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This is the first aggregate AU metric that uses point-of-care, antimicrobial administration data electronically reported to a national surveillance system to enable risk-adjusted, AU comparisons across multiple hospitals. Endorsed by the National Quality Forum, SAARs provide AU benchmarks that stewardship programs can use to help drive improvements.

Background In the 2011 US hospital prevalence survey of healthcare-associated infections and antimicrobial use 50% of patients received antimicrobial medications on the survey date or day before. More hospitals have since established antimicrobial stewardship programs. We repeated the survey in 2015 to determine antimicrobial use prevalence and describe changes since 2011. Methods The Centers for Disease Control and Prevention’s Emerging Infections Program sites in 10 states each recruited ≤25 general and women’s and children’s hospitals. Hospitals selected a survey date from May–September 2015. Medical records for a random patient sample on the survey date were reviewed to collect data on antimicrobial medications administered on the survey date or day before. Percentages of patients on antimicrobial medications were compared; multivariable log-binomial regression modeling was used to evaluate factors associated with antimicrobial use. Results Of 12 299 patients in 199 hospitals, 6084 (49.5%; 95% CI, 48.6–50.4%) received antimicrobials. Among 148 hospitals in both surveys, overall antimicrobial use prevalence was similar in 2011 and 2015, although the percentage of neonatal critical care patients on antimicrobials was lower in 2015 (22.8% vs 32.0% [2011]; P = .006). Fluoroquinolone use was lower in 2015 (10.1% of patients vs 11.9% [2011]; P < .001). Third- or fourth-generation cephalosporin use was higher (12.2% vs 10.7% [2011]; P = .002), as was carbapenem use (3.7% vs 2.7% [2011]; P < .001). Conclusions Overall hospital antimicrobial use prevalence was not different in 2011 and 2015; however, differences observed in selected patient or antimicrobial groups may provide evidence of stewardship impact.

This cross-sectional study evaluates the appropriateness of antimicrobial use for hospitalized patients treated for community-acquired pneumonia or a urinary tract infection present at admission or for patients who had received fluoroquinolone or intravenous vancomycin treatment.

Background The Standardized Antimicrobial Administration Ratio (SAAR) is a risk-adjusted metric of antimicrobial use (AU) developed by the CDC in 2015 as a tool for hospital antimicrobial stewardship programs (ASPs) to track and compare AU to a national benchmark. In 2018, CDC updated the SAAR by expanding the locations and antimicrobial categories for which SAARs can be calculated and by modeling adult and pediatric locations separately. Methods We identified eligible patient care locations and defined SAAR antimicrobial categories. Predictive models were developed for eligible adult and pediatric patient care locations using negative binomial regression applied to nationally aggregated AU data from locations reporting ≥9 months of 2017 data to the National Healthcare Safety Network (NHSN). Results 2017 baseline SAAR models were developed for seven adult and eight pediatric SAAR antimicrobial categories using data reported from 2,156 adult and 170 pediatric locations across 457 hospitals. The inclusion of step-down units and general hematology-oncology units in adult 2017 baseline SAAR models and the addition of SAARs for narrow-spectrum beta-lactam agents, antifungals predominantly used for invasive candidiasis, antibacterial agents posing the highest risk for Clostridioides difficile infection, and azithromycin (pediatrics only) expand the role SAARs can play in ASP efforts. Final risk-adjusted models are used to calculate predicted antimicrobial days, the denominator of the SAAR, for 40 SAAR types displayed in NHSN. Conclusions SAARs can be used as a metric to prompt investigation into potential overuse or underuse of antimicrobials and to evaluate the effectiveness of ASP interventions.

Objective The rapid spread of SARS-CoV-2 throughout key regions of the United States (U.S.) in early 2020 placed a premium on timely, national surveillance of hospital patient censuses. To meet that need, the Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN), the nation’s largest hospital surveillance system, launched a module for collecting hospital COVID-19 data. This paper presents time series estimates of the critical hospital capacity indicators during April 1–July 14, 2020. Design From March 27–July 14, 2020, NHSN collected daily data on hospital bed occupancy, number of hospitalized patients with COVID-19, and availability/use of mechanical ventilators. Time series were constructed using multiple imputation and survey weighting to allow near real-time daily national and state estimates to be computed. Results During the pandemic’s April peak in the United States, among an estimated 431,000 total inpatients, 84,000 (19%) had COVID-19. Although the number of inpatients with COVID-19 decreased during April to July, the proportion of occupied inpatient beds increased steadily. COVID-19 hospitalizations increased from mid-June in the South and Southwest after stay-at-home restrictions were eased. The proportion of inpatients with COVID-19 on ventilators decreased from April to July. Conclusions The NHSN hospital capacity estimates served as important, near-real time indicators of the pandemic’s magnitude, spread, and impact, providing quantitative guidance for the public health response. Use of the estimates detected the rise of hospitalizations in specific geographic regions in June after declining from a peak in April. Patient outcomes appeared to improve from early April to mid-July.

To assess uptake of the Centers for Disease Control and Prevention's Core Elements of Hospital Antibiotic Stewardship Programs, we analyzed stewardship practices as reported in the 2015 National Healthcare Safety Network's Annual Hospital Survey. Hospital uptake of all 7 core elements increased from 40.9% in 2014 to 48.1% in 2015.

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