Hospital surfaces are known to contribute to the spread of healthcare-associated antimicrobial pathogens. Environmental sampling can help locate reservoirs and determine intervention strategies, although sampling and detection can be labor intensive. Composite approaches may help reduce time and costs associated with sampling and detection. We investigated optimum surface areas for sampling antimicrobial-resistant organisms (AROs) with a single side of cellulose sponge, created theoretical composites (TC) by adding recovery results from multiple optimum areas, then compared the TC to the standard Centers for Disease Control and Prevention sampling method (one sponge using all sides, whole tool; (WT)). Five AROs were evaluated: carbapenemase-producing KPC+ Klebsiella pneumoniae (KPC), Acinetobacter baumannii (AB), methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus faecalis (VRE) and Clostridioides difficile spores (CD). Steel coupons comprising four surface areas (323; 645; 1,290 and 2,258 cm2) were inoculated, dried, and sampled with one sampling pass using the larger side (face) or the smaller side (edge) of a pre-moistened cellulose sponge tool. Based on the optimum areas determined for each organism, composite areas were 1,290 cm2 for MRSA and VRE, 1,936 cm2 for AB, 2,580 cm2 for CD spores and 3,870 cm2 for KPC. Total colony forming units (CFU) recovered using a composite approach was greater or comparable than using multiple WT samplings (over the same area as the composite) for MRSA, VRE and AB (130%; 144% and 95%) yet less than if using multiple WT samplings for KP and CD (47% and 66%). We propose a conservative composite sampling strategy if the target organism is unknown; 323 cm2 sampling area for each of the four sides of the sponge, (1290 cm2 total). The conservative composite sampling strategy improved the recovery of KP (from 47% to 85% of multiple WT samplings), while MRSA, VRE, AB and CD (131%; 144%; 97% and 66%) remained within 5% to that of the optimum area TC.

BACKGROUND: The field of healthcare epidemiology is increasingly focused on identifying, characterizing, and addressing social determinants of health (SDOH) to address inequities in healthcare quality. To identify evidence gaps, we examined recent systematic reviews examining the association of race, ethnicity, and SDOH with inpatient quality measures. METHODS: We searched Medline via OVID for English language systematic reviews from 2010 to 2022 addressing race, ethnicity, or SDOH domains and inpatient quality measures in adults using specific topic questions. We imported all citations to Covidence (www.covidence.org, Veritas Health Innovation) and removed duplicates. Two blinded reviewers assessed all articles for inclusion in 2 phases: title/abstract, then full-text review. Discrepancies were resolved by a third reviewer. RESULTS: Of 472 systematic reviews identified, 39 were included. Of these, 23 examined all-cause mortality; 6 examined 30-day readmission rates; 4 examined length of stay, 4 examined falls, 2 examined surgical site infections (SSIs) and one review examined risk of venous thromboembolism. The most evaluated SDOH measures were sex (n = 9), income and/or employment status (n = 9), age (n = 6), race and ethnicity (n = 6), and education (n = 5). No systematic reviews assessed medication use errors or healthcare-associated infections. We found very limited assessment of other SDOH measures such as economic stability, neighborhood, and health system access. CONCLUSION: A limited number of systematic reviews have examined the association of race, ethnicity and SDOH measures with inpatient quality measures, and existing reviews highlight wide variability in reporting. Future systematic evaluations of SDOH measures are needed to better understand the relationships with inpatient quality measures.

Antimicrobial use (AU) data reported to the National Healthcare Safety Network's Antimicrobial Use and Resistance Module between January 2019 and July 2022 were analyzed to assess the impact of the COVID-19 pandemic on inpatient antimicrobial use.

OBJECTIVES: To evaluate the incidence of a candidate definition of healthcare facility-onset, treated Clostridioides difficile (CD) infection (cHT-CDI) and to identify variables and best model fit of a risk-adjusted cHT-CDI metric using extractable electronic heath data. METHODS: We analyzed 9,134,276 admissions from 265 hospitals during 2015-2020. The cHT-CDI events were defined based on the first positive laboratory final identification of CD after day 3 of hospitalization, accompanied by use of a CD drug. The generalized linear model method via negative binomial regression was used to identify predictors. Standardized infection ratios (SIRs) were calculated based on 2 risk-adjusted models: a simple model using descriptive variables and a complex model using descriptive variables and CD testing practices. The performance of each model was compared against cHT-CDI unadjusted rates. RESULTS: The median rate of cHT-CDI events per 100 admissions was 0.134 (interquartile range, 0.023-0.243). Hospital variables associated with cHT-CDI included the following: higher community-onset CDI (CO-CDI) prevalence; highest-quartile length of stay; bed size; percentage of male patients; teaching hospitals; increased CD testing intensity; and CD testing prevalence. The complex model demonstrated better model performance and identified the most influential predictors: hospital-onset testing intensity and prevalence, CO-CDI rate, and community-onset testing intensity (negative correlation). Moreover, 78% of the hospitals ranked in the highest quartile based on raw rate shifted to lower percentiles when we applied the SIR from the complex model. CONCLUSIONS: Hospital descriptors, aggregate patient characteristics, CO-CDI burden, and clinical testing practices significantly influence incidence of cHT-CDI. Benchmarking a cHT-CDI metric is feasible and should include facility and clinical variables.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic may have impacted outpatient antibiotic prescribing in low- and middle-income countries such as Brazil. However, outpatient antibiotic prescribing in Brazil, particularly at the prescription level, is not well-described. METHODS: We used the IQVIA MIDAS database to characterize changes in prescribing rates of antibiotics commonly prescribed for respiratory infections (azithromycin, amoxicillin-clavulanate, levofloxacin/moxifloxacin, cephalexin, and ceftriaxone) among adults in Brazil overall and stratified by age and sex, comparing prepandemic (January 2019-March 2020) and pandemic periods (April 2020-December 2021) using uni- and multivariate Poisson regression models. The most common prescribing provider specialties for these antibiotics were also identified. RESULTS: In the pandemic period compared to the prepandemic period, outpatient azithromycin prescribing rates increased across all age-sex groups (incidence rate ratio [IRR] range, 1.474-3.619), with the greatest increase observed in males aged 65-74 years; meanwhile, prescribing rates for amoxicillin-clavulanate and respiratory fluoroquinolones mostly decreased, and changes in cephalosporin prescribing rates varied across age-sex groups (IRR range, 0.134-1.910). For all antibiotics, the interaction of age and sex with the pandemic in multivariable models was an independent predictor of prescribing changes comparing the pandemic versus prepandemic periods. General practitioners and gynecologists accounted for the majority of increases in azithromycin and ceftriaxone prescribing during the pandemic period. CONCLUSIONS: Substantial increases in outpatient prescribing rates for azithromycin and ceftriaxone were observed in Brazil during the pandemic with prescribing rates being disproportionally different by age and sex. General practitioners and gynecologists were the most common prescribers of azithromycin and ceftriaxone during the pandemic, identifying them as potential specialties for antimicrobial stewardship interventions.

NHSN total IV antimicrobial start rates for patients on hemodialysis decreased from 2016-2020, | but rates unsupported by NHSN surveillance documentation did not. |  80% of reporting hemodialysis facilities had ≥1 unsupported IV antimicrobial start and the | fraction of facilities with unsupported starts increased yearly. |  Accurate reporting and timely review of antimicrobial data is critical to understanding | prescribing trends and practices in hemodialysis facilities.

OBJECTIVE: To examine temporal changes in coverage with a complete primary series of coronavirus disease 2019 (COVID-19) vaccination and staffing shortages among healthcare personnel (HCP) working in nursing homes in the United States before, during, and after the implementation of jurisdiction-based COVID-19 vaccination mandates for HCP. SAMPLE AND SETTING: HCP in nursing homes from 15 US jurisdictions. DESIGN: We analyzed weekly COVID-19 vaccination data reported to the Centers for Disease Control and Prevention's National Healthcare Safety Network from June 7, 2021, through January 2, 2022. We assessed 3 periods (preintervention, intervention, and postintervention) based on the announcement of vaccination mandates for HCP in 15 jurisdictions. We used interrupted time-series models to estimate the weekly percentage change in vaccination with complete primary series and the odds of reporting a staffing shortage for each period. RESULTS: Complete primary series vaccination among HCP increased from 66.7% at baseline to 94.3% at the end of the study period and increased at the fastest rate during the intervention period for 12 of 15 jurisdictions. The odds of reporting a staffing shortage were lowest after the intervention. CONCLUSIONS: These findings demonstrate that COVID-19 vaccination mandates may be an effective strategy for improving HCP vaccination coverage in nursing homes without exacerbating staffing shortages. These data suggest that mandates can be considered to improve COVID-19 coverage among HCP in nursing homes to protect both HCP and vulnerable nursing home residents.

The above article1 is linked to the following commentary: | https://doi.org/10.1017/ice.2021.377 | ::: which was erroneously placed in Volume 42, Issue 11 of ICHE. The publisher apologizes for this error.

We agree with Chaturvedi and Ostrosky-Zeichner that Candida species are important health care–associated pathogens. The Emerging Infections Program of the Centers for Disease Control and Prevention has conducted population-based candidemia surveillance in metropolitan Atlanta and Baltimore since 2008. Although the incidence of candidemia decreased from 2008 through 2013, resistance to echinocandins and multidrug resistance increased.1 Data from our hospital surveys affirm that candida species are common causes of health care–associated infections,2 and there was no significant difference in the percentage of patients with infections caused by candida species in 2011 (32 of 504, 6.3%) and 2015 (26 of 427, 6.1%) (P=0.87 according to mid-P exact test). Among all patients surveyed, there was no significant difference in the percentage with a health care–associated infection due to candida species in 2011 (31 of 11,282, 0.27%) and 2015 (25 of 12,299, 0.20%) (P=0.26 according to mid-P exact test). Changes in the incidence of candida infections and the prevalence of antifungal resistance should be closely monitored, and more information is needed regarding the most effective approaches to the prevention of candida infections in health care facilities, particularly those infections caused by resistant and transmissible yeasts such as C. auris.

INTRODUCTION: Racial and ethnic minorities are disproportionately affected by end-stage kidney disease (ESKD). ESKD patients on dialysis are at increased risk for Staphylococcus aureus bloodstream infections, but racial, ethnic, and socioeconomic disparities associated with this outcome are not well described. METHODS: Surveillance data from the 2020 National Healthcare Safety Network (NHSN) and the 2017-2020 Emerging Infections Program (EIP) were used to describe bloodstream infections among patients on hemodialysis (hemodialysis patients) and were linked to population-based data sources (CDC/Agency for Toxic Substances and Disease Registry [ATSDR] Social Vulnerability Index [SVI], United States Renal Data System [USRDS], and U.S. Census Bureau) to examine associations with race, ethnicity, and social determinants of health. RESULTS: In 2020, 4,840 dialysis facilities reported 14,822 bloodstream infections to NHSN; 34.2% were attributable to S. aureus. Among seven EIP sites, the S. aureus bloodstream infection rate during 2017-2020 was 100 times higher among hemodialysis patients (4,248 of 100,000 person-years) than among adults not on hemodialysis (42 of 100,000 person-years). Unadjusted S. aureus bloodstream infection rates were highest among non-Hispanic Black or African American (Black) and Hispanic or Latino (Hispanic) hemodialysis patients. Vascular access via central venous catheter was strongly associated with S. aureus bloodstream infections (NHSN: adjusted rate ratio [aRR] = 6.2; 95% CI = 5.7-6.7 versus fistula; EIP: aRR = 4.3; 95% CI = 3.9-4.8 versus fistula or graft). Adjusting for EIP site of residence, sex, and vascular access type, S. aureus bloodstream infection risk in EIP was highest in Hispanic patients (aRR = 1.4; 95% CI = 1.2-1.7 versus non-Hispanic White [White] patients), and patients aged 18-49 years (aRR = 1.7; 95% CI = 1.5-1.9 versus patients aged ≥65 years). Areas with higher poverty levels, crowding, and lower education levels accounted for disproportionately higher proportions of hemodialysis-associated S. aureus bloodstream infections. CONCLUSIONS AND IMPLICATIONS FOR PUBLIC HEALTH PRACTICE: Disparities exist in hemodialysis-associated S. aureus infections. Health care providers and public health professionals should prioritize prevention and optimized treatment of ESKD, identify and address barriers to lower-risk vascular access placement, and implement established best practices to prevent bloodstream infections.

Nursing home residents have been disproportionately affected by COVID-19; older age, comorbidities, and the congregate nature of nursing homes place residents at higher risk for infection and severe COVID-19-associated outcomes, including death (1). Studies have demonstrated that receipt of a primary COVID-19 mRNA vaccination series (2) and monovalent booster doses (3) is effective in reducing COVID-19-related morbidity and mortality in this population. Public health recommendations for staying up to date with COVID-19 vaccination have been revised throughout the pandemic response, most recently to include an updated (bivalent) booster dose, which protects against both the ancestral strain of SARS-CoV-2 and recent Omicron variants BA.4 and BA.5 (4). However, data on the effectiveness of staying up to date, including with bivalent booster doses, are lacking among nursing home residents. CDC's National Healthcare Safety Network (NHSN) analyzed surveillance data to examine weekly incidence rates of COVID-19 among nursing home residents by up-to-date vaccination status (receipt of a bivalent booster dose or completion of a primary series or receipt of a monovalent booster dose within the previous 2 months [i.e., not yet eligible to receive a bivalent booster dose]).* Up-to-date vaccination status among nursing home residents remained low throughout the study period, increasing to 48.9% by the week ending January 8, 2023. During October 10, 2022-January 8, 2023, the COVID-19 weekly incidence rates (new cases per 1,000 nursing home residents) among residents who were not up to date with COVID-19 vaccination were consistently higher than those among residents who were up to date. Moreover, the weekly incidence rate ratios (IRRs) indicated that residents who were not up to date with COVID-19 vaccines had a higher risk for acquiring SARS-CoV-2 than their up-to-date counterparts (IRR range = 1.3-1.5). It is critical that nursing home residents stay up to date with COVID-19 vaccines and receive a bivalent booster dose to maximize protection against COVID-19.

OBJECTIVES: To evaluate the prevalence of hospital-onset bacteremia and fungemia (HOB), identify hospital-level predictors, and to evaluate the feasibility of an HOB metric. METHODS: We analyzed 9,202,650 admissions from 267 hospitals during 2015-2020. An HOB event was defined as the first positive blood-culture pathogen on day 3 of admission or later. We used the generalized linear model method via negative binomial regression to identify variables and risk markers for HOB. Standardized infection ratios (SIRs) were calculated based on 2 risk-adjusted models: a simple model using descriptive variables and a complex model using descriptive variables plus additional measures of blood-culture testing practices. Performance of each model was compared against the unadjusted rate of HOB. RESULTS: Overall median rate of HOB per 100 admissions was 0.124 (interquartile range, 0.00-0.22). Facility-level predictors included bed size, sex, ICU admissions, community-onset (CO) blood culture testing intensity, and hospital-onset (HO) testing intensity, and prevalence (all P < .001). In the complex model, CO bacteremia prevalence, HO testing intensity, and HO testing prevalence were the predictors most associated with HOB. The complex model demonstrated better model performance; 55% of hospitals that ranked in the highest quartile based on their raw rate shifted to a lower quartile when the SIR from the complex model was applied. CONCLUSIONS: Hospital descriptors, aggregate patient characteristics, community bacteremia and/or fungemia burden, and clinical blood-culture testing practices influence rates of HOB. Benchmarking an HOB metric is feasible and should endeavor to include both facility and clinical variables.

BACKGROUND: The microbiologic etiologies, clinical manifestations, and antimicrobial treatment of neonatal infections differ substantially from infections in adult and pediatric patient populations. In 2019, the Centers for Disease Control and Prevention developed neonatal-specific (Standardized Antimicrobial Administration Ratios SAARs), a set of risk-adjusted antimicrobial use metrics that hospitals participating in the National Healthcare Safety Network's (NHSN's) antimicrobial use surveillance can use in their antibiotic stewardship programs (ASPs). METHODS: The Centers for Disease Control and Prevention, in collaboration with the Vermont Oxford Network, identified eligible patient care locations, defined SAAR agent categories, and implemented neonatal-specific NHSN Annual Hospital Survey questions to gather hospital-level data necessary for risk adjustment. SAAR predictive models were developed using 2018 data reported to NHSN from eligible neonatal units. RESULTS: The 2018 baseline neonatal SAAR models were developed for 7 SAAR antimicrobial agent categories using data reported from 324 neonatal units in 304 unique hospitals. Final models were used to calculate predicted antimicrobial days, the SAAR denominator, for level II neonatal special care nurseries and level II/III, III, and IV NICUs. CONCLUSIONS: NHSN's initial set of neonatal SAARs provides a way for hospital ASPs to assess whether antimicrobial agents in their facility are used at significantly higher or lower rates compared with a national baseline or whether an individual SAAR value is above or below a specific percentile on a given SAAR distribution, which can prompt investigations into prescribing practices and inform ASP interventions.

OBJECTIVE: To evaluate hospital-level variation in using first-line antibiotics for Clostridioides difficile infection (CDI) based on the burden of laboratory-identified (LabID) CDI. METHODS: Using data on hospital-level LabID CDI events and antimicrobial use (AU) for CDI (oral/rectal vancomycin or fidaxomicin) submitted to the National Healthcare Safety Network in 2019, we assessed the association between hospital-level CDI prevalence (per 100 patient admissions) and rate of CDI AU (days of therapy per 1,000 days present) to generate a predicted value of AU based on CDI prevalence and CDI test type using negative binomial regression. The ratio of the observed to predicted AU was then used to identify hospitals with extreme discordance between CDI prevalence and CDI AU, defined as hospitals with a ratio outside of the intervigintile range. RESULTS: Among 963 acute-care hospitals, rate of CDI prevalence demonstrated a positive dose-response relationship with rate of CDI AU. Compared with hospitals without extreme discordance (n = 902), hospitals with lower-than-expected CDI AU (n = 31) had, on average, fewer beds (median, 106 vs 208), shorter length of stay (median, 3.8 vs 4.2 days), and higher proportion of undergraduate or nonteaching medical school affiliation (48% vs 39%). Hospitals with higher-than-expected CDI AU (n = 30) were similar overall to hospitals without extreme discordance. CONCLUSIONS: The prevalence rate of LabID CDI had a significant dose-response association with first-line antibiotics for treating CDI. We identified hospitals with extreme discordance between CDI prevalence and CDI AU, highlighting potential opportunities for data validation and improvements in diagnostic and treatment practices for CDI.

OBJECTIVE: To evaluate if facility-level vaccination after an initial vaccination clinic was independently associated with COVID-19 incidence adjusted for other factors in January 2021 among nursing home residents. DESIGN: Ecological analysis of data from the CDC's National Healthcare Safety Network (NHSN) and from the CDC's Pharmacy Partnership for Long-Term Care Program. SETTING AND PARTICIPANTS: CMS-certified nursing homes participating in both NHSN and the Pharmacy Partnership for Long-Term Care Program. METHODS: A multivariable, random intercepts, negative binomial model was applied to contrast COVID-19 incidence rates among residents living in facilities with an initial vaccination clinic during the week ending January 3, 2021 (n = 2843), vs those living in facilities with no vaccination clinic reported up to and including the week ending January 10, 2021 (n = 3216). Model covariates included bed size, resident SARS-CoV-2 testing, staff with COVID-19, cumulative COVID-19 among residents, residents admitted with COVID-19, community county incidence, and county social vulnerability index (SVI). RESULTS: In December 2020 and January 2021, incidence of COVID-19 among nursing home residents declined to the lowest point since reporting began in May, diverged from the pattern in community cases, and began dropping before vaccination occurred. Comparing week 3 following an initial vaccination clinic vs week 2, the adjusted reduction in COVID-19 rate in vaccinated facilities was 27% greater than the reduction in facilities where vaccination clinics had not yet occurred (95% confidence interval: 14%-38%, P < .05). CONCLUSIONS AND IMPLICATIONS: Vaccination of residents contributed to the decline in COVID-19 incidence in nursing homes; however, other factors also contributed. The decline in COVID-19 was evident prior to widespread vaccination, highlighting the benefit of a multifaced approach to prevention including continued use of recommended screening, testing, and infection prevention practices as well as vaccination to keep residents in nursing homes safe.

OBJECTIVES: To determine the impact of the coronavirus disease 2019 (COVID-19) pandemic on healthcare-associated infection (HAI) incidence in US hospitals, national- and state-level standardized infection ratios (SIRs) were calculated for each quarter in 2020 and compared to those from 2019. METHODS: Central-line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), ventilator-associated events (VAEs), select surgical site infections, and Clostridioides difficile and methicillin-resistant Staphylococcus aureus (MRSA) bacteremia laboratory-identified events reported to the National Healthcare Safety Network for 2019 and 2020 by acute-care hospitals were analyzed. SIRs were calculated for each HAI and quarter by dividing the number of reported infections by the number of predicted infections, calculated using 2015 national baseline data. Percentage changes between 2019 and 2020 SIRs were calculated. Supporting analyses, such as an assessment of device utilization in 2020 compared to 2019, were also performed. RESULTS: Significant increases in the national SIRs for CLABSI, CAUTI, VAE, and MRSA bacteremia were observed in 2020. Changes in the SIR varied by quarter and state. The largest increase was observed for CLABSI, and significant increases in VAE incidence and ventilator utilization were seen across all 4 quarters of 2020. CONCLUSIONS: This report provides a national view of the increases in HAI incidence in 2020. These data highlight the need to return to conventional infection prevention and control practices and build resiliency in these programs to withstand future pandemics.

Nursing home and long-term care facility residents live in congregate settings and are often elderly and frail, putting them at high risk for infection with SARS-CoV-2, the virus that causes COVID-19, and severe COVID-19-associated outcomes; therefore, this population was prioritized for early vaccination in the United States (1). Following rapid distribution and administration of the mRNA COVID-19 vaccines (Pfizer-BioNTech and Moderna) under an Emergency Use Authorization by the Food and Drug Administration (2), observational studies among nursing home residents demonstrated vaccine effectiveness (VE) ranging from 53% to 92% against SARS-CoV-2 infection (3-6). However, concerns about the potential for waning vaccine-induced immunity and the recent emergence of the highly transmissible SARS-CoV-2 B.1.617.2 (Delta) variant(†) highlight the need to continue to monitor VE (7). Weekly data reported by the Centers for Medicaid & Medicare (CMS)-certified skilled nursing facilities or nursing homes to CDC's National Healthcare Safety Network (NHSN)(§) were analyzed to evaluate effectiveness of full vaccination (2 doses received ≥14 days earlier) with any of the two currently authorized mRNA COVID-19 vaccines during the period soon after vaccine introduction and before the Delta variant was circulating (pre-Delta [March 1-May 9, 2021]), and when the Delta variant predominated(¶) (Delta [June 21-August 1, 2021]). Using 17,407 weekly reports from 3,862 facilities from the pre-Delta period, adjusted effectiveness against infection for any mRNA vaccine was 74.7% (95% confidence interval [CI] = 70.0%-78.8%). Analysis using 33,160 weekly reports from 11,581 facilities during an intermediate period (May 10-June 20) found that the adjusted effectiveness was 67.5% (95% CI = 60.1%-73.5%). Analysis using 85,593 weekly reports from 14,917 facilities during the Delta period found that the adjusted effectiveness was 53.1% (95% CI = 49.1%-56.7%). Effectiveness estimates were similar for Pfizer-BioNTech and Moderna vaccines. These findings indicate that mRNA vaccines provide protection against SARS-CoV-2 infection among nursing home residents; however, VE was lower after the Delta variant became the predominant circulating strain in the United States. This analysis assessed VE against any infection, without being able to distinguish between asymptomatic and symptomatic presentations. Additional evaluations are needed to understand protection against severe disease in nursing home residents over time. Because nursing home residents might remain at some risk for SARS-CoV-2 infection despite vaccination, multiple COVID-19 prevention strategies, including infection control, testing, and vaccination of nursing home staff members, residents, and visitors, are critical. An additional dose of COVID-19 vaccine might be considered for nursing home and long-term care facility residents to optimize a protective immune response.

BACKGROUND: The Centers for Disease Control and Prevention's (CDC's) National Healthcare Safety Network (NHSN) is the most widely used health care-associated infection (HAI) and antimicrobial use and resistance surveillance program in the United States. Over 37,000 health care facilities participate in the program and submit a large volume of surveillance data. These data are used by the facilities themselves, the CDC, and other agencies and organizations for a variety of purposes, including infection prevention, antimicrobial stewardship, and clinical quality measurement. Among the summary metrics made available by the NHSN are standardized infection ratios, which are used to identify HAI prevention needs and measure progress at the national, regional, state, and local levels. OBJECTIVE: To extend the use of geospatial methods and tools to NHSN data, and in turn to promote and inspire new uses of the rendered data for analysis and prevention purposes, we developed a web-enabled system that enables integrated visualization of HAI metrics and supporting data. METHODS: We leveraged geocoding and visualization technologies that are readily available and in current use to develop a web-enabled system designed to support visualization and interpretation of data submitted to the NHSN from geographically dispersed sites. The server-client model-based system enables users to access the application via a web browser. RESULTS: We integrated multiple data sets into a single-page dashboard designed to enable users to navigate across different HAI event types, choose specific health care facility or geographic locations for data displays, and scale across time units within identified periods. We launched the system for internal CDC use in January 2019. CONCLUSIONS: CDC NHSN statisticians, data analysts, and subject matter experts identified opportunities to extend the use of geospatial methods and tools to NHSN data and provided the impetus to develop NHSNViz. The development effort proceeded iteratively, with the developer adding or enhancing functionality and including additional data sets in a series of prototype versions, each of which incorporated user feedback. The initial production version of NHSNViz provides a new geospatial analytic resource built in accordance with CDC user requirements and extensible to additional users and uses in subsequent versions.

During March 27-July 14, 2020, the CDC's National Healthcare Safety Network extended its surveillance to hospital capacities responding to COVID-19 pandemic. The data showed wide variations across hospitals in case burden, bed occupancies, ventilator usage, and healthcare personnel and supply status. These data were used to inform emergency responses.

BACKGROUND: We analyzed 2017 healthcare facility-onset (HO) vancomycin-resistant Enterococcus (VRE) bacteremia data to identify hospital-level factors that were significant predictors of HO-VRE using the Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN) multidrug-resistant organism and Clostridioides difficile reporting module. A risk-adjusted model that can be used to calculate the number of predicted HO-VRE bacteremia events in a facility was developed, thus enabling the calculation of VRE standardized infection ratios (SIRs). METHODS: Acute-care hospitals reporting at least 1 month of 2017 VRE bacteremia data were included in the analysis. Various hospital-level characteristics were assessed to develop a best-fit model and subsequently derive the 2018 national and state SIRs. RESULTS: In 2017, 470 facilities in 35 states participated in VRE bacteremia surveillance. Inpatient VRE community-onset prevalence rate, average length of patient stay, outpatient VRE community-onset prevalence rate, and presence of an oncology unit were all significantly associated (all 95% likelihood ratio confidence limits excluded the nominal value of zero) with HO-VRE bacteremia. The 2018 national SIR was 1.01 (95% CI, 0.93-1.09) with 577 HO bacteremia events reported. CONCLUSION: The creation of an SIR enables national-, state-, and facility-level monitoring of VRE bacteremia while controlling for individual hospital-level factors. Hospitals can compare their VRE burden to a national benchmark to help them determine the effectiveness of infection prevention efforts over time.

IMPORTANCE: Hospital antimicrobial consumption data are widely available; however, large-scale assessments of the quality of antimicrobial use in US hospitals are limited. OBJECTIVE: To evaluate the appropriateness of antimicrobial use for hospitalized patients treated for community-acquired pneumonia (CAP) or urinary tract infection (UTI) present at admission or for patients who had received fluoroquinolone or intravenous vancomycin treatment. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study included data from a prevalence survey of hospitalized patients in 10 Emerging Infections Program sites. Random samples of inpatients on hospital survey dates from May 1 to September 30, 2015, were identified. Medical record data were collected for eligible patients with 1 or more of 4 treatment events (CAP, UTI, fluoroquinolone treatment, or vancomycin treatment), which were selected on the basis of common infection types reported and antimicrobials given to patients in the prevalence survey. Data were analyzed from August 1, 2017, to May 31, 2020. EXPOSURE: Antimicrobial treatment for CAP or UTI or with fluoroquinolones or vancomycin. MAIN OUTCOMES AND MEASURES: The percentage of antimicrobial use that was supported by medical record data (including infection signs and symptoms, microbiology test results, and antimicrobial treatment duration) or for which some aspect of use was unsupported. Unsupported antimicrobial use was defined as (1) use of antimicrobials to which the pathogen was not susceptible, use in the absence of documented infection signs or symptoms, or use without supporting microbiologic data; (2) use of antimicrobials that deviated from recommended guidelines; or (3) use that exceeded the recommended duration. RESULTS: Of 12 299 patients, 1566 patients (12.7%) in 192 hospitals were included; the median age was 67 years (interquartile range, 53-79 years), and 864 (55.2%) were female. A total of 219 patients (14.0%) were included in the CAP analysis, 452 (28.9%) in the UTI analysis, 550 (35.1%) in the fluoroquinolone analysis, and 403 (25.7%) in the vancomycin analysis; 58 patients (3.7%) were included in both fluoroquinolone and vancomycin analyses. Overall, treatment was unsupported for 876 of 1566 patients (55.9%; 95% CI, 53.5%-58.4%): 110 of 403 (27.3%) who received vancomycin, 256 of 550 (46.6%) who received fluoroquinolones, 347 of 452 (76.8%) with a diagnosis of UTI, and 174 of 219 (79.5%) with a diagnosis of CAP. Among patients with unsupported treatment, common reasons included excessive duration (103 of 174 patients with CAP [59.2%]) and lack of documented infection signs or symptoms (174 of 347 patients with UTI [50.1%]). CONCLUSIONS AND RELEVANCE: The findings suggest that standardized assessments of hospital antimicrobial prescribing quality can be used to estimate the appropriateness of antimicrobial use in large groups of hospitals. These assessments, performed over time, may inform evaluations of the effects of antimicrobial stewardship initiatives nationally.

Data reported to the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN) were analyzed to understand the potential impact of the COVID-19 pandemic on central line-associated bloodstream infections (CLABSIs) in acute care hospitals. Descriptive analysis of the Standardized Infection Ratio (SIR) was conducted by locations, location type, geographic area, and bed size.

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.

Liver transplant recipients are at high risk for surgical site infections (SSIs). Limited data are available on SSI epidemiology following liver transplant procedures (LTPs). We analyzed data on SSIs from 2015-2018 reported to CDC's National Healthcare Safety Network to determine rates, pathogen distribution, and antimicrobial resistance after LTPs and other hepatic, biliary, or pancreatic procedures (BILIs). LTP and BILI SSI rates were 5.7% and 5.9%, respectively. The odds of SSI after LTP were lower than after BILI (adjusted odds ratio = 0.70, 95% confidence interval 0.57-0.85). Among LTP SSIs, 43.1% were caused by Enterococcus spp., 17.2% by Candida spp., and 15.0% by coagulase-negative Staphylococcus spp. (CNS). Percentages of SSIs caused by Enterococcus faecium or CNS were higher after LTPs than BILIs, whereas percentages of SSIs caused by Enterobacteriaceae, Enterococcus faecalis, or viridans streptococci were higher after BILIs. Antimicrobial resistance was common in LTP SSI pathogens, including E. faecium (69.4% vancomycin-resistant); E. coli (68.8% fluoroquinolone-non-susceptible, 44.7% extended spectrum cephalosporin [ESC]-non-susceptible); and K. pneumoniae and K. oxytoca (39.4% fluoroquinolone-non-susceptible, 54.5% ESC-non-susceptible). National LTP SSI pathogen and resistance data can help prioritize studies to determine effective interventions to prevent SSIs and reduce antimicrobial resistance in liver transplant recipients.

During the beginning of the coronavirus disease 2019 (COVID-19) pandemic, nursing homes were identified as congregate settings at high risk for outbreaks of COVID-19 (1,2). Their residents also are at higher risk than the general population for morbidity and mortality associated with infection with SARS-CoV-2, the virus that causes COVID-19, in light of the association of severe outcomes with older age and certain underlying medical conditions (1,3). CDC's National Healthcare Safety Network (NHSN) launched nationwide, facility-level COVID-19 nursing home surveillance on April 26, 2020. A federal mandate issued by the Centers for Medicare & Medicaid Services (CMS), required nursing homes to commence enrollment and routine reporting of COVID-19 cases among residents and staff members by May 25, 2020. This report uses the NHSN nursing home COVID-19 data reported during May 25-November 22, 2020, to describe COVID-19 rates among nursing home residents and staff members and compares these with rates in surrounding communities by corresponding U.S. Department of Health and Human Services (HHS) region.* COVID-19 cases among nursing home residents increased during June and July 2020, reaching 11.5 cases per 1,000 resident-weeks (calculated as the total number of occupied beds on the day that weekly data were reported) (week of July 26). By mid-September, rates had declined to 6.3 per 1,000 resident-weeks (week of September 13) before increasing again, reaching 23.2 cases per 1,000 resident-weeks by late November (week of November 22). COVID-19 cases among nursing home staff members also increased during June and July (week of July 26 = 10.9 cases per 1,000 resident-weeks) before declining during August-September (week of September 13 = 6.3 per 1,000 resident-weeks); rates increased by late November (week of November 22 = 21.3 cases per 1,000 resident-weeks). Rates of COVID-19 in the surrounding communities followed similar trends. Increases in community rates might be associated with increases in nursing home COVID-19 incidence, and nursing home mitigation strategies need to include a comprehensive plan to monitor local SARS-CoV-2 transmission and minimize high-risk exposures within facilities.

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.

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.

Surgical site infections (SSIs) are among the most common healthcare-associated infections in low- and middle-income countries. To encourage establishment of actionable and standardized SSI surveillance in these countries, we propose simplified surveillance case definitions. Here, we use NHSN reports to explore concordance of these simplified definitions to NHSN as 'reference standard.'

OBJECTIVE: To describe pathogen distribution and rates for central-line-associated bloodstream infections (CLABSIs) from different acute-care locations during 2011-2017 to inform prevention efforts. METHODS: CLABSI data from the Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN) were analyzed. Percentages and pooled mean incidence density rates were calculated for a variety of pathogens and stratified by acute-care location groups (adult intensive care units [ICUs], pediatric ICUs [PICUs], adult wards, pediatric wards, and oncology wards). RESULTS: From 2011 to 2017, 136,264 CLABSIs were reported to the NHSN by adult and pediatric acute-care locations; adult ICUs and wards reported the most CLABSIs: 59,461 (44%) and 40,763 (30%), respectively. In 2017, the most common pathogens were Candida spp/yeast in adult ICUs (27%) and Enterobacteriaceae in adult wards, pediatric wards, oncology wards, and PICUs (23%-31%). Most pathogen-specific CLABSI rates decreased over time, excepting Candida spp/yeast in adult ICUs and Enterobacteriaceae in oncology wards, which increased, and Staphylococcus aureus rates in pediatric locations, which did not change. CONCLUSIONS: The pathogens associated with CLABSIs differ across acute-care location groups. Learning how pathogen-targeted prevention efforts could augment current prevention strategies, such as strategies aimed at preventing Candida spp/yeast and Enterobacteriaceae CLABSIs, might further reduce national rates.

BACKGROUND: Prevention of Clostridioides difficile infection (CDI) is a national priority and may be facilitated by deployment of the Targeted Assessment for Prevention (TAP) Strategy, a quality improvement framework providing a focused approach to infection prevention. This article describes the process and outcomes of TAP Strategy implementation for CDI prevention in a healthcare system. METHODS: Hospital A was identified based on CDI surveillance data indicating an excess burden of infections above the national goal; hospitals B and C participated as part of systemwide deployment. TAP facility assessments were administered to staff to identify infection control gaps and inform CDI prevention interventions. Retrospective analysis was performed using negative-binomial, interrupted time series (ITS) regression to assess overall effect of targeted CDI prevention efforts. Analysis included hospital-onset, laboratory-identified C. difficile event data for 18 months before and after implementation of the TAP facility assessments. RESULTS: The systemwide monthly CDI rate significantly decreased at the intervention (beta2, -44%; P = .017), and the postintervention CDI rate trend showed a sustained decrease (beta1 + beta3; -12% per month; P = .008). At an individual hospital level, the CDI rate trend significantly decreased in the postintervention period at hospital A only (beta1 + beta3, -26% per month; P = .003). CONCLUSIONS: This project demonstrates TAP Strategy implementation in a healthcare system, yielding significant decrease in the laboratory-identified C. difficile rate trend in the postintervention period at the system level and in hospital A. This project highlights the potential benefit of directing prevention efforts to facilities with the highest burden of excess infections to more efficiently reduce CDI rates.