Exposure Health Informatics Methods for Environment Epidemiological Research

Abstract

Traditional environmental epidemiology has relied on assessing individual exposures and their effects. Exposure science methods provides opportunities to advance epidemiological studies considering multiple and consequent exposures and over extended periods of time. Materializing the exposome concept requires methods to collect, integrate and assimilate data from multiple exposures including at personal levels, genomics, personal activities, locations, socio-behaviors, biomarkers of exposures, physiological effects, and health outcomes - all with appropriate spatiotemporal descriptions and associated uncertainties in measurements. Such an exposome requires integration of data from wearable and stationary sensors, environmental monitors, physiology, medication use and other clinical data. In addition, such an integration needs to have a high spatial-temporal resolution for correlating times and location of exposures to occurrences of conditions and their severities. This would require filling any gaps in the measured data with modeled data along with characterization of any uncertainties. Informatics is the scientific field that deals with biomedical information, data, and knowledge - their storage, retrieval, and optimal use for problem solving and decision-making. Recently, informatics methods are being developed, evaluated and utilized in sphere of environmental epidemiological and the exposome. In this symposium, we define the novel informatics sub-field, Exposure Health Informatics, and discuss challenges that require development of informatics methods. We then formally describe informatics methods that address these challenges, and their implementation as a scalable computation infrastructure, the Exposure Health Informatics Ecosystem (EHIE). EHIE is a comprehensive, standards-based, open-source informatics platform that provides semantically consistent, metadata-driven, event-based management of exposomic data consisting of sensor data acquisition pipelines, participant and researcher facing tools, computational modeling, and big data integration platform. Finally, with PRISMS pediatric asthma, Environmental Children’s Health Outcomes, diabetes and molecular epidemiological studies as exemplars, we discuss the generalizability of these multi-scale and multi-omics informatics methods for providing epidemiological researchers with robust reproducible pipelines.