Redefining Rurality, Prioritizing Self-Perception
The geography of where a person lives undeniably affects their health. Each community has unique resources and faces different challenges and facilitators in meeting the needs of their residents. The choice of geographic definition used in analysis has consequences in monitoring health outcomes and identifying disparities by geography.
Current methodologies for classifying geography tend to focus on urban areas and often define rural as "not urban." Common geographic definitions rely on quantifiable characteristics of a region, including housing and population density, land-use, economic characteristics, or administrative boundaries. Additionally, there are differences in life experiences in rural areas not captured by these metrics. This project sought to highlight the differences in rural areas of New York State by including residents' self-perception of their community. Rural areas are not homogeneous; within areas that are labeled rural by standard definitions there are towns and villages, small cities, communities bordering metropolitan areas, and deeply remote lands.
We developed a new classification of geography (Geoclassification) that 1) promotes equity by prioritizing data that represent the lived experience of residents, and 2) better reflects regional geography with more granularity, using census tracts as a unit of measure, especially in areas considered rural. We leveraged existing datasets to create our new Geoclassification. Our process combines the Rural-Urban Commuting Area (RUCA) codes from the U.S. Department of Agriculture, the Urbanization Perceptions Small Area Index (UPSAI) from the Department of Housing and Urban Development, and population density. By using nationally available standard datasets with a systematic approach, our methodology can be easily updated when new data is available. Developing Geoclassification at the census tract level adds the ability to delineate borders including city lines, towns and villages, and types of rural areas.
Using the definitions for each data source, we developed a matrix of all possible combinations of cases for the RUCA codes (1-10) and UPSAI categories (urban, suburban, and rural). We then assigned each case combination to one of six geographic categories: Urban, Suburban, Rural – Transitional, Rural – Population Center, Rural, or Correctional Facilities. Tracts with a population that is 50% or more incarcerated were labeled as Correctional Facilities. We applied the categorization to all New York State census tracts and reviewed each combination case to evaluate the need to introduce thresholds based on population density. We assessed our decision matrix using our knowledge of the state and by connecting with regional experts across the state to validate. After developing Geoclassification for the 2010 census tract definitions, we mapped our results to 2020 census tract definitions using crosswalks from NHGIS and the US Census Bureau.
This classification can be combined with additional datasets to analyze access, utilization, and health outcomes. Upon applying Geoclassification to statewide datasets, we observed measurable differences in emergency department and hospitalization utilization rates between each geographic type, as well as premature mortality and life expectancy. As a part of our commitment to data equity principles, Common Ground Health has adopted this classification as one way of identifying geography-based disparities.
For more detail on our process or to explore applications of this method, contact us at [email protected]
Try our interactive map with Geoclassification applied:
