Citations 2
From Housing to Hotspots: Integrating a Housing-Based Measure of Individual Socioeconomic Status with Geospatial Analysis to Target Colorectal Cancer Screening in Rural Communities
Yao, Rebecca , Beenken, Madison, Watson, Dave , Wheeler, Philip H., Finch, Mike, Kelleher, Dan , Anil, Gokhan , Anderson, Trent, Madden, Kathy A., Okuno, Scott H. , Odedina, Folakemi T., Park, Eunice , Dugani, Sagar B., Sosso, Jessica , Sabarish, Shivani , Singh, Gurpreet, Lugo-Fagundo, Nahyr, Kim, W. Ray, Walker-Mcgill, Cheryl L., Juhn, Young J. , Lynch, Brian , Wi, Chung‐Il , Madden, Kathy A., Westfall, Erin , Sharma, Pravesh , Dugani, Sagar B. , Foss, Randy , Hidaka, Brandon H. , Singh, Gurpreet, Howick V, James F , Calvin, Andrew D. , Walker-McGill, Cheryl Lynn, Rennert, Lior , Cerhan, James R.
No container title available | September 3, 2026 | Volume: No volume available | Issue: No issue available | Pages: No page available
ABSTRACT Purpose This 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. Methods We 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). Findings Among 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). Conclusion Lower 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.
Beyond Rurality: Individual Socioeconomic Status and Chronic Disease Prevalence
Sabarish, Shivani , Wi, Chung-Il, Beenken, Madison, Watson, Dave , Prissel, Christine M. , Wheeler, Philip H., Kelleher, Dan , Anil, Gokhan , Park, Eunice Y., Singh, Gurpreet, Lugo-Fagundo, Nahyr S, Howick, Jimmy F., Walker-Mcgill, Cheryl L., DUGANI, SAGAR, Pongdee, Thanai, Sosso, Jessica , Foss, Randy , Varkey, Prathibha, Juhn, Young J. , Wi, Chung‐Il , Wi, Chung-Ii , Patten, Christi A. , Wheeler, Philip, Lugo-Fagundo, Nahyr, Howick V, James F, Sharma, Pravesh , Dugani, Sagar B , Sosso, Jessica L , Foss, Randy M, Foss, Randy , Brockman, Tabetha A. , Wheeler, Philip H., Anderson, Trent, Lugo‐Fagundo, Nahyr , Walker-McGill, Cheryl Lynn, Hidaka, Brandon H. , Sharma, Pravesh , Dugani, Sagar B. , Pongdee, Thanai , Garovic, Vesna D.
No container title available | April 6, 2026 | Volume: No volume available | Issue: No issue available | Pages: No page available
ABSTRACT Importance Rural-urban disparities in chronic disease prevalence are well established; however, the extent to which individual-level socioeconomic status (SES) contributes to these disparities remains unclear. Objective To examine the associations of rurality and SES with the prevalence of five most burdensome chronic diseases among adults. Design We conducted a retrospective cross-sectional study of adults across 27 Upper Midwest counties using the Expanded Rochester Epidemiology Project (E-REP) medical record data linkage system to evaluate associations between rurality, SES and chronic disease prevalence. Prevalence of clinically diagnosed asthma, diabetes, hypertension, coronary heart disease, and mood disorders was identified from International Classification of Diseases ICD9/10 codes over a five-year period (2014-2019). Setting Population based Participants Adults over 18 years residing in the 27 E-REP counties, excluding those missing rural-urban residence status. Exposure HOUSES index, an individual-level measure of SES, served as the primary measure, while rurality based on Rural Urban Commuting Area (RUCA) codes 4-10 was the secondary measure. Main Outcome Prevalence of the five clinically diagnosed chronic diseases was identified using ICD9/10 codes from 2014–2019. Mixed effect logistic regression models were used and adjusted for demographics and general medical examination receipt, to assess rural-urban and SES differences for prevalence of each chronic disease. Results Among 455,802 adults with available HOUSES index, 42.8% lived in rural areas, 53.8% were female and 87.4% were non-Hispanic White. In the unadjusted analysis, rural and urban populations showed comparable asthma and CHD prevalence, while mood disorders, hypertension, and diabetes were more common in urban areas. After adjusting for demographic factors and healthcare utilization, rural-urban differences were no longer statistically significant, whereas SES remained strongly associated with all diseases in a dose response manner (e.g., adjusted Odds Ratio for hypertension (ref: HOUSES index Q4): 1.14, 1.27, and 1.42 for HOUSES index Q3, Q2, and Q1, respectively). Conclusions and Relevance Individual-level SES measured by the HOUSES index, was more strongly associated with chronic disease prevalence than rurality, supporting its integration into population health assessment and risk stratification.