54 Public Health Reports / 2011 Supplement 3 / Volume 126
Incorporating Geospatial Capacity
within Clinical Data Systems to Address
Social Determinants of Health
Karen Frederickson Comer,
Shaun Grannis, MD, MSb,c
Brian E. Dixon, MPA, PhDc
David J. Bodenhamer, PhDa
Sarah E. Wiehe, MD, MPHb,c
aIndiana University-Purdue University Indianapolis, School of Liberal Arts, The Polis Center, Indianapolis, IN
bIndiana University, School of Medicine, Indianapolis, IN
cRegenstrief Institute, Inc., Indianapolis, IN
Address correspondence to: Karen Frederickson Comer, MLA, Indiana University-Purdue University Indianapolis, School of Liberal Arts,
The Polis Center, 1200 Waterway Blvd., Indianapolis, IN 46202; tel. 317-274-2296; fax 317-278-1830; e-mail <firstname.lastname@example.org>.
©2011 Association of Schools of Public Health
Linking electronic health record (EHR) systems with community information sys-
tems (CIS) holds great promise for addressing inequities in social determinants
of health (SDH). While EHRs are rich in location-specific data that allow us to
uncover geographic inequities in health outcomes, CIS are rich in data that
allow us to describe community-level characteristics relating to health. When
meaningfully integrated, these data systems enable clinicians, researchers, and
public health professionals to actively address the social etiologies of health
This article describes a process for exploring SDH by geocoding and
integrating EHR data with a comprehensive CIS covering a large metropolitan
area. Because the systems were initially designed for different purposes and
had different teams of experts involved in their development, integrating
them presents challenges that require multidisciplinary expertise in informat-
ics, geography, public health, and medicine. We identify these challenges and
the means of addressing them and discuss the significance of the project as a
model for similar projects.
Geospatial Capacity, Clinical Data Systems, and SDH 55
Public Health Reports / 2011 Supplement 3 / Volume 126
Electronic health record (EHR) systems have the poten-
tial to improve the quality of care and decrease overall
health-care utilization costs.1,2 By augmenting clinical
data captured in a health information exchange (HIE)
with spatially enabled community data, these systems
also can more effectively identify and characterize
public health trends and events, predict future public
health outcomes, and help devise more effective health
interventions.3,4 In this article, we discuss the role of
spatially enabled data and community information
systems (CIS) in the context of health care.
Spatial data describe the geospatial location(s) of
patients and associated geographic entities, such as
neighborhoods, census tracts, or counties. These data
also may include attributes associated with the patient
or the geographic entities in which the patient lives.
Residential street address is commonly recorded in
electronic patient records. Addresses are translated
into spatial data via a geocoding process called address
matching. This process involves matching the input
address with addresses in a digital reference map
and extracting the associated geographic coordinates
(latitude and longitude) to define the position of the
associated point on the earth. Once geographic coor-
dinates are defined for a patient record, they can be
used to identify any other place that shares this loca-
tion or any catchment area associated with it (e.g., ZIP
code or neighborhood). As health disparities are often
geographically specific, it is particularly important to
consider place to understand and address their causal
Community data in this context refer to both com-
positional and contextual characteristics of the areas,
or geographic entities, associated with a location. Com-
positional data can refer to population characteristics
(e.g., adolescent fertility rates or socioeconomic status
[SES]) and contextual data can refer to proximity
to risk factors (e.g., nearness to high-crime areas or
affordable clinical care).10–15 Contextual data can also
refer to qualitative data, such as written histories of a
place or interviews with residents. Community data
often describe well-known geographic entities, such
as neighborhoods, census tracts, and counties. Alter-
natively, community data may be defined by specific
criteria, such as distance zones around a point loca-
tion. Depending on the analysis and the theorized
geographic level of influence, areas of interest may
include the associated neighborhood, primary care
service area, and/or county, among others.16
Data from the U.S. decennial censuses are com-
monly used to describe demographic, socioeconomic,
and housing characteristics of a place. The annual
American Community Survey data promise to provide
population and housing data more regularly, although
these data often have insufficient sample sizes to
analyze the context of small geographic entities.17 In
addition, census geographic entities, such as census
tracts, may not be the most relevant to examine social
processes influencing health.5,8
CIS, an ideal source of compositional and contex-
tual community data, typically are developed by local
stakeholders interested in assembling data to help
assess local issues. In addition to U.S. census data,
CIS commonly integrate data from a wide variety of
state and local sources18 that provide more detailed
and varied information not covered by the census,
such as crime incidence or availability of community
resources. Although some CIS focus primarily on
neighborhood-level indicators, others provide data at
multiple geographic levels to support a wider range of
uses, including multilevel analysis, and/or allow data
to be aggregated to custom boundaries of interest to
the user. Once local administrative datasets are incor-
porated into a CIS, these data are typically updated on
a recurring basis and made publicly available.
Equally important, selected data and associated CIS
indicators reflect local concerns and interests, which
can be informative to the researcher studying social
determinants of health (SDH). Additionally, because
CIS commonly incorporate geographic information
systems to geocode, integrate, and visualize data, these
systems can provide a good source of geocoding exper-
tise, tools, and reference data.
We demonstrate the challenges and means of lever-
aging EHR systems and CIS by integrating two well-
established information systems in central Indiana.
The interdisciplinary Indiana Center of Excellence in
Public Health Informatics manages a large EHR system
called the Indiana Network for Patient Care (INPC).
The Polis Center, also at Indiana University-Purdue
University Indianapolis, has developed and manages
the SAVI Community Information System (hereafter,
INPC is one of the nation’s most comprehensive and
longest-running HIEs. It is operated by the Regenstrief
Institute, an internationally recognized informatics
and health-care research organization dedicated to
improving health through research, development,
and education. In addition to providing clinical data
at the point of care to more than 14,000 physicians,
INPC provides statewide syndromic surveillance, public
56 Practice Articles
Public Health Reports / 2011 Supplement 3 / Volume 126
health case detection, and physician alerting services
to local and state public health. Its data repository
contains more than one billion coded standardized
clinical observations dating back more than 30 years
and receives 350,000 to one million clinical transactions
daily from more than 200 data sources, including nearly
80 emergency departments and 35 hospitals, more
than 100 clinics, local and state health departments,
and multiple ancillary care data sources.19 In addition
to offering daily access to community-wide clinical
data to providers in routine health-care settings and
supporting real-time public health surveillance, INPC
offers a rich source of clinical data for researchers to
study pediatric obesity, adolescent and young adult
sexually transmitted infections (STIs), and asthma,
among other health topics.19–29
SAVI is one of the leading CIS noted by the U.S. Gov-
ernment Accountability Office.30 SAVI was developed
and is managed by The Polis Center, which is dedicated
to using collaboration, interdisciplinary research, and
knowledge of advanced spatial technologies to provide
reliable information for improving communities in
Indiana and beyond. SAVI collects, geocodes, orga-
nizes, and presents integrated data on communities
in the 11-county Indianapolis metropolitan statistical
area drawn from more than 30 federal, state, and local
providers, all linked to the lowest available geographic
level (often, the street address). It is the nation’s largest
CIS, with more than 10,000 time-series variables from
1980 to the present, including welfare, education,
health, public safety, housing, and demographics. SAVI
also includes information on the locations of health
facilities, health and human services, community
facilities, and associated service areas. In addition to
providing compositional and contextual data for local
public health research, SAVI provides data for cross-site
(multiple city and institution) studies on public health
and other community issues.24–29
Until recently, researchers who wanted to link
patient and community data from INPC and SAVI had
to do so manually on a project-by-project basis. This
ad hoc approach was inefficient and lacked the flex-
ibility for data analysis that an integrated system could
provide. Overlapping datasets would be extracted for
geocoding by different researchers, with the results not
returned for subsequent use. Also, when researchers
who had geocoded their own datasets shared their
data, there was often uncertainty about the relative
accuracy of the geocodes, particularly because there
was not a standard method for capturing spatial meta-
data or documenting the associated methodology and
reference data used. In addition to being redundant,
this manual approach was time-consuming. To address
this problem, an INPC-SAVI team is augmenting the
clinical data with geospatial attributes by designing,
implementing, deploying, and evaluating a near real-
time geocoding process capable of handling high-
transaction volumes. Within this system linkage, all
current and incoming patient records are geocoded,
which allows the clinical data to be spatially associated
with extensive, locally developed datasets about the
social, economic, and physical environment.
Requirements for the initial version of this system
were defined based on a relatively simple administra-
tive use (i.e., generation of aggregate-level counts of
notifiable disease cases per county). The accuracy and
completeness of geocoding required for this admin-
istrative use are not as stringent as needed for many
clinical and research uses. Additional use cases have
been and will continue to be defined to guide the ongo-
ing refinement and expansion of system requirements.
Example use case
To help demonstrate the opportunities and chal-
lenges associated with systemized integration of data
from INPC with geospatial attributes and associated
community data of SAVI, we describe an example use
case—disparities in the social determinants of STIs.
Despite dramatic health disparities in STI rates by
race/ethnicity and SES, previous work has recognized
the limitations of individual-level and behavioral
approaches in effectively targeting STI risk factors31,32
and has called for further investigation of social33 and
structural34 characteristics as potential factors for con-
tributing to the geospatial clustering of STIs.12,30,33,35–42
Investigation of how STI cases cluster geographically
may identify key associations with the social and physi-
cal environment and, thus, inform research on STI
prevalence and disparities. Considering geographic
context and subsequently addressing characteristics
of core areas contributing to spatial heterogeneity of
STIs is crucial in effectively tailoring and implementing
interventions.33 Furthermore, determining significant
“areas of exposure” may be important in targeting
prevention efforts and understanding mechanisms of
local sexual networks.
In considering how to integrate community-level
exposures and STI risk, we have added to a conceptual
model recently presented and discussed by Buffardi et
al. (Figure).43 In this model, ecosocial and individual
psychosocial factors (distal risk factors) contribute to
STI risk via altered partner characteristics and high-
risk sexual behaviors (proximal risk factors). We have
modified this model by adding another layer to the
distal risk factors box, which includes compositional
and contextual measures. We hypothesize that these
Geospatial Capacity, Clinical Data Systems, and SDH 57
Public Health Reports / 2011 Supplement 3 / Volume 126
factors may also modify partner characteristics and
high-risk sexual behaviors, thus increasing STI acquisi-
tion and diagnosis.
For the use case, we are interested in assessing associ-
ations between an individual’s disease risk (occurrence
of a particular STI during that year) and the selected
community measures (low SES, incarceration rates,
marriageable males, percentage of vacant houses, and
institutional resources) at different geographic levels
to evaluate significance of exposure proximity. We use
incarceration as an example case because STIs have
been well-documented in incarcerated populations
and among arrestees,44–48 including adolescents.49–55 In
addition, area-level incarceration has been linked to
area-level STI rates. Researchers have suggested that
associations between STI prevalence and incarceration
rates may reflect the impact of ex-offenders on the
general population.56 Incarceration may also produce
a change in network structures while community mem-
bers are incarcerated.32,56,57 Communities with high
incarceration rates may also carry disproportionate
risk through contact with individuals with high STI
risk prior to their incarceration.58
Most studies of community risk factors rely on
commonly available administrative boundaries, such
as census-tract boundaries, to define a patient’s area
of exposure. This is likely a decision of convenience
rather than a scientifically based one. While we are
interested in developing measures at the census-tract
level for comparison with previous studies, we are also
interested in developing measures for zones around
a patient’s residence (e.g., 200 meters, 400 meters,
and 800 meters). As such, we identified the following
data-processing and integration steps for our example
1. Geocoding patient addresses with documented positive
STI test results. Residential address indicates where the
individual likely spent a significant portion of his/her
time, although other relevant exposures may include
those associated with place of work, school, and other
daily activities.59,60 Multiple residential addresses can
be stored if a patient changed residence between
clinical visits. Access to these time-series address data
allows analysis of effects of residential mobility and
longitudinal exposures. Once geographic coordinates
are defined for a patient record, they can be used to
identify the associated areas (i.e., census tracts) for
which compositional and contextual data are available.
Figure. Conceptual model based on Buffardi et al.a in which distal risk factors, including community measures,
contribute to STI risk via proximal risk factors
aBuffardi AL, Thomas KK, Holmes KK, Manhart LE. Moving upstream: ecosocial and psychosocial correlates of sexually transmitted infections
among young adults in the United States. Am J Public Health 2008;98:1128-36.
STI 5 sexually transmitted infection
SES 5 socioeconomic status
58 Practice Articles
Public Health Reports / 2011 Supplement 3 / Volume 126
2. Geocoding residential addresses associated with indi-
vidual incarceration cases. We are interested in where
convicted criminals lived vs. where crimes occurred.
While crime event data are collected and geocoded
by SAVI, incarceration data currently are not. As such,
individual-level incarceration data must be collected
from other agencies and geocoded.
3. Aggregating geocoded incarceration data to geographic
areas of interest. Using the generated geographic coor-
dinates of each positive STI, we will be able to calculate
incarceration rates for various geographies, including
census boundaries or buffer zones.
4. Linking STI cases with other community variables based
on geographic units of interest. The remaining commu-
nity variables of interest are available from SAVI at the
census-tract level. We will be able to readily link these
variables to the STI cases using census-tract identifica-
tion as the joining field. For analysis using our buffer
zones of interest, we will be able aggregate the available
record-level data in SAVI to the generated buffer zones.
BArrIErS TO InTEGrATIOn
While the integration of spatially enabled EHR systems
with CIS holds great promise for understanding health
disparities, the automated integration of these data
Differing user requirements
The list of use cases that could potentially be supported
by such an integrated system is endless. Also, the output
of spatial data integration and analysis will need to
be translated into formats useful for a wide range of
public health users and accessible to multiple agencies
and jurisdictions. Prioritizing use cases and associated
system requirements is important for project planning
and managing stakeholder expectations.
Varying geocoding methodologies
and reference data
Geocoding capabilities are available via a wide range
of geographic information system tools and services.
In addition, there are multiple types of reference data
(e.g., street address centerlines, property parcels, and
address points) and sources (e.g., federal, commercial,
and academic) for any given geographic area. Differ-
ent reference data may produce different geospatial
attributes. Thus, appropriate selection depends on the
use case and an associated assessment of available data,
including the cost and associated data types, geom-
etries, accuracies, and resolutions. Finally, geocoding
technology and reference layers will continue to evolve.
Managing data-sharing partnerships
Both INPC and SAVI had to develop relationships and
trust among their data-sharing partners; INPC and SAVI
must continuously demonstrate that their partners are
getting value from sharing their data and that their
interests are being protected. This sociocultural com-
ponent is a greater challenge than the technical design
and implementation of such a system. Data-sharing
memoranda of understanding include oversight pro-
visions, guarantees of confidentiality, and guides on
allowable data use. Only aggregate data at a geographic
level that appropriately protects the confidentiality of
an individual’s data are publicly released. In addition
to assurances of data security, many stakeholders seek
the greatest community benefit possible from their
data-sharing efforts. To increase the possible uses of
the integrated data while maintaining individual con-
fidentiality, we have discussed the future integration
of geomasking techniques into the system.
ADDrESSInG THE CHAllEnGES
To meaningfully facilitate interdisciplinary research of
SDH, the integration of clinical and community data
systems requires active collaboration among informa-
tion scientists, geographers, data providers, clinicians,
and public health investigators experienced in using
clinical, as well as community, data. Specifically, the
collaboration helps to accomplish the following tasks.
Define needs. The productive synthesis of geospatially
enabled clinical and community data requires careful
consideration of the data needs and uses associated
with different groups, including, but not limited to,
data system administrators, clinicians, public health
researchers, public health agencies, and nongovern-
mental public health organizations. Multidisciplinary
collaboration on system design can require extensive
communication efforts due to different theoretical
frameworks and terminology. Clear documentation
of agreed-upon data classifications, standards, and
processes is essential.
Identify standards. To develop integrated data systems
that meaningfully facilitate exploration of SDH, we
need input from all collaborators experienced in
using clinical and community data. The coordinated
integration of an EHR system with a CIS provides the
opportunity to build consistency in geocoding methods
and reference data. Use of consistent methods and
reference data for geocoding clinical and community
datasets is necessary to avoid varying assignment of
geographic coordinates biasing subsequent analysis.
Geospatial Capacity, Clinical Data Systems, and SDH 59
Public Health Reports / 2011 Supplement 3 / Volume 126
As such, choice of geocoding technology and refer-
ence maps requires expert knowledge of geographic
information science and an understanding of use cases.
Leverage relationships. Acquisition of administrative
datasets, particularly at the individual record level,
typically requires memoranda of understanding with
the source data provider(s), usually with strict data-
handling protocols designed to protect confidentiality.
Establishing a memorandum of understanding can be
time-consuming. With the explicit, written permission
of source providers and Institutional Review Board
approval, many of the individual-level data geocoded
for CIS inclusion can be made available for research
and other public health uses in a de-identified or aggre-
gated format. CIS typically have data advisory commit-
tees composed of community stakeholders that recom-
mend datasets for inclusion based on understanding
the needs of the local community. These committees
provide a valuable source of community perspective.
Supporting use by multiple public health sectors
Ultimately, we want to make our integrated data
available for public health uses. An EHR system in
combination with a CIS can be used to significantly
augment existing community health assessment tools.
These tools typically report health outcomes by county
or other geographic administrative unit relevant for
policymaking but not necessarily for understanding
SDH. Our envisioned system will incorporate the
generated spatial attributes into existing standards-
based, bidirectional communications between public
health and clinical stakeholders. For this full potential
to be realized, multiple public health sectors must
become invested, contributing members of use case
The benefits of integrating EHR systems with CIS
include a better fit between the intended use of clinical,
spatial, and community data and the associated geopro-
cessing options, with the ultimate aim of developing
a sound theoretical and practical guide for applying
these data for addressing SDH. Benefits also include
the opportunity to generate and test new hypotheses
to address the social etiologies of health disparities.
Our integration work will lead to the identification of
nexus elements in system design that will inform the
work of other systems’ integration efforts and continu-
ally improve upon our current efforts. For instance,
system integration will address whether geospatial data
may improve patient-matching methods that are used
to aggregate patient data.
The growth in the number of EHR systems and CIS
nationwide holds promise for this type of system inte-
gration and the associated benefits to occur in cities
and states across the nation. In 2009, there were an
estimated 193 initiatives across the U.S. pursuing HIE
activities.61 Additional investment from the American
Recovery and Reinvestment Act of 2009 is likely to
increase this number.62 The Centers for Disease Con-
trol and Prevention (CDC) has invested in HIE via
the Nationwide Health Information Network (NHIN),
envisioned to be a network of networks allowing for
data integration across states and regions among clini-
cal and public health information systems.63,64 CDC also
has funded initiatives that would connect HIEs to state
health departments and state health departments to
CDC via NHIN.65,66
U.S. agencies are increasingly developing, integrat-
ing, and using local data for community building and
policymaking, as reflected by the growth and activity
of the National Neighborhood Indicators Partnership
(NNIP), which grew from six original partner cities in
1995 to 36 in 2011 and has been involved in many multi-
city initiatives to advance the development and use of
local CIS. All NNIP partner cities have built advanced,
spatially enabled data systems on neighborhood condi-
tions. The local specificity of these data systems makes
them a valuable resource for those interested in investi-
gating the relationships between health and prevention
and social, cultural, and other environmental factors.
A 2004 U.S. Government Accountability Office study
surveyed such systems for potential inclusion in a
national indicators system.29 These systems serve dif-
ferent purposes, some with comprehensive missions
(e.g., SAVI), and others focused on specific topics such
as education or housing; however, most use geospatial
information technologies and have efficient geocod-
ing mechanisms.67 Despite the significant potential
for geospatial integration with HIE, CIS do not have
a standard design, as most have developed in situ in
response to local interests and needs68 and, thus, their
possibilities and challenges vary.
While it is not uncommon to geocode clinical data
for ad hoc analyses and queries, it is uncommon to
routinely maintain an up-to-date geocoded patient
registry containing billions of clinical data results using
operational automated mechanisms. We believe that
this type of system will become more common in the
future, and our system serves as an example of a novel
data-enhancement process that links clinical data to
important community-level information. Once the
integration of CIS as a core part of HIE infrastructure
becomes accepted practice, more in-depth investigation
of SDH disparities will be possible.
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Public Health Reports / 2011 Supplement 3 / Volume 126
Understanding SDH is critical in addressing health
disparities. However, relatively few research projects use
individually identified data, tracked longitudinally with
point-level address data, possibly contributing to the
reason that resulting findings and associated interven-
tions have been inadequate to address the problem.
With the nationwide development of HIEs and CIS,
there is an increased opportunity for meaningful
compositional, contextual, and geographic analysis of
SDH. To realize the full potential of this opportunity,
several challenges must be met, including the need
for (1) a multidisciplinary approach; (2) research in
related fields, such as information science, geoinfor-
matics, informatics, and health geographics; and (3)
tools that can be used by multiple public health sectors.
The benefits, however, clearly outweigh the challenges.
With system integration recently completed, includ-
ing the geocoding of more than 27 million historic
clinical records and an additional 300,000 new clinical
records every evening, we are poised to proceed with
analysis of the linked data for several local research
projects, including the study of STIs, obesity, and dia-
betes health disparities.
With our advancing technology, interdisciplinary col-
laborations, and developing experience, we will aptly be
able to investigate further and hopefully address fully
health disparities resulting from social determinants.
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