CHaracterizing Effects of Air Quality In Maternal, Newborn and Child Health: The CHEAQI-MNCH Research Project

表征空气质量对孕产妇、新生儿和儿童健康的影响：CHEAQI-MNCH 研究项目

• 批准号: 10713481
• 负责人: Tamara Govindasamy
• 金额: $ 25万
• 依托单位: CENTRE/SEXUAL HLTH/HIV AID RES/ZIMBABWE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-14 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10713481
• 关键词: Acceleration; Address; Adverse effects; Africa; Africa South of the Sahara; African; Air; Air Pollution; Appointment; Biological; Child; Child Health; Childbirth; Climate; Clinical; Cohort Studies; Collaborations; Communities; Complex; Country; Coupled; Data; Data Analyses; Data Analytics; Data Science; Data Scientist; Derivation procedure; Deterioration; Development; Ecosystem; Ensure; Environmental Health; Environmental Pollution; Environmental Risk Factor; Epidemiology; Exposure to; Food; Funding; Future; Generations; Goals; Health; Health care facility; Heat Stress Disorders; Heat Waves; Human; In Situ; Individual; Industrialization; Industry; Infant; Institution; Intervention; Investigation; Joints; Knowledge; Life; Link; Low Birth Weight Infant; Low income; Machine Learning; Maternal Health; Maternal and Child Health; Measurement; Measures; Michigan; Names; Outcome; Policies; Pollution; Population; Population Growth; Postdoctoral Fellow; Poverty; Pre-Eclampsia; Predisposition; Pregnancy; Pregnant Women; Premature Birth; Proxy; Research; Research Personnel; Research Project Grants; Resources; Risk; SMART health; Socioeconomic Factors; Spontaneous abortion; System; Techniques; Temperature; Testing; Translating; Translations; United States National Institutes of Health; Universities; Urbanization; Validation; Vulnerable Populations; Washington; Water; Zimbabwe; adaptive intervention; adverse birth outcomes; adverse outcome; air monitoring; ambient air pollution; burden of illness; career; climate change; climate impact; climate-related health; cost; data repository; doctoral student; experience; health care availability; improved; innovation; interest; knowledge translation; neonatal health; neonate; pollutant; prenatal exposure; programs; promote resilience; prospective; remote sensing; resilience; response; sensor; skills; statistical and machine learning; stillbirth; tool

Air pollution is a leading contributor to the global disease burden, which is a crucial concern as the air quality across sub-Saharan Africa significantly and rapidly deteriorates with accelerated urbanization, industrialization, and population growth. The synergistic association between heat waves and air pollution is expected to exacerbate with the changing climate, which poses a crucial threat to the health of vulnerable populations in low-income settings. Studies which indicate associations between maternal and prenatal exposure to environmental pollution and adverse health outcomes, highlight the need for further investigation in African populations, as such vulnerable subpopulations are not consistently investigated. Pregnant women who are exposed to heat stress coupled with air pollution are more susceptible to adverse birth outcomes including; miscarriages, stillbirth, preterm birth, low birth weight, and preeclampsia. Developing appropriate health sector responses and adaptive interventions relies on identifying these vulnerable populations along with their level of environmental risk. Socio-economic factors such as poverty, food and water insecurity, and limited access to healthcare facilities perpetuate vulnerability among these communities. Impacts of pollution exposure over periods of increased temperatures are difficult to measure and require refined data science and analytical approaches. The current poor networks of ground sensors for measuring air quality, piecemeal approaches to quantifying associations with adverse health outcomes and dearth of translation from evidence to intervention warrants a paradigm shift in approach. To address the lack of understanding of the environmental risk impacts on the changing epidemiology in sub-Saharan Africa, the proposed research project will aim to quantify the current and future impacts of air pollution on maternal and neonatal health through innovative data science approaches such as machine learning, by accelerating low-cost characterization of pollution exposure data while understanding its associations with adverse outcomes related to pregnancy, childbirth and early life. Further to this we will develop adaptive interventions that will help pregnant women and their children counter the risk imposed by exposure to pollutants and build resilience against the high odds of adverse health outcomes. The CHEAQI-MNCH project will provide an opportunity for emerging data scientists and researchers in Africa to engage, collaborate and develop transferrable skills, while contributing to a continental resource center for knowledge translation and dissemination within the fields of climate and health.

空气污染是全球疾病负担的主要因素，这是一个至关重要的问题 随着撒哈拉以南非洲地区的空气质量急剧恶化， 加速城市化、工业化和人口增长。协同关联 热浪和空气污染之间的关系预计将随着气候变化而加剧， 这对低收入环境中弱势群体的健康构成严重威胁。 研究表明，母亲和产前接触 环境污染和不利的健康结果，强调需要进一步 在非洲人群中进行的调查，因为这些弱势亚群并不一致 研究了孕妇暴露在热应激加上空气污染中， 更容易出现不良分娩结果，包括流产、死胎、早产， 低出生体重和先兆子痫制定适当的卫生部门应对措施， 适应性干预措施依赖于识别这些弱势群体沿着他们的水平 环境风险。社会经济因素，如贫困、粮食和水不安全，以及 获得保健设施的机会有限，使这些社区长期处于弱势地位。 在气温升高期间，污染暴露的影响难以衡量 并需要完善的数据科学和分析方法。目前网络的贫困程度 测量空气质量的地面传感器，量化关联的零碎方法 对健康产生不利影响，缺乏从证据到干预措施的转化 方法的范式转变。解决对环境风险缺乏了解的问题 对撒哈拉以南非洲不断变化的流行病学的影响，拟议的研究项目 旨在量化空气污染对孕产妇和新生儿的当前和未来影响 通过机器学习等创新的数据科学方法， 污染暴露数据的低成本特征，同时了解其与 与怀孕、分娩和生命早期有关的不良后果。在此基础上，我们将开发 适应性干预措施将帮助孕妇及其子女应对风险 并建立对不利健康的高几率的恢复能力 结果。CHEAQI-MNCH项目将为新兴数据科学家提供机会 和非洲的研究人员参与，合作和发展可转移的技能， 为知识翻译和传播的大陆资源中心做出贡献 在气候和健康领域。