression Treatment to Unleash HIV Public Health Benefits

说明(申请人提供)：为响应NIH/PEPFAR RFA的实施科学和影响评估，本申请涉及以下优先领域：将艾滋病毒初级保健与共同发病(抑郁症)相结合，并审查抑郁症治疗作为促进艾滋病毒护理坚持和预防传播的一种手段。在我们的研究的基础上，强调了抑郁症在几个公共卫生战线上阻碍抗击艾滋病毒疾病的努力的作用，以及尽管我们观察到抗抑郁药对艾滋病毒/艾滋病患者(PLHA)的疗效，但整个撒哈拉以南非洲地区(SSA)的艾滋病毒护理计划中相对缺乏抑郁症治疗，拟议的项目将评估建立可持续抑郁症治疗能力的任务转移方法。缺乏训练有素的心理健康专家是提供治疗的一个主要障碍，然而，克服人力资源限制的任务转移方法已经成功地在SSA扩大了ART规模，并在与非艾滋病毒患者的协作护理模式中提供了抑郁症治疗；然而，拟议的研究可能是与PLHA和SSA对这种模式的第一次研究。我们将测试一种基于算法的、由护士驱动的抗抑郁治疗管理模式，该模式包括(1)通过在所有患者每次就诊时进行常规抑郁症筛查来促进病例识别；(2)培训护士通过执行初始评估、监测症状和副作用并提出基于算法的剂量建议来协助初级保健机构实施抗抑郁治疗；以及(3)由精神科专家进行分层监督和监测，以确保护理的安全性和质量。使用群组随机设计，任务转移模式将在六家诊所实施，而其他六家诊所将使用标准的医生驱动模式。在每个地点，将对150名筛查抑郁症呈阳性的医学稳定患者进行随机抽样，为期12个月，以评估两种治疗模式在抗抑郁药物治疗摄取和抑郁变化的护理质量指标上是否有所不同。我们还将评估抑郁症的变化是否与关键的经济和公共卫生结果有关，包括工作和养家糊口的能力、持续使用避孕套和坚持艾滋病毒护理。在治疗模式开始前的两周内以及治疗模式开始后的一年和两年内，将在每个地点随机选择100名患者的新样本进行时间和运动评估，以评估对临床效率和患者流动的影响。如果我们证明这种抑郁症治疗的任务转移方法是可行的、有效的，并很好地整合到临床系统中，它将建立一个模式，解决建立可持续抑郁症治疗能力的人力资源挑战。

DESCRIPTION (provided by applicant): In response to the NIH/PEPFAR RFA for implementation science and impact evaluation, this application addresses these priority areas: integration of HIV primary care and a common co-morbidity (depression), and examining depression treatment as a means to promote HIV care adherence, and prevention of transmission. Building on our research that highlights the role of depression in thwarting efforts to combat HIV disease on several public health fronts, and the relative absence of depression treatment in HIV care programs throughout sub-Saharan Africa (SSA) despite our observed efficacy of antidepressants with persons living with HIV/AIDS (PLHA), the proposed project will evaluate a task-shifting approach to building the capacity for sustainable depression treatment. Lack of trained mental health specialists is a major barrier to provision of treatment, yet task-shifting approaches to overcoming human resource limitations have been successful for ART scale-up in SSA, as well as provision of depression treatment in collaborative care models with non-HIV patients; however, the proposed study may be the first study of such a model with PLHA, and in SSA. We will test an algorithm-based, nurse-driven model for managing antidepressant treatment that includes (1) case identification facilitated by routine depression screening at each clinic visit for all patients, (2) training nurses to assist primary care provides in implementing antidepressant treatment by performing the initial evaluation, monitoring symptoms and side effects, and making algorithm-based dose recommendations, and (3) layers of supervision and monitoring by psychiatric specialists to ensure safety and quality of care. Using a cluster- randomization design, the task-shifting model will be implemented at six clinics, while six other clinics will use the standard physician-driven model. At each site, a random sample of 150 medically stable patients who screen positive for depression will be followed for 12 months to assess whether the two models of treatment differ on the quality of care indicators of antidepressant treatment uptake and change in depression. We will also assess whether change in depression is associated with key economic and public health outcomes including ability to work and provide for one's family, consistent condom use, and HIV care adherence. Time-and-motion assessments will be done with new samples of 100 randomly selected patients at each site during two week periods before, and one and two years after, initiation of the treatment models to assess impact on clinic efficiency and patient flow. If we demonstrate that this task-shifting approach to depression treatment is feasible, effective and well incorporated into clinic systems, it will establish a model that addresses the human resource challenges to building the capacity for sustainable depression treatment.