Regression-based modeling of pairwise genomic linkage data identifies risk factors for healthcare-associated infection transmission: Application to carbapenem-resistant transmission in a long-term care facility.

BACKGROUND

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.

METHODS

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 (CRKP) in a long-term acute care hospital (LTACH). Using rectal surveillance data collected over one year, we fit three pairwise regression models with three different metrics of genomic relatedness for pairs of case isolates, a proxy for transmission linkage: 1) single-nucleotide variant genomic distance, 2) closest genomic donor, 3) 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.

RESULTS

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.

CONCLUSIONS

Taken together, our results demonstrate that pairwise regression models combining relevant genomic and epidemiologic data are useful tools for identifying HAI transmission risks.