A cluster of Rhizobium radiobacter isolates isolated from six unique surgical tissue cultures prompted an investigation ultimately identifying a pseudo-outbreak linked to errant laboratory tissue processing with contaminated, nonsterile saline. Timely response and multidisciplinary collaboration led to tangible system-level interventions and avoidance of unnecessary antibiotic exposures.

Background: Drug manufacturing and distribution is a complex, global process. The global drug supply chain is prone to disruptions associated with geopolitical issues, trade, civil unrest, severe weather, and pandemics, all of which have the potential to affect medication supply and result in drug shortages. To our knowledge, the extent to which the supply of antimicrobials is threated due to disruptions in the drug supply chain in the United States is unknown. We examined trends and duration of disruptions to the drug supply chain for antimicrobials. Methods: Manufacturer reports of supply disruptions were extracted from the Food and Drug Administration (FDA) and the American Society for Health-Systems Pharmacists (ASHP) websites and merged on the agent-formulation level. For each month of the study period, a drug was considered to have an active supply chain issue if an FDA or ASHP shortage or recall report overlapped with that month for ≥15 days, or if a discontinuation had occurred within the previous 3 months. Total months of supply chain issues were summed for antimicrobials overall, at the agent formulation , and class levels. A Mann-Kendall test was used to determine the significance of trends in supply-chain issues. Results: Of 105 antimicrobials purchased in the United States, 74 (70%) had a supply-chain issue for ≥1 month from January 15, 2017, to June 30, 2022. Combined, the 74 agents had 1,611 total months of supply-chain issues over the 66-month study period. Agents from the penicillin class were most frequently affected (ie, 80% of penicillins had supply-chain issues for 206 months), but cephalosporins had supply-chain issues for the longest duration (66% of cephalosporins for 653 months). From 2017–2021, supply-chain issues decreased significantly for penicillins and quinolones (tests of trend, P = .01 and .02, respectively). No trend was identified for the other classes or antimicrobials overall. Interestingly, supply-chain issues for most classes did not increase with seasonal increases in antimicrobial use. Also, supply-chain issues affected 33 antimicrobial agents for at least half of the study period, and supply-chain issues affected ampicillin-sulbactam, cefotaxime, ceftazidime, cefotetan, cefepime, clindamycin, vancomycin for 100% of the study period. Conclusions: Drug supply-chain issues commonly affect antimicrobials and are not improving for most classes. Drug supply-chain issues cause significant strain on healthcare, including drug procurement, access to optimal therapy, and poses challenges to prescribing and antimicrobial stewardship. To decrease the threat to the antibacterial drug supply, action should be taken to strengthen the drug supply chain to ensure access to these essential medicines.

Disclosures: None

Background: Antimicrobial resistance (AMR) is a global health threat, particularly in refugee populations, due to challenges posed by migration. Little guidance has been provided by public health agencies regarding antimicrobial stewardship specific to this demographic. Studies have primarily focused on encampment areas abroad. We sought to better understand health literacy and beliefs regarding AMR in local refugee and immigrant populations in southeastern Michigan. Methods: From November 1, 2022 to March 10, 2023, we distributed an anonymous questionnaire to adult patients at four primary care clinics in Southeastern Michigan and made it available online. The questionnaire collected demographic information and used 5-point Likert scale responses regarding antibiotic use in children with symptoms of respiratory infection. We binarized the questions and responses to determine whether respondents provided the preferred response and added these to create an overall health literacy score, then used simple linear and multivariable linear regression modeling to identify demographic variables independently associated with the health literacy score. Chi-squared and Mann-Whitney tests were also performed where appropriate. Results: Immigrants and refugees/asylum-seekers from low or middle-income countries (group A, n = 109) were compared to native-born Americans and immigrants from high-income countries (group B, n = 171) with participants from 40 countries (Figure 1). Age distribution did not differ between groups, while group B had generally longer duration of living in the United States (Figure 2). Differences were found in other demographic categories except female gender, with group B reporting higher income, educational levels, and English ability (Figure 3). Simple linear regression revealed that all demographic variables except age significantly correlated with responses (Figure 4). Multivariable linear regression showed that female gender, educational level, and age correlated with greater health literacy, while being in group A trended towards significance with respect to correlating with lesser health literacy (Figure 5). Conclusions: Immigrants and refugees/asylum-seekers from LMICs demonstrated beliefs suggesting deficits in knowledge of AMR compared to native-born Americans and those from high-income countries, independent of other potentially confounding demographic characteristics. Female gender, educational level, and age independently correlated with greater health literacy. These results could inform future patient-centered antimicrobial stewardship educational interventions in certain target populations such as immigrants and refugees/asylum-seekers in the United States.

Disclosures: None

Background: Historically, diagnosis of urinary tract infections (UTIs) has been divided into 3 categories based on symptoms and urine culture results: not UTI, asymptomatic bacteriuria (ASB), or UTI. However, some populations (eg, older adults, catheterized patients) may not present with signs or symptoms referrable to the urinary tract or have chronic lower urinary tract symptoms (LUTS), making the diagnosis of UTI challenging. We sought to understand the clinical presentation of patients who receive urine tests in a cohort of diverse hospitals. Methods: This retrospective descriptive cohort study included all adult noncatheterized inpatient and ED encounters with paired urinalysis and urine cultures (24 hours apart) from 5 community and academic hospitals in 3 states (NC, VA, GA) between January 1, 2017, and December 31, 2019. Trained abstractors collected clinical and demographic data using a 60-question REDCap survey. The study group met with multidisciplinary experts (ID, geriatrics, urology) to define the “continuum of UTI” (Table 1), which includes 2 new categories: (1) LUTS to capture patients with chronic lower urinary tract symptoms and (2) bacteriuria of unclear significance (BUS) to capture patients who do not clinically meet criteria for ASB or UTI (eg, older adults who present with delirium and bacteriuria). The newly defined categories were compared to current guideline-based categories. We further compared ASB, BUS, and UTI categories using a lower bacterial threshold of 1,000 colony-forming units. Results: In total, 220,531 encounters met study criteria. After using a random number generator and removing duplicates, 3,392 encounters were included. Based on current IDSA guidelines, the prevalence of ASB was 32.1% (n = 975), and prevalence of patients with “not UTI” was 1,614 (53%). Applying the expert panel’s new “continuum of UTI” definitions, the prevalence of “not UTI” patients decreased to 1,147 (37.7%), due to reassignment of 467 patients (15.3%)to LUTS. The prevalence of ASB decreased by 24% due to reassignment to BUS. Lowering the bacterial threshold had a slight impact on the number of definitive UTIs (14.9 vs 15.9%) (Table 1). Conclusions: Our rigorous review of laboratory and symptom data from a diverse population dataset revealed that diagnostic uncertainty exists when assessing patients with suspicion for UTI. We propose moving away from dichotomous approach of ASB versus UTI and using the “continuum of UTI” for stewardship conversations. This approach will allow us to develop nuanced deprescribing interventions for patients with LUTS or BUS (eg, watchful waiting, shorter course therapy) that account for the unique characteristics of these populations.

Disclosures: None

Our institution sought to evaluate our antimicrobial stewardship empiric treatment recommendations for Salmonella. Results from 36 isolates demonstrated reduced susceptibilities to fluoroquinolones with 1 isolate susceptible only to ceftriaxone. Analysis supports the current recommendation of empiric ceftriaxone therapy for severe infection and updated recommendation for sulfamethoxazole-trimethoprim in non-severe infections.

Background: Antibiotic use (AU) data are needed to improve prescribing in long-term care facilities (LTCFs). CMS requires AU tracking in LTCFs (effective 2017). Although most LTCFs have limited resources for AU tracking, LTCFs contract with LTCF pharmacies to dispense, monitor, and review medications. The objective of our analysis was to report LTCF antibiotic prescribing and characterize temporal changes from 2013 to 2021. Methods: We estimated annual systemic AU rates using prescription dispenses and resident census data from PharMerica, a LTCF-pharmacy services provider that covers ~20% of LTCFs nationwide, although the number of LTCFs and residents serviced by PharMerica varied over time (Fig. 1). We included LTCFs with ≥4 months of antibiotic dispensing and 12 months of census data. We identified courses by collapsing the same drug dispensed to the same resident within 3 days of the preceding end date. Course duration was calculated as the difference between the end and dispense dates. We reported yearly AU rates as courses per 1,000 residents and days of therapy (DOT) per 1,000 resident days from 2013 to 2021. We compared AU rates (percentage change) and antibiotic courses by class and agent (absolute percent difference) between 2013 and 2021. Results: From 2013 to 2021, AU course rates reported as antibiotic courses per 1,000 residents decreased (percentage change, −28%), with a notable increase in 2020 (Fig. 1). However, the median course duration remained the same (Table 1). The AU decline was mostly driven by decreases in fluoroquinolone courses (absolute difference, −10%, most commonly levofloxacin) and macrolide courses (−2%, most commonly azithromycin) (Figs. 2 and 3). Increases in cephalosporin courses (absolute difference, +7%, most commonly cephalexin) and tetracycline courses (+5%, most commonly doxycycline) were also observed (Figs. 2 and 3). During this period, AU DOT rates reported as DOT per 1,000 resident days decreased (percentage change, −13%) (Table 1). Conclusions: The LTCF AU rates, especially for fluoroquinolones, have decreased in recent years with associated shifts in the distribution of antibiotic classes. This finding may be due to CMS stewardship requirements and increased awareness of adverse events, including the FDA fluoroquinolone warnings. The observed increase in 2020 could be secondary to changes in prescribing practices and resident population during the COVID-19 pandemic. Opportunities to improve prescribing in LTCFs include optimizing treatment duration and leveraging LTCF-pharmacy resources to provide stewardship expertise and support AU tracking and reporting.

Disclosures: None

Background: Overall, ~12% of outpatient visits result in an antibiotic prescription, and 30% of those prescriptions are inappropriate. Behavioral nudges help influence practitioner behavior. We hypothesized that peer comparison combined with a behavioral nudge (a patient alert letter) would influence prescribers to reduce antibiotic prescriptions and improve antimicrobial stewardship in the outpatient setting. We pilot-tested this intervention in outpatient primary care clinics associated with a large Veterans Affairs (VA) medical center. Methods: We conducted a clustered randomized controlled trial of 12 community-based outpatient clinics. All practitioners in the intervention arm received quarterly comparative feedback reports and, when indicated, quarterly patient alert letters. Comparative feedback reports gave personalized feedback about antibiotic prescriptions for upper respiratory tract infections, comparing the recipient’s antibiotic prescriptions to the average for all practitioners at the primary care clinics included in our study. Patient alert letters notified practitioners to patients in their panel with recently detected Clostridioides difficile or resistant organism and their antibiotic exposures. We assessed outpatient visits during the preintervention period (April–September 2020), the intervention period (October 2020–September 2021), and the postintervention period (October 2021–September 2022). A mixed-effects logistic regression model predicting antibiotic prescriptions compared the arms across these periods. Results: The outpatient populations observed in the intervention and control arms were similar during each phase of the study. Prior to the intervention, the average proportion of visits with an antibiotic prescription was lower among clinics in the intervention arm (1.4% vs 1.8% in control arm; P = .45). This difference broadened slightly during the intervention period (1.4% vs 2.1%, respectively; P = .03) and the postintervention period (1.3% vs 2.1%, respectively; P = .01) (Fig. 1). Throughout the study, clinics in the intervention arm typically used more doxycycline and azithromycin and less amoxicillin-clavulanate and sulfamethoxazole-trimethoprim compared to clinics in the control arm. (Fig. 2). In the 6-month preintervention period, which coincided with the early phase of the COVID-19 pandemic, antibiotic prescriptions in the intervention compared to control clinics were similar. During the intervention and postintervention periods, the proportion of visits with an antibiotic prescription remained steady for clinics in the intervention arm and increased for those in the control arm. These results suggest that this pilot study using a low-intensity intervention consisting of comparative feedback reports and patient alert letters was successful in influencing the antibiotic prescribing behavior of primary care clinicians practicing in community-based outpatient clinics affiliated with a VA medical center.

Financial support: This study was funded by Merck.

Disclosures: None

Background: Healthcare settings without access to infectious diseases experts may struggle to implement effective antibiotic stewardship programs. We previously described a successful pilot project using the Veterans Affairs (VA) telehealth system to form a Videoconference Antimicrobial Stewardship Team (VAST) that connected multidisciplinary teams from rural VA medical centers (VAMCs) with infectious diseases experts at geographically distant locations. VASTs discussed patients from the rural VAMC, with the overarching goal of supporting antibiotic stewardship. This project is currently ongoing. Here, we describe preliminary outcomes describing the cases discussed, recommendations made, and acceptance of those recommendations among 4 VASTs. Methods: Cases discussed at any of the 4 participating intervention sites were independently reviewed by study staff, noting the infectious disease diagnoses, recommendations made by infectious diseases experts and, when applicable, acceptance of those recommendations at the rural VAMC within 1 week. Discrepancies between independent reviewers were discussed and, when consensus could not be reached, discrepancies were discussed with an infectious diseases clinician. Results: The VASTs serving 4 different rural VAMCs discussed 96 cases involving 92 patients. Overall, infection of the respiratory tract was the most common syndrome discussed by VASTs (Fig. 1). The most common specific diagnoses among discussed cases were cellulitis (n = 11), acute cystitis (n = 11), wounds (n = 11), and osteomyelitis (n = 10). Of 172 recommendations, 41 (24%) related to diagnostic imaging or laboratory results and 38 (22%) were to change the antibiotic agent, dose, or duration (Fig. 2). Of the 151 recommendations that could be assessed via chart review, 122 (81%) were accepted within 1 week. Conclusions: These findings indicate successful implementation of telehealth to connect clinicians at rural VAMCs with an offsite infectious diseases expert. The cases represented an array of common infectious syndromes. The most frequent recommendations pertained to getting additional diagnostic information and to adjusting, but not stopping, antibiotic therapy. These results suggest that many of the cases discussed warrant antibiotics and that VASTs may use the results of diagnostic studies to tailor that therapy. The high rate of acceptance suggests that the VASTs are affecting patient care. Future work will describe VAST implementation at 4 additional VAMCs, and we will assess whether using telehealth to disseminate infectious diseases expertise to rural VAMCs supports changes in antibiotic use that align with principles of antimicrobial stewardship.

Disclosures: None

Through the Centers for Medicare and Medicaid Services Promoting Interoperability Program, more hospitals will be reporting to the National Healthcare Safety Network Antimicrobial Use (AU) Option. We highlight the next steps and opportunities for measurement of AU to optimize prescribing.

This project surveyed Veterans’ COVID-19 vaccination beliefs and status. 1,080 (30.8%) Veterans responded. Factors associated with being unvaccinated, identified using binomial logistic regression, included negative feelings about vaccines (OR = 3.88, 95%CI = 1.52, 9.90) and logistical difficulties such as finding transportation (OR = 1.95, 95%CI = 1.01, 3.45). This highlights the need for education about and access to vaccination.

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

Despite the considerable advances in the last years, the health information systems for health surveillance still need to overcome some critical issues so that epidemic detection can be performed in real time. For instance, despite the efforts of the Brazilian Ministry of Health (MoH) to make COVID-19 data available during the pandemic, delays due to data entry and data availability posed an additional threat to disease monitoring. Here, we propose a complementary approach by using electronic medical records (EMRs) data collected in real time to generate a system to enable insights from the local health surveillance system personnel. As a proof of concept, we assessed data from São Caetano do Sul City (SCS), São Paulo, Brazil. We used the “fever” term as a sentinel event. Regular expression techniques were applied to detect febrile diseases. Other specific terms such as “malaria,” “dengue,” “Zika,” or any infectious disease were included in the dictionary and mapped to “fever.” Additionally, after “tokenizing,” we assessed the frequencies of most mentioned terms when fever was also mentioned in the patient complaint. The findings allowed us to detect the overlapping outbreaks of both COVID-19 Omicron BA.1 subvariant and Influenza A virus, which were confirmed by our team by analyzing data from private laboratories and another COVID-19 public monitoring system. Timely information generated from EMRs will be a very important tool to the decision-making process as well as research in epidemiology. Quality and security on the data produced is of paramount importance to allow the use by health surveillance systems.

Background: Pathogen transmission among staff and residents in nursing homes can vary depending on their interactions and by the amount of time a resident receives care in the facility. Understanding the relative differences in transmission rates between and among staff and residents can identify the pathways that contributed most to the spread of SARS-CoV-2 in US nursing homes. Further exploring relative differences by categorizing facilities by residents’ lengths of stay can identify priority categories for intervention. Methods: Using US National Healthcare Safety Network (NHSN) surveillance data on resident and staff cases, vaccination, and resident deaths during June 2020–June 2022, we estimated SARS-CoV-2 transmission among and between residents and staff. We used a Bayesian inversion of a susceptible–exposed–infected–removed–virus–death (SEIRVD) compartmental model to produce the estimates. The facilities were divided into those with median length of stay (LOS) among the residents of 10 weeks. Additional inputs included the incidence and vaccination levels of the county where each facility was located. For the compartmental model, all data were averaged to form a representative facility for each category. Transmission was estimated separately for 3 periods: (1) June 2020–March 2021 as before the SARS-CoV-2 delta variant, (2) April 2021—October 2021 during SARS-CoV-2 delta variant dominance, and (3) November 2021—June 2022 during the prevalence of the SARS-CoV-2 omicron variant. Results: Regardless of facility category, transmission was highest from staff to residents or resident to resident (Fig.). These estimates of transmission were highest during the pre–SARS-CoV-2 delta variant phase. Transmission in that phase was highest in the facilities with LOS >10 weeks from staff to residents at 0.88 per week (95% credible interval [CrI], 0.06–1.85), in the facilities with LOS 6–10 weeks from staff to residents at 0.68 per week (95% CrI, 0.03–1.78), and in the facilities with LOS <6 weeks between residents at 0.47 per week (95% CrI, 0.02–0.95). Conclusions: Staff-to-resident or resident-to-resident transmission were the dominant pathways of spread of SARS-CoV-2 across the periods or the facility categories. Facilities with LOS 6 weeks or longer had higher median transmission estimates across the periods and transmission routes compared to facilities with LOS less than 6 weeks, implying that when prioritization of intervention resources is needed, facilities caring for populations with longer stays could be prioritized.

Disclosures: None

Background: Data regarding the effects of the SARS-COV-2 (COVID-19) pandemic on healthcare-associated infections (HAIs) in Canadian acute-care hospitals are limited. We examined the impact of the COVID-19 pandemic on HAIs and antimicrobial resistant organisms in hospitals participating in the Canadian Nosocomial Infection Surveillance Program. Methods: We analyzed 13,406 HAIs including adult mixed intensive care unit (ICU) central-line–associated bloodstream infections (CLABSIs), and healthcare-associated (HA) Clostridioides difficile infection (CDI), methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections (BSI), vancomycin-resistant Enterococcus (VRE) BSI, and carbapenemase-producing Enterobacterales (CPE) infections collected using standardized case definitions and questionnaires from 29–64 hospitals participating in the Canadian Nosocomial Infection Surveillance Program (CNISP) from January 2018 to December 2021. We used a generalized linear mixed model with quasi-Poisson distribution to assess step and slope changes in monthly HAI rates between the pre–COVID-19 pandemic period (January 1, 2018–February 29, 2020; 26 time points) and the COVID-19 pandemic period (March 1, 2020–December 31, 2021; 22 time points). Results were reported as incidence rate ratios (IRRs) with 95% confidence intervals (CIs) and adjusted for seasonality, hospital clustering, and hospital characteristics of interest. Results: In the CNISP network, 7,352 (55%) HAIs were reported in the prepandemic period and 6,054 (45%) in the pandemic period. Median age was significantly younger during the pandemic period compared to the prepandemic period among patients with HA-CDI, HA-MRSA BSI, and adult mixed ICU CLABSIs, and more than half of cases among all reported HAIs were male (range, 52%–65%). The 30-day all-cause in-hospital mortality rate did not significantly change between the prepandemic and pandemic periods for all reported HAIs and was highest among HA-VRE BSIs (34%). Modeling results indicated that the COVID-19 pandemic was associated with an immediate increase in HA-CDI and adult mixed ICU CLABSI rates whereas HA-MRSA BSI, HA-CPE and HA-VRE BSI rates immediately decreased. However, pandemic status did not have a statistically significant lasting impact on monthly rate trends for all reported HAIs after adjusting for seasonality, clustering, and hospital covariates (Fig. 1 and 2). Adjusted IRRs for all HAIs ranged from 1.00 to 1.01 (95% CI, 0.94–0.99 to 1.01–1.05).

Conclusions: Although the COVID-19 pandemic placed a significant burden on the Canadian healthcare system, the immediate impact on monthly rates of HAIs in Canadian acute-care hospitals was not sustained over time. Understanding the epidemiological effects of the COVID-19 pandemic in the context of changing patient populations, and clinical and infection control practices, are essential to inform the continued management and prevention of HAIs in Canadian acute-care settings.

Disclosures: None

Background: Many hospitals test all patients for SARS-CoV-2 upon admission to prevent silent transmission to other patients and healthcare workers. The utility of universal admission testing has been questioned, however, due to resource constraints, care delays, and sparse data on its impact on nosocomial infections. England and Scotland stopped requiring universal admission testing on August 31, 2022, and September 28, 2022, respectively. We assessed associations between these changes and hospital-onset SARS-CoV-2 infection rates. Methods: We used public data from National Health Service England and Public Health Scotland on hospital-onset SARS-CoV-2 infections, defined as cases diagnosed >7 days after admission, between July 1, 2021, and December 16, 2022. Because hospital-onset infections are driven by SARS-CoV-2 community incidence rates, we calculated the weekly ratio between hospital-onset versus community-onset SARS-CoV-2 admissions (diagnosed ≤7 days from admission) and assessed for temporal changes associated with stopping universal admission testing using interrupted time-series analysis. The study was divided into 3 periods: sARS-CoV-2 delta-variant dominance with admission testing, SARS-CoV-2 omicron-variant dominance with admission testing (starting December 14, 2021), and SARS-CoV-2 omicron-variant dominance without admission testing. Results: During the study period, there were 518,379 COVID-19 admissions in England, including 398,264 community-onset and 120,115 hospital-onset cases, and 46,517 COVID-19 admissions in Scotland, including 34,183 community-onset and 12,334 hospital-onset cases. The mean weekly ratio of new hospital-onset SARS-CoV-2 infections versus community-onset admissions in England rose from 0.12 during the SARS-CoV-2 delta-variant surge to 0.33 during the SARS-CoV-2 omicron-variant surge to 0.48 after universal admission testing ended (Fig.). There was a significant immediate level change both after the SARS-CoV-2 delta-to-omicron variant transition (92% relative increase; 95% CI, 58%–127%) and after admission testing ended (32% relative increase; 95% CI, 14%–50%). Likewise, the mean weekly ratios rose from 0.11 to 0.43 to 0.89 during their analogous periods in Scotland, with significant level changes both after SARS-CoV-2 delta-to-omicron variant transition (113% relative increase; 95% CI, 54%–172%) and after admission testing ended (72% relative increase; 95% CI, 43%–100%). No significant trend changes were observed. Conclusions: Stopping asymptomatic screening of hospitalized patients in 2 national health systems was associated with significant increases in hospital-onset SARS-CoV-2 infections. Nosocomial SARS-CoV-2 remains a common and potentially morbid complication, with reported mortality rates for nosocomial infections by SARS-CoV-2 omicron variant ranging from 5% to 13%. Preventing infections in vulnerable populations remains an important safety goal. Hospitals should exercise caution when considering reductions in SARS-CoV-2 admission screening.

Disclosures: None

Clinical guidelines recommend device removal for cardiovascular implantable electronic device (CIED) infection management. In this retrospective, nationwide cohort, 60.8% of CIED infections received guideline-concordant care. One-year mortality was higher among those without procedural management (25% vs 16%). Factors associated with receipt of device procedures included pocket infections and positive microbiology.