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Prissel, Christine M.

0009-0003-8569-8697

Mayo Clinic

Mayo Clinic

Citations 2

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

10.64898/2026.04.02.26350063 Get It

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.
Integrating Environmental Data with Medical Data in a Records-Linkage System to Explore Groundwater Nitrogen Levels and Child Health Outcomes
Prissel, Christine M. , Klinger, Gregory S., Rocca, Walter A. , Grossardt, Brandon R , Rocca, Walter A , Grossardt, Brandon R. , Sauver, Jennifer L. St.

International Journal of Environmental Research and Public Health | March 14, 2023 | Volume: 20 | Issue: 6 | Pages: 5116

10.3390/ijerph20065116 Get It

Background: The Rochester Epidemiology Project (REP) medical records-linkage system offers a unique opportunity to integrate medical and residency data with existing environmental data, to estimate individual-level exposures. Our primary aim was to provide an archetype of this integration. Our secondary aim was to explore the association between groundwater inorganic nitrogen concentration and adverse child and adolescent health outcomes. Methods: We conducted a nested case-control study in children, aged seven to eighteen, from six counties of southeastern Minnesota. Groundwater inorganic nitrogen concentration data were interpolated, to estimate exposure across our study region. Residency data were then overlaid, to estimate individual-level exposure for our entire study population (n = 29,270). Clinical classification software sets of diagnostic codes were used to determine the presence of 21 clinical conditions. Regression models were adjusted for age, sex, race, and rurality. Results: The analyses support further investigation of associations between nitrogen concentration and chronic obstructive pulmonary disease and bronchiectasis (OR: 2.38, CI: 1.64–3.46) among boys and girls, thyroid disorders (OR: 1.44, CI: 1.05–1.99) and suicide and intentional self-inflicted injury (OR: 1.37, CI: >1.00–1.87) among girls, and attention deficit conduct and disruptive behavior disorders (OR: 1.34, CI: 1.24–1.46) among boys. Conclusions: Investigators with environmental health research questions should leverage the well-enumerated population and residency data in the REP.