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 common surveillance concepts included in Laboratory-Identified (LabID) Event Reporting [Chapter 12 of the NHSN Patient Safety Manual - Multidrug-Resistant Organism &Clostridioides difficile Infection (MDRO/CDI) Module] used with validation efforts. The intent of the case study series is to foster standardized application of the NHSN surveillance definitions and encourage accurate event determination among Infection Preventionists (IPs).

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 common surveillance concepts included in the Patient Safety Component, Chapter 9 - Surgical Site Infection Event (SSI). The intent of the case study series is to foster standardized application of the NHSN HAI surveillance definitions and encourage accurate HAI event determination among Infection Preventionists (IPs).

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.

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.

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.

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.

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.

BACKGROUND: A point-prevalence survey that was conducted in the United States in 2011 showed that 4% of hospitalized patients had a health care-associated infection. We repeated the survey in 2015 to assess changes in the prevalence of health care-associated infections during a period of national attention to the prevention of such infections. METHODS: At Emerging Infections Program sites in 10 states, we recruited up to 25 hospitals in each site area, prioritizing hospitals that had participated in the 2011 survey. Each hospital selected 1 day on which a random sample of patients was identified for assessment. Trained staff reviewed medical records using the 2011 definitions of health care-associated infections. We compared the percentages of patients with health care-associated infections and performed multivariable log-binomial regression modeling to evaluate the association of survey year with the risk of health care-associated infections. RESULTS: In 2015, a total of 12,299 patients in 199 hospitals were surveyed, as compared with 11,282 patients in 183 hospitals in 2011. Fewer patients had health care-associated infections in 2015 (394 patients [3.2%; 95% confidence interval {CI}, 2.9 to 3.5]) than in 2011 (452 [4.0%; 95% CI, 3.7 to 4.4]) (P<0.001), largely owing to reductions in the prevalence of surgical-site and urinary tract infections. Pneumonia, gastrointestinal infections (most of which were due to Clostridium difficile [now Clostridioides difficile]), and surgical-site infections were the most common health care-associated infections. Patients' risk of having a health care-associated infection was 16% lower in 2015 than in 2011 (risk ratio, 0.84; 95% CI, 0.74 to 0.95; P=0.005), after adjustment for age, presence of devices, days from admission to survey, and status of being in a large hospital. CONCLUSIONS: The prevalence of health care-associated infections was lower in 2015 than in 2011. To continue to make progress in the prevention of such infections, prevention strategies against C. difficile infection and pneumonia should be augmented. (Funded by the Centers for Disease Control and Prevention.).

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.

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.

This is the first case study published in a series in the American Journal of Infection Control (AJIC) since the Centers for Disease Control and Prevention/ National Healthcare Safety Network (NHSN) surveillance definition update of 2016. These cases represent some of the complex patient scenarios IPs have encountered in their daily surveillance of healthcare-associated infections (HAI) using NHSN procedural approach and definitions. Case study objectives have been previously published. (1) | With each case, a link to an online survey is provided, where you may enter answers to questions and receive immediate feedback in the form of correct answers and explanations. All individual participant answers will remain confidential, although it is the authors' intention to share a summary of the survey responses at a later date. Cases, answers, and explanations have been reviewed and approved by NHSN staff. We hope that you will take advantage of this offering, and we look forward to your active participation.

This is the first case study published in a series in the American Journal of Infection Control since the Centers for Disease Control and Prevention/National Healthcare Safety Network (NHSN) surveillance definition update of 2015. These cases reflect some of the complex patient scenarios infection control professionals (ICPs) have encountered in their daily surveillance of health care-associated infections (HAI) using NHSN definitions.

This is the second case study published in a series in the American Journal of Infection Control since the Centers for Disease Control and Prevention/National Healthcare Safety Network (NHSN) surveillance definition update of 2015. These cases reflect some of the complex patient scenarios infection control professionals (ICPs) have encountered in their daily surveillance of health care-associated infections (HAIs) using NHSN definitions