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

描述（由申请人提供）：为了响应NIH/PEPFAR RFA的实施科学和影响评估，本申请涉及这些优先领域：整合艾滋病毒初级保健和常见的合并症（抑郁症），并检查抑郁症治疗作为促进艾滋病毒护理依从性和预防传播的手段。我们的研究强调了抑郁症在挫败几个公共卫生战线上抗击艾滋病毒疾病的努力中的作用，以及撒哈拉以南非洲（SSA）艾滋病毒护理计划中抑郁症治疗的相对缺乏，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，拟议的项目将评估任务转移方法，以建立可持续抑郁症治疗的能力。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，但任务转移的方法，克服人力资源的限制已经成功的抗逆转录病毒治疗的规模在SSA，以及提供抑郁症治疗的合作护理模式与非艾滋病毒患者，但是，拟议的研究可能是第一个研究这样的模式与PLHA，并在SSA。我们将测试一种基于算法的护士驱动模型，用于管理抗抑郁治疗，包括（1）在每次诊所访视时对所有患者进行常规抑郁症筛查，以促进病例识别，（2）培训护士，通过进行初步评估，监测症状和副作用，并提出基于算法的剂量建议，协助初级保健提供者实施抗抑郁治疗，及（3）由精神科专家进行层层监督及监察，以确保护理的安全及质素。采用分组随机化设计，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。在每个研究中心，随机抽取150名抑郁症筛查阳性的医学稳定患者，随访12个月，以评估两种治疗模式在抗抑郁治疗摄入和抑郁症变化的护理质量指标上是否存在差异。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相关，包括工作和养家糊口的能力，坚持使用避孕套和艾滋病毒护理依从性。在治疗模型启动前两周以及启动后一年和两年，将对每个研究中心随机选择的100例患者的新样本进行时间和运动评估，以评估对诊所效率和患者流量的影响。如果我们证明这种任务转移的抑郁症治疗方法是可行的，有效的，并很好地融入临床系统，它将建立一个模型，解决人力资源的挑战，建立可持续的抑郁症治疗的能力。

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.