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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.

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.

Objective: To describe the effect of a microbiology comment nudge on antibiotic use for asymptomatic bacteriuria (ASB)., Design: Single-center, before-and-after, quasi-experimental study., Setting: Community-based, public, not-for-profit teaching hospital in the southeastern United States., Participants: Adult inpatients with a positive urine culture and the absence of urinary tract infection signs and symptoms., Intervention: Implementation of a microbiology comment nudge on urine cultures., Results: In total, 204 patients were included in the study. Antibiotics were less likely to be continued beyond 72 hours in the postimplementation group: 57 (55%) of 104 versus 38 (38%) of 100 ( P = .016). They were less likely to have antibiotics continued beyond 48 hours: 60 (58%) of 104 versus 43 (43%) of 100 ( P = .036). They were also less likely to have antibiotics prescribed at discharge 35 (34%) of 104 versus 20 (20%) of 100 ( P = .028). In addition, they had fewer total antibiotic days of therapy: 4 (IQR, 1-6) versus 1 (IQR, 0-6) ( P = .022)., Conclusion: Microbiology comment nudging may contribute to less antibiotic utilization in patients with ASB.

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.

During March 27-July 14, 2020, the Centers for Disease Control and Prevention'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.

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.

Data reported to the Centers for Disease Control and Prevention's National Healthcare Safety Network (CDC 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 location, location type, geographic area, and bed size.

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.

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.

Objective: The rapid spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) throughout key regions of the United States 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 coronavirus disease 2019 (COVID-19) data. We present time-series estimates of the critical hospital capacity indicators from April 1 to July 14, 2020., Design: From March 27 to July 14, 2020, the NHSN collected daily data on hospital bed occupancy, number of hospitalized patients with COVID-19, and the availability and/or 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 from April to July, the proportion of occupied inpatient beds increased steadily. COVID-19 hospitalizations increased from mid-June in the South and Southwest regions 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 they declined from a peak in April. Patient outcomes appeared to improve from early April to mid-July.

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 (β2, -44%; P = .017), and the postintervention CDI rate trend showed a sustained decrease (β1 + β3; -12% per month; P = .008). At an individual hospital level, the CDI rate trend significantly decreased in the postintervention period at hospital A only (β1 + β3, -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.

Objective: Describe common pathogens and antimicrobial resistance patterns for healthcare-associated infections (HAIs) that occurred during 2015-2017 and were reported to the Centers for Disease Control and Prevention's (CDC's) National Healthcare Safety Network (NHSN)., Methods: Data from central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), ventilator-associated events (VAEs), and surgical site infections (SSIs) were reported from acute-care hospitals, long-term acute-care hospitals, and inpatient rehabilitation facilities. This analysis included device-associated HAIs reported from adult location types, and SSIs among patients ≥18 years old. Percentages of pathogens with nonsusceptibility (%NS) to selected antimicrobials were calculated for each HAI type, location type, surgical category, and surgical wound closure technique., Results: Overall, 5,626 facilities performed adult HAI surveillance during this period, most of which were general acute-care hospitals with <200 beds. Escherichia coli (18%), Staphylococcus aureus (12%), and Klebsiella spp (9%) were the 3 most frequently reported pathogens. Pathogens varied by HAI and location type, with oncology units having a distinct pathogen distribution compared to other settings. The %NS for most pathogens was significantly higher among device-associated HAIs than SSIs. In addition, pathogens from long-term acute-care hospitals had a significantly higher %NS than those from general hospital wards., Conclusions: This report provides an updated national summary of pathogen distributions and antimicrobial resistance among select HAIs and pathogens, stratified by several factors. These data underscore the importance of tracking antimicrobial resistance, particularly in vulnerable populations such as long-term acute-care hospitals and intensive care units.

Objective: To describe common pathogens and antimicrobial resistance patterns for healthcare-associated infections (HAIs) among pediatric patients that occurred in 2015-2017 and were reported to the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN)., Methods: Antimicrobial resistance data were analyzed for pathogens implicated in central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), ventilator-associated pneumonias (VAPs), and surgical site infections (SSIs). This analysis was restricted to device-associated HAIs reported from pediatric patient care locations and SSIs among patients <18 years old. Percentages of pathogens with nonsusceptibility (%NS) to selected antimicrobials were calculated by HAI type, location type, and surgical category., Results: Overall, 2,545 facilities performed surveillance of pediatric HAIs in the NHSN during this period. Staphylococcus aureus (15%), Escherichia coli (12%), and coagulase-negative staphylococci (12%) were the 3 most commonly reported pathogens associated with pediatric HAIs. Pathogens and the %NS varied by HAI type, location type, and/or surgical category. Among CLABSIs, the %NS was generally lowest in neonatal intensive care units and highest in pediatric oncology units. Staphylococcus spp were particularly common among orthopedic, neurosurgical, and cardiac SSIs; however, E. coli was more common in abdominal SSIs. Overall, antimicrobial nonsusceptibility was less prevalent in pediatric HAIs than in adult HAIs., Conclusion: This report provides an updated national summary of pathogen distributions and antimicrobial resistance patterns among pediatric HAIs. These data highlight the need for continued antimicrobial resistance tracking among pediatric patients and should encourage the pediatric healthcare community to use such data when establishing policies for infection prevention and antimicrobial stewardship.

The antimicrobial use (AU) option within the National Healthcare Safety Network summarizes antimicrobial prescribing data as a standardized antimicrobial administration ratio (SAAR). A hospital's antimicrobial stewardship program found that greater involvement of an infectious disease physician in prospective audit and feedback procedures was associated with reductions in SAAR values across multiple antimicrobial categories. Infect Control Hosp Epidemiol 2017;38:721-723.

OBJECTIVE To determine the typical microbial bioburden (overall bacterial and multidrug-resistant organisms [MDROs]) on high-touch healthcare environmental surfaces after routine or terminal cleaning. DESIGN Prospective 2.5-year microbiological survey of large surface areas (>1,000 cm2). SETTING MDRO contact-precaution rooms from 9 acute-care hospitals and 2 long-term care facilities in 4 states. PARTICIPANTS Samples from 166 rooms (113 routine cleaned and 53 terminal cleaned rooms). METHODS Using a standard sponge-wipe sampling protocol, 2 composite samples were collected from each room; a third sample was collected from each Clostridium difficile room. Composite 1 included the TV remote, telephone, call button, and bed rails. Composite 2 included the room door handle, IV pole, and overbed table. Composite 3 included toileting surfaces. Total bacteria and MDROs (ie, methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci [VRE], Acinetobacter baumannii, Klebsiella pneumoniae, and C. difficile) were quantified, confirmed, and tested for drug resistance. RESULTS The mean microbial bioburden and range from routine cleaned room composites were higher (2,700 colony-forming units [CFU]/100 cm2; ≤1-130,000 CFU/100 cm2) than from terminal cleaned room composites (353 CFU/100 cm2; ≤1-4,300 CFU/100 cm2). MDROs were recovered from 34% of routine cleaned room composites (range ≤1-13,000 CFU/100 cm2) and 17% of terminal cleaned room composites (≤1-524 CFU/100 cm2). MDROs were recovered from 40% of rooms; VRE was the most common (19%). CONCLUSIONS This multicenter bioburden summary provides a first step to determining microbial bioburden on healthcare surfaces, which may help provide a basis for developing standards to evaluate cleaning and disinfection as well as a framework for studies using an evidentiary hierarchy for environmental infection control. Infect Control Hosp Epidemiol 2016;1426-1432.

OBJECTIVE To describe antimicrobial resistance patterns for healthcare-associated infections (HAIs) that occurred in 2011-2014 and were reported to the Centers for Disease Control and Prevention's National Healthcare Safety Network. METHODS Data from central line-associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated pneumonias, and surgical site infections were analyzed. These HAIs were reported from acute care hospitals, long-term acute care hospitals, and inpatient rehabilitation facilities. Pooled mean proportions of pathogens that tested resistant (or nonsusceptible) to selected antimicrobials were calculated by year and HAI type. RESULTS Overall, 4,515 hospitals reported that at least 1 HAI occurred in 2011-2014. There were 408,151 pathogens from 365,490 HAIs reported to the National Healthcare Safety Network, most of which were reported from acute care hospitals with greater than 200 beds. Fifteen pathogen groups accounted for 87% of reported pathogens; the most common included Escherichia coli (15%), Staphylococcus aureus (12%), Klebsiella species (8%), and coagulase-negative staphylococci (8%). In general, the proportion of isolates with common resistance phenotypes was higher among device-associated HAIs compared with surgical site infections. Although the percent resistance for most phenotypes was similar to earlier reports, an increase in the magnitude of the resistance percentages among E. coli pathogens was noted, especially related to fluoroquinolone resistance. CONCLUSION This report represents a national summary of antimicrobial resistance among select HAIs and phenotypes. The distribution of frequent pathogens and some resistance patterns appear to have changed from 2009-2010, highlighting the need for continual, careful monitoring of these data across the spectrum of HAI types. Infect Control Hosp Epidemiol 2016;1-14.

OBJECTIVE We compared risk for surgical site infection (SSI) following surgical breast procedures among 2 patient groups: those whose procedures were performed in ambulatory surgery centers (ASCs) and those whose procedures were performed in hospital-based outpatient facilities. DESIGN Cohort study using National Healthcare Safety Network (NHSN) SSI data for breast procedures performed from 2010 to 2014. METHODS Unconditional multivariate logistic regression was used to examine the association between facility type and breast SSI, adjusting for American Society of Anesthesiologists (ASA) Physical Status Classification, patient age, and duration of procedure. Other potential adjustment factors examined were wound classification, anesthesia use, and gender. RESULTS Among 124,021 total outpatient breast procedures performed between 2010 and 2014, 110,987 procedure reports submitted to the NHSN provided complete covariate data and were included in the analysis. Breast procedures performed in ASCs carried a lower risk of SSI compared with those performed in hospital-based outpatient settings. For patients aged ≤51 years, the adjusted risk ratio was 0.36 (95% CI, 0.25-0.50) and for patients >51 years old, the adjusted risk ratio was 0.32 (95% CI, 0.21-0.49). CONCLUSIONS SSI risk following breast procedures was significantly lower among ASC patients than among hospital-based outpatients. These findings should be placed in the context of study limitations, including the possibility of incomplete ascertainment of SSIs and shortcomings in the data available to control for differences in patient case mix. Additional studies are needed to better understand the role of procedural settings in SSI risk following breast procedures and to identify prevention opportunities. Infect Control Hosp Epidemiol 2016;1-7.

We assessed the impact of a reflex urine culture protocol, an intervention aimed to reduce unnecessary urine culturing, in intensive care units at a tertiary care hospital. Significant decreases in urine culturing rates and reported rates of catheter-associated urinary tract infection followed implementation of the protocol.

BACKGROUND Case mix index (CMI) has been used as a facility-level indicator of patient disease severity. We sought to evaluate the potential for CMI to be used for risk adjustment of National Healthcare Safety Network (NHSN) healthcare-associated infection (HAI) data. METHODS NHSN facility-wide laboratory-identified Clostridium difficile infection event data from 2012 were merged with the fiscal year 2012 Inpatient Prospective Payment System (IPPS) Impact file by CMS certification number (CCN) to obtain a CMI value for hospitals reporting to NHSN. Negative binomial regression was used to evaluate whether CMI was significantly associated with healthcare facility-onset (HO) CDI in univariate and multivariate analysis. RESULTS Among 1,468 acute care hospitals reporting CDI data to NHSN in 2012, 1,429 matched by CCN to a CMI value in the Impact file. CMI (median, 1.49; interquartile range, 1.36-1.66) was a significant predictor of HO CDI in univariate analysis (P<.0001). After controlling for community onset CDI prevalence rate, medical school affiliation, hospital size, and CDI test type use, CMI remained highly significant (P<.0001), with an increase of 0.1 point in CMI associated with a 3.4% increase in the HO CDI incidence rate. CONCLUSIONS CMI was a significant predictor of NHSN HO CDI incidence. Additional work to explore the feasibility of using CMI for risk adjustment of NHSN data is necessary. Infect. Control Hosp. Epidemiol. 2015;37(1):19-25.

OBJECTIVES To determine the impact of mucosal barrier injury laboratory-confirmed bloodstream infections (MBI-LCBIs) on central-line-associated bloodstream infection (CLABSI) rates during the first year of MBI-LCBI reporting to the National Healthcare Safety Network (NHSN) DESIGN Descriptive analysis of 2013 NHSN data SETTING Selected inpatient locations in acute care hospitals METHODS A descriptive analysis of MBI-LCBI cases was performed. CLABSI rates per 1,000 central-line days were calculated with and without the inclusion of MBI-LCBIs in the subset of locations reporting ≥1 MBI-LCBI, and in all locations (regardless of MBI-LCBI reporting) to determine rate differences overall and by location type. RESULTS From 418 locations in 252 acute care hospitals reporting ≥1 MBI-LCBIs, 3,162 CLABSIs were reported; 1,415 (44.7%) met the MBI-LCBI definition. Among these locations, removing MBI-LCBI from the CLABSI rate determination produced the greatest CLABSI rate decreases in oncology (49%) and ward locations (45%). Among all locations reporting CLABSI data, including those reporting no MBI-LCBIs, removing MBI-LCBI reduced rates by 8%. Here, the greatest decrease was in oncology locations (38% decrease); decreases in other locations ranged from 1.2% to 4.2%. CONCLUSIONS An understanding of the potential impact of removing MBI-LCBIs from CLABSI data is needed to accurately interpret CLABSI trends over time and to inform changes to state and federal reporting programs. Whereas the MBI-LCBI definition may have a large impact on CLABSI rates in locations where patients with certain clinical conditions are cared for, the impact of MBI-LCBIs on overall CLABSI rates across inpatient locations appears to be more modest. Infect. Control Hosp. Epidemiol. 2015;37(1):2-7.

Objective: To develop a method for calculating the number of healthcare-associated infections (HAIs) that must be prevented to reach a HAI reduction goal and identifying and prioritizing healthcare facilities where the largest reductions can be achieved., Setting: Acute care hospitals that report HAI data to the Centers for Disease Control and Prevention's National Healthcare Safety Network. METHODS :The cumulative attributable difference (CAD) is calculated by subtracting a numerical prevention target from an observed number of HAIs. The prevention target is the product of the predicted number of HAIs and a standardized infection ratio goal, which represents a HAI reduction goal. The CAD is a numeric value that if positive is the number of infections to prevent to reach the HAI reduction goal. We calculated the CAD for catheter-associated urinary tract infections for each of the 3,639 hospitals that reported such data to National Healthcare Safety Network in 2013 and ranked the hospitals by their CAD values in descending order., Results: Of 1,578 hospitals with positive CAD values, preventing 10,040 catheter-associated urinary tract infections at 293 hospitals (19%) with the highest CAD would enable achievement of the national 25% catheter-associated urinary tract infection reduction goal., Conclusion: The CAD is a new metric that facilitates ranking of facilities, and locations within facilities, to prioritize HAI prevention efforts where the greatest impact can be achieved toward a HAI reduction goal.