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

基本信息

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
项目类别：
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

项目摘要

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，这是至关重要的关注点随着撒哈拉以南非洲地区的空气质量显着，迅速恶化加速城市化，工业化和人口增长。协同协会在热浪和空气污染之间有望随着气候变化而加剧，这对低收入环境中脆弱人群的健康构成了至关重要的威胁。表明孕产妇和产前暴露之间关联的研究环境污染和不利的健康结果，强调了进一步的需求非洲人口的调查，因为这种脆弱的亚群并不始终如一调查。接触热应激的孕妇加上空气污染更容易受到不利的出生结果的影响。流产，死产，早产，低出生体重和先兆子痫。制定适当的卫生部门的反应和自适应干预措施依赖于确定这些脆弱人群及其水平环境风险。社会经济因素，例如贫困，食物和水不安全感，以及在这些社区中，获得医疗机构的机会有限。在温度升高时期，污染暴露的影响很难衡量并需要精致的数据科学和分析方法。当前的贫困网络的网络测量空气质量的零碎方法的接地传感器来量化关联从不良的健康结果和从证据到干预令的翻译缺乏进近的范式转变。解决对环境风险的缺乏理解拟议的研究项目对撒哈拉以南非洲的流行病学不断变化的影响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创新的数据科学方法（例如机器学习，加速）健康污染暴露数据的低成本表征，同时了解其与与怀孕，分娩和早期生活有关的不利结果。除此之外，我们将发展适应性干预措施将帮助孕妇及其子女应对风险暴露于污染物并为不良健康的高几率增强弹性而施加结果。 Cheaqi-Mnch项目将为新兴数据科学家提供机会非洲的研究人员参与，协作和发展可转让技巧，而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。