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

项目概要/摘要 疟疾是撒哈拉以南非洲最严重的公共卫生问题之一。学龄儿童最多 通常感染疟疾寄生虫，估计有2亿人处于危险之中。学校疟疾筛查- 在疟疾流行国家，15岁以下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）。基于疟疾快速诊断测试的干预措施已被证明是有效的。 有效，但在常规基础上用疟疾RDT进行大规模筛查既昂贵又不切实际。因此，在本发明中， 学龄儿童往往被排除在外。在这方面，疟疾RDT的风险分层（预筛选）可 在疟疾的诊断和管理中发挥关键作用。我们假设血液和 血红蛋白水平和急性未分化发热性疾病评估可对学龄儿童进行风险分层 他们将受益于疟疾RDT并避免不必要的RDT。学龄儿童的疟疾感染是 与贫血密切相关。因此，非侵入性血液血红蛋白水平读数可以是非常有益的 用于识别无症状（未检测到）的无发热疟疾感染。我们将利用我们最近 开发的mHealth方法，可以可靠地预测血液血红蛋白水平的数字照片， 用低端智能手机拍摄的内眼睑在目标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）。的 提出的机器学习方法将混合深度学习和统计学习， 使用未经改装的智能手机精确测量学龄儿童的血红蛋白含量。在 目标2（R33阶段），我们将开发一个移动健康风险分层模型，以确定疟疾RDT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， 确定将受益于疟疾RDT并需要确诊疟疾的患者。我们将 进一步制定一个先进的风险分层模型，可以预测分子检测证实的疟疾。在 目标3（R33阶段），我们将实施一个移动健康应用程序，将疟疾风险分层与 电子健康记录（EHR）系统。我们将把移动健康技术整合到一个安卓系统中- 基于EHR的集成移动的应用程序，适用于我们的社区卫生工作者（CHW）和卫生设施 研究设置。我们还将包括数字报告平台，以取代基于纸张的患者数据收集 并允许自动传输到我们的研究设置中当前使用的EHR系统。后 我们预期，如果该项目顺利完成，学龄儿童中疟疾的诊断和管理将得到改善， 为社区卫生工作者和卫生设施提供较少依赖硬件的移动医疗技术。拟议 数据驱动和互联的移动医疗技术可以最大限度地扩大全国范围内的成本效益 在卢旺达的疟疾诊断和管理，可能提供流动性，简单性和负担得起的， 在其他资源有限的环境中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.