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.
Emerging and expanding technologies, including text mining of EHR data and small, deployable air samplers are making more comprehensive environmental health research possible. The proposed research will utilize these technologies to first elucidate pathways linking unconventional natural gas development to adverse birth outcomes and later to asthma onset and acute respiratory infection in the same children. This research sup- ports the NIEHS mission to develop bioinformatics tools to conduct interdisciplinary research on interactions of multiple exposures including environmental and social factors, with implications for energy policy that seeks to protect the public's health.
