Journal article
Contrasting methods to operationalize antibiotic exposure in clinical research: a real-world application on health care-associated Clostridioides difficile infection
American journal of epidemiology, v 194(5), pp 1448-1459
07 May 2025
PMID: 39191653
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The goal of this article is to summarize common methods of antibiotic operationalization used in clinical research and demonstrate methods for exposure variable selection. We demonstrate 3 methods for modeling exposure, using data from a case-control study on Clostridioides difficile infection in hospitalized patients: (1) factor analysis, (2) logistic regression models, and 3) least absolute shrinkage and selection operator (LASSO) regression. The factor analysis identified 8 variables contributing the most variation in the data set: any antibiotic exposure; number of antibiotic classes; number of antibiotic courses; dose; and specific classes monobactam, beta-lactam-beta-lactamase inhibitors, rifamycin, and cephalosporin. The logistic regression models resulting in the best model fit used predictors representing any antibiotic exposure and the proportion of a patient's hospitalization that they were receiving antibiotics. The LASSO model selected 22 variables for inclusion in the predictive model, of which 10 were antibiotic exposure variables, including any antibiotic exposure; classes beta-lactam-beta-lactamase inhibitors, carbapenem, cephalosporin, fluoroquinolone, monobactam, rifamycin, sulfonamides, and miscellaneous; and proportion of hospitalization that antibiotic treatment was administered. Investigators studying antibiotic use should consider multiple characteristics of exposure informed by their research question and the theory on how antibiotics may affect the distribution of the outcome in their target population.
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Details
- Title
- Contrasting methods to operationalize antibiotic exposure in clinical research: a real-world application on health care-associated Clostridioides difficile infection
- Creators
- Jessica L. Webster (Corresponding Author) - Drexel Univ, Dornsife Sch Publ Hlth, Dept Epidemiol & Biostat, 3215 Market St, Philadelphia, PA 19104 USAStephen Eppes - Christiana Care Health SystemBrian K. Lee - Drexel University, Epidemiology and BiostatisticsNicole S. Harrington - Christiana HospitalNeal D. Goldstein - Drexel University, Epidemiology and Biostatistics
- Publication Details
- American journal of epidemiology, v 194(5), pp 1448-1459
- Publisher
- Oxford University Press
- Number of pages
- 12
- Grant note
- Pfizer, Inc. to Drexel University; Pfizer
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:001421922500001
- Scopus ID
- 2-s2.0-105004650376
- Other Identifier
- 991021899175104721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Public, Environmental & Occupational Health