Electronic health record (EHR) systems have the potential 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 coordinates are defined for a patient record, they can be used to identify any other place that shares this location 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 factors.5–9
Community data in this context refer to both compositional and contextual characteristics of the areas, or geographic entities, associated with a location. Compositional 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. Alternatively, community data may be defined by specific criteria, such as distance zones around a point location. 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 commonly 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 contextual 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 incorporated 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 expertise, tools, and reference data.