=Paper=
{{Paper
|id=Vol-3805/ICBO-2022_paper_6982
|storemode=property
|title=Environmental Health Language Collaborative (EHLC): a Route to Environmental Health Science Data Harmonization
|pdfUrl=https://ceur-ws.org/Vol-3805/ICBO-2022_paper_6982.pdf
|volume=Vol-3805
|authors=Anna Maria Masci,Stephanie Holmgren,Charles Schmitt,Rima Habre,Anne E. Thessen,Rebecca Boyles,Carmen J. Marsit
|dblpUrl=https://dblp.org/rec/conf/icbo/MasciHSHTBM22
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==Environmental Health Language Collaborative (EHLC): a Route to Environmental Health Science Data Harmonization==
https://ceur-ws.org/Vol-3805/ICBO-2022_paper_6982.pdf
Environmental Health Language Collaborative (EHLC): a route to
environmental health science data harmonization
Anna Maria Masci 1, Stephanie Holmgren 1, Charles Schmitt1, Rima Habre2, Anne E Thessen3,
Rebecca Boyles4, Carmen Marsit5.
1
Office of Data Science, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park,
North Carolina, USA
2
University of Southern California, Los Angeles, CA, USA
3
University of Colorado Anschutz Medical Campus, Center for Health AI, Aurora CO, USA
4
Center for Data Modernization Solutions, RTI International. Durham, NC. USA
5
Gangarose Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta,
GA, USA
Abstract
Standard language is critical for helping
scientists share, compare, and reanalyze
data. The increased use of automatization
and AI technologies has made the adoption
of machine interpretable language essential.
Due to the broadness of the domains that are
under the environmental health umbrella
there is not yet a set of common standard
terminologies. To address this lack of
standardized language, NIEHS has launched
the Environmental Health Language
Collaborative (EHLC)
https://www.niehs.nih.gov/research/progra
ms/ehlc/index.cfm. This is a new initiative
to advance community development and
application of a harmonized language for
describing Environmental Health Science
(EHS) research.
As a first step toward the development of
standard terminology, a working group of
environmental health researchers and
NIEHS program officers established an
initial set of four general use cases. Here we
present one of the initial use cases on place-
based exposures. This preliminary work is
intended to be expanded as the community
develops. EHLC is seeking larger
community involvement as well as
additional use cases. NIEHS encourages
anyone interested in advancing this mission
to engage in this community.
Keywords
Ontology; controlled vocabulary; data reuse; FAIR data metadata; taxonomy; standards;
semantic; environmental health; toxicology; community of practice; community driven,
geospatial, place, location, exposure.
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
1
1. Introduction are inconsistencies and gaps in the
terminologies and ontologies used within
Environmental health (EH) is a science that subfields, but scientific language is often
studies the effect of exposure to domain-specific and standardizing or even
environmental factors on human health. The harmonizing language across subfields is
definition of environment is wide and especially challenging.
includes the “totality of exposures we face To address the need for common language,
throughout our lives, e.g., the food we ingest, NIEHS has launched the Environmental
the air we breathe, the objects we touch, the Health Language Collaborative (EHLC)[4]
psychological stresses we face, the activities https://www.niehs.nih.gov/research/program
in which we engage” [1] EH research is not s/ehlc/index.cfm. This is a new initiative to
just focused on external exposures, but also advance community development and
considers the molecules in our body that application of a harmonized language for
derive from external exposures, the describing Environmental Health Science
environmental influences we receive through (EHS) research.
our parents, the socio-economic factors that
play into disparities in health as well as The proposed mission of this community is
research that seeks to remediate and reduce to:
the impact of these factors, e.g., by
engineering plants that can remove or reduce • Apply language standards and best
pollutants. practices for accurate environmental
The EH field covers a diversity of domains health data and knowledge
and methodologies, such as environmental representation
epidemiology, toxicology, clinical and • Cultivate a vocabulary aware
translational research, immunology, environmental health community
microbiology, exposure science, social through training and education
science, and environmental engineering. • Foster community-based
Progress in EH research depends on the development of harmonized
ability to compare, contrast, and integrate data vocabularies, terminologies, and
from across the field, which requires adoption ontologies
of the principles of Findable, Accessible, • Identify use cases for applying
Integrable, and Reusable (FAIR) [2, 3]data. knowledge organization systems in
FAIR requires the use of either common or research
comparable language in describing scientific • Promote and develop methods and
data, metadata, and findings. tools for applying harmonized
The breadth of the EH field challenges the use language in research
of a common language, not only because there
ICBO 2022, September 25–28, 2022, Ann Arbor, MI, USA
EMAIL: mascia2@niehs.nih.gov (Anna Maria masci.);
holmgre1@niehs.nih.gov (Stephanie Holmgren.);
charles.schmitt@nih.gov (Charles Schmitt.); habre@usc.edu (
Rima Habre.); annethessen@gmail.com (Anne E. Thessen);
rboyles@rti.org ( Rebecca Boyles); carmen.j.marsit@emory.edu
(Carmen Marsit).
ORCID: 0000-0003-1940-6740 (Anna Maria Masci.); 0000-
0002-3148-2263 (Charles Schmitt); 0000-0002-2908-3327 ( Anne
E. Thessen); 0000-0003-0073-6854 (Rebecca Boyles); 0000-
0003-4566-150X (Carmen Marsit).
2022 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
©️
A first step towards a practical approach has 5. What do my unique exposure
been asking what scientific questions would conditions based on where I live and
benefit most from development and adoption work (E.g., Geographical Location,
of a harmonized language standard? A Occupation, Regulations, Hobbies)
working group of environmental health indicate about potential risks to my
researchers and NIEHS program officers health?
developed an initial set of five general use Due to space limitation, we present the very
cases examples as starting points for preliminary work done for use case five to
community discussion. Working groups led begin building the ontological representation
by use case champions were formed from the of environmental exposures and social
community to address each use case. The stressors or factors assessed based on place
working group teams decided to focus all the and geospatial information.
use cases on the effect of Particulate Matter
(PM) main component of the air pollutants on 2. Methods and Results
Asthma as a common theme to unite their
work. ‘Asthma is a disease of the respiratory Geospatial data are composed of three general
tract which is caused by a combination of components Object, Event, Location, and
environmental and genetic factors’ [5, 6]. each of these components has specific
‘Particulate matter is an environmental material characteristics that are time related Fig (1).
which is composed of microscopic portions of
solid or liquid material suspended in another
environmental material’[7, 8]. PM derives
from multiple different sources, such as
vehicle and industrial emissions from fossil
fuel combustion, cigarette smoke, and
burning organic matter, such as wildfires, as
well as chemical reactions that can form PM
from precursors.
WHO has estimated that 4.2 million deaths
occur as a result of exposure to ambient
(outdoor) air pollution. (
https://www.who.int/health-topics/air-
pollution - tab=tab_2 ). Figure 1. The main components associated
Although all the five use cases focus on with geospatial data.
Asthma and PM, each of them is trying to
answer different questions: Members of the Geospatial Working Group
1. What data exists for a given started by looking at an available data set
chemical/endpoint/exposure from the Personalized Environment and
scenario? Genes Study (PEGS)
2. How best to combine data from (https://www.niehs.nih.gov/research/clinical/
multiple independent studies? studies/pegs/index.cfm).
3. Given measures of biological The study’s panel of experts had already
responses to one or more exposures, identified the initial set of essential
what are the biological processes that components to represent the geospatial data.
might be related to the observed
changes? Because we are using an existing list of data
4. What are the biomarkers, phenotypes, elements, the first step was to look at the OBO
and/or outcomes that can be measured Foundry ontologies [9] to see if those data
and used as an indicator of exposure? elements were already captured in existing
ontologies. Figure 2 shows a representation of
the data elements that were captured and their We then explored the ability of ontology to
relations. The different box color represents capture more specific geographical types of
the different ontologies from which the terms information. Figure 3 shows a list of terms
were imported: Exposure Ontology (ExO) related to geographic location. There are
[10], Gazetteer (GZ) terms like latitude measurement datum,
(http://environmentontology.github.io/gaz/), longitude measurement datum that have been
Ontology of Biomedical Investigations (OBI) already described in Ontology of Biomedical
[11, 12], phenotype and trait ontology Investigations. Other terms like geographical
(PATO) [13]. In italic are the relations from identifier (GEO ID) and Buffer zone, which
the relation Ontology (RO) [14] that we have are commonly used in geospatial studies, are
used to link the terms. In addition to the not present in any ontology.
imported terms, new terms have been The Census Bureau and other state and
identified as ‘stressor detection assay’ and federal agencies are responsible for assigning
‘stressor detector’. For the stressor detection geographic identifiers, or GEOIDs, to
assay we are proposing the following geographic entities to facilitate the
definition: ‘an assay that aims to detect organization, presentation, and exchange of
exposure stressor’. We are proposing the geographic and statistical data.
stressor detection to be a child of a more (https://www.census.gov/programs-
general term assay defined in OBI. surveys/geography/guidance/geo-
identifiers.html)
We have classified the GEO ID term as
identifier class defined in the IAO
(https://obofoundry.org/ontology/iao.html).
We have modified the Census Bureau
definition for the GEO ID to be ‘is an
identifier composed by numeric codes that
uniquely identify all administrative/legal and
statistical geographic areas for which the
Figure 2. Ontological representation of an Census Bureau tabulates data.’
exposure event. The different box colors Another term that we needed to represent is a
indicate the different ontologies from which Buffer Zone. This is a very common term used
the terms were imported. The gray boxes to define a zone and its characteristics, that
indicate the term has not been found in any are the object of the study. Although there is
ontology. The green filled box highlights the this term in ENVO its classification under
term that is present in Figure 2 as well as administrative region does not fit with our
Figure 3. usage of the term. In our use case the buffer
zone is used to define a zone from which
The second additional term is a stressor collecting data (point, line, area) that is
detector. equidistant from the stressor.
We are proposing the following definition: Is As is shown in Figure 3 classification of this
a role that inheres in a material entity, and term is still under discussion as well as how
which is realized through a process of to relate it to a specific geographic location.
exposure stressor detection.
These two new terms as well as their
definitions have been proposed to the
ontology community.
The red triangle in Figure 2 represents the
term that is the linking node between Figures
2 and 3.
Environmental Health Language
Collaborative. Members of Environmental
Health Language Collaborative Geospatial
working group
5. References
Figure 3. Ontological representation of the
geographical specific entities. The different Uncategorized References
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