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 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- 流行国家的儿童年龄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（RDT）的干预措施已被证明是 有效，但常规使用疟疾 RDT 进行大规模筛查既昂贵又不切实际。因此， 学龄儿童常常被排除在外。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发挥着至关重要的作用。我们假设血液的组合 血红蛋白水平和急性未分化发热性疾病评估可以对学龄儿童进行风险分层 谁将从疟疾 RDT 中受益并避免不必要的 RDT。学龄儿童的疟疾感染是 与贫血密切相关。因此，无创血液血红蛋白水平读数非常有益 用于识别无症状（未检测到）的无热疟疾感染。我们将利用最近的优势 开发了 mHealth 方法，可以通过数字照片可靠地预测血液中的血红蛋白水平 低端智能手机拍摄的内眼睑。在目标 1（R21 阶段）中，我们将完善 mHealth 血液 适用于卢旺达学龄儿童（6 至 15 岁）的血红蛋白计算算法。这 所提出的机器学习方法将混合深度学习和统计学习，以准确和 使用未经修改的智能手机精确测量学龄儿童血液中的血红蛋白含量。在 目标 2（R33 阶段），我们将开发移动医疗风险分层模型，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 确定将从疟疾 RDT 中受益并需要疟疾确诊的患者。我们将 进一步制定先进的风险分层模型，可以预测分子测试确诊的疟疾。在 目标 3（R33 阶段），我们将实施一个移动医疗应用程序，将疟疾风险分层与 现有的电子健康记录（EHR）系统。我们将把移动医疗技术融入到Android- 为我们的社区卫生工作者 (CHW) 和卫生机构提供基于 EHR 集成的移动应用程序 学习设置。我们还将包括一个数字报告平台，以取代纸质患者数据收集 供社区卫生工作者使用，并允许自动传输到我们研究环境中当前使用的 EHR 系统中。后 成功完成后，我们期望改善学龄儿童的疟疾诊断和管理， 通过减少对硬件依赖的移动医疗技术为社区卫生工作者和医疗机构提供支持。拟议的 数据驱动和互联的移动医疗技术可以最大限度地在全国范围内推广具有成本效益的医疗服务 卢旺达的疟疾诊断和管理，有可能为人们提供流动性、简单性和可负担性 在其他资源有限的环境中进行快速和可扩展的适应。