SCH: EXP: Smart integration of community crowdsourced data for real-time individualized disease risk assessment

SCH：EXP：智能整合社区众包数据，进行实时个体化疾病风险评估

• 批准号: 1551036
• 负责人: Rumi Chunara
• 金额: $ 45.75万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1551036&HistoricalAwards=false
• 关键词: SCH; EXP; Smart; integration; community

This project addresses a major inefficiency in healthcare today: an under-informed, homogeneous approach to patient care that doesn't consider community disease prevalence and its effect on an individual's disease risk in an actionable manner. The approach is to combine real-time diagnostic information from select community members via an easy to use, gargle-based immunoassay detection test with more abundant contemporaneous community symptom reports via an online informatics platform. The hypothesis is that the likelihood of a virological diagnosis of influenza (flu) can be accurately predicted for the individual using real-time flu self-diagnostics and symptom information from their community, and can influence a user to take appropriate measures to prevent disease spread. This approach enables testing of several concepts regarding (1) the specificity and sensitivity of a rapid flu diagnostic test for Influenza A and B detection compared to a laboratory polymerase chain reaction test, (2) generation of valid user-contributed diagnostic information at scale (3), potential of real-time contextual data to be used to calculate influenza risk, (4) use and acceptance of this information by individuals. The team brings together experience in building and studying novel crowdsourced data sources for disease surveillance and expertise in software, graphic design, epidemiology, medicine, and engineering, to develop user-friendly systems that the community will employ at scale.The proposed system has substantial potential for beneficial societal impact through acertaining and changing the way people approach healthcare. Empowering individuals to generate and act on health information impresses on them the importance of surveillance and educates them to become more involved and accountable for their health. In order to maximize the impact and sustainability of the system, this system also functions as an educational tool. Clear open communication of anonymized and privacy-protected data to the general public in an easy to interpret manner will assist individuals, policy-makers and other researchers in using contextual disease risk information. Undergraduate and graduate students from a variety of backgrounds will have opportunity to participate in the research. Collaborators at the American Public Health Association and their associated groups concerned with the health of minority populations such as The Association of Minority Health Professions Schools and The Latina Health Project will assist in engaging a diverse group of participants nationwide.

该项目解决了当今医疗保健中的一个主要低效率问题：一种信息不足的同质化患者护理方法，不考虑社区疾病的流行及其对个人疾病风险的影响。该方法是将通过易于使用的、基于漱口的免疫测定检测测试从选择的社区成员获得的实时诊断信息与通过在线信息平台获得的更丰富的同期社区症状报告联合收割机相结合。该假设是，可以使用来自其社区的实时流感自我诊断和症状信息来准确预测个体的流感病毒学诊断的可能性，并且可以影响用户采取适当的措施来防止疾病传播。该方法能够测试几个概念，涉及（1）与实验室聚合酶链反应检测相比，快速流感诊断检测对甲型和B流感检测的特异性和灵敏度，（2）生成有效的用户贡献的诊断信息，（3）实时背景数据用于计算流感风险的潜力，（4）个人对该信息的使用和接受。该团队汇集了在构建和研究用于疾病监测的新型众包数据源方面的经验，以及软件、图形设计、流行病学、医学和工程方面的专业知识，以开发用户友好的系统，供社区大规模使用。拟议的系统具有巨大的潜力，通过确定和改变人们对待医疗保健的方式，产生有益的社会影响。使个人有能力生成健康信息并采取行动，使他们认识到监测的重要性，并教育他们更多地参与自己的健康并对其负责。为了最大限度地扩大该系统的影响和可持续性，该系统还发挥教育工具的作用。以易于解释的方式向公众公开匿名和受隐私保护的数据，将有助于个人，政策制定者和其他研究人员使用相关的疾病风险信息。来自不同背景的本科生和研究生将有机会参与这项研究。美国公共卫生协会及其关注少数民族健康的相关团体（如少数民族健康学校协会和拉丁健康项目）的合作者将协助在全国范围内吸引不同群体的参与者。