OBJECTIVE: Resilience of the healthcare system has been described as the ability to absorb, adapt, and respond to stress while maintaining the provision of safe patient care. We quantified the impact that stressors associated with the COVID-19 pandemic had on patient safety, as measured by central line-associated bloodstream infections (CLABSIs) reported to the Centers for Disease Control and Prevention's National Healthcare Safety Network. DESIGN: Acute care hospitals were mandated to report markers of resource availability (staffing and hospital occupancy with COVID-19 inpatients) to the federal government between July 2020 and June 2021. These data were used with community levels of COVID-19 to develop a statistical model to assess factors influencing rates of CLABSIs among inpatients during the pandemic. RESULTS: After risk adjustment for hospital characteristics, measured stressors were associated with increased CLABSIs. Staff shortages for more than 10% of days per month were associated with a statistically significant increase of 2 CLABSIs per 10,000 central line days versus hospitals reporting staff shortages of less than 10% of days per month. CLABSIs increased with a higher inpatient COVID-19 occupancy rate; when COVID-19 occupancy was 20% or more, there were 5 more CLABSIs per 10,000 central line days versus the referent (less than 5%). CONCLUSIONS: Reporting of data pertaining to hospital operations during the COVID-19 pandemic afforded an opportunity to evaluate resilience of US hospitals. We demonstrate how the stressors of staffing shortages and high numbers of patients with COVID-19 negatively impacted patient safety, demonstrating poor resilience. Understanding stress in hospitals may allow for the development of policies that support resilience and drive safe care.

Sepsis, life-threatening organ dysfunction secondary to infection, contributes to at least 1.7 million adult hospitalizations and at least 350,000 deaths annually in the United States. Sepsis care is complex, requiring the coordination of multiple hospital departments and disciplines. Sepsis programs can coordinate these efforts to optimize patient outcomes. The 2022 National Healthcare Safety Network (NHSN) annual survey evaluated the prevalence and characteristics of sepsis programs in acute care hospitals. Among 5,221 hospitals, 3,787 (73%) reported having a committee that monitors and reviews sepsis care. Prevalence of these committees varied by hospital size, ranging from 53% among hospitals with 0-25 beds to 95% among hospitals with >500 beds. Fifty-five percent of all hospitals provided dedicated time (including assigned protected time or job description requirements) for leaders of these committees to manage a program and conduct daily activities, and 55% of committees reported involvement with antibiotic stewardship programs. These data highlight opportunities, particularly in smaller hospitals, to improve the care and outcomes of patients with sepsis in the United States by ensuring that all hospitals have sepsis programs with protected time for program leaders, engagement of medical specialists, and integration with antimicrobial stewardship programs. CDC's Hospital Sepsis Program Core Elements provides a guide to assist hospitals in developing and implementing effective sepsis programs that complement and facilitate the implementation of existing clinical guidelines and improve patient care. Future NHSN annual surveys will monitor uptake of these sepsis core elements.

Data from the National Healthcare Safety Network were analyzed to assess the impact of COVID-19 on the incidence of healthcare-associated infections (HAI) during 2021. Standardized infection ratios were significantly higher than those during the prepandemic period, particularly during 2021-Q1 and 2021-Q3. The incidence of HAI was elevated during periods of high COVID-19 hospitalizations.

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.

To assess potential changes in the pathogens attributed to central-line-associated bloodstream infections between 2019 and 2020, hospital data from the National Healthcare Safety Network were analyzed. Compared to 2019, increases in the proportions of pathogens identified as Enterococcus faecalis and coagulase-negative staphylococci were observed during 2020.

This case study is part of a series centered on the Centers for Disease Control and Prevention's National Healthcare Safety Network's (NHSN) health care-associated infection (HAI) surveillance definitions. This is the first analytic case study published in AJIC since the CDC/ NHSN updated its HAI risk adjustment models and rebaselined the standardized infection ratios (SIRs) in 2015. This case describes a scenario that Infection Preventionists (IPs) have encountered during their analysis of surgical site infection (SSI) surveillance data. The case study is intended to illustrate how specific models can impact the SIR results by highlighting differences in the criteria for NHSN's older and newer risk models: the original versions and the updated models introduced in 2015. Understanding these differences provides insight into how SSI SIR calculations differ between the older and newer NHSN baseline models. NHSN plans to produce another set of HAI risk adjustment models in the future, using newer HAI incidence and risk factor data. While the timetable for these changes remains to be determined, the statistical methods used to produce future models and SIR calculations will continue the precedents that NHSN has established. An online survey link is provided where participants may confidentially answer questions related to the case study and receive immediate feedback in the form of correct answers, explanations, rationales, and summary of teaching points. Details of the case study, answers, and explanations have been reviewed and approved by NHSN staff. We hope that participants take advantage of this educational offering and thereby gain a greater understanding of the NHSN's HAI data analysis. There are 2 baselines available for SSI standardized infection ration (SIRs) in the National Healthcare Safety Network (NHSN); one based on the 2006-2008 national aggregate data and another based on the 2015 data. Each of the 2 baselines has a different set of inclusion criteria for the SSI data, which impact the calculation of the SIR. In this case study, we focused on the impact of the inclusion of PATOS in the calculation of the 2006-2008 baseline SSI SIR and the exclusion of PATOS from the calculation of the 2015 baseline SSI SIR. In the 2006-2008 baseline SSI SIRs, PATOS events and the procedures to which they are linked are included in the calculation of the SSI SIR whereas in the 2015 baseline SSI SIRs, PATOS events and the procedures to which they are linked are excluded from the calculation of the SSI SIR. Meaning, if we control for all other inclusion criteria other than PATOS data for both baselines, we will notice differences in the number of observed events as well as the number of predicted infections for the 2 baselines. For details of the 2015 baseline and risk adjustment calculation, please review the NHSN Guide to the SIR referenced below. For details of the 2006-2008 baseline4 and risk adjustment, please see the SHEA paper "Improving Risk-Adjusted Measures of Surgical Site Infection for the National Healthcare Safety Network" by author Yi Mu.

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.

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.

Using data from the National Healthcare Safety Network (NHSN), we assessed changes to intensive care unit (ICU) bed capacity during the early months of the COVID-19 pandemic. Changes in capacity varied by hospital type and size. ICU beds increased by 36%, highlighting the pressure placed on hospitals during the pandemic.

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.

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.

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.

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.

BACKGROUND: In 2012, the Centers for Disease Control and Prevention launched the Long-term Care Facility (LTCF) Component of the National Healthcare Safety Network (NHSN) designed for LTCFs to monitor Clostridium difficile infections (CDIs), urinary tract infections (UTIs), infections due to multidrug-resistant organisms, including methicillin-resistant Staphylococcus aureus (MRSA), and infection prevention process measures. METHODS: We describe characteristics and reporting patterns of facilities enrolled in the first 3 years of the surveillance system and rate estimates for CDI, UTI, and MRSA data submitted between 2013 and 2015. RESULTS: From 2013-2015, 279 LTCFs were enrolled and eligible to report to the NHSN with variability in reporting from year to year. Crude rate estimates pooled over these 3 years from reporting facilities were 0.98 incident LTCF-onset CDI cases per 10,000 resident days, 0.59 UTI cases per 1,000 resident days, and 0.10 LTCF-onset MRSA cases per 1,000 resident days. CONCLUSIONS: These initial data demonstrate the capability of the NHSN LTCF Component as a national surveillance system for monitoring infections in LTCFs. Further investigation is needed to understand factors associated with successful enrollment and reporting. As participation increases, data from a larger group of LTCFs will be used to establish national baselines and track prevention goals.

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.

We examined reported policies for the control of common multidrug-resistant organisms (MDROs) in US healthcare facilities using data from the National Healthcare Safety Network Annual Facility Survey. Policies for the use of Contact Precautions were commonly reported. Chlorhexidine bathing for preventing MDRO transmission was also common among acute care hospitals.

BACKGROUND: The National Action Plan to Combat Antibiotic Resistant Bacteria calls for all U.S. hospitals to improve antibiotic prescribing as a key prevention strategy for resistance and Clostridium difficile Antibiotic stewardship programs (ASPs) will be important in this effort but implementation is not well understood. METHODS: We analyzed the 2014 National Healthcare Safety Network (NHSN) Annual Hospital Survey to describe ASPs in U.S. acute care hospitals as defined by CDC's Core Elements for Hospital Antibiotic Stewardship Programs. Univariate analyses were used to assess stewardship infrastructure and practices by facility characteristics and a multivariate model determined factors associated with meeting all ASP core elements. RESULTS: Among 4,184 U.S. hospitals, 39% reported having an ASP that met all seven core elements. Although hospitals with greater than 200 beds (59%) were more likely to have ASPs; one in four (25%) of hospitals with less than 50 beds reported achieving all seven CDC-defined core elements of a comprehensive ASP. The percent of hospitals in each state that reported all seven elements ranged from 7% to 58%. In the multivariate model, written support (adjusted RR 7.2 [95% CI, 6.2-8.4]; P<0.0001) or salary support (adjusted RR 1.5 [95% CI, 1.4-1.6]; P<0.0001) were significantly associated with having a comprehensive ASP. CONCLUSIONS: Our findings show that ASP implementation varies across the U.S. and provide a baseline to monitor progress toward national goals. Comprehensive ASPs can be established in facilities of any size and hospital leadership support for antibiotic stewardship appears to drive the establishment of ASPs.

BACKGROUND: Health care-associated antibiotic-resistant (AR) infections increase patient morbidity and mortality and might be impossible to successfully treat with any antibiotic. CDC assessed health care-associated infections (HAI), including Clostridium difficile infections (CDI), and the role of six AR bacteria of highest concern nationwide in several types of health care facilities. METHODS: During 2014, approximately 4,000 short-term acute care hospitals, 501 long-term acute care hospitals, and 1,135 inpatient rehabilitation facilities in all 50 states reported data on specific infections to the National Healthcare Safety Network. National standardized infection ratios and their percentage reduction from a baseline year for each HAI type, by facility type, were calculated. The proportions of AR pathogens and HAIs caused by any of six resistant bacteria highlighted by CDC in 2013 as urgent or serious threats were determined. RESULTS: In 2014, the reductions in incidence in short-term acute care hospitals and long-term acute care hospitals were 50% and 9%, respectively, for central line-associated bloodstream infection; 0% (short-term acute care hospitals), 11% (long-term acute care hospitals), and 14% (inpatient rehabilitation facilities) for catheter-associated urinary tract infection; 17% (short-term acute care hospitals) for surgical site infection, and 8% (short-term acute care hospitals) for CDI. Combining HAIs other than CDI across all settings, 47.9% of Staphylococcus aureus isolates were methicillin resistant, 29.5% of enterococci were vancomycin-resistant, 17.8% of Enterobacteriaceae were extended-spectrum beta-lactamase phenotype, 3.6% of Enterobacteriaceae were carbapenem resistant, 15.9% of Pseudomonas aeruginosa isolates were multidrug resistant, and 52.6% of Acinetobacter species were multidrug resistant. The likelihood of HAIs caused by any of the six resistant bacteria ranged from 12% in inpatient rehabilitation facilities to 29% in long-term acute care hospitals. CONCLUSIONS: Although there has been considerable progress in preventing some HAIs, many remaining infections could be prevented with implementation of existing recommended practices. Depending upon the setting, more than one in four of HAIs excluding CDI are caused by AR bacteria. IMPLICATIONS FOR PUBLIC HEALTH PRACTICE: Physicians, nurses, and health care leaders need to consistently and comprehensively follow all recommendations to prevent catheter- and procedure-related infections and reduce the impact of AR bacteria through antimicrobial stewardship and measures to prevent spread.

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.

This report is a summary of Device-associated (DA) Module data collected by hospitals participating in the National Healthcare Safety Network (NHSN) for events occurring from January through December 2013 and reported to the Centers for Disease Control and Prevention (CDC) by June 1, 2014. This report updates previously published DA Module data from NHSN and provides contemporary comparative rates.1 Figure 1 provides a brief summary of highlights from this report. This report complements other NHSN reports, including national and state-specific progress reports for select healthcare-associated infections (HAIs).2

This report is a summary of Device-associated (DA) Module data collected by hospitals participating in the National Healthcare Safety Network (NHSN) for events occurring from January through December 2012 and reported to the Centers for Disease Control and Prevention (CDC) by July 1, 2013. This report updates previously published DA Module data from NHSN and provides contemporary comparative rates.1 Figure 1 provides a brief summary of key findings from this report. This report complements other NHSN reports, including national and state-specific reports of standardized infection ratios (SIRs) for select healthcare-associated infections (HAIs).2, 3

This report is a summary of Device-associated (DA) Module data collected by hospitals participating in the National Healthcare Safety Network (NHSN) for events occurring from January through December 2011 and reported to the Centers for Disease Control and Prevention (CDC) by August 1, 2012. This report updates previously published DA Module data from NHSN and provides contemporary comparative rates.1 This report complements other NHSN reports, including national and state-specific reports of standardized infection ratios (SIRs) for select healthcare-associated infections (HAIs).2,3,4 | NHSN data collection, reporting, and analysis are organized into three components: Patient Safety, Healthcare Personnel Safety, and Biovigilance, and use standardized methods and definitions in accordance with specific module protocols.5,6,7 Institutions may use modules singly or simultaneously, but once selected, they must be used for a minimum of one calendar month for the data to be included in CDC analyses. All infections are categorized using standard CDC definitions that include laboratory and clinical criteria.7 The DA Module may be used by facilities other than hospitals, including outpatient dialysis centers. A report of data from this module for outpatient dialysis centers was published separately.8 NHSN facilities contributing HAI surveillance data to this report did so voluntarily, in response to state mandatory reporting requirements or in compliance with the Centers for Medicare and Medicaid Services’ (CMS) Hospital Inpatient Quality Reporting (IQR) Program. CDC aggregated these data into a single national database for 2011, consistent with the stated purposes of NHSN, which were to: | Collect data from a sample of healthcare facilities in the United States to permit valid estimation of the magnitude of adverse events among patients and healthcare personnel. | Collect data from a sample of healthcare facilities in the United States to permit valid estimation of the adherence to practices known to be associated with prevention of these adverse events. | Analyze and report collected data to permit recognition of trends. | Provide facilities with risk-adjusted metrics that can be used for inter-facility comparisons and local quality improvement activities. | Assist facilities in developing surveillance and analysis methods that permit timely recognition of patient and healthcare worker safety problems and prompt intervention with appropriate measures. | Conduct collaborative research studies with NHSN member facilities (e.g., describe the epidemiology of emerging healthcare-associated infection [HAI] and pathogens, assess the importance of potential risk factors, further characterize HAI pathogens and their mechanisms of resistance, and evaluate alternative surveillance and prevention strategies). | Comply with legal requirements – including but not limited to state or federal laws, regulations, or other requirements – for mandatory reporting of healthcare facility-specific adverse event, prevention practice adherence, and other public health data. | Enable healthcare facilities to report HAI and prevention practice adherence data via NHSN to the U.S. Centers for Medicare and Medicaid Services (CMS) in fulfillment of CMS’s quality measurement reporting requirements for those data. | Provide state departments of health with information that identifies the healthcare facilities in their state that participate in NHSN. | Provide to state agencies, at their request, facility-specific, NHSN patient safety component and healthcare personnel safety component adverse event and prevention practice adherence data for surveillance, prevention, or mandatory public reporting. | Patient- and facility-specific data reported to CDC are kept confidential in accordance with sections 304, 306, and 308(d) of the Public Health Service Act (42 USC 242b, 242k, and 242m(d)).

This report is a summary of Device-Associated (DA) module data collected by hospitals participating in the National Healthcare Safety Network (NHSN) for events occurring between January and December 2009 and reported to the Centers for Disease Control and Prevention (CDC) by October 18, 2010. This report updates previously published DA module data from the NHSN and provides contemporary comparative rates.1 Procedure-Associated module data will be reported separately. Surgical site infection data will be reported as standardized infection ratios using new logistic regression models, and postprocedure pneumonia rates for 2009 are available on the NHSN's public Web site. This report complements other NHSN reports, including national and state-specific standardized infection ratios for selected health care–associated infections (HAIs).2, 3, 4 | The NHSN was established in 2005 to integrate and supersede 3 legacy surveillance systems at the CDC: the National Nosocomial Infections Surveillance system, the Dialysis Surveillance Network, and the National Surveillance System for Healthcare Workers. NHSN data collection, reporting, and analysis are organized into 3 components—Patient Safety, Healthcare Personnel Safety, and Biovigilance—and use standardized methods and definitions in accordance with specific module protocols.5, 6, 7 The modules may be used singly or simultaneously, but once selected, they must be used for a minimum of 1 calendar month. All infections are categorized using standard CDC definitions that include laboratory and clinical criteria.7 The DA module may be used by facilities other than hospitals, including long-term care facilities and outpatient dialysis centers. A report of data from this module for outpatient dialysis centers has been published separately.8 For this report, only data from the Patient Safety component are presented. NHSN facilities report their HAI surveillance data voluntarily or in response to state mandatory reporting requirements. The CDC aggregates these data into a single national database for the stated purposes in place in 2009, as follows: | • | Collect data from a sample of US health care facilities to permit valid estimation of the magnitude of adverse events among patients and health care personnel. | • | Collect data from a sample of US health care facilities to permit valid estimation of the adherence to practices known to be associated with prevention of these adverse events. | • | Analyze and report collected data to permit recognition of trends. | • | Provide facilities with risk-adjusted metrics that can be used for interfacility comparisons and local quality improvement activities. | • | Assist facilities in developing surveillance and analysis methods that permit timely recognition of patient and health care worker safety problems and prompt intervention with appropriate measures. | • | Conduct collaborative research studies with NHSN member facilities (eg, describe the epidemiology of emerging HAIs and pathogens, assess the importance of potential risk factors, further characterize HAI pathogens and their mechanisms of resistance, and evaluate alternative surveillance and prevention strategies). | | The identity of each NHSN facility is kept confidential by the CDC in accordance with Sections 304, 306, and 308(d) of the Public Health Service Act [42 USC 242b, 242K, and 242m(d)].

This report is a summary of Device-Associated (DA) and Procedure-Associated (PA) module data collected and reported by hospitals and ambulatory surgical centers participating in the National Healthcare Safety Network (NHSN) from January 2006 through December 2008 as reported to the Centers for Disease Control and Prevention (CDC) by July 6, 2009. This report updates previously published DA and PA module data from the NHSN.1 | The NHSN was established in 2005 to integrate and supersede 3 legacy surveillance systems at the CDC: the National Nosocomial Infections Surveillance (NNIS) system, the Dialysis Surveillance Network (DSN), and the National Surveillance System for Healthcare Workers (NaSH). Similar to the NNIS system, NHSN facilities voluntarily report their health care–associated infection (HAI) surveillance data for aggregation into a single national database for the following purposes: | • | Estimation of the magnitude of HAIs | • | Monitoring of HAI trends | • | Facilitation of interfacility and intrafacility comparisons with risk-adjusted data that can be used for local quality improvement activities | • | Assistance to facilities in developing surveillance and analysis methods that permit timely recognition of patient safety problems and prompt intervention with appropriate measures. | | In addition, many facilities use these same data to comply with state reporting mandates. Identity of all NHSN facilities is kept confidential by the CDC in accordance with Sections 304, 306, and 308(d) of the Public Health Service Act [42 USC 242b, 242k, and 242m(d)].