Seroepidemiologic methods to identify hotspots of trachoma and predict future infection

确定沙眼热点并预测未来感染的血清流行病学方法

• 批准号: 9974479
• 负责人: Benjamin F Arnold
• 金额: $ 8.77万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-09 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9974479
• 关键词: Age; Antibiotics; Antibodies; Antibody Response; Area; Azithromycin; Biological Markers; Blindness; Characteristics; Child; Chlamydia trachomatis; Cholera; Clinical; Cluster randomized trial; Communicable Diseases; Country; Data; Data Science; Development; Disease; Enrollment; Ethiopia; Eye Infections; Face; Funding; Future; Goals; Handwashing; Heterogeneity; Hygiene; Immunoglobulin G; Individual; Infection; Intervention; Language; Machine Learning; Malaria; Masks; Measurement; Measures; Methods; Monitor; Operative Surgical Procedures; Pharmaceutical Preparations; Population; Prevalence; Public Health; Receiver Operating Characteristics; Relative Risks; Resolution; Sanitation; Scanning; Serological; Seroprevalences; Source; Surveys; Symptoms; Testing; Time; Trachoma; United States National Institutes of Health; Visit; Water; Work; World Health Organization; base; clinical biomarkers; clinical predictors; design; high risk population; improved; infection risk; machine learning method; method development; novel; population based; programs; randomized trial; remote sensing; sample collection; sex; transmission process

Project Summary / Abstract Background: Trachoma, caused by ocular infection with Chlamydia trachomatis, is the leading cause of infectious blindness worldwide and has been targeted for global elimination as a public health problem by 2020. This goal will be achieved in many countries, but some regions in Ethiopia maintain persistently high levels of infection despite >10 years of intensive control activities. A small proportion of the population likely harbors the majority of trachoma infections, with foci of infection (“hotspots”) at or below the village scale; the operational challenge is accurately predicting where they are with existing data. Advances in machine learning and spatial data science have demonstrated marked improvements in the spatial resolution of predictions for diseases like malaria. Among available biomarkers of trachoma, IgG antibody responses in children could enable more accurate predictions because they integrate exposure over time and reflect recent transmission. Aims: The principal aims of this study are to evaluate whether antibody measurements can identify stable hotspots of trachoma infection, and whether a novel machine learning approach can accurately predict village- level trachoma infection forward in time (up to 3 years). We hypothesize that infection will be concentrated in the population and that hotspots of infection will be at the village level. We further hypothesize that antibody measurements in young children will provide a stable source of information about trachoma transmission that will enable us to accurately predict villages with high levels of future C. trachomatis infection. Methods: To test our hypotheses, we will draw on measurements from a well characterized population across 40 villages enrolled in a NIH-funded cluster randomized trial in Ethiopia’s Amhara Region (U10-EY023939). The three-year trial is designed to measure the effect of improved water, sanitation, and handwashing (WASH) on trachoma infection in the absence of azithromycin treatment. The trial has collected clinical and biomarker measurements from approximately 2,400 children ages 0-9 years at enrollment and in annual visits over 3 years. We will characterize the spatial scale of transmission using the !-statistic, which equals the relative risk of infection within different distances of cases. We will use a permutation-based, spatial scan statistic to identify hotspots using IgG antibody and PCR measures in each year, and will determine if they are stable over time. Using geospatial ensemble machine learning, we will predict trachoma seroprevalence as a function of remotely sensed, geospatial information and limited enrollment characteristics. We will rank order villages by predicted seroprevalence, and will assess the proportion of PCR C. trachomatis infections in top-ranked villages 1, 2, and 3 years later. We will repeat the analysis using predicted clinical symptoms as a comparator. The development of methods to make accurate, fine-scale predictions of future C. trachomatis infection will lay the groundwork for a future adaptive randomized trial that preferentially allocates more intensive intervention to villages predicted at enrollment to have high future levels of infection.

项目概要/摘要 背景：沙眼是由沙眼衣原体眼部感染引起的，是引起沙眼的主要原因。 世界范围内的传染性失明已成为全球消除的公共卫生问题 2020年，这一目标将在许多国家实现，但埃塞俄比亚部分地区保持持续较高水平 尽管进行了 10 年以上的强化控制活动，但仍有一小部分人口可能受到感染。 存在大部分沙眼感染，感染灶（“热点”）等于或低于村庄规模； 运营挑战是利用现有数据准确预测它们的位置。 和空间数据科学已经证明预测的空间分辨率显着提高 在沙眼的可用生物标志物中，儿童的 IgG 抗体反应可能会导致疟疾等疾病。 可以实现更准确的预测，因为它们整合了一段时间内的暴露情况并反映了最近的传播情况。 目的：本研究的主要目的是评估抗体测量是否可以识别稳定的抗体。 沙眼感染的热点地区，以及一种新颖的机器学习方法是否可以准确预测村庄 我们努力将沙眼感染的时间提前（最多3年）。 人口以及感染热点将在村庄一级，我们进一步追踪该抗体。 对幼儿进行测量将提供有关沙眼传播的稳定信息来源 将使我们能够准确预测未来沙眼衣原体感染水平较高的村庄。 方法：为了检验我们的假设，我们将利用来自各地区特征明确的人群的测量结果 40 个村庄参加了 NIH 资助的埃塞俄比亚阿姆哈拉地区整群随机试验 (U10-EY023939)。 这项为期三年的试验旨在衡量改善水、卫生设施和洗手 (WASH) 的效果 该试验收集了未经阿奇霉素治疗的沙眼感染的临床和生物标志物。 大约 2,400 名 0-9 岁儿童在入学时和每年超过 3 次的访问中进行测量 我们将使用 ! 统计量来表征传播的空间规模，该统计量等于相对风险。 我们将使用基于排列的空间扫描统计来计算病例不同距离内的感染情况。 每年使用 IgG 抗体和 PCR 措施识别热点，并确定它们是否稳定 使用地理空间集成机器学习，我们将预测沙眼血清流行率作为时间的函数。 我们将根据遥感、地理空间信息和有限的招生特征对村庄进行排序。 预测血清流行率，并将评估 PCR 沙眼衣原体感染在排名最高的人群中所占的比例 我们将在 1、2 和 3 年后使用预测的临床症状作为比较来重复分析。 开发对未来沙眼衣原体感染进行准确、精细预测的方法将为 为未来的适应性随机试验奠定基础，该试验优先分配更密集的干预 入学时预计村庄未来的感染率很高。