Developing data science solutions to mitigate the health impacts of climate change in Africa: the HE2AT Center

开发数据科学解决方案以减轻非洲气候变化对健康的影响：HE2AT 中心

• 批准号: 10314149
• 负责人: Matthew Francis Chersich
• 金额: $ 130万
• 依托单位: WITS HEALTH CONSORTIUM (PTY), LTD
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10314149
• 关键词: Address; Adopted; Africa; Africa South of the Sahara; African; Air; Basic Science; Behavioral; Behavioral Sciences; Biometry; Birth Weight; Centers for Disease Control and Prevention (U.S.); Characteristics; Climate; Climatology; Clinical; Clinical Medicine; Collaborations; Communities; Computers; Dangerousness; Data; Data Analyses; Data Science; Data Scientist; Data Sources; Development; Disease; Ecosystem; Elements; Environmental Exposure; Environmental Health; Event; Future; General Population; Geography; Goals; Government; Health; Health Sciences; Heat Waves; Housing; Individual; Industrialization; Industry; Influentials; Institution; Ivory Coast; Knowledge; Link; Low income; Machine Learning; Morbidity - disease rate; Outcome; Output; Participant; Pilot Projects; Planets; Policies; Pre-Eclampsia; Pregnant Women; Premature Birth; Preparation; Public Health; Research; Research Activity; Research Priority; Research Project Grants; Resources; Risk; Risk Factors; Scientist; Series; Social Sciences; Software Design; South Africa; Specificity; System; Techniques; Technology; Temperature; Testing; Time; Training; Training Support; United States; Universities; Vertebral column; Vulnerable Populations; Weather; Work; base; built environment; burden of illness; career; climate change; climate data; climate impact; cohort; complex data; computer science; data ecosystem; data management; data repository; data reuse; digital; doctoral student; extreme heat; global temperature; hazard; health data; high risk; high risk population; innovation; interest; machine learning algorithm; maternal outcome; mortality; neonatal outcome; neonate; programs; prospective; response; risk prediction; social; statistics; urban area; urban setting

HE2AT Center Overall - Project summary/abstract The world's climate is changing rapidly, with global temperatures having risen more than 1°C since the industrial revolution, and a further 0.5°C increase is likely by 2040. Heat waves and rising temperatures have major, though underappreciated, health implications, particularly for vulnerable populations in low-income settings. The overarching objective of the Heat and Health African Transdisciplinary Center (HE2AT Center) is to develop innovative solutions to mitigate the health impacts of climate change in Africa. The consortium of academic and non-academic partners is drawn from across sub-Saharan Africa and from the United States, and constitutes a transdisciplinary group, including heat physiologists, biomedical and climate content experts, public health practitioners, social-behavioral scientists as well as statisticians, and computer and data scientists. The Center will systematically develop a data ecosystem containing biomedical data, integrated with weather, air quality and other environmental data, and other geospatial data within two existing highly- complementary data platforms (IBM-PAIRS and the University of Cape Town). Over five years we will implement two Research Projects and 10-12 Pilot Projects, all streamlined and supported by the Administration, Data Management and Analysis, and Training and Engagement Cores. The first Project will implement an innovative data science approach to characterize the clinical outcomes of heat exposure in pregnant women and neonates. We will reuse data from cohorts and trials among pregnant women and neonates conducted across sub-Saharan Africa since the year 2000. Data from systematically identified studies will be integrated in an Individual Participant Data platform from data repositories and data owners. Then, analyses of relationships between heat exposure and outcomes (preterm birth, birth weight and pre- eclampsia) will inform quantification of heat-related disease burden. Finally, taking all findings together, we will pilot a district-level climate change indicator, the first of its kind. The second Project assesses the burden of heat-related morbidity in vulnerable urban settings using geospatial and heat hazard analyses in Abidjan, Cote d'Ivoire and Johannesburg, South Africa. This Project uses more complex data and data sources on the built environment and topography, for example, to assess heat-health impacts, and how these vary across urban geographies. Activities will inform development of an Early Warning System, including a digital App that delivers information to people on their forecasted risks of heat-health disease, based on their individualized risk profile, as determined by a machine learning algorithm which takes into account weather conditions, individual characteristics, geolocation and other factors that drive risk. These systems are a central element in heatwave responses, allow for adequate preparations for heat events, which is especially important for vulnerable groups and industry. We will collaborate closely with other Hubs and parts of the DS-I Africa consortium, supporting them to incorporate climate data within their research activities, and vice versa.

HE 2AT中心整体-项目总结/摘要 世界气候正在迅速变化，自2010年以来，全球气温上升了1摄氏度以上。 工业革命，到2040年可能会进一步增加0.5°C。热浪和气温上升 对健康的重大影响，特别是对低收入国家的弱势群体， 设置.热与健康非洲跨学科中心（HE 2AT中心）的总体目标是 制定创新解决方案，减轻气候变化对非洲健康的影响。组成的联合体 学术和非学术合作伙伴来自撒哈拉以南非洲和美国， 并组成了一个跨学科小组，包括热生理学家，生物医学和气候内容专家， 公共卫生从业人员、社会行为科学家以及统计学家、计算机和数据 科学家该中心将系统地开发一个包含生物医学数据的数据生态系统， 天气、空气质量和其他环境数据，以及两个现有高度- 补充数据平台（IBM-PAIRS和开普敦大学）。五年后，我们将 实施两个研究项目和10-12个试点项目，所有项目都得到精简和支持， 管理、数据管理和分析以及培训和参与核心。第一个项目将 实施创新的数据科学方法，以表征热暴露的临床结果， 孕妇和新生儿。我们将重新使用来自孕妇队列和试验的数据， 自2000年以来在撒哈拉以南非洲进行的新生儿研究。数据来自系统识别的 研究将整合到来自数据存储库和数据所有者的个人参与者数据平台中。 然后，分析了热暴露与结局（早产、出生体重和早产儿）的关系， 子痫）将告知热相关疾病负担的量化。最后，综合所有的调查结果，我们将 试行地区一级的气候变化指标，这是第一个此类指标。第二个项目评估了 利用地理空间和热危害分析在科特迪瓦阿比让脆弱城市环境中与热有关的发病率 科特迪瓦和南非约翰内斯堡。本项目使用更复杂的数据和数据源， 例如，环境和地形，以评估热对健康的影响，以及这些影响在城市中的变化 地理活动将为预警系统的开发提供信息，包括一个数字应用程序， 根据人们的个性化风险，向他们提供有关热健康疾病预测风险的信息 由考虑天气条件的机器学习算法确定的个人概况， 特征、地理位置和其他因素。这些系统是热浪的核心要素 响应，允许为高温事件做好充分准备，这对弱势群体尤其重要 和行业我们将与其他中心和DS-I非洲联盟的部分成员密切合作， 各国政府鼓励它们将气候数据纳入其研究活动，反之亦然。