An RCT to Improve the South African Government's Community Health Workers' Capacities to Deliver Evidence-based Interventions for Optimizing HIV Outcomes and Reducing its Comorbidities

摘要 非洲的卫生服务，特别是以社区为基础的卫生服务的特点是效率低下， 能力有限，人员培训不足。在这些系统中，医疗质量是最重要的 关于人员：培训、监测和持续的执行质量。南非正在部署 65,000名社区卫生工作者(CHW)，然而，目前几乎没有任何系统，也没有 关于如何培训、监测和确保这些CHW提供高质量护理的系统尚未规划。在试行中 在过去的六个月里，在南非的东开普省，我们发现25%的CHW没有上班 任何时候，当监管第一次被外包的时候。这样的性能和要监控的故障 表现，并谴责缺席的后果，往往是典型的CHW服务交付#年 中低收入国家(LMIC)。建立在成功的有效性研究结果的基础上 在南非开普敦的城镇，我们的目标是展示培训、监测和数据信息 监督将导致CHW在妇幼保健(MCH)方面比前两项有显著的好处 多年来，特别是艾滋病毒携带者的母亲、抑郁的母亲和十几岁的母亲。在来自 省政府、Philani的创新培训和监测战略旨在证明我们 可以缩小我们知道的(基于科学的)和我们为改善农村健康所做的事情之间的差距 目前缺乏问责和效率的社区。 加州大学洛杉矶分校、斯泰伦博什大学、齐斯图勒医院和菲拉尼母婴健康和营养信托基金 将在政府资助的CHW提供的第二次家访有效性RCT上进行合作。 加州大学洛杉矶分校将随机选择24个匹配的地区，每个地区都有政府资助的CHW：1)接受过家访培训， 受监督和例行监测(即Philani干预模式[PIM]，n=420名妇女，12个地区)；或 标准护理(SC)控制CHW的家访，他们接受当前标准的培训、监测和 监督(n=420名妇女，12个地区)。在样本中，25%的人将是青春期母亲，30%的人将是MLH； 25%的人会在怀孕期间饮酒；22%的人会感到抑郁。将进行独立评估 StellenBosch大学在怀孕期间，出生后两周内，6个月后，15个月和24个月后。我们 将构建一个全面的分析测试，在综合测试的同时检查多个结果。 我们将监测MLH的艾滋病毒检测、与护理的联系、治疗依从性和保留；孕产妇健康 指标(体重、糖尿病、精神健康状况、伴侣关系、饮酒和伴侣暴力)；以及 儿童的身体发育、认知、情感和行为调整。我们预计PIM将显著改善 总体结果和高危亚组(MLH、抑郁、青少年、饮酒)的结果。分析了 从CHW(持续时间、频率、内容区域、使用的技能、电话联系)进行实时流程测量 在手机上收集的数据将使我们能够设计和重新设计实施战略。

Abstract Health services and especially community-based health services in Africa are characterized by inefficiency, limited capacity, and poorly-trained personnel. The quality of care in these systems is dependent most heavily on personnel: training, monitoring, and ongoing quality of the implementation. South Africa is deploying 65,000 community health workers (CHW), however, there are almost no systems currently, nor are the systems yet planned for how to train, monitor, and ensure quality care is delivered by these CHW. In a pilot over the last six months in the Eastern Cape of South Africa, we found 25% of CHW not showing up for work at any time, when supervision was out-sourced for the first time. Such performance and failures to monitor performance, with consequences of censuring non-attendance, are often typical of CHW service delivery in low and middle income countries (LMIC). Building on the results of a successful effectiveness study in the townships of Cape Town, South Africa, we aim to show that training, monitoring, and data-informed supervision will result in CHW having significant benefits in maternal and child health (MCH) over the first two years of life, particularly mothers living with HIV, depressed mothers, and teenage mothers. With support from the provincial government, Philani's innovative training and monitoring strategies aim to demonstrate that we can decrease the gap between what we know (based on science) and what we do to improve health in rural communities that currently lack accountability and efficiency. UCLA, Stellenbosch University, Zithulele Hospital, and the Philani Maternal, Child Health and Nutrition Trust will collaborate on this second effectiveness RCT of home visiting delivered by government-funded CHW. UCLA will randomize 24 matched areas, each with government-funded CHW to either: 1) home visiting trained, supervised, and routinely monitored (i.e., the Philani Intervention Model [PIM], n=420 women, 12 areas); or 2) a standard care (SC) control home visiting by CHW who receive current standards of training, monitoring, and supervision (n=420 women, 12 areas). Within the sample, 25% will be adolescent mothers; 30% will be MLH; 25% will drink alcohol in pregnancy; and 22% will be depressed. Independent assessments will be conducted by Stellenbosch University during pregnancy, within two weeks of post-birth, 6, 15, and 24 months later. We will construct an overall analytic test which examines multiple outcomes simultaneously with an omnibus test. We will monitor MLH's HIV testing, linkage to care, treatment adherence and retention; maternal health indicators (weight, diabetes, mental health status, partnerships, alcohol use, and partner violence); and, the child's physical growth, cognitive, affective, and behavioral adjustment. We expect PIM to significantly improve outcomes overall and for the high risk subgroups (MLH, depressed, adolescents, alcohol-using). Analyses of the real-time process measures from CHW (duration, frequency, content area, skill used, phone contacts) collected on mobiles will allow us to design and redesign implementation strategies.