Improving the accuracy of malaria surveillance with serology and parasite genetic data

利用血清学和寄生虫遗传数据提高疟疾监测的准确性

基本信息

批准号：
10350174
负责人：
Jessica Briggs
金额：
$ 20.04万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-12-08 至 2026-11-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10350174
关键词：
Africa South of the Sahara Age Antibodies Antibody Response Award Beds Biological Biometry Biostatistical Methods Catchment Area Child Collaborations Communicable Diseases Communities Country Cross-Sectional Studies DNA Data Diagnostic Diagnostic tests Disease Surveillance Epidemiologist Epidemiology Funding Generations Genetic Genomics Goals Gold Health Health Services Accessibility Health care facility Immunity Incidence Individual Infrastructure Insecticides Intervention Laboratory Scientists Link Malaria Measurement Measures Mentored Patient-Oriented Research Career Development Award Mentorship Methods Microscopy Modeling Molecular Morbidity - disease rate Outcome Parasites Pathway interactions Persons Plasmodium falciparum Population Population Surveillance Positioning Attribute Prevalence Public Health Practice Randomized Rapid diagnostics Research Research Priority Resolution Risk Sampling Serology Serology test Site Statistical Models Surveillance Methods Surveys Techniques Testing Time Training Translating Uganda United States National Institutes of Health Validation Work base burden of illness career cohort deep sequencing density experience improved mortality novel programs response risk stratification serological marker skills surveillance data surveillance network transmission process vector control

项目摘要

Project Abstract Despite the critical need for high quality data in order to plan, implement, and evaluate malaria control interventions, malaria surveillance is particularly poor in high burden countries such as Uganda. Malaria molecular surveillance (MMS), which evaluates parasite DNA and host antibodies present in biological samples to derive epidemiologically actionable information, has the potential to improve upon current surveillance methods; however, there is limited use of these data outside of the research setting. A current research priority is to understand how serologic and parasite genetic data can be used to enhance routine malaria surveillance methods and evaluate malaria control interventions. Recently, our team was funded to directly measure malaria incidence via enhanced passive surveillance within the catchment areas around 64 health facilities throughout Uganda. These 64 clusters will then be randomized to receive one of two types of novel bednets, and cross-sectional surveys will be performed at each site 12, 24, and 36 months after the roll- out of the intervention has been completed. This K23 project offers an outstanding opportunity to leverage samples from cross-sectional surveys at these 64 sites to test the hypothesis that MMS will enable us to estimate malaria incidence with more accuracy than parasite prevalence (PfPR), using enhanced incidence data as the gold standard. Our approach will be to use established molecular techniques, including multiplex serologic assays, qPCR, and amplicon deep-sequencing, to generate molecular metrics from samples collected in these cross-sectional surveys; we will then build statistical models using these molecular metrics as variables to estimate incidence as the outcome. Aim 1 is to use serologic metrics to improve the estimation of malaria incidence in children <5 years compared to standard models based primarily on PfPR. Aim 2 is to use parasite DNA-based metrics to improve the estimation of malaria incidence in children <5 years compared to standard models based primarily on PfPR. In Aim 3, we will identify the set of MMS metrics that best estimates changes in incidence over time to determine how changes in MMS metrics (both serologic and parasite DNA-based) between each survey timepoint can be used to accurately predict changes in incidence from year to year at each site. To complete this project, I will need additional mentorship and training in seroepidemiology and biostatistical methods in addition to field experience in public health and surveillance activities as outlined in this proposal. This K23 award will provide the crucial link in my transition from a laboratory scientist to achieving my career goal of becoming a molecular epidemiologist with a focus on public health surveillance, with the skills to effectively utilize molecular data to evaluate, develop, and apply population level interventions for malaria control and elimination. I will emerge from this award prepared for a strong NIH R01 application focused on utilizing molecular data to enhance malaria surveillance in settings with poor health infrastructure in order to better target malaria control interventions.

项目摘要尽管规划、实施和评估疟疾控制迫切需要高质量数据在乌干达等高负担国家，疟疾监测尤其薄弱。疟疾分子监测 (MMS)，评估生物体内存在的寄生虫 DNA 和宿主抗体样本以获得流行病学上可行的信息，有可能改进当前的监视方法；然而，这些数据在研究环境之外的使用有限。电流研究重点是了解如何使用血清学和寄生虫遗传数据来加强日常工作疟疾监测方法并评估疟疾控制干预措施。最近，我们的团队获得了资助通过加强流域内的被动监测直接测量疟疾发病率 64 乌干达各地的卫生设施。然后，这 64 个集群将被随机分配以接收两种类型中的一种新型蚊帐，并将在滚动后 12、24 和 36 个月在每个地点进行横断面调查退出干预已经完成。这个 K23 项目提供了一个绝佳的机会来利用从这 64 个地点进行的横断面调查中获取样本，以检验 MMS 将使我们能够使用增加的发病率来估计疟疾发病率比寄生虫患病率 (PfPR) 更准确数据作为黄金标准。我们的方法将是使用已建立的分子技术，包括多重技术血清学检测、qPCR 和扩增子深度测序，从样本中生成分子指标在这些横断面调查中收集的信息；然后我们将使用这些分子指标建立统计模型作为估计发生率作为结果的变量。目标 1 是使用血清学指标来改进估计与主要基于 PfPR 的标准模型相比，<5 岁儿童的疟疾发病率。目标 2 是使用基于寄生虫 DNA 的指标来改进对 5 岁以下儿童疟疾发病率的估计主要基于 PfPR 的标准模型。在目标 3 中，我们将确定最适合的 MMS 指标集估计发病率随时间的变化，以确定 MMS 指标（血清学和每个调查时间点之间基于寄生虫 DNA 的）可用于准确预测发病率的变化每个地点年复一年。为了完成这个项目，我需要额外的指导和培训除了公共卫生和监测方面的现场经验外，还具有血清流行病学和生物统计方法本提案中概述的活动。这个 K23 奖项将为我从实验室科学家实现我的职业目标，成为一名关注公众的分子流行病学家健康监测，具有有效利用分子数据进行评估、开发和应用的技能控制和消除疟疾的人口层面干预措施。我将从这个奖项中脱颖而出，为强大的 NIH R01 应用程序侧重于利用分子数据来加强疟疾监测卫生基础设施薄弱，无法更好地开展疟疾控制干预措施。