Changes in the number of intensive care unit beds in US hospitals during the early months of the coronavirus disease 2019 (COVID-19) pandemic.

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.

Impact of coronavirus disease 2019 (COVID-19) on US Hospitals and Patients, April-July 2020.

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.

Hospital capacities and shortages of healthcare resources among US hospitals during the coronavirus disease 2019 (COVID-19) pandemic, National Healthcare Safety Network (NHSN), March 27-July 14, 2020.

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.

Laboratory-identified vancomycin-resistant enterococci bacteremia incidence: A standardized infection ratio prediction model.

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.

How infection present at time of surgery (PATOS) data impacts your surgical site infection (SSI) standardized infection ratios (SIR), with focus on the complex 30-day SSI SIR model.

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.

Rates and causative pathogens of surgical site infections attributed to liver transplant procedures and other hepatic, biliary, or pancreatic procedures, 2015-2018.

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 to 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); Escherichia coli (68.8% fluoroquinolone non-susceptible and 44.7% extended spectrum cephalosporin [ESC] non-susceptible); and Klebsiella pneumoniae and K. oxytoca (39.4% fluoroquinolone non-susceptible and 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.

Healthcare-associated infections studies project: An American Journal of Infection Control and National Healthcare Safety Network data quality collaboration.

This case study is part of a series centered on the Centers for Disease Control and Prevention/National Healthcare Safety Network (NHSN) healthcare-associated infection (HAI) surveillance definitions. This specific case study focuses on the application of the Pneumonia (PNEU), Ventilator-associated event (VAE), and Bloodstream infections (BSI) surveillance definitions to a patient with COVID-19. The intent of the case study series is to foster standardized application of the NHSN HAI surveillance definitions among Infection Preventionists (IPs) and encourage accurate determination of HAI events.

Pathogens causing central-line-associated bloodstream infections in acute-care hospitals-United States, 2011-2017.

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.

Health care-associated infections studies project: an American Journal of Infection Control and National Healthcare Safety Network data quality collaboration.

This case study is part of a series centered on the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN) health care-associated infection (HAI) surveillance definitions. The intent of the case study series is to foster standardized application of the NHSN HAI surveillance definitions among infection preventionists and to promote accurate determination of HAI events. These cases reflect some of the complex patient scenarios that infection preventionists have encountered in their daily surveillance of HAIs using NHSN definitions. Objectives have been previously published.1.

Accuracy of catheter-associated urinary tract infections reported to the National Healthcare Safety Network, January 2010 through July 2018.

BACKGROUND: Surveillance of health care-associated, catheter-associated urinary tract infections (CAUTI) are the corner stone of infection prevention activity. The Centers for Disease Control and Prevention's National Healthcare Safety Network provides standard definitions for CAUTI surveillance, which have been updated periodically to increase objectivity, credibility, and reliability of urinary tract infection definitions. Several state health departments have validated CAUTI data that provided insights into accuracy of CAUTI reporting and adherence to CAUTI definition. METHODS: Data accuracy measures included pooled mean sensitivity, specificity, positive predictive value, and negative predictive value. Total CAUTI error rate was computed as proportion of mismatches among total records. The impact of 2015 CAUTI definition changes were tested by comparing pooled accuracy estimates of validations prior to 2015 with post-2015. RESULTS: At least 19 state health departments conducted CAUTI validations and indicated pooled mean sensitivity of 88.3%, specificity of 98.8%, positive predictive value of 93.6%, and negative predictive value of 97.6% of CAUTI reporting to the National Healthcare Safety Network. Among CAUTIs misclassified (121), 66% were underreported and 34% were overreported. CAUTI classification error rate declined significantly from 4.3% (pre-2015) to 2.4% (post-2015). Reasons for CAUTI misclassifications included: misapplication of CAUTI definition, misapplication of general health care-associated infection definitions, and clinical judgement over surveillance definition. CONCLUSIONS: CAUTI underreporting is a major concern; validations provide transparency, education, and relationship building to improve reporting accuracy.

Health care-associated infections studies project: An American Journal of Infection Control and National Healthcare Safety Network data quality collaboration case study: Bloodstream infection-patient injection into vascular access 2018.

This case study is part of a series centered on the Centers for Disease Control and Prevention/National Healthcare Safety Network (NHSN) health care-associated infection surveillance definitions. These cases reflect some of the complex patient scenarios infection preventionists have encountered in their daily surveillance of health care-associated infections using NHSN definitions and protocols. Teaching points for this case study are.

State health department validations of central line-associated bloodstream infection events reported via the National Healthcare Safety Network.

BACKGROUND: Numerous state health departments (SHDs) have validated central line-associated bloodstream infection (CLABSI) data, and results from these studies provide important insights into the accuracy of CLABSI reporting to the National Healthcare Safety Network (NHSN) and remediable shortcomings in adherence to the CLABSI definition and criteria. METHODS: State CLABSI validation results were obtained from peer-reviewed publications, reports on SHD Web sites, and via personal communications with the SHD health care-associated infections coordinator. Data accuracy measures included pooled mean sensitivity, specificity, positive predictive value, and negative predictive value. Total CLABSI error rate was computed as the proportion of mismatches among total records reviewed. When available, reasons for CLABSI misclassification reported by SHDs were reviewed. RESULTS: At least 23 SHDs that have completed CLABSI validations indicated sensitivity (pooled mean, 82.9%), specificity (pooled mean, 98.5%), positive predictive value (pooled mean, 94.1%), and negative predictive value (pooled mean, 95.9%) of CLABSI reporting. The pooled error rate of CLABSI reporting was 4.4%. Reasons for CLABSI misclassification included incorrect secondary bloodstream infection attribution, misapplication of CLABSI definition, missed case finding, applying clinical over surveillance definitions, misapplication of laboratory-confirmed bloodstream infection 2 definition, and misapplication of general NHSN definitions. CONCLUSIONS: CLABSI underreporting remains a major concern; validations conducted by SHDs provide an important impetus for improved reporting. SHDs are uniquely positioned to engage facilities in collaborative validation reviews that allow transparency, education, and relationship building.

Healthcare-associated infections studies project: An American Journal of Infection Control and National Healthcare Safety Network data quality collaboration: Location mapping.

This case study is part of a series centered on the Centers for Disease Control and Prevention/National Healthcare Safety Network (NHSN) health care-associated infection (HAI) surveillance definitions. This specific case study focuses on appropriately mapping locations within an NHSN-enrolled facility. The intent of the case study series is to foster standardized application of the NHSN HAI surveillance definitions among IPs and encourage accurate determination of HAI events. 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 and explanations and rationales. 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 NHSN HAI surveillance definitions.

Health care-associated infections studies project: An American Journal of Infection Control and National Healthcare Safety Network data quality collaboration.

This case study is part of a series centered on the Centers for Disease Control and Prevention/National Healthcare Safety Network (NHSN) health care-associated infection (HAI) surveillance definitions. This specific case study focuses on the definitions and protocols used to make HAI infection determinations, such as the infection window period and secondary bloodstream infection attribution period. The case reflects the real-life and complex patient scenarios that infection preventionists (IPs) face when identifying and reporting HAIs to NHSN. The intent of the case study series is to foster standardized application of the NHSN HAI surveillance definitions among IPs and encourage accurate determination of HAI events. 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 and explanations and rationales. 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 NHSN HAI surveillance definitions.

Assessment of the accuracy and consistency in the application of standardized surveillance definitions: A summary of the American Journal of Infection Control and National Healthcare Safety Network case studies, 2010-2016.

BACKGROUND: The Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN) surveillance definitions are the most widely used criteria for health care-associated infection (HAI) surveillance. NHSN participants agree to conduct surveillance in accordance with the NHSN protocol and criteria. To assess the application of these standardized surveillance specifications and offer infection preventionists (IPs) opportunities for ongoing education, a series of case studies, with questions related to NHSN definitions and criteria were published. METHODS: Beginning in 2010, case studies with multiple-choice questions based on standard surveillance criteria and protocols were written and published in the American Journal of Infection Control with a link to an online survey. Participants anonymously submitted their responses before receiving the correct answers. RESULTS: The 22 case studies had 7,950 respondents who provided 27,790 responses to 75 questions during the first 6 years. Correct responses were selected 62.5% of the time (17,376 out of 27,290), but ranged widely (16%-87%). In a subset analysis, 93% of participants self-identified as IPs (3,387 out of 3,640), 4.5% were public health professionals (163 out of 3,640), and 2.5% were physicians (90 out of 3,640). IPs responded correctly (62%) more often than physicians (55%) (P = .006). CONCLUSIONS: Among a cohort of voluntary participants, accurate application of surveillance criteria to case studies was suboptimal, highlighting the need for continuing education, competency development, and auditing.

Health care-associated infections studies project: An American Journal of Infection Control and National Healthcare Safety Network data quality collaboration.

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. The intent of the case study series is to foster standardized application of the NHSN's HAI surveillance definitions among infection preventionists and accurate determination of HAI events. This specific case study focuses on the definitions found within the surgical site infection (SSI) protocol. It aims to reflect the real life and complex patient scenario surrounding a bloodstream infection that is secondary to an SSI and the application of the Present at the Time of Surgery event detail. 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 and explanations and rationales. 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 surveillance definitions.

Incidence and Characteristics of Ventilator-Associated Events Reported to the National Healthcare Safety Network in 2014.

OBJECTIVE: Ventilator-associated event surveillance was introduced in the National Healthcare Safety Network in 2013, replacing surveillance for ventilator-associated pneumonia in adult inpatient locations. We determined incidence rates and characteristics of ventilator-associated events reported to the National Healthcare Safety Network. DESIGN, SETTING, AND PATIENTS: We analyzed data reported from U.S. healthcare facilities for ventilator-associated events that occurred in 2014, the first year during which ventilator-associated event surveillance definitions were stable. We used negative binomial regression modeling to identify healthcare facility and inpatient location characteristics associated with ventilator-associated events. We calculated ventilator-associated event incidence rates, rate distributions, and ventilator utilization ratios in critical care and noncritical care locations and described event characteristics. MEASUREMENTS AND MAIN RESULTS: A total of 1,824 healthcare facilities reported 32,772 location months of ventilator-associated event surveillance data to the National Healthcare Safety Network in 2014. Critical care unit pooled mean ventilator-associated event incidence rates ranged from 2.00 to 11.79 per 1,000 ventilator days, whereas noncritical care unit rates ranged from 0 to 14.86 per 1,000 ventilator days. The pooled mean proportion of ventilator-associated events defined as infection-related varied from 15.38% to 47.62% in critical care units. Pooled mean ventilator utilization ratios in critical care units ranged from 0.24 to 0.47. CONCLUSIONS: We found substantial variability in ventilator-associated event incidence, proportions of ventilator-associated events characterized as infection-related, and ventilator utilization within and among location types. More work is needed to understand the preventable fraction of ventilator-associated events and identify patient care strategies that reduce ventilator-associated events.