Data Science and Analytical Core [Parent Title: PREVENTING INFANT INFECTIONS WITH IMPLEMENTATION SCIENCE IN MALAWI]

数据科学和分析核心 [父标题：在马拉维通过实施科学预防婴儿感染]

• 批准号: 10701194
• 负责人: Maganizo Chagomerana
• 金额: $ 20.31万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10701194
• 关键词: AIDS clinical trial group; Acceleration; Active Learning; Adherence; Aptitude; Biometry; Breast Feeding; Caring; Childhood; Clinical Investigator; Clinical Trials; Clinical Trials Network; Collaborations; Collection; Common Data Element; Competence; Conceptions; Data; Data Analyses; Data Analytics; Data Collection; Data Management Resources; Data Science; Database Management Systems; Databases; Dedications; Development; Discipline; Electronics; Ensure; Epidemiology; Evaluation; Faculty; Family; Funding; Goals; HIV prevention trials network; Health; Incidence; Infant; Information Systems; Intervention; Link; Malawi; Measurement; Mentors; Methods; Modeling; Monitor; Mothers; Observational Study; Outcome; Paper; Parents; Patient Outcomes Assessments; Prevalence; Program Research Project Grants; Quality Control; Registries; Regulation; Research; Research Design; Research Methodology; Research Personnel; Research Project Grants; Resource Sharing; Risk Factors; Sample Size; Scientist; Security; Source; Statistical Data Interpretation; System; Systems Development; Training; United States National Institutes of Health; Vertical Disease Transmission; Viral Load result; Woman; algorithm development; analytical method; built environment; clinical care; cohort; data management; data quality; design; diverse data; implementation research; implementation science; improved; infant infection; intervention effect; maternal outcome; mathematical model; meetings; novel strategies; pediatric human immunodeficiency virus infection; perinatal HIV; pre-exposure prophylaxis; prediction algorithm; pregnant; prevent; programs; quality assurance; skills; statistics; synergism; tool; transmission process; uptake

Data Science and Analytical Core – Summary Achieving elimination of mother to child transmission (EMTCT) will depend on successful collaboration of scientists and practitioners from diverse fields. Essential among these fields are analytical disciplines, especially epidemiology, biostatistics, and implementation science. These disciplines are dedicated to applying appropriate conceptual frameworks and analytical methods to ensure accurate measurement and valid inference in health research. The goal of the Data Science and Analytical Core is to ensure that synergies across the research projects are realized by collaborating with the investigators and developing capacity to produce and manage high quality data for evaluating perinatal HIV transmission, ART uptake and adherence, and PrEP uptake and adherence through the following specific aims: Aim 1. Develop data management systems and support data management activities for the three program research projects. The UNC Project Malawi (UNC-PM) has an established data department responsible for managing data for large NIH funded clinical trials networks, observational cohorts, Implementation research, and registries. The data department has consistently achieved outstanding evaluations of all data metrics, while meeting external monitoring criteria. UNC Project has successfully extracted routine data from MOH electronic databases and paper-based registers and linking the same to research data when implementing some of its studies.The Data Science and Analytical Core will utilize these skills to support the projects by designing data collection tools, creating and managing databases, extracting routine data from MOH databases, and setting up data quality assurance and control systems. Aim 2. Support analytical aspects of the research projects from design to dissemination. The research projects proposed in this application are thematically focused on EMTCT but require a broad range of analytical aptitudes to assess data from diverse sources. All projects require cross-cutting analytical competencies The Data Science and Analytical Core and UNC-PM affiliated faculty will directly support these analytical needs to achieve the overall aims. Aim 3. Estimate the effects of the proposed interventions on pediatric and maternal outcomes and accelerating EMTCT if expanded nationally. The proposed projects involve implementing novel strategies to accelerate EMTCT in Malawi. We will develop mathematical models to quantify and compare the effect of these interventions in averting both maternal and pediatric HIV infections, improving viral load suppression, and increasing retention in care for mother-infant pairs at the national level. Aim 4. Create a capacity building environment for embedded junior investigators new to data management and statistical analysis through didactic training and experiential learning. The Core will host and support the program mentoring activities by providing one-on-one consultancies and didactic training in data management, research methods, and analysis for mentored clinical investigators and researchers.

数据科学和分析核心-摘要 实现消除母婴传播(EMTCT)将取决于 来自不同领域的科学家和从业者。这些领域中必不可少的是分析学科， 特别是流行病学、生物统计学和实施学。这些学科致力于应用 适当的概念框架和分析方法，以确保准确测量和有效 健康研究中的推理。数据科学和分析核心的目标是确保协同效应 所有研究项目都是通过与调查人员合作和发展能力来实现的 制作和管理用于评估围产期艾滋病毒传播、抗逆转录病毒药物吸收和依从性的高质量数据， 以及通过以下具体目标吸收和遵守PrEP： 目标1.开发数据管理系统并支持这三个机构的数据管理活动 计划研究项目。北卡罗来纳大学马拉维项目(UNC-PM)有一个既定的数据部门 负责管理NIH资助的大型临床试验网络、观察队列、 实施研究和登记。数据部门一直取得了卓越的成绩 评估所有数据指标，同时满足外部监测标准。北卡罗来纳大学项目已成功 从卫生部电子数据库和纸质登记册中提取常规数据，并将其链接到 在实施其一些研究时研究数据。数据科学和分析核心将利用这些 通过设计数据收集工具、创建和管理数据库、提取 卫生部数据库中的常规数据，并建立数据质量保证和控制系统。目标2. 支持研究项目从设计到传播的分析方面。研究项目 此应用程序中提出的建议以EMTCT为主题，但需要广泛的分析 有能力评估来自不同来源的数据。所有项目都需要交叉分析能力 数据科学与分析核心学院和北卡罗来纳大学PM附属学院将直接支持这些分析需求 实现总体目标。目标3.评估拟议干预措施对儿童和儿童的影响 如果在全国范围内推广，产妇结局和加速母婴传播。拟议的项目涉及 在马拉维实施新的战略以加速紧急母婴传播。我们将开发数学模型来 量化和比较这些干预措施在避免孕产妇和儿科艾滋病毒感染方面的效果， 改善病毒载量抑制，并在国家一级增加母婴护理的保留率。 目标4.为新接触数据的初级调查人员创造能力建设环境 通过教学培训和体验式学习进行管理和统计分析。核心意志 通过提供一对一咨询和授课培训来主持和支持项目指导活动 指导临床研究人员和研究人员的数据管理、研究方法和分析。