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

利用血液分析的移动健康光谱对学龄儿童疟疾进行风险分层

• 批准号: 10527037
• 负责人: Young L Kim
• 金额: $ 17.61万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10527037
• 关键词: 15 year old; Acute; Africa; Africa South of the Sahara; Algorithms; Android; Anemia; Artemisinins; Biomedical Engineering; Blood; Cause of Death; Cellular Phone; Chemoprevention; Child; Child Health; Collection; Color; Combined Modality Therapy; Community Health Aides; Computational algorithm; Country; Data; Data Collection; Diagnosis; Diagnostic tests; Doctor of Philosophy; Electronic Health Record; Eyelid structure; Fever; Goals; Health; Health Technology; Health care facility; Healthcare Systems; Hemoglobin; Hemoglobin concentration result; Hybrids; Infection; Intervention; Investments; Learning; Machine Learning; Malaria; Malaria Diagnosis; Malaria Diagnostic; Mass Screening; Measurement; Measures; Methods; Mobile Health Application; Modeling; Molecular; Paper; Parasites; Patients; Performance; Phase; Plasmodium falciparum; Play; Public Health; Rapid diagnostics; Reading; Reporting; Research; Resolution; Resource-limited setting; Resources; Risk; Role; Rwanda; School-Age Population; Schools; Spectrum Analysis; Structure of palpebral conjunctiva; System; Telemedicine; Test Result; Testing; Undifferentiated; Universities; age group; associated symptom; base; clinically relevant; cognitive enhancement; cognitive function; cost effective; deep learning; digital; eHealth; empowered; human capital; imaging Segmentation; improved; mHealth; malaria infection; malaria transmission; mobile application; response; risk stratification; scale up; screening; standard of care; statistical learning; transmission process

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.

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。学龄的孩子最多 通常感染疟疾寄生虫，估计有2亿危险。疟疾筛查学校 - 流行国家的年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）。疟疾快速诊断测试（RDT）的干预措施已证明是 有效，但通常会在常规的疟疾RDT上进行大规模筛查是昂贵且不切实际的。因此， 学龄儿童经常被排除在外。在这方面，疟疾RDT的风险分层（预筛查）可以 在疟疾的诊断和管理中起关键作用。我们假设血液结合 血红蛋白水平和急性未分化的高温疾病评估可能会使学龄儿童风险划分 谁将受益于疟疾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，非侵入性血红蛋白水平读数可能是非常有益的 用于识别无症状（未发现的）Afebrile疟疾感染。我们将利用我们最近的优势 开发的MHealth方法可以可靠地预测来自数字照片 低端智能手机采用的内眼眼睑。在AIM 1（R21阶段）中，我们将完善MHealth的血液 血红蛋白计算算法应用于卢旺达的学龄儿童（6至15岁）。这 提出的机器学习方法将使深度学习和统计学习融合到准确和 使用未修饰的智能手机，精确测量学龄儿童血红蛋白含量。在 AIM 2（R33阶段），我们将开发一个MHealth风险分层模型，以确定疟疾RDT的需求 在学龄儿童中。我们将研究MHealth血液血红蛋白评估的附加值 识别将受益于疟疾RDT的患者，并且需要确认性疟疾诊断。我们将 进一步制定了一个晚期风险分层模型，该模型可以预测分子测试确认的疟疾。在 AIM 3（R33阶段），我们将实施MHealth应用程序，该应用程序将疟疾风险分层整合到 现有的电子健康记录（EHR）系统。我们将将MHealth技术纳入Android- 基于社区卫生工作者的EHR集成移动应用程序（CHW）和我们的医疗机构 学习设置。我们还将包括一个数字报告平台，以取代基于纸张的患者数据收集 对于CHW，可以在我们的研究环境中自动传输到当前使用的EHR系统。后 成功完成，我们希望改善学龄儿童的疟疾诊断和管理， 通过使用较少依赖硬件的MHealth技术赋予CHW和健康设施的能力。提议 数据驱动和连接的MHealth技术可以最大程度地提高全国性的成本效益 卢旺达的疟疾诊断和管理，可能提供机动性，简单性和负担能力 在其他资源有限的设置中快速可扩展的适应。