Selective and Cascade Reporting of Antimicrobial Susceptibility Testing Results and Its Impact on Antimicrobial Resistance Surveillance-National Healthcare Safety Network, April 2020 to March 2021.

Selective or cascade reporting (SR/CR) of antimicrobial susceptibility testing (AST) results is a strategy for antimicrobial stewardship. SR/CR is often achieved by suppressing AST results of secondary drugs in electronic laboratory reports. We assessed the extent of SR/CR and its impact on cumulative antibiograms (CAs) in a large cohort of U.S. hospitals submitting AST data to the CDC's National Healthcare Safety Network (NHSN) through electronic data exchange. The NHSN calls for hospitals to extract AST data from their electronic systems. We analyzed the AST reported for Escherichia coli (blood and urine) and Staphylococcus aureus (blood and lower respiratory tract [LRT]) isolates from April 2020 to March 2021, used AST reporting patterns to assign SR/CR reporting status for hospitals, and compared their CAs. Sensitivity analyses were done to account for those potentially extracted complete data. At least 35% and 41% of the hospitals had AST data that were suppressed in more than 20% blood isolates for E. coli and S. aureus isolates, respectively. At least 63% (blood) and 50% (urine) routinely reported ciprofloxacin or levofloxacin for E. coli isolates; and 60% (blood) and 59% (LRT) routinely reported vancomycin for S. aureus isolates. The distribution of CAs for many agents differed between high SR/CR and low- or non-SR/CR hospitals. Hospitals struggled to obtain complete AST data through electronic data exchange because of data suppression. Use of SR/CR can bias CAs if incomplete data are used. Technical solutions are needed for extracting complete AST results for public health surveillance. IMPORTANCE This study is the first to assess the extent of using selective and/or cascade antimicrobial susceptibility reporting for antimicrobial stewardship among U.S. hospitals and its impact on cumulative antibiograms in the context of electronic data exchange for national antimicrobial resistance surveillance.

National Healthcare Safety Network 2018 Baseline Neonatal Standardized Antimicrobial Administration Ratios.

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

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 COVID-19 pandemic on central-line-associated bloodstream infections during the early months of 2020, National Healthcare Safety Network.

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.

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.

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.

US Emergency Department Visits Attributed to Medication Harms, 2017-2019.

IMPORTANCE: Assessing the scope of acute medication harms to patients should include both therapeutic and nontherapeutic medication use. OBJECTIVE: To describe the characteristics of emergency department (ED) visits for acute harms from both therapeutic and nontherapeutic medication use in the US. DESIGN, SETTING, AND PARTICIPANTS: Active, nationally representative, public health surveillance based on patient visits to 60 EDs in the US participating in the National Electronic Injury Surveillance System-Cooperative Adverse Drug Event Surveillance Project from 2017 through 2019. EXPOSURES: Medications implicated in ED visits, with visits attributed to medication harms (adverse events) based on the clinicians' diagnoses and supporting data documented in the medical record. MAIN OUTCOMES AND MEASURES: Nationally weighted estimates of ED visits and subsequent hospitalizations for medication harms. RESULTS: Based on 96 925 cases (mean patient age, 49 years; 55% female), there were an estimated 6.1 (95% CI, 4.8-7.5) ED visits for medication harms per 1000 population annually and 38.6% (95% CI, 35.2%-41.9%) resulted in hospitalization. Population rates of ED visits for medication harms were higher for patients aged 65 years or older than for those younger than 65 years (12.1 vs 5.0 [95% CI, 7.4-16.8 vs 4.1-5.8] per 1000 population). Overall, an estimated 69.1% (95% CI, 63.6%-74.7%) of ED visits for medication harms involved therapeutic medication use, but among patients younger than 45 years, an estimated 52.5% (95% CI, 48.1%-56.8%) of visits for medication harms involved nontherapeutic use. The proportions of ED visits for medication harms involving therapeutic use were lowest for barbiturates (6.3%), benzodiazepines (11.1%), nonopioid analgesics (15.7%), and antihistamines (21.8%). By age group, the most frequent medication types and intents of use associated with ED visits for medication harms were therapeutic use of anticoagulants (4.5 [95% CI, 2.3-6.7] per 1000 population) and diabetes agents (1.8 [95% CI, 1.3-2.3] per 1000 population) for patients aged 65 years and older; therapeutic use of diabetes agents (0.8 [95% CI, 0.5-1.0] per 1000 population) for patients aged 45 to 64 years; nontherapeutic use of benzodiazepines (1.0 [95% CI, 0.7-1.3] per 1000 population) for patients aged 25 to 44 years; and unsupervised medication exposures (2.2 [95% CI, 1.8-2.7] per 1000 population) and therapeutic use of antibiotics (1.4 [95% CI, 1.0-1.8] per 1000 population) for children younger than 5 years. CONCLUSIONS AND RELEVANCE: According to data from 60 nationally representative US emergency departments, visits attributed to medication harms in 2017-2019 were frequent, with variation in products and intent of use by age.

Ecological Analysis of the Decline in Incidence Rates of COVID-19 Among Nursing Home Residents Associated with Vaccination, United States, December 2020-January 2021.

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

Building an Interactive Geospatial Visualization Application for National Health Care-Associated Infection Surveillance: Development Study.

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.

Rates of COVID-19 Among Residents and Staff Members in Nursing Homes - United States, May 25-November 22, 2020.

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

National Healthcare Safety Network Standardized Antimicrobial Administration Ratios (SAARs): A Progress Report and Risk Modeling Update Using 2017 Data.

BACKGROUND: The Standardized Antimicrobial Administration Ratio (SAAR) is a risk-adjusted metric of antimicrobial use (AU) developed by the Centers for Disease Control and Prevention (CDC) in 2015 as a tool for hospital antimicrobial stewardship programs (ASPs) to track and compare AU with a national benchmark. In 2018, CDC updated the SAAR by expanding the locations and antimicrobial categories for which SAARs can be calculated and by modeling adult and pediatric locations separately. METHODS: We identified eligible patient-care locations and defined SAAR antimicrobial categories. Predictive models were developed for eligible adult and pediatric patient-care locations using negative binomial regression applied to nationally aggregated AU data from locations reporting ≥9 months of 2017 data to the National Healthcare Safety Network (NHSN). RESULTS: 2017 Baseline SAAR models were developed for 7 adult and 8 pediatric SAAR antimicrobial categories using data reported from 2156 adult and 170 pediatric locations across 457 hospitals. The inclusion of step-down units and general hematology-oncology units in adult 2017 baseline SAAR models and the addition of SAARs for narrow-spectrum B-lactam agents, antifungals predominantly used for invasive candidiasis, antibacterial agents posing the highest risk for Clostridioides difficile infection, and azithromycin (pediatrics only) expand the role SAARs can play in ASP efforts. Final risk-adjusted models are used to calculate predicted antimicrobial days, the denominator of the SAAR, for 40 SAAR types displayed in NHSN. CONCLUSIONS: SAARs can be used as a metric to prompt investigation into potential overuse or underuse of antimicrobials and to evaluate the effectiveness of ASP interventions.

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.

Antimicrobial-resistant pathogens associated with adult healthcare-associated infections: Summary of data reported to the National Healthcare Safety Network, 2015-2017.

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.

Antimicrobial-resistant pathogens associated with pediatric healthcare-associated infections: Summary of data reported to the National Healthcare Safety Network, 2015-2017.

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.

A Collaborative Multicenter QI Initiative to Improve Antibiotic Stewardship in Newborns.

OBJECTIVES: To determine if NICU teams participating in a multicenter quality improvement (QI) collaborative achieve increased compliance with the Centers for Disease Control and Prevention (CDC) core elements for antibiotic stewardship and demonstrate reductions in antibiotic use (AU) among newborns. METHODS: From January 2016 to December 2017, multidisciplinary teams from 146 NICUs participated in Choosing Antibiotics Wisely, an Internet-based national QI collaborative conducted by the Vermont Oxford Network consisting of interactive Web sessions, a series of 4 point-prevalence audits, and expert coaching designed to help teams test and implement the CDC core elements of antibiotic stewardship. The audits assessed unit-level adherence to the CDC core elements and collected patient-level data about AU. The AU rate was defined as the percentage of infants in the NICU receiving 1 or more antibiotics on the day of the audit. RESULTS: The percentage of NICUs implementing the CDC core elements increased in each of the 7 domains (leadership: 15.4%-68.8%; accountability: 54.5%-95%; drug expertise: 61.5%-85.1%; actions: 21.7%-72.3%; tracking: 14.7%-78%; reporting: 6.3%-17.7%; education: 32.9%-87.2%; P < .005 for all measures). The median AU rate decreased from 16.7% to 12.1% (P for trend < .0013), a 34% relative risk reduction. CONCLUSIONS: NICU teams participating in this QI collaborative increased adherence to the CDC core elements of antibiotic stewardship and achieved significant reductions in AU.

Digital Platforms as a Method of Invention for Infection Surveillance.

Background: The history of large-scale technological advances, such as the digital revolution in our era, suggests that core technologies yield wide benefits by serving as a method of invention, spawning new tools and techniques that surpass the performance of their predecessors. Methods: Digital platforms provide a method of invention in the health sector by enabling innovations in data collection, use, and sharing. Although wide adoption of computerized information technology in healthcare has produced mixed results, the advent of mobile health (mHealth) creates new opportunities for device-mediated advances in surgical and public health practice. Conclusion: Mobile solutions for collecting, using, and sharing patient-generated health data after surgery can yield important benefits for post-operative monitoring, whether the data are used to evaluate and manage individual patients or track infections and other outcomes in patient populations.

Using NHSN's Antimicrobial Use Option to Monitor and Improve Antibiotic Stewardship in Neonates.

BACKGROUND: The Antimicrobial Use (AU) Option of the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN) is a surveillance resource that can provide actionable data for antibiotic stewardship programs. Such data are used to enable measurements of AU across hospitals and before, during, and after stewardship interventions. METHODS: We used monthly AU data and annual facility survey data submitted to the NHSN to describe hospitals and neonatal patient care locations reporting to the AU Option in 2017, examine frequencies of most commonly reported agents, and analyze variability in AU rates across hospitals and levels of care. We used results from these analyses in a collaborative project with Vermont Oxford Network to develop neonatal-specific Standardized Antimicrobial Administration Ratio (SAAR) agent categories and neonatal-specific NHSN Annual Hospital Survey questions. RESULTS: As of April 1, 2018, 351 US hospitals had submitted data to the AU Option from at least 1 neonatal unit. In 2017, ampicillin and gentamicin were the most frequently reported antimicrobial agents. On average, total rates of AU were highest in level III NICUs, followed by special care nurseries, level II-III NICUs, and well newborn nurseries. Seven antimicrobial categories for neonatal SAARs were created, and 6 annual hospital survey questions were developed. CONCLUSIONS: A small but growing percentage of US hospitals have submitted AU data from neonatal patient care locations to NHSN, enabling the use of AU data aggregated by NHSN as benchmarks for neonatal antimicrobial stewardship programs and further development of the SAAR summary measure for neonatal AU.

Association of State Laws With Influenza Vaccination of Hospital Personnel.

INTRODUCTION: Healthcare personnel influenza vaccination can reduce influenza illness and patient mortality. State laws are one tool promoting healthcare personnel influenza vaccination. METHODS: A 2016 legal assessment in 50 states and Washington DC identified (1) assessment laws: mandating hospitals assess healthcare personnel influenza vaccination status; (2) offer laws: mandating hospitals offer influenza vaccination to healthcare personnel; (3) ensure laws: mandating hospitals require healthcare personnel to demonstrate proof of influenza vaccination; and (4) surgical masking laws: mandating unvaccinated healthcare personnel to wear surgical masks during influenza season. Influenza vaccination was calculated using data reported in 2016 by short-stay acute care hospitals (n=4,370) to the National Healthcare Safety Network. Hierarchical linear modeling in 2018 examined associations between reported vaccination and assessment, offer, or ensure laws at the level of facilities nested within states, among employee and non-employee healthcare personnel and among employees only. RESULTS: Eighteen states had one or more healthcare personnel influenza vaccination-related laws. In the absence of any state laws, facility vaccination mandates were associated with an 11-12 percentage point increase in mean vaccination coverage (p<0.0001). Facility-level mandates were estimated to increase mean influenza vaccination coverage among all healthcare personnel by 4.2 percentage points in states with assessment laws, 6.6 percentage points in states with offer laws, and 3.1 percentage points in states with ensure laws. Results were similar in analyses restricted only to employees although percentage point increases were slightly larger. CONCLUSIONS: State laws moderate the effect of facility-level vaccination mandates and may help increase healthcare personnel influenza vaccination coverage in facilities with or without vaccination requirements.

Adherence of Newborn-Specific Antibiotic Stewardship Programs to CDC Recommendations.

: media-1vid110.1542/5839992664001PEDS-VA_2017-4322Video Abstract BACKGROUND: The Centers for Disease Control and Prevention (CDC) published the Core Elements of Hospital Antibiotic Stewardship Programs (ASPs), while the Choosing Wisely for Newborn Medicine Top 5 list identified antibiotic therapy as an area of overuse. We identify the baseline prevalence and makeup of newborn-specific ASPs and assess the variability of NICU antibiotic use rates (AURs). METHODS: Data were collected using a cross-sectional audit of Vermont Oxford Network members in February 2016. Unit measures were derived from the 7 domains of the CDC's Core Elements of Hospital ASPs, including leadership commitment, accountability, drug expertise, action, tracking, reporting, and education. Patient-level measures included patient demographics, indications, and reasons for therapy. An AUR, defined as the number of infants who are on antibiotic therapy divided by the census that day, was calculated for each unit. RESULTS: Overall, 143 centers completed structured self-assessments. No center addressed all 7 core elements. Of the 7, only accountability (55%) and drug expertise (62%) had compliance >50%. Centers audited 4127 infants for current antibiotic exposure. There were 725 infants who received antibiotics, for a hospital median AUR of 17% (interquartile range 10%-26%). Of the 412 patients on >48 hours of antibiotics, only 26% (107 out of 412) had positive culture results. CONCLUSIONS: Significant gaps exist between CDC recommendations to improve antibiotic use and antibiotic practices during the newborn period. There is wide variation in point prevalence AURs. Three-quarters of infants who received antibiotics for >48 hours did not have infections proven by using cultures.

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.