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原因，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城镇化、工业化和人口增长加快。协同协会随着气候变化，热浪和空气污染之间的矛盾预计会加剧，这对低收入环境中弱势群体的健康构成重大威胁。研究表明母亲和产前暴露于环境污染和不良健康后果，凸显了进一步采取措施的必要性对非洲人口进行调查，因为这些弱势群体的情况并不一致调查了。暴露于热应激和空气污染的孕妇更容易受到不良分娩结果的影响，包括；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应对措施和适应性干预措施依赖于识别这些弱势群体及其水平的环境风险。社会经济因素，例如贫困、粮食和水不安全，以及获得医疗保健设施的机会有限，使这些社区的脆弱性长期存在。气温升高期间污染暴露的影响很难测量并需要完善的数据科学和分析方法。目前网络状况较差用于测量空气质量的地面传感器，量化关联的零碎方法健康结果不佳且缺乏从证据到干预依据的转化方法的范式转变。解决对环境风险缺乏了解的问题拟议的研究项目对撒哈拉以南非洲不断变化的流行病学的影响旨在量化空气污染当前和未来对孕产妇和新生儿的影响通过机器学习等创新数据科学方法，通过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、分娩和早年有关的不良后果。在此基础上我们将进一步开发适应性干预措施将帮助孕妇及其孩子应对风险减少因接触污染物而造成的影响，并增强抵御不利健康的可能性的能力结果。 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、合作和发展可转移技能，同时为大陆知识翻译和传播资源中心做出贡献在气候和健康领域。