Using GIS Technology to Define and Assess a Rurality Scheme Suitable for Decision Support in Health and Patient Services

Using GIS Technology to Define and Assess a Rurality Scheme Suitable for Decision Support in Health and Patient Services

Liora Sahar, Rentonia Williams, Arthi Rao, Kassandra I. Alcaraz, Kenneth M. Portier
Copyright: © 2018 |Pages: 17
DOI: 10.4018/IJAGR.2018070101
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Labeling geographic areas into rural or urban classes has implications to public policy, distribution of funds and services, as well as analysis and research. Rural-Urban classifications are often limited to dichotomous, county-based comparisons on geographies that can be too large and diverse to effectively assess and resource health care needs and services. Using an existing census tract based classification system, a modified rurality classification layer is proposed and used as a foundation layer in support of research, mission and income programs at a National Non-profit Organization. This system is analyzed by integrating with health services and program data to better understand accessibility and availability of health services, to assess available program offerings, and to evaluate the geographic distribution of health care providers and facilities. The analysis illustrates how identifying intra-rural differences can play a pivotal role in understanding patient needs, assigning adequate resources and can further support public health programs and policy implementation.
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Rurality is not an easily defined concept and its definition often depends on the questions being asked (Larson & Hart, 2003). Properly labeling geographic areas to rural or urban classes is important since it has implications to public policy, to distribution of funds and services, as well as to research and analysis. There is a tendency to define “rural” by what it is not, such as NOT urban or NOT metropolitan. Several common rural/urban definitions exist that typically employ characteristics such as population density and geographic location. “Rurality … exists along a continuum and varies extensively based on proximity to a central place, community size, population density, total population, and various social and economic factors” (George, 2008, p. 1). There is also the common perception of rural areas as open ranges with low population density, defined by agriculturally-driven economic activities (Hart, Larson, & Lishner, 2005). These perceptions of rurality linger despite drastic shifts in urbanization and the evolution of ‘rural’ economies (Schaeffer, Kahsai, & Jackson, 2012).

In the US, various geographic levels are available for rural classification, but the county is still the primary level used, mainly because of data availability. Though common, analysis by county level is challenging due to its tendency to mask the heterogeneity of population characteristics, socio-economic status and other indicators (United States Department of Agriculture [USDA], 2014b). Isserman (2005) discusses the “County Trap”, that is, defining counties as solely urban or rural, and suggests that a county is a mix of rural and urban areas and cannot be treated as a homogeneous unit. Hall, Kaufman, and Ricketts (2006) found that dichotomous classification of counties masks the heterogeneity of very rural areas when looking at health care access outcomes in those communities. Census tracts however, are smaller, consistent statistical divisions of a county (Branch, 2016) and with recent shifts in available datasets, and advancements in Geographic Information Systems (GIS), there is an opportunity to more carefully and accurately identify the range of rural and urban places (Ricketts, Johnson-Web, & Taylor, 1998). Rural classification systems at the small-area level (e.g. census tract, zip code) are less likely to lead to misclassifications, tend to address heterogeneity within counties, and allow discovery of intra-rural differences (Inagami et al., 2015).

Different groups, including federal agencies, rural advocacy/support organizations, and research centers have addressed “Rural” definitions and challenges. Among federal agencies, the Census Bureau (Census) and the U.S. Office of Management and Budget (OMB), have taken the lead and developed two common rural/urban classification schemas by defining and using the Urban (Urbanized areas/Urban clusters) and Core Based Statistical Areas (Metropolitan (Metro)/Micropolitan (Micro)) schema respectively. Rural advocacy and support organizations such as the Rural Policy Research Center (RUPRI) and the Rural Assistance Center (RAC) seek to address and inform on the challenges, needs, and opportunities of rural America, and support development of strategies to improve rural health and human services (Rural Assistance Center, 2015; Rural Policy Research Institute, 2015).

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