From Housing to Hotspots: Integrating a Housing-Based Measure of Individual Socioeconomic Status with Geospatial Analysis to Target Colorectal Cancer Screening in Rural Communities
- 1Mayo Clinic
Abstract
PurposeThis study assesses the association between colorectal cancer (CRC) screening and a validated, housing-based measure of individual-level socioeconomic status (SES, called HOUSES hereafter) within rural communities and determines whether HOUSES-integrated geospatial analysis can be used to tailor interventions.
MethodsWe used CRC screening data from a subset of Mayo Clinic Midwest patients living in cities without ready access to routine care in the Mayo Clinic Health System in 2019 to represent rural communities. At the individual level, we assessed the association between CRC screening rates and the HOUSES index, adjusting for age, sex, race/ethnicity, comorbidity, distance from home address to clinic, and area deprivation index, using a multilevel mixed-effects logistic regression model. Additionally, we conducted geospatial analysis to examine the correlation between hotspots of 1) lower CRC screening rates and 2) lower SES of the subject population (HOUSES quartile 1).
FindingsAmong 34,489 individuals (median age 64.0 years, 52.4% female), those with the lowest SES (HOUSES Q1) had 37% lower odds of being CRC screening adherent than those with the highest SES (HOUSES Q4) (adj. OR [95% CI]: 0.63 [0.58-0.69]). In the 14 identified HOUSES Q1 hotspots, there was a significant correlation in counts of HOUSES Q1 and low CRC screening (correlation coefficient=0.81).
ConclusionLower SES was significantly associated with lower CRC screening among rural populations. HOUSES-enabled geospatial analysis identified geographic hotspots with lower CRC screening rates for targeted interventions to address disparities in CRC screening in rural communities. HOUSES may be a useful digital tool for cancer preventive care and research.
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BibTeX
@misc{yao2026from,
title = {{From Housing to Hotspots: Integrating a Housing-Based Measure of Individual Socioeconomic Status with Geospatial Analysis to Target Colorectal Cancer Screening in Rural Communities}},
author = {R. Yao and C.-I. Wi and M. J. Beenken and D. Watson and P. H. Wheeler and M. Finch and D. P. Kelleher and G. Anil and T. Anderson and K. Madden and S. H. Okuno and F. T. Odedina and E. C. Westfall and E. Y. Park and P. Sharma and S. Dugani and R. M. Foss and B. H. Hidaka and J. L. Sosso and S. Sabarish and G. Singh and N. Lugo-Fagundo and J. Howick and W. R. Kim and A. D. Calvin and C. L. Walker-Mcgill and L. Rennert and Y. J. Juhn and J. R. Cerhan and B. A. Lynch},
howpublished = {medRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.28.26361444},
url = {https://www.medrxiv.org/content/10.64898/2026.08.28.26361444v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - From Housing to Hotspots: Integrating a Housing-Based Measure of Individual Socioeconomic Status with Geospatial Analysis to Target Colorectal Cancer Screening in Rural Communities
AU - R. Yao
AU - C.-I. Wi
AU - M. J. Beenken
AU - D. Watson
AU - P. H. Wheeler
AU - M. Finch
AU - D. P. Kelleher
AU - G. Anil
AU - T. Anderson
AU - K. Madden
AU - S. H. Okuno
AU - F. T. Odedina
AU - E. C. Westfall
AU - E. Y. Park
AU - P. Sharma
AU - S. Dugani
AU - R. M. Foss
AU - B. H. Hidaka
AU - J. L. Sosso
AU - S. Sabarish
AU - G. Singh
AU - N. Lugo-Fagundo
AU - J. Howick
AU - W. R. Kim
AU - A. D. Calvin
AU - C. L. Walker-Mcgill
AU - L. Rennert
AU - Y. J. Juhn
AU - J. R. Cerhan
AU - B. A. Lynch
PY - 2026
DA - 2026/08/28
PB - medRxiv
DO - 10.64898/2026.08.28.26361444
UR - https://www.medrxiv.org/content/10.64898/2026.08.28.26361444v1
AB - PurposeThis study assesses the association between colorectal cancer (CRC) screening and a validated, housing-based measure of individual-level socioeconomic status (SES, called HOUSES hereafter) within rural communities and determines whether HOUSES-integrated geospatial analysis can be used to tailor interventions. MethodsWe used CRC screening data from a subset of Mayo Clinic Midwest patients living in cities without ready access to routine care in the Mayo Clinic Health System in 2019 to represent rural communities. At the individual level, we assessed the association between CRC screening rates and the HOUSES index, adjusting for age, sex, race/ethnicity, comorbidity, distance from home address to clinic, and area deprivation index, using a multilevel mixed-effects logistic regression model. Additionally, we conducted geospatial analysis to examine the correlation between hotspots of 1) lower CRC screening rates and 2) lower SES of the subject population (HOUSES quartile 1). FindingsAmong 34,489 individuals (median age 64.0 years, 52.4% female), those with the lowest SES (HOUSES Q1) had 37% lower odds of being CRC screening adherent than those with the highest SES (HOUSES Q4) (adj. OR [95% CI]: 0.63 [0.58-0.69]). In the 14 identified HOUSES Q1 hotspots, there was a significant correlation in counts of HOUSES Q1 and low CRC screening (correlation coefficient=0.81). ConclusionLower SES was significantly associated with lower CRC screening among rural populations. HOUSES-enabled geospatial analysis identified geographic hotspots with lower CRC screening rates for targeted interventions to address disparities in CRC screening in rural communities. HOUSES may be a useful digital tool for cancer preventive care and research.
ER -