The Impact of Unconventional Natural Gas Development on Maternal, Perinatal, and Childhood Health: an Electronic Health Record Approach

非常规天然气开发对孕产妇、围产期和儿童健康的影响：电子健康记录方法

• 批准号: 10016282
• 负责人: Joan A Casey
• 金额: $ 24.9万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10016282
• 关键词: Acute respiratory infection; Address; Affect; Age; Air; Air Pollutants; Air Pollution; Anxiety; Asthma; Award; Biological Models; Birth; Birth Weight; Caring; Catchment Area; Chemicals; Child; Child Care; Child Health; Childhood; Childhood Asthma; Chronic; Code; Cohort Studies; Complex; Data; Data Collection; Development; Diagnosis; Electronic Health Record; Environment; Environmental Epidemiology; Environmental Exposure; Environmental Health; Environmental Policy; Environmental Risk Factor; Exposure to; Geographic Information Systems; Geography; Gestational Diabetes; Goals; Health; Health system; Healthcare Systems; Hospitals; Hypertension; Infant Care; Inflammation; Injury; Institution; Interdisciplinary Study; Irritants; Joints; Link; Logistics; Longevity; Maternal Health; Maternal and Child Health; Measures; Mediating; Mediation; Mediator of activation protein; Mental Depression; Methods; Mission; Modeling; Mothers; National Institute of Environmental Health Sciences; Natural Gas; Nature; Neonatal; Outcome; Outcome Study; Particulate Matter; Pathway interactions; Pennsylvania; Perinatal; Phase; Policies; Policy Making; Population Heterogeneity; Poverty; Pregnancy; Premature Birth; Primary Health Care; Production; Psychological Stress; Public Health; Questionnaires; Race; Reading; Reporting; Research; Respiratory System; Risk Factors; Sleep Disorders; Socioeconomic Status; Stress; Technology; Term Birth; Time; Training; Work; adverse birth outcomes; air monitoring; air sampling; apprenticeship; asthma exacerbation; base; bioinformatics tool; career; data mining; design; early childhood; electronic data; elementary school; environmental stressor; epidemiology study; experience; experimental study; health data; improved; in utero; pediatric patients; population health; psychosocial; respiratory; respiratory health; skills; social; social determinants; social factors; social stress; social stressor; stressor; study population; text searching; volatile organic compound

PROJECT SUMMARY/ABSTRACT Recent studies have linked unconventional natural gas development (UNGD) to adverse birth outcomes. The relative contributions of specific risk factors, such as related air pollutants, socioeconomic status, and maternal health during pregnancy, however, remain unclear, as do the consequences of UNGD for childhood health. The objective of this K99/R00 application is to use UNGD in Pennsylvania as a model system–because it repre- sents a rapid, widespread social experiment with chemical and psychosocial exposures and as well as communi- ty changes–to disentangle the effects of environmental and social co-exposures on maternal, neonatal, and child health. The proposed project will link UNGD activity to mothers' electronic health records (EHRs) in combination with primary data collected by questionnaires, passive air samplers, and geographic information systems. EHR data are particularly well suited for environmental health research because they provide inexpensive access to longitudinal health data on large and diverse populations (i.e., in terms of age, socioeconomic status, race, and geography). Considerable logistical and analytic skills are required to optimize use of EHR data, supplement it with primary data collection, and complete causal analyses. The K99 is designed to augment the candidate's prior research experience through coursework, apprenticeships in environmental epidemiology, and directed readings, with specific training in: (1) maternal and child health; (2) EHR text mining; (3) causal mediation analysis; (4) primary data collection; and (5) analysis of co-exposures. The skills gained during this award are critical to the long-term goal to use EHR data from multiple healthcare systems to conduct environmental epi- demiology studies across the lifecourse, in order to inform environmental policy-making. The proposed research will utilize Geisinger Health System's EHR data, which provides access to >15,000 births that have spatial and temporal overlap with UNGD in Pennsylvania. Aim 1 (K99 phase) combines text mining strategies and diagnosis codes to extract mothers' pregnancy-related health conditions from EHR data and then applies causal inference methods to evaluate pregnancy-related hypertension, gestational diabetes, sleep disorders, depression, and anx- iety as mediators of the observed associations between UNGD and term birth weight and preterm birth. Aim 2 (K99 phase) pilots primary data collection of chronic social stressors via questionnaires and ambient air samples near elementary schools attended by Geisinger pediatric patients. The R00 phase builds upon K99 data collec- tion and follows the primary care infants until 2021 (ages 8-15 years) to evaluate associations of types and timing of UNGD activity in relation to asthma diagnosis and acute respiratory infection. Aims 3-4 (R00 phase) begin to disentangle the environmental and social determinants of childhood respiratory outcomes. The proposal ad- dresses logistical and analytic challenges in environmental epidemiology and will prepare the applicant for an independent research career. This work will evaluate joint effects of environmental and social stressors on health across the lifespan and advance use of EHR data in environmental epidemiology.

项目概要/摘要 最近的研究将非常规天然气开发（UNGD）与不良出生结果联系起来。 特定风险因素的相对贡献，例如相关空气污染物、社会经济地位和孕产妇 然而，怀孕期间的健康状况仍不清楚，UNGD 对儿童健康的影响也是如此。 该 K99/R00 应用程序的目标是使用宾夕法尼亚州的 UNGD 作为模型系统 - 因为它代表 发起了一项快速、广泛的社会实验，涉及化学和社会心理暴露以及交流 ty 变化——理清环境和社会共同暴露对孕产妇、新生儿和儿童的影响 拟议的项目将把 UNGD 活动与母亲的电子健康记录 (EHR) 结合起来。 通过问卷、被动空气采样器和地理信息系统收集的主要数据。 数据特别适合环境健康研究，因为它们提供了廉价的获取途径 大量且多样化人群的纵向健康数据（即年龄、社会经济地位、种族和 需要大量的后勤和分析技能来优化 EHR 数据的使用并对其进行补充。 K99 旨在增强候选人的主要数据收集和完整的因果分析。 通过课程作业、环境流行病学学徒和指导获得的先前研究经验 阅读，并接受以下方面的专门培训：(1) 孕产妇和儿童健康；(2) 电子病历文本挖掘；(3) 因果调解； 分析；(4) 原始数据收集；以及 (5) 共同暴露分析 在该奖项中获得的技能是。 对于使用来自多个医疗保健系统的 EHR 数据进行环境监测的长期目标至关重要 整个生命周期的流行病学研究，以便为环境决策提供信息。 将利用 Geisinger Health System 的 EHR 数据，该数据可访问超过 15,000 名拥有空间和信息的新生儿 与宾夕法尼亚州 UNGD 的时间重叠目标 1（K99 阶段）结合了文本挖掘策略和诊断。 从 EHR 数据中提取母亲与怀孕相关的健康状况的代码，然后应用因果推断 评估妊娠相关高血压、妊娠糖尿病、睡眠障碍、抑郁症和焦虑症的方法 iety 作为观察到的 UNGD 与足月出生体重和早产之间关联的中介。 （K99阶段）试点通过问卷和环境空气样本收集慢性社会压力源的主要数据 R00 阶段建立在 K99 数据收集的基础上。 并跟踪初级保健婴儿直至 2021 年（8-15 岁），以评估类型和时间的关联 目标 3-4（R00 阶段）开始开展与哮喘诊断和急性呼吸道感染相关的 UNGD 活动。 该提案阐明了儿童呼吸系统结果的环境和社会决定因素。 解决环境流行病学中的后勤和分析挑战，并使申请人做好准备 这项工作将评估环境和社会压力对健康的共同影响。 整个生命周期并推进 EHR 数据在环境流行病学中的使用。