mHealth-Community Health Worker tool for comprehensive post-cesarean follow-up in rural Rwanda

项目摘要/摘要 越来越多的人接受剖腹产(剖腹产)，这有助于降低亚 撒哈拉非洲(SSA)；然而，随着剖腹产率的增加，与剖腹产率相关的比率也在上升 并发症。而在农村SSA经阴道分娩的妇女通常在他们的 来自社区卫生工作者(CHW)的家庭，大多数项目要求通过剖腹产分娩的妇女 返回健康中心或医院进行跟进，因为他们的护理增加了复杂性。设施- 以此为基础的随访可能会给母亲带来经济上的灾难性和身体上的负担，导致护理工作的延误 并增加发病风险。 这项提案的总体目标是开发一种安全的移动健康(MHealth)工具，以支持CHW领导的家庭- 卢旺达农村地区在术后第5天和第10天通过剖腹产分娩的妇女的基础随访。 在第一个R21阶段，我们将为现有的基于照片的手术部位感染(SSI)开发一个软件库 在智能手机上本地运行的诊断算法(没有互联网或手机网络连接)(目标1a)。我们 将与Insight tiv Technologies合作开发全面的mHealth-CHW工具，包括 SSI诊断算法软件库，并将三个CHW可用性评估集成到设计中 阶段(目标1b)。最后，我们将在30个CHW(AIM)组中测试新工具的可用性和可接受性 2)。当我们达到80%的可用性和可接受性时，我们将继续进入第二阶段。 赠款的R33阶段将通过以下方式研究mHealth-CHW工具用于剖腹产后续行动(目标3)的有效性 前瞻性跟踪450名通过剖腹产和实施mHealth-CHW工具分娩的妇女，并跟踪- 在5号舱和10号舱的家中。然后这些妇女将在16号舱返回医院进行体检 检查，我们将比较通过mHealth-CHW工具确定的诊断和并发症 从体检来的。然后，我们将通过随机化来评估剖腹产并发症的诊断时间。 通过mHealth-CHW工具对1350名妇女进行跟踪，对比护理标准(目标4)。最后，我们将评估 焦点组40例剖宫产产妇对mHealth-CHW随访的可接受性 讨论(目标5)。我们还将进行两个系列的研究培训，一个是定量方法，另一个是 定性方法，增强我们团队未来领导mHealth研究的能力。

Project Summary/Abstract Increased access to cesarean sections (c-sections) has contributed to the decline of maternal mortality in sub- Saharan Africa (SSA); however, as the rate of c-sections has increased, so has the rate of c-section related complications. While women who deliver vaginally in rural SSA often receive postpartum follow-up care in their homes from community health workers (CHWs), most programs require that women who deliver via c-section return to health centers or hospitals for follow-up because of the increased complexity of their care. Facility- based follow-up can be financially catastrophic and physically burdensome for mothers, leading to delays in care and increased risk for morbidity. The overall goal of this proposal is to develop a safe mobile health (mHealth) tool to support CHW-led home- based follow-up for women delivering via c-section in rural Rwanda at postoperative days (PODs) 5 and 10. In the first R21 phase, we will develop a software library for an existing photo-based surgical site infection (SSI) diagnostic algorithm to run locally on a smartphone (without internet or cell network connection) (Aim 1a). We will work with Insightiv Technologies to develop the comprehensive mHealth-CHW tool, including incorporating the SSI diagnostic algorithm software library and integrating three CHW usability assessments into the design phase (Aim 1b). Finally, we will test the usability and acceptability of the new tool in a group of 30 CHWs (Aim 2). When we achieve 80% usability and acceptability, we will continue to the second phase. The R33 phase of the grant will study the validity of the mHealth-CHW tool for c-section follow-up (Aim 3) by prospectively following 450 women delivering via c-section and implementing the mHealth-CHW tool and follow- up in their homes at PODs 5 and 10. The women will then return to the hospital at POD 16 for a physical examination and we will compare diagnoses and complications identified via the mHealth-CHW tool to those from the physical exam. We will then evaluate the time-to-diagnosis for c-section complications by randomizing 1350 women to follow-up by the mHealth-CHW tool versus standard of care (Aim 4). Finally, we will assess the acceptability of mHealth-CHW follow-up in 40 women who have delivered via c-section through focus group discussions (Aim 5). We will also conduct two research training series, one in quantitative methods and one in qualitative methods, to strengthen our team's ability to lead mHealth research in the future.