Understanding the impact of mpox-related hospitalizations for medical versus infection control indications in New York City Background: New York City (NYC) accounted for 15-20% of new mpox infections at the peak of the 2022-2023 United States outbreak. Globally, 8% of mpox patients required hospitalization. We investigated the proportion of mpox hospitalizations for medical versus infection control indications at two large healthcare systems in the New York metropolitan area. Methods: We included all patients admitted to NYU Langone Health or NYC Health + Hospitals for laboratory-confirmed mpox between May 1, 2022, and April 28, 2023. We analyzed demographic information, reasons for hospitalization, length of stay, number and type of co-infections, healthcare encounters, complications, and treatments received. Results: Sixty-five patients were hospitalized for mpox, with 8 (12%) admitted primarily for infection control isolation (Table 1). Median age was 35 years (IQR=31-40), 69% were cisgender men, and 38% were Black. Those hospitalized primarily for isolation were more likely to reside in a homeless shelter (50% vs. 9%, p < 0 .01) and less likely to have a private residence (25% vs. 81%, p < 0 .01) than those hospitalized for medical indications. Those hospitalized for medical indications were more likely to have HIV (63% vs. 25%, p=0.04), secondary bacterial infections (67% vs. 25%, p=0.02), and to receive antibiotics (82% vs. 25%, p < 0 .01) (Tables 2 and 3). There was no significant difference in median cumulative length of stay per patient (p=0.69) between those hospitalized for medical versus isolation purposes. Most admissions for medical indications were for soft tissue superinfection (40%), severe pharyngitis and/or proctitis (28%) and pain management (20%). There was no significant difference in the proportion of tecovirimat receipt (65% vs. 38%, p=0.14) between those hospitalized for medical versus isolation purposes. Conclusion: Infection control isolation accounted for a significant proportion (12%) of mpox hospitalizations and was associated with a similar median length of stay per patient as hospitalization for medical indications. Our small cohort limits statistical power for comparison between groups. However, our findings argue for increased community-based isolation capacity. This may reduce unnecessary hospitalizations during future outbreaks, particularly amongst unsheltered individuals or those living in congregate settings.

Disclosure: Madeline DiLorenzo: Stocks - Abbvie, Amgen Inc., Becton Dickinson, Biogen Inc., Bristol Myers and Squibb, CVS Health, Davita Inc., Elevance Health, Gilead, Henry Schein, Hologic Inc., Humana Inc., Jazz Pharmaceuticals, Laboratory Corp, Merck and Co., Quest Diagnostics, ResMed Inc., Teladoc Health, Vertex Pharmaceuticals, West Pharmaceuticals

Background: The phenomena of emerging infectious diseases accelerating once they reach healthcare facilities have been well documented. Outbreaks of MERS-CoV, SARS-CoV, and COVID-19 have led to in-hospital transmission where the initial patient infects healthcare workers, patients, visitors, etc., with infection control policies unable to curtail the spread early on. We refer to this phenomenon as nosocomial amplification. Nosocomial amplification causes an undue burden on a hospital that’s already strained from the pandemic. We aimed to understand which hospital-level parameters impact the community most and vice versa. Methods: We adapted an SEIR compartmental model to have two interconnected units, a community unit and a hospital special care unit, to determine the number of COVID-19 acquisitions in each of them over a hypothetical year. The model was stochastically simulated using Gillespie’s Direct Method for 1000 iterations. A parameter sensitivity analysis assessed the effects each parameter had on the model. The original values of all parameters were allowed to vary +/- 50%. The number of simulation acquisitions was normalized as a percent change from the original model’s mean acquisition. Results: Our analysis found that parameters impacting the community had a disproportionate impact on COVID-19 acquisitions in the hospital as compared to the special care unit, as did the parameters governing the level of asymptomatic transmission. Transmission between healthcare workers facilitated within-hospital transmission even when strict patient-based cohorting and testing were in place. Extensive community-level transmission was also found to readily overwhelm hospital-level infection control at realistic levels of effectiveness and compliance. Conclusion: These findings illustrate that hospitals and the community are tightly linked systems. Hospitals may reintroduce infection into the community that might have contained or mitigated ongoing outbreaks or introduce the disease into a disease-free population; community transmission puts tremendous pressure on infection control. In the future, we can model policies to curb an existing COVID-19 outbreak or subsequent outbreaks to avoid or minimize nosocomial amplification, thus improving the disproportionate burdens on the healthcare system.

Background: The epidemiology of Clostridioides difficile infection (CDI) is complex, and the COVID-19 pandemic has had extreme impacts on known risk factors such as comorbidity burden and antibiotic prescribing. However, whether these changes have affected the incidence of hospital-associated CDI (HA-CDI) remains unknown. We compared incidence and trends of HA-CDI before and after the pandemic onset, and we assessed the impact of changes in key CDI-related risk factors. Methods: We conducted an interrupted time-series study (March 2018–March 2021) of adult inpatients hospitalized 4 or more days with no known CDI on admission at a 576-bed academic medical center. Our primary outcome was monthly HA-CDI per 10,000 patient days. We performed segmented linear regression to compare the preinterruption trend in HA-CDI rate to the postinterruption slope and level change. We established a series of 30-day intervals before and after the interruption timepoint of March 23, 2020, which corresponds with the Oregon stay-at-home executive order. The data included 24 preinterruption time points and 12 postinterruption time points. We also assessed changes in slope and trend for known HA-CDI risk factors. Results: We included 34,592 inpatient encounters in our prepandemic period and 10,932 encounters in our postinterruption period. The mean prepandemic HA-CDI rate was 4.07 cases per 10,000 patient days. After the pandemic onset, the rate was 3.6 per 10,000 patient days. However, the observed differences in rate (both in terms of slope and level) were not statistically significant (P = .90 for level; P = .60 for slope change). We observed a significant decrease in admissions per 30 days (1,441 vs 911; level-change P < .0001) and a slight increase in the mean number of Elixhauser comorbidities (1.96 vs 2.07; level-change P = .05). We also observed significant increases in both frequency and intensity of antibiotic use, with an increase average days of therapy per encounter (5.8 vs 7.2; level-change P = .01; slope-change P < .0001) and in antibiotic spectrum index (ASI) points per antibiotic day (4.4 vs 4.9; P < .0001). We observed a consistent downward trend for case days of CDI colonization pressure per hospital day (preinterruption slope P < .0001), which remained consistent after the pandemic onset (P = 0.5 for postinterruption slope change) (Fig. 1). Conclusions: Despite significant increases in high-intensity antibiotic use and comorbidity burden, we did not observe significant differences in HA-CDI after the pandemic onset. This may be due to the significant decrease in colonization pressure in the postpandemic period. Further research is required to fully understand the impact of the pandemic-related changes on

Disclosures: None

Background: Modeling is a cost-effective way to evaluate interventions pertaining to hospital infection acquisitions, such as staffing levels. Increasing the number of nurses in an intensive care unit affects rates of HAI transmission. The way compartmental models are often formulated assumes that there is a never-ending series of tasks for workers to perform. Our previous models used a baseline of 1:3 nurse:patient ratio, and we kept the number of tasks the same across staffing ratios. We wanted to understand how having a finite number of tasks, using this baseline number, across staffing levels affected HAI acquisitions. Methods: We used a stochastic mathematical model of methicillin-resistant Staphylococcus aureus (MRSA) to study the impact of changes in staffing and a finite pool of tasks on hospital-associated acquisitions. For a 15-bed intensive care unit (ICU), we have 1 intensivist, and we set the nurse:patient ratios at 1:1, 1:1.5, 1:2.5, 1:3, 1:5, and 1:7.5, to represent the extreme ends of staffing levels and more moderate values in line with critical care society guidelines. Each model was run 1,000 times. The outcome of each scenario is the median number of hospital-associated MRSA acquisitions in 1 year from those 1,000 runs. Results: Treating the 1:3 nurse:patient ratio as the baseline, with 45 MRSA acquisitions per year, increasing the number of nurses from 5 to 6 (moving to a 1:2.5 nurse:patient ratio) had a relative risk (RR) of 0.77, suggesting that a small change in nurse staffing levels might have an outsized impact on rates. More dramatic changes had correspondingly larger swings in MRSA acquisition rates, with 1:1 nurse:patient ratio scenarios having an RR of 0.17, and at the other extreme, a 1:7.5 nurse:patient ratio having an RR of 4.66. Comparing the infinite to finite models, the ratios with more nurses had lower acquisition rates, with decreases ranging from 20% to 50%. Ratios with fewer nurses in the ICU showed 100%–400% increases in the number of acquisitions. All results were statistically significant. Conclusions: As nurse:patient ratios go up, the burden of direct-care tasks fall on fewer people, which has a direct impact on HAI rates. Our model demonstrates this hypothesis. Therefore, appropriate staffing should be considered in infection control guidelines, and the cost of staffing should be weighed against its impact on infection prevention as well as other areas of patient care. In this study, we considered only the impact from changes in contact patterns emerging from different staffing levels. Further insights may exist when considering other outcomes that also accompany increased staffing.

Disclosures: None

Background: COVID-19 has presented hospitals with unique challenges. An SHEA Research Network survey showed that 40% reported “limited” or worse levels of personal protective equipment (PPE) and that 13% were self-producing PPE to address those deficits, including 3D-printed items. However, we do not know how efficiently, if at all, 3D-printed materials can be disinfected. Additionally, 2 filaments, PLACTIVE and PUREMENT, claim to be antimicrobial; they use copper nanocomposites and silver ions to reduce bacterial populations. We assessed how PLACTIVE and PUREMENT may be contaminated and how well they reduce contamination, and how readily polylactic acid (PLA), a standard 3D-printed material, may be disinfected. Methods: We grew methicillin-resistant and -susceptible Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae on 3D-printed disks and conducted bacterial survival assays to determine whether bacteria grow on PLA, PLACTIVE, and PUREMENT. We performed a time series (with 3- and 24-hour dry times) followed by serial dilutions to attain colony-forming unit (CFU) averages for each strain per disk. To determine whether 3D-printed material can be cleaned, we used 70% EtOH on PLA only. We conducted the same time series followed by a disinfectant time series (with dry times 30 seconds, 2.5, minutes, 5 minutes, and 10 minutes). Again, serial dilutions were performed to attain the PLA CFU averages with disinfectant. The CFU averages from the control group (PLA) and testing group (PLACTIVE and PUREMENT) were compared to see how well the antimicrobial material decreased bacterial load. We also compared the CFU averages of PLA with and without disinfectant to see how well 70% EtOH decreased bacterial load. Results: 3D-printed material is readily contaminated with bacteria common in hospitals and can sustain that contamination. Antimicrobial materials, PLACTIVE and PUREMENT, had lower levels of bacterial contamination when compared to PLA. However, disinfected disks had lower overall CFU averages than those that were not, but the level of disinfection was variable and bacterial populations recovered hours after disinfection application. Conclusions: Proper disinfection and using appropriate 3D-printed materials are essential to limiting bacterial contamination. 3D printers and their products can be invaluable for hospitals, especially when supplies are low and healthcare worker safety is paramount. Environmental services should be made aware of the presence of antimicrobial 3D-printed materials, and patients should be discouraged from printing their own items for use in hospital environments.

Disclosures: None

This SHEA white paper identifies knowledge gaps and challenges in healthcare epidemiology research related to coronavirus disease 2019 (COVID-19) with a focus on core principles of healthcare epidemiology. These gaps, revealed during the worst phases of the COVID-19 pandemic, are described in 10 sections: epidemiology, outbreak investigation, surveillance, isolation precaution practices, personal protective equipment (PPE), environmental contamination and disinfection, drug and supply shortages, antimicrobial stewardship, healthcare personnel (HCP) occupational safety, and return to work policies. Each section highlights three critical healthcare epidemiology research questions with detailed description provided in supplementary materials. This research agenda calls for translational studies from laboratory-based basic science research to well-designed, large-scale studies and health outcomes research. Research gaps and challenges related to nursing homes and social disparities are included. Collaborations across various disciplines, expertise and across diverse geographic locations will be critical.

Background: Healthcare-associated transmission of methicillin-resistant Staphylococcus aureus (MRSA) remains a persistent problem despite advances in prevention. The use of chlorhexidine gluconate (CHG) as a means of decolonizing patients, either through targeted decolonization or daily bathing, is a frequently used measure to supplement other MRSA reduction interventions. However, there is room for new and innovative decolonizing agents. We explored the potential utility of a long-acting CHG-like disinfectant with a persistent protective effect as well as an immediate decolonizing action in the prevention of MRSA acquisition as well as the subsequent development of clinical illness and MRSA-related mortality. Methods: We modeled MRSA transmission throughout an 18-bed intensive care unit, based on previously published models. A baseline model with no daily decolonizing protocol was used as a baseline and was compared to a scenario assuming that patients were bathed with CHG, which decolonizes them but provides no ongoing protection, as well as a scenario involving a hypothetical treatment that both decolonizes and provides ongoing protection from subsequent colonization. We varied the duration and efficacy of this protection to fully explore the potential utility of such a treatment. Results: The results of the simulations are shown in Fig. 1, where duration and efficacy of protection varied. The number of MRSA acquisitions from each combination is depicted as a single point, with blue points indicating correspondingly fewer MRSA acquisitions. Overall, improved efficacy of the hypothetical disinfectant resulted in immediate improvements in MRSA acquisition rates when compared to the baseline. To see major improvements in the MRSA acquisition rate due to the duration of infection, that duration must be well above 10 hours in many scenarios. There is also little evidence of synergy between the two. Conclusions: Based on recent results suggesting CHG has a relatively modest per-use efficacy (<.20), there is room for improvement in the formulation and administration of decolonizing agents. Although there has been considerable excitement about the possibility of long-acting agents that not only decolonize but provide long-acting protection against colonization, these results suggest that such protection would only result in markedly decreased acquisition rates only if that duration of protection was extremely long, or if the agent itself was also considerably more efficacious than CHG. These results may be used to help consider the necessary study size for clinical studies of these agents in the future, or to set research priorities and properly calibrate expectations.

Funding: None

Disclosures: None

To understand hospital policies and practices as the COVID-19 pandemic accelerated, the Society for Healthcare Epidemiology of America (SHEA) conducted a survey through the SHEA Research Network (SRN). The survey assessed policies and practices around the optimization of personal protection equipment (PPE), testing, healthcare personnel policies, visitors of COVID-19 patients in relation to procedures, and types of patients. Overall, 69 individual healthcare facilities responded in the United States and internationally, for a 73% response rate.

The modern healthcare system involves complex interactions among microbes, patients, providers, and the built environment. It represents a unique and challenging setting for control of the emergence and spread of infectious diseases. We examine an extension of the perspectives and methods from ecology (and especially urban ecology) to address these unique issues, and we outline 3 examples: (1) viewing patients as individual microbial ecosystems; (2) the altered ecology of infectious diseases specifically within hospitals; and (3) ecosystem management perspectives for infection surveillance and control. In each of these cases, we explore the accuracy and relevance of analogies to existing urban ecological perspectives, and we demonstrate a few of the potential direct uses of this perspective for altering research into the control of healthcare-associated infections.

Infect Control Hosp Epidemiol. 2016;1507–1512