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Spatial pattern and risk factors of resistance to important antibiotics among E. coli from veterans in seven U.S. Midwest states
Journal article   Open access   Peer reviewed

Spatial pattern and risk factors of resistance to important antibiotics among E. coli from veterans in seven U.S. Midwest states

Zhuo Tang, Qianyi Shi, Shinya Hasegawa, Margaret Carrel, Jacob Oleson and Michihiko Goto
Antimicrobial stewardship & healthcare epidemiology : ASHE, Vol.6(1), e35
01/01/2026
DOI: 10.1017/ash.2025.10292
PMCID: PMC12854879
PMID: 41625132
url
https://doi.org/10.1017/ash.2025.10292View
Published (Version of record) Open Access

Abstract

Background: Effective antibiotic stewardship programing in clinical settings necessitates a good understanding of local prevalences of antimicrobial resistance and important patient and community risk factors. However, most studies are limited in sample size and geographic coverage. Methods: This study utilized phenotypic resistance data of Escherichia coli from the Veteran’s Health Administration of the United States (U.S.), incorporating 126,777 unique cultures from veteran outpatients from seven Midwest states from 2010 to 2023, to examine the spatial pattern and important individual- and county-level risk factors for resistance to four important classes of antibiotics. We utilized Bayesian conditional autoregressive zero-inflated Poisson regression models to generate smoothed rates of resistance in each county and multilevel logistic regression models to detect risk factors for resistance. Results: High overall rates of resistance were seen for fluoroquinolone (29%) and TMP-SMX (22%). Geographic variation was seen among and between antibiotic classes. Certain urban regions in the southern parts of Illinois, Indiana, and Ohio had higher local resistance rates for fluoroquinolone and TMP-SMX. Being male, having diabetes, and previous exposure to antibiotics are significant risk factors for all classes of antibiotics while the significance of other risk factors varied across classes. Conclusion: Diverse geographic patterns of resistance level may reflect differences in local prescribing practices, while the differential correlations with risk factors likely reflect their clinical indications and prescribing patterns in clinical settings. The local resistance rates and risk factors for different classes of antibiotics should provide important guidance in practicing empirical prescribing and antibiotic stewardship in clinical settings.
Antibiotics Bacterial Infections Comorbidity Diabetes Drug Resistance E Coli Precipitation Age Chronic obstructive pulmonary disease Data warehouses Datasets Ethnicity Family income Gender Patients Risk factors Urban areas Variables

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