Risk stratification of malaria among school-age children with mHealth spectroscopy of blood analysis

项目摘要/摘要 疟疾是撒哈拉以南非洲最严重的公共卫生问题之一。学龄儿童是最 普遍感染疟疾寄生虫，估计有2亿人面临风险。学校疟疾筛查- 流行国家的适龄儿童在两个方面至关重要：疟疾传播和教育表现 (人力资本投资)。基于疟疾快速诊断试验(RDT)的干预措施已表明 有效，但常规使用疟疾RDTs进行大规模筛查既昂贵又不切实际。结果, 学龄儿童经常被排除在外。在这方面，疟疾RDTs的风险分层(预筛选)可以 在疟疾的诊断和管理中发挥关键作用。我们假设一组血迹 血红蛋白水平和急性未分化发热疾病评估可以对学龄儿童进行风险分层 世卫组织将受益于疟疾RDTs，并避免不必要的RDTs。学龄儿童中的疟疾感染是 与贫血密切相关。因此，无创血色素水平读数可能非常有益。 用于识别无症状(未发现)的无热性疟疾感染。我们将利用我们最近的 开发的mHealth方法可以可靠地从数字照片中预测血液中的血红蛋白水平 低端智能手机拍摄的内眼皮。在目标1(R21阶段)，我们将完善mHealth血液 血红蛋白计算算法适用于卢旺达学龄儿童(6至15岁)。这个 提出的机器学习方法将深度学习和统计学习相结合，以准确和 使用未经改装的智能手机精确测量学龄儿童的血液血红蛋白含量。在……里面 目标2(R33阶段)，我们将开发一个mHealth风险分层模型来确定疟疾RDTs的需求 在学龄儿童中。我们将调查mHealth血液血红蛋白评估的附加值 确定将从疟疾RDTs中受益并需要确诊疟疾诊断的患者。我们会 进一步制定一个先进的风险分层模型，可以预测经分子测试确认的疟疾。在……里面 目标3(R33阶段)，我们将实施mHealth应用程序，将疟疾风险分层与 现有电子健康记录(EHR)系统。我们将把移动健康技术整合到安卓系统中- 基于EHR的集成移动应用程序，适用于我们的社区卫生工作者(CHW)和卫生机构 检查设置。我们还将包括一个数字报告平台，以取代纸质的患者数据收集 对于CHW，并允许在我们的研究环境中自动传输到当前使用的EHR系统。之后 成功完成后，我们预计将改善学龄儿童的疟疾诊断和管理， 通过使用对硬件依赖性较低的移动健康技术来增强CHW和卫生设施的能力。建议数 数据驱动和互联的移动健康技术可以最大限度地在全国范围内扩大成本效益 卢旺达的疟疾诊断和管理，可能为以下方面提供移动性、简单性和可负担性 在其他资源有限的情况下进行快速和可扩展的适应。

PROJECT SUMMARY/ABSTRACT Malaria is one of the most serious public health problems in sub-Saharan Africa. School-age children are most commonly infected with malaria parasites with an estimated 200 million at risk. Malaria screening for school- age children in endemic countries is critical in two aspects: malaria transmission and educational performance (human capital investment). Malaria rapid diagnostic test (RDT)-based interventions have shown to be effective, but mass screening with malaria RDTs on a routine basis is expensive and impractical. As a result, school-age children are often excluded. In this respect, risk stratification (prescreening) for malaria RDTs can play a critical role in the diagnosis and management of malaria. We hypothesize that a combination of blood hemoglobin level and acute undifferentiated febrile illness assessments can risk-stratify school-age children who will benefit from malaria RDTs and avoid unnecessary RDTs. Malaria infections in school-age children are strongly associated with anemia. Thus, noninvasive blood hemoglobin level readings can be highly beneficial for identifying asymptomatic (undetected) afebrile malaria infections. We will take advantage of our recently developed mHealth method that can reliably predict blood hemoglobin levels from digital photographs of the inner eyelid taken by a low-end smartphone. In Aim 1 (R21 phase), we will perfect an mHealth blood hemoglobin computation algorithm applied to school-age children (6 to 15 years of age) in Rwanda. The proposed machine learning approach will hybridize deep learning and statistical learning to accurately and precisely measure blood hemoglobin content among school-age children using an unmodified smartphone. In Aim 2 (R33 phase), we will develop an mHealth risk-stratification model to determine the need of malaria RDTs among school-age children. We will investigate the added value of mHealth blood hemoglobin assessments in identifying patients who will benefit from malaria RDTs and will need confirmatory malaria diagnosis. We will further formulate an advanced risk-stratification model that can forecast molecular test-confirmed malaria. In Aim 3 (R33 phase), we will implement an mHealth application integrating malaria risk stratification with the existing electronic health record (EHR) system. We will incorporate the mHealth technology into an Android- based EHR-integrated mobile application for community health workers (CHWs) and health facilities in our study settings. We will also include a digital reporting platform to replace paper-based patient data collection for CHWs and allow for automatic transmission into the currently used EHR system in our study settings. After successful completion, we expect to improve malaria diagnosis and management among school-age children, by empowering CHWs and health facilities with less hardware-dependent mHealth technologies. The proposed data-driven and connected mHealth technologies can maximize the nationwide scale-up of cost-effective malaria diagnosis and management in Rwanda, potentially offering mobility, simplicity, and affordability for rapid and scalable adaptation in other resources-limited settings.