Leveraging the Rural-Urban Commuting Area Tool to Address Geographic Disparities in Cancer Care: A Dual-Application...

Area of Science:

• Public Health
• Geographic Information Systems (GIS)
• Cancer Epidemiology

Background:

• Cancer disparities research requires accurate geographic data to identify and address health inequities.
• Existing county-level classifications may lack the granularity needed to capture the nuances of rural-urban divides.
• Standardization is crucial for meeting data requirements, such as those from the National Institutes of Health (NIH) Helping to End Addiction Long-term (HEAL) Initiative.

Purpose of the Study:

• To develop and validate an open-access Rural-Urban Commuting Area (RUCA) tool for standardized geographic coding in cancer disparities research.
• To meet NIH HEAL Initiative Common Data Element requirements and support institutional catchment area analyses.
• To provide census tract-level precision for improved geographic classification.

Main Methods:

• Developed a web-based tool integrating US Department of Agriculture RUCA codes, census tract data, and electronic health records.
• Implemented the tool using Wake Forest Cancer Center's 2023 registry data (n=21,219).
• Conducted a systematic comparison with county-level Rural-Urban Continuum Code (RUCC) classifications using 18,714 cancer cases across 336 ZIP codes for breast, colon, and lung cancers.

Main Results:

• 19.51% (n=4,140) of cancer patients resided in rural areas, with 4.81% (n=1,022) in the most rural census tracts (RUCA codes 7-10).
• A 9.4% disagreement between RUCA and RUCC classifications was observed, affecting 1,765 patients.
• Twenty-eight ZIP codes classified as rural by RUCA were within metropolitan counties by RUCC, misclassifying 109 patients with cancer.

Conclusions:

• The RUCA tool provides essential census tract-level precision, overcoming limitations of county-level classifications in cancer research.
• This dual-application framework aligns institutional analyses with national standardization efforts.
• The tool identified 109 patients misclassified by traditional methods, enabling enhanced targeted interventions for rural cancer care access.