=Paper=
{{Paper
|id=Vol-2137/ws_ONCONTO_paper_3.pdf
|storemode=property
|title=Ontology of Cancer Related Social-Ecological Variables
|pdfUrl=https://ceur-ws.org/Vol-2137/ws_ONCONTO_paper_3.pdf
|volume=Vol-2137
|authors=Dharani K. Balasubramanian,Jamillah Z. Khan,Jiang Bian,Yi Guo,William R. Hogan,Amanda Hicks
|dblpUrl=https://dblp.org/rec/conf/icbo/Balasubramanian17
}}
==Ontology of Cancer Related Social-Ecological Variables==
https://ceur-ws.org/Vol-2137/ws_ONCONTO_paper_3.pdf
Ontology of cancer related social-ecological variables
Dharani K. Balasubramanian1,*, Jamillah Z. Khan1, Jiang Bian1, Yi Guo1, William R
Hogan1, Amanda Hicks1
1
Department of Health Outcomes and Policy, University of Florida, USA
ABSTRACT approach can be used in improving health outcomes by taking
Several social-ecological (SE) factors affect human behavior. Analysis
steps to intervene on the variables of influence (Diez-Roux,
of these factors is an integral part of behavior research. An efficient method
of scrutinizing these predictors is multilevel analysis. Social Ecological 2000). By integrating data with the help of OCRSEV (K.
Model (SEM) is a multilevel framework that helps to capture all the variables Balasubramanian, 2017), multilevel analysis of SE predictors
at five levels: individual, interpersonal, organizational, community, and can be performed.
policy. This work aims to develop a reference ontology with classes that
correspond to SE predictors that influence cancer diagnosis, beginning with Social Ecological Model (SEM) is a conceptual model that
the individual level of SEM. This ontology is built with an aim to aid data has been in use in the public health community as the
integration in order to carry out multilevel analysis of the integrated data. foundation of multilevel intervention design and
The broad hypothesis is that, if all the variables gathered from various
sources and at different levels of the SEM are configured in an ontology, implementation (Moore A et al., 2015). SEM helps to explore
there will be enough information to identify and visualize association the predictors of a disease at multiple levels and analyze
between these variables and health outcomes. This work is focused on 13 SE them. There are several SE factors contributing to a wide
variables which were first identified by performing a scoping literature
review. Manually curated terms corresponding to these variables were
range of health disparities in society (Adler & Newman,
aligned with existing ontology classes. The ontology of cancer related social- 2002). In this work, we refer to the SEM of health promotion
ecological variables (OCRSEV) is built upon the Basic Formal Ontology 2.0 (CDC – Social Ecological Model – CCRP, 2017) to stratify
(BFO 2.0) and conforms to Open Biomedical Ontologies (OBO) Foundry’s the SE variables of late-stage cancer diagnosis (LSCD). The
best practices. Future work is planned to extend the ontology for variables in
other levels of SEM and map the PCORnet Common Data Model (PCORnet SEM framework has five levels: individual, interpersonal,
CDM) data and other relevant data with these variables in the ontology. organizational, community, and policy. The theory of SEM
model implies that the individual is the target of this
1 INTRODUCTION interrelated system, placing the individual in the center of the
One of the greatest advances in explaining the predictors model and all other levels representing SE variables around
of disease is the identification of social and psychological the individual level in a concentric manner (Fig. 1). When
conditions of individuals (Chapter 4: Social Risk Factors, each of those variables is identified and analyzed, public
2001). These conditions may influence the morbidity and health activities can be implemented at each level, thereby
mortality directly through physiological processes and increasing synergies of intervention to improve the overall
indirectly through behavioral pathways. People living in health outcomes of individuals.
areas with socioeconomic disadvantages and cultural barriers
such as lack of health insurance, low income level, and
negative opinions toward cancer screening are more likely to
be diagnosed with cancer at late-stage (Wang, Luo and
McLafferty, 2010). Treatment is less effective and there is a
lower chance of survival with those who have late-stage
diagnosed. It is critical to identify and enhance methods to
detect cancer at an earlier stage. Earlier cancer diagnosis
would save tens of millions of dollars annually and increase
chances of effective treatment and survival (Why is early
diagnosis important?, 2015).
Multilevel analysis in public health research is a statistical
strategy which involves parallel examinations of individual
Fig. 1. Social Ecological Model, adapted by the CDC (CDC –
and group level factors. This method has a hierarchical Social Ecological Model – CCRP, 2017).
approach with multiple levels that classify factors. This
*
To whom correspondence should be addressed: kbdhaar@ufl.edu
1
Ontology of cancer related social-ecological variables
The structure of this ontology is based on Web Ontology different sources such as PubMed, Web of Science, Google
Language 2 (OWL 2) models for different individual level SE Scholar, etc., that affect cancer diagnosis. A total of 79, 29,
variables and their relations with the SEM. For our use-case, 59, 62 and 36 variables were collected for cancer types breast,
and because this study focused on individual-level variables lung, prostate, colon/colorectal and cervical respectively
affecting cancer diagnosis, we investigated five databases from a total of 43 articles. The variables from the literature
with Florida patient data and selected one that relates the were then extracted and stratified into the five levels of SEM.
most to the individual factors of the SEM. The PCORnet As a preliminary step in constructing the ontology, the
Common Data Model (CDM) conveys a specification that most common individual-level variables were selected to be
defines a standard organization and representation of data for used in the development of our ontology. A total of 13
the PCORnet Distributed Research Network. (PCORnet variables were selected: comorbidity, age, blood pressure,
Common Data Model – PCORnet, 2017). This CDM holds body mass index range, education, employment, income
patient-centered data that include many individual-focused status, medical cost, socioeconomic status, tobacco-use,
variables such as demographics and vitals. With this model, ethnicity, race and gender. The references for these variables
relevant data from any PCORnet database can be mapped can be found on GitHub (K. Balasubramanian, 2017). Of
with its corresponding classes in the ontology. these variables, gender, ethnicity, race and tobacco-use can
Semantic data integration (SI) combines heterogeneous be found in PCORnet CDM.
data from various sources and integrates them by leveraging The competency questions (CQ) used in the development
the semantic content that is embedded in these sources of this ontology are:
(Livingston et al., 2015). It brings together data from various (1) What are some diseases that are cancer?
sources by relating the true meaning of data from one source (2) What are some parts of SEM that are levels?
with another. Ontology mapping is key to SI of data which (3) What are some variables represented by SEM?
helps to identify similarities between ontologies and (4) What are some variables represented by the SEM at the
determine the concepts that represent these correspondences individual level?
in order to enable reasoning. Other data integration methods (5) What are some variables represented by the SEM at the
may not use semantic standards, which are mandatory to individual level, that are also present in PCORnet
define the meaning of the data and finding its roots (Noy, CDM database?
2004). Our ontology can be extended for other levels of SEM (6) What are some variables represented by the SEM at the
and linked with corresponding data from different databases individual level, that are also present in PCORnet
thereby providing semantic data integration. CDM database and are identity data?
The Ontology for Medically Related Social Entities Based on these competency questions, reusable classes
(OMRSE) (Hogan, Garimalla, & Tariq, 2011)(Hicks et al., and object properties from existing ontologies relating to our
2016) covers the domain of social entities related to 13 variables and the five cancer types were identified using
healthcare and is similar to OCRSEV as it includes terms Ontobee (Ontobee, 2017) and BioPortal (NCBO BioPortal,
describing SE variables such as demographics, but different 2017). The ontology was developed in Protégé 5.1 (Research,
in the sense that it does not follow a multi-level model 2017) and conforms to OBO Foundry’s (Smith et al., 2007)
approach. OCRSEV is useful in knowledge representation of best practices. The upper-level ontology is BFO 2 (Grenon &
SE variables and specifying their level in SEM. Smith, 2004; Arp et al., 2015). Ontofox (Xiang et al., 2010)
was used to import the ontologies and the individual terms
Scope of the Ontology: The ontology will include terms that
for this project.
are related to SE variables of five types of cancer diagnosis
namely breast, cervical, lung, prostate and colon/colorectal. Imported Ontologies: The structure of the ontology was
The ontology will focus on some of the key variables in the heavily influenced by OMRSE. OMRSE utilizes BFO as its
individual level of SEM. The specific aims of this project are: upper-level ontology. OMRSE has many classes that are
● To collect SE variables related to cancer and those which reused in OCRSEV. The Vital Sign Ontology (VSO)
act as possible determinants of late-stage cancer (Goldfain et al., 2011) also uses BFO as its upper-level
diagnosis based on a scoping literature review ontology and focuses on vital sign measurement data, vital
● To develop an ontology for SE factors responsible for sign measuring processes, and devices used to measure
cancer diagnosis at the individual level of SEM. human vital signs. OMRSE, along with VSO were both
● To include those variables that match with the individual imported ontologies. Classes related to “blood pressure” from
level variables in PCORnet CDM. VSO are reused in OCRSEV. The two selected ontology
imports serve as a foundation for the creation of this ontology
2 METHODS and provides the skeleton of the future work which involving
This work involved performing a scoping literature review the inclusion of variables beyond the individual-level.
(Arksey et al., 2007) to collect all the SE variables from
2
Ontology of cancer related social-ecological variables
Imported terms: Apart from entire ontologies, some classes comorbidity” was created as a subclass of “comorbidity” and
and relations were also imported for the construction of defined as a comorbidity with a primary diagnosis of cancer.
OCRSEV. A total of 70 terms were imported from the Human This was required to relate comorbidity with cancer. “tobacco
Disease Ontology (DOID) (Kibbe et al., 2014) for the five use role” and “tobacco use behavior” were created according
types of cancer (cervical cancer, colon cancer, breast cancer, to the model created for “individual tobacco use range
lung cancer, prostate cancer). The terms were arranged in variable”. For the PCORnet database and variables,
BFO 2 following the hierarchy of the Cell Line Ontology “PCORnet CDM database” was created as a subclass of
(CLO) (CLO, 2017). “Body Mass Index” was imported from “database” imported from ERO (Fig. 2) first and then,
Clinical Measurement Ontology (CMO) (Shimoyama et al., “PCORnet tobacco use range variable” was created as a
2012; Smith et al., 2013). To determine where to place this subclass of “individual tobacco use range variable”.
class in BFO 2, the same class from Taxonomy for
Rehabilitation of Knee Conditions (TRAK) (Button et al.,
2013) was referred to.
Existing definitions for variables such as comorbidity, age,
weight, and height encouraged us to reuse these definitions in
place of creating our own definitions. “Comorbidity” was
imported from the Ontology for Minimum Information
About Biobank data Sharing (OMIABIS) (Brochhausen et
al., 2013), and qualities such as “height” and “weight” were Fig. 2. Variable terms and database terms
imported using the Phenotypic Quality Ontology (PATO)
(WG, 2017). “Age” used for modeling the created term “age
range variable” was imported from Ontology for Biomedical
Investigations (OBI). Since OCRSEV is constructed with
numerous variables and categorical variables, classes
“variable” which is a subclass of “data item” and “categorical
variable” which is a subclass of “variable” were reused from
Apollo Structured Vocabulary (Apollo SV) (Hogan et al.,
2016).
“Hypertension” and its related classes were imported from
Obstetric and Neonatal Ontology (ONTONEO) (Farinelli et
al., 2016) as they are useful creating class restrictions in the Fig. 3. Identity datum terms
model for one of the created classes “blood pressure range The class “model” was created as a subclass of “directive
variable”. “tobacco”, “tobacco material”, “chewing tobacco information entity” in BFO following the placement of “data
behavior”, “smoking behavior” and related classes were representational model” from Apollo SV. Since SEM of
imported from Ontology for Biobanking (OBIB) health promotion is a model that represents SE variables and
(Brochhausen et al., 2016) as they were useful in developing is encoded in the specification of CDC’s SEM, CCRP, 2015,
the model for the created class “individual tobacco use this model is a directive information entity, similar to the
variable”. Since the ontology uses PCORnet CDM database “data representational model” from Apollo SV. “Multilevel
variables, the class “database” was imported from Eagle-I model” was created as a subclass of “model” and “socio-
Research Resource Ontology (ERO) (Vasilevsky et al., economic model” was created as a subclass of “multi-level
2012). model”. “Socio ecological model level” which is a part or a
Created Classes: SE variables were created as subclasses of component of the socioecological model was created as a
categorical variable. The classes include “age range subclass of “information content entity”. The five levels of
variable”, “individual tobacco use variable”, “individual socio-ecological model, namely, “socio ecological model
education level variable”, “individual employment status individual level”, “socio ecological model interpersonal
variable”, “individual income status variable”, “individual level”, “socio ecological model organizational level”, “socio
socioeconomic status variable”, “individual medical cost ecological model community level” and “socio ecological
variable”, “body mass index range variable” and “blood model policy level” were created as subclasses of “socio
pressure range variable”. This covers nine of 13 variables ecological model level” (Fig. 4). The class “body mass” was
(Fig. 2). Of the remaining four variables, three classes, created as a subclass of “mass” in quality of BFO, as required
namely, “racial identity datum”, “ethnic identity datum” and by the model created for “body mass index range variable”.
“gender identity datum” were reused from OMRSE while Although “body mass” is available in Vertebrate Trait (VT)
“comorbidity” was reused from OMIABIS. “Cancer
3
Ontology of cancer related social-ecological variables
ontology (Park C et al., 2013), we did not reuse this class as social social A social ecological model level which
ecological ecological represents the social variables influencing an
VT does not have BFO as its upper level ontology. model model level individual or a population, specifically
policy containing variables related to Medicare and
level Medicaid at population level, including
proportion of population in managed care health
plan and proportion of vulnerable cancer
population.
Table 1 Competency Questions, Description Logic queries, and
expected results for validation
Fig. 4. Social ecological model and its levels Class restrictions: To create class restrictions for the created
Classes “PCORnet ethnicity identity datum” and classes, object properties such as “part of”, “has part”, “is
“PCORnet racial identity datum” and their subclasses were about”, and “inheres in” were used. Some relations such as
used from the model created for PCORowl by OneFlorida “correlated with” were imported from Relations Ontology
team at the University of Florida (Fig. 3) (Hicks et al., 2017). (RO) (Mungall, 2017) to relate the qualities or roles of the
variables with cancer. From the literature review, we could
Definitions: WordNet (Miller, 1990) and NCI Thesaurus say that some of the variables discussed in this paper are
(Sioutos et al., 2007), Wikipedia (Wikipedia, 2017), Medical “correlated with” the condition “cancer” (K.
Subject Headings (Medical Subject Headings, 2017), Apollo Balasubramanian, D., 2017). “is determined by” and
SV, PATO and other existing ontologies were referred to for “determines” were imported from The Drug-Drug
crafting the definitions and modified as required based on the Interactions Ontology (DINTO) (Herrero-Zazo et al., 2015)
requirement and genus proximus (Seppälä et al., 2016) for all to relate the variables with qualities or other conditions. For
the terms created in OCRSEV. CDC’s SEM for CCRP and example, since “body mass index” is a measurement of
Wikipedia were referred for crafting definitions of SEM and related body weight to height of an individual, we can say
its levels. SEM levels’ definitions are in Table 1. that “body mass index” is determined by “height” and
Class Genus Definition “weight”. As an overall structure, all 13 variables are a “part
Proximus of” some “social ecological model level” and every “social
social information An information content entity which is a ecological model level” is a part of some “social ecological
ecological content component of social ecological model which
model entity represents various personal or environmental model”. Since this work focusses only on individual level, all
level variables that influence individuals and are 13 variables are a “part of” some “social ecological model
grouped together based on some common individual level”. All PCORnet CDM variables are a “part
characteristic.
social social A social ecological model level which
of” some “PCORnet CDM database”. These class
ecological ecological represents the personal variables influencing an descriptions were necessary to answer the six competency
model model level individual, specifically containing all the questions.
individual variables related to the individual's attributes,
An example of a representation of “age range variable” is
level including age, gender, race, ethnicity, education,
income, employment, co-morbid conditions, given in Fig. 5. Eight of 13 variables were modeled in the
presence of health insurance, tobacco use and same way by relating the variable with cancer and Homo
year of diagnosis. sapiens. Every variable is a data item conveying information
social social A social ecological model level which
ecological ecological represents the social variables influencing an about some other entity and can be mapped with its
model model level individual or a population, specifically corresponding data (instances) from database. In Fig,5, “age
interperso containing variables related to individual's range variable” is a data item which conveys information
nal level relationship with family, friends, healthcare
providers, community health workers or patient
about the quality “age” which “inheres in” human being.
navigators who can influence individual's Every SE variable is represented by a level in SEM. In our
behavior or attitude. example, “age range variable” is represented by “social
social social A social ecological model level which ecological model individual level” which is a part of “social
ecological ecological represents the social variables influencing an
model model level individual or a population, specifically ecological model”. Hence, all SE variables which are a part
organizati containing variables related to the availability of of some level, all levels which are a part of SEM and the SEM
onal level health care, including the number of primary are information content entities in IAO. One could draw
care physicians and health facilities available in
the individual's area of residence. parallels between the structure of SEM in OCRSEV and the
social social A social ecological model level which class “document” in IAO. Just as “variable” is a part of
ecological ecological represents the social variables influencing an “social ecological model level” which is a part of “social
model model level individual or a population, specifically
communit containing all the variables related to the
ecological model” in OCRSEV, “acknowledgements
y level individual's area of residence, including area- section” is a part of “document part” which is a part of
level poverty, rural residency, area-level “document” in IAO.
smoking and alcohol consumption rate and area-
level hospital utilization rate.
4
Ontology of cancer related social-ecological variables
3 RESULTS
A total of 25 classes were created of which 13 classes
represent nine SE variables, five represent the five levels of
SEM, three were created for SEM hierarchy and one each for
PCORnet CDM specification, PCORnet CDM database,
cancer comorbidity and body mass. A total of 30 class
restrictions were created. The imported ontologies, classes
and relations, created classes and class restrictions helped in
not only answering all six competency questions but also in
representing models of the 13 variables. The results were
validated by running the DL queries as explained in the
methods section. The actual results exactly matched with the
expected results.
Fig. 5. RDF model for “age range variable” 4 DISCUSSION
The ontology was validated using Description Logic (DL) OCRSEV is an open-ended ontology that can be extended
queries in Protégé 5.1. The DL queries along with the with additional variables in SEM. The principles of
expected results are shown in Table 2. classification, “disjointness” and “consistent differentia” are
Competency Description Logic Expected Results followed for all the created classes. This project precludes
Questions Query
the principle of “exhaustiveness” as it can include any
What are some disease and cancer All 5 types of cancer (breast,
diseases that are colorectal, lung, prostate and number of variables in each level of the SEM. This project is
cancer? cervical) and its related limited to only some key SE variables of cancer and LSCD,
cancer types only five types of cancer and only one multi-level model.
What are some (‘part of’ some ‘socio ecological model
levels of SEM? ‘social ecological individual level’ This work is a preliminary step in providing for data
model’) and ‘social ‘socio ecological model integration tasks which will in turn aid in multilevel analysis
ecological model interpersonal level’ of SE predictors. Future work will involve creating models
level’ ‘socio ecological model
organizational level’
for SE variables of all five levels of SEM and adding those
‘socio ecological model models to the ontology. The aboutness of the variables will
community level’ also be explained with regards to specifying their level in
‘socio ecological model SEM as part of future work. Different databases for all the
policy level’
What variables are ‘information content All 13 variables along with all variables will be identified and respective fields will be
represented by entity’ and (‘part of’ of their subclasses mapped with the classes in OCRSEV. These mappings will
SEM? some ‘social be created using an add-on tool for Protégé 5.1 called Ontop
ecological model
level’) (Calvanese et al., 2016).
What variables are ‘information content All 13 variables along with
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