Environmental Data Integration to Assess Impacts of Shale Gas Development on Perinatal Health Outcomes and Childhood Cancers

环境数据整合评估页岩气开发对围产期健康结果和儿童癌症的影响

• 批准号: 10025379
• 负责人: Mary D Willis
• 金额: $ 1.44万
• 依托单位: OREGON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10025379
• 关键词: Accounting; Address; Affect; Air; Air Pollution; American; Aromatic Polycyclic Hydrocarbons; Big Data; Birth; Birth Weight; Carcinogens; Child; Child Health; Childhood; Communities; Complex; Congenital Abnormality; Country; Data; Data Science; Data Sources; Databases; Development; Economics; Environmental Exposure; Environmental Hazards; Environmental Health; Environmental Pollutants; Environmental Wind; Epidemiology; Exposure to; Fetal Development; Flare; Foundations; Fracture; Future; Gases; Goals; Guidelines; Health; Home environment; Incidence; Industrialization; Industry; Infant; Link; Malignant Childhood Neoplasm; Maternal Exposure; Medicine; Methods; Modeling; Natural Gas; Ontology; Outcome; Particulate Matter; Pathway interactions; Policies; Population; Premature Birth; Prevalence; Process; Production; Public Health; Regulation; Research; Retrospective cohort; Risk; Risk Assessment; Risk Estimate; Risk Factors; Site; Source; Techniques; Teratogens; Texas; Time; United States; Variant; Vital Statistics; adverse birth outcomes; ambient air pollution; cohort; data integration; data registry; epidemiology study; evidence base; hazard; health data; in utero; innovation; insight; large scale data; neoplasm registry; novel; perinatal health; pollutant; prenatal exposure; volatile organic compound

PROJECT SUMMARY While ambient air pollution is a well-recognized risk factor for adverse infant and childhood health outcomes, inadequate research exists on the health impacts of air emissions from emerging industries such as shale gas development (SGD). The SGD industry rapidly expanded from under 30,000 sites in 2000 to over 300,000 sites in 2016, so now approximately 17.6 million Americans now live within one mile of a drilling site. Given this substantial population exposure, there is an immediate need to determine the health risks associated with SGD air emissions and develop effective methods to evaluate and reduce exposure to emerging hazards. This study will use data science and big data techniques to integrate environmental data with health information to assess the impact of SGD on infants and children who are exposed to the SGD industry in utero. Specific health data will be derived from a large retrospective birth cohort (n=5,275,799) with full maternal addresses with linkages to birth defect and childhood cancer registries from 1996 through 2009, which corresponds to the rapid increase in Texas SGD activity. Texas is the largest shale gas producer in the country and 16% of its population (4.5 million people) lives within 1 mile of drilling, thus this is the ideal cohort to study this exposure. Aim 1 builds novel spatial-temporal exposure metrics from administrative and proprietary data sources to capture multiple pathways by which SGD may affect local populations, including specific SGD processes (e.g. production, flaring), traffic from the industry, and wind direction between homes and drilling. To date, these sources have not been used in large-scale data integration projects. By assessing policy-relevant SGD exposures, these metrics represent a substantial advancement over previous exposure assessments used in epidemiology and risk assessment studies, which can be applied to SGD as well as future threats. Aim 2 applies these spatial-temporal metrics to the geocoded birth cohort to quantify the impact of specific SGD processes and related exposures on adverse birth outcomes, birth defects, and childhood cancers. This analysis uses a unique causal-inference framework that leverages cross-disciplinary epidemiological, economic, and ontological methods. The results of the health analyses will provide further insights into which SGD exposures influence perinatal health outcomes as well as the policy guidelines that can help reduce risks for local communities. The proposed research will synthesize spatial exposure assessment methods, advance environmental health data science techniques, and develop causal-inference models to produce robust risk estimates for SGD exposures. Findings from the proposed study will provide a better understanding of how SGD is affecting local communities by providing the foundational evidence for the effects of SGD exposure on infant and children’s health. Beyond the risks associated with SGD, this project will establish novel methods to assess other local environmental hazards and help bridge multiple disciplinary gaps among epidemiology, exposure assessment, data science, and economics by demonstrating a causal inference framework not often applied in public health studies.

项目摘要 虽然环境空气污染是不良婴儿和儿童健康结果的公认危险因素，但 研究对新兴行业（例如页岩气）的空气排放的健康影响不足 开发（SGD）。 SGD行业从2000年的30,000个站点迅速扩展到300,000多个站点 在2016年，现在大约有1760万美国人居住在钻井场地的一英里范围内。鉴于此 大量人口暴露，立即需要确定与SGD相关的健康风险 空气排放并开发有效的方法来评估和减少面临新兴危害的暴露。这项研究 将使用数据科学和大数据技术将环境数据与健康信息整合在一起以评估 SGD对暴露于子宫内SGD行业的婴儿和儿童的影响。特定的健康数据 将源自带有完整材料地址的大型回顾性出生队列（n = 5,275,799） 从1996年至2009年开始出生缺陷和儿童期癌症注册表，这对应于快速增加 在德克萨斯州SGD活动中。德克萨斯州是该国最大的页岩气生产商，占其人口的16％（4.5 百万人）居住在钻井1英里之内，因此这是研究这种曝光的理想人群。 AIM 1构建小说 从行政和专有数据源中捕获多个途径的时空暴露指标 SGD可能会影响当地人口，包括特定的SGD流程（例如生产，燃烧），流量 来自行业以及家庭和钻探之间的风向。迄今为止，这些来源尚未使用 大规模数据集成项目。通过评估与政策相关的SGD暴露，这些指标代表 在流行病学和风险评估中使用的先前暴露评估比以前的暴露评估方面的大幅度进步 研究，可以应用于SGD以及未来的威胁。 AIM 2将这些时空指标应用于 地理编码的出生队列，旨在量化特定SGD流程和相关暴露对广告的影响 出生结果，出生缺陷和童年癌。该分析使用独特的因果推断框架 这利用跨学科的流行病学，经济和本体论方法。健康结果 分析将提供进一步的见解，以影响SGD暴露会影响围产期健康状况以及 拟议的研究将合成 空间暴露评估方法，提高环境健康数据科学技术并发展 因果推断模型，以产生SGD暴露的强大风险估计。拟议研究的发现 通过提供基础，将更好地了解SGD如何影响当地社区 SGD暴露对婴儿和儿童健康的影响的证据。超越与SGD相关的风险， 该项目将建立新的方法来评估其他当地环境危害并帮助桥梁多重 通过证明流行病学，暴露评估，数据科学和经济学之间的纪律处分 一个因果推理框架通常不适用于公共卫生研究。