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Regression-based modeling of pairwise genomic linkage data identifies risk factors for healthcare-associated pathogen transmission: application to carbapenem-resistant Klebsiella pneumoniae transmission in a long-term care facility
Journal article   Open access   Peer reviewed

Regression-based modeling of pairwise genomic linkage data identifies risk factors for healthcare-associated pathogen transmission: application to carbapenem-resistant Klebsiella pneumoniae transmission in a long-term care facility

Hannah Steinberg, Timileyin Adediran, Mary K. Hayden, Evan Snitkin and Jon Zelner
Microbiology spectrum, Vol.14(2), e0245225
02/03/2026
DOI: 10.1128/spectrum.02452-25
PMID: 41524388
url
https://doi.org/10.1128/spectrum.02452-25View
Published (Version of record) Open Access

Abstract

Pathogen whole-genome sequencing (WGS) has significant potential for improving healthcare-associated infection (HAI) outcomes. However, methods for integrating WGS with epidemiologic data to quantify risks for pathogen spread remain underdeveloped. To identify analytic strategies for conducting WGS-based HAI surveillance in high-burden settings, we modeled patient- and facility-level transmission risks of carbapenem-resistant Klebsiella pneumoniae (CRKP) in a long-term acute care hospital (LTACH). Using rectal surveillance data collected over 1 year, we fit three pairwise regression models with three different metrics of genomic relatedness for pairs of case isolates, a proxy for transmission linkage: (i) single-nucleotide variant genomic distance, (ii) closest genomic donor, and (iii) common genomic cluster. To assess the performance of these approaches under real-world conditions defined by passive surveillance, we conducted a sensitivity study including only cases detected by admission surveillance or clinical symptoms. Genomic relatedness between pairs of isolates was associated with room sharing in two of the three models and overlapping stays on a high-acuity unit in all models, echoing previous findings from LTACH settings. In our sensitivity analysis, qualitative findings were robust to the exclusion of cases that would not have been identified with a passive surveillance strategy; however, uncertainty in all estimates also increased markedly. Taken together, our results demonstrate that pairwise regression models combining relevant genomic and epidemiologic data are useful tools for identifying HAI transmission risks.IMPORTANCEWhole-genome sequencing of healthcare-associated infections (HAIs) is becoming more common, and new methods are necessary to integrate these data with epidemiologic risk factors to quantify transmission drivers. We demonstrate how pairwise regression models, in which the outcome of a regression model represents genomic similarity between a pair of isolates, can identify known transmission risk factors of carbapenem-resistant Klebsiella pneumoniae in a long-term acute care facility. Such pairwise regression models could be used with rich epidemiologic data in other settings to identify important risk factors of endemic HAI transmission.
 Epidemiology Medical Microbiology Nosocomial Infections Virology Antimicrobial Resistance Clinical Microbial Genomics Clinical Microbiology and Infectious Diseases Genome Sequencing Healthcare-Associated Infections Infection Prevention and Control Microbial Physiology and Genetics Nosocomial Transmission Outbreak Investigation in Healthcare Facilities Pathogen Genome Sequencing Public Health Microbiology Research Article Surveillance and Reporting of Healthcare-Associated Infections Transmission and Spread Transmission of Drug Resistant Bacteria

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