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Background: Carbapenemase genes in carbapenem-resistant Enterobacterales (CP-CRE) may be transmitted between patients and bacteria. Reported rates of carbapenemase genes vary widely, and it is unclear whether having a carbapenemase gene portends worse outcomes given that all patients with CRE infections have limited treatment options. Methods: Using active population- and laboratory-based active surveillance data collected by the US CDC-funded Georgia Emerging Infections Program from 2011 to 2020, we assessed the frequency of carbapenemase genes in a convenience sample of CRE isolates using whole-genome sequencing (WGS), and we investigated risk factors for carbapenemase positivity. Only the first isolate per patient in a 30-day period was included. We compared characteristics of patients with CP-CRE and non–CP-CRE. Using multivariable log binomial regression, we assessed the association of carbapenemase gene positivity and 90-day mortality. Results: Of 284 CRE isolates, 171 isolates (60.2%) possessed a carbapenemase gene (Table 1), and KPC-3 was the most common carbapenemase gene (80.7%), with only 7 isolates possessing NDM (Table 2). No isolates possessed >1 carbapenemase gene, and most isolates were from urine (82.4%) (Table 1). Carbapenemase gene positivity was associated with lower age, male sex, black race, infection with Klebsiella pneumoniae, polymicrobial infection, having an indwelling medical device, receiving chronic dialysis, and prior stay in a long-term acute-care hospital, long-term care facility, and/or prior hospitalization in the last year. The 90-day mortality rates were similar in patients with non–CP-CRE and CP-CRE: 24.8% versus 25.7% (P = .86). In multivariable analysis, carbapenemase gene presence was not associated with 90-day mortality (adjusted risk ratio, 0.82; 95% CI, 0.50–1.35) when adjusting for CCI, infection with Klebsiella pneumoniae, and chronic dialysis use. Conclusions: The frequency of CP-CRE among CRE was high in this study, but unlike prior studies, the 90-day mortality rates wer similar in patients with CP-CRE compared to non–CP-CRE. Our results provide novel associations (eg, lower age, male sex, infection with Klebsiella pneumoniae, and indwelling medical devices) that infection preventionists could use to target high-risk patients for screening or isolation prior to CP-CRE detection.

Background: Environmental contamination increases risk for Clostridioides difficile infection (CDI) given that spores can remain on a hospital bed, floor, sink, and light switch despite appropriate cleaning measures. Using real-time asset management software (AgileTrac, GE Healthcare) for beds we examined the risk of a patient developing hospital-onset CDI (HO-CDI) when staying in a hospital bed that had a previous occupant with CDI. Methods: We retrospectively identified all patients in tracked beds from April 2018 to August 2019 to identify hospital-onset CDI (HO-CDI), defined as a positive PCR test for C. difficile in a patient hospitalized for >3 days. A patient was defined as being exposed to a potentially “contaminated” bed if within the preceding 7 days from their HO-CDI diagnosis they resided in a hospital bed that, within the prior 90 days, had held an occupant with CDI (Fig. 1). We used multivariable logistic regression to evaluate the association between being exposed to a contaminated bed and HO-CDI. Model covariates were chosen a priori based on known risk factors for CDI. As a sensitivity analysis, we varied the length of time that a bed could stay contaminated from 90 to 60, 30, 14, and 7 days. Results: We analyzed 25,032 hospital encounters representing 18,860 unique patients; we identified 237 (0.9%) hospital encounters with HO-CDI (Table 1). The Elixhauser comorbidity score, being exposed to a contaminated bed, and receiving antibiotics or a proton pump inhibitor (PPI) during the hospital admission were all associated with HO-CDI in the univariable analysis (Table 2). In the adjusted multivariable model, being exposed to a contaminated bed remained a significant risk factor for HO-CDI (OR, 1.60; 95% CI, 1.22–2.08) even after controlling for known risk factors for CDI including age >65, elevated Elixhauser score, and recent antibiotic or PPI use (Table 2). In the sensitivity analysis in which we adjusted the time a bed was considered contaminated after CDI, being exposed to a contaminated bed remained a risk factor for HO-CDI, with a similar odds ratios as the original model (Table 2). Conclusions: Residing in a hospital bed that contained a previous occupant with CDI is a risk factor for developing HO-CDI. Hospital epidemiologists, infection control personnel, and environmental services staff should consider this association when developing CDI risk mitigation strategies.

The identification of floral visitation by pollinators provides an opportunity to improve our understanding of the fine-scale ecological interactions between plants and pollinators, contributing to biodiversity conservation and promoting ecosystem health. In this review, we outline the various methods which can be used to identify floral visitation, including plant-focused and insect-focused methods. We reviewed the literature covering the ways in which DNA metabarcoding has been used to answer ecological questions relating to plant use by pollinators and discuss the findings of this research. We present detailed methodological considerations for each step of the metabarcoding workflow, from sampling through to amplification, and finally bioinformatic analysis. Detailed guidance is provided to researchers for utilisation of these techniques, emphasising the importance of standardisation of methods and improving the reliability of results. Future opportunities and directions of using molecular methods to analyse plant–pollinator interactions are then discussed.