Early in the COVID-19 pandemic, when the novel coronavirus SARS-CoV-2 spilled into the United States and spawned devastating outbreaks in Albany, Georgia, and multiple other cities, news media organizations served an important public health function. Journalists gathered and reported information about a new infectious disease peril, and they used increasing tolls of cases, hospitalizations, and deaths as a shorthand form of risk communication. However, there were ample reasons from the start to question the completeness, accuracy, and fairness of the information that local news sources provided, and reporters repeated in numerous accounts of the Albany hotspot from March to July 2020. The story that went viral adhered to and supported a standard but strikingly deficient explanation of how novel infectious diseases wreak widespread havoc. The conventional outbreak narrative, exemplified by the Albany news coverage, frames causality, spread, and repercussions in ways that implicate personal behaviors while diminishing or disregarding population-level drivers of epidemics and the contribution of institutional lapses in healthcare safety. A second, closely related ramification of this responsibility framing is stigmatization of specific individuals or groups when they are singled out on the basis of an attribute, such as their race/ethnicity, religion, or sexual orientation, and identified as bearers and spreaders of a communicable disease. As the COVID-19 pandemic once again demonstrated, and the Albany story epitomizes, the conventional outbreak narrative sends strong stigma cues while leaving large gaps in the information needed to contend more equitably and effectively with emerging infectious diseases.

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.

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.

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.

Abstract 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.

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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.

AbstractData reported to the Centers for Disease Control and Prevention’s National Healthcare Safety Network (CDC NHSN) were analyzed to understand the potential impact of the COVID-19 pandemic on central-line–associated bloodstream infections (CLABSIs) in acute-care hospitals. Descriptive analysis of the standardized infection ratio (SIR) was conducted by location, location type, geographic area, and bed size.

BACKGROUND 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 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.

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. (c) 2021 The Authors. Published by Elsevier Inc. on behalf of AMDA -The Society for Post-Acute and Long-Term Care Medicine. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

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.

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.

Background: Water management programs (WMPs) are needed to minimize the growth and transmission of opportunistic pathogens in healthcare facility water systems. In 2017, the Centers for Medicare & Medicaid Service (CMS) began requiring that certified hospitals in the United States have water management policies and procedures; in response, the National Healthcare Safety Network (NHSN) Annual Hospital Survey included new, voluntary questions on practices regarding water management and monitoring. Of 4,929 hospitals surveyed in 2017, 3,821 (77.5%) reported having a WMP. Of these 3,821 facilities, 86.9% reported regular monitoring of water temperature; 66.2% monitored disinfectant (eg, residual chlorine); 63.1% used specific tests for Legionella; and 35.6% performed heterotrophic plate counts (HPCs). We analyzed new, 2018 hospital survey data to assess further progress toward meeting CMS requirements for WMPs. Methods: We analyzed 2018 NHSN Annual Hospital Survey responses for facilities that reported on WMPs in 2017. Responses included information regarding risk assessments for Legionella and other waterborne pathogens as well as details regarding WMP teams and water-monitoring practices. WMP team members were categorized as administrative (hospital administrator, compliance officer, risk or quality management), epidemiology or infection control (epidemiologist or infection preventionist, other clinical), or environmental or facilities (consultant, facility manager or engineer, equipment or chemical supplier, maintenance). Statistical significance was assessed using the McNemar test, where appropriate. Results: Of hospitals reporting on WMPs in 2017, 4,087 of 4,929 (83%) responded again in 2018. The proportion of facilities that reported having a WMP increased from 3,258 of 4,087 (79.7%) in 2017 to 3,647 of 4,087 (89.2%) in 2018 (P < .0001). Of the 3,647 hospitals that reported having a WMP in 2018, 95.9% had conducted a risk assessment for waterborne pathogens; 67.3% of these facilities had most recently done so within 1 year of the survey. WMP teams had representation from environmental or facilities staff at 98.8% of hospitals, epidemiology or infection control staff at 89.8% of hospitals, and administrative staff at 71.7% of hospitals. Of facilities with WMPs in 2018, 90.5% reported regular monitoring of water temperature, 72.2% disinfectant, 67.4% tests for Legionella, and 48.8% HPCs. Conclusions: More hospitals reported having a WMP in 2018 than 2017. However, ~1 in 10 respondents lacked a WMP. Differences in water monitoring practices across facilities potentially reflect a lack of standardization in how WMPs are implemented. Some hospital WMPs do not incorporate routine monitoring of water temperature and disinfectant, which is a basic practice. CDC continues to develop tools, resources, and training to support facility WMP teams in meeting CMS requirements and protecting patients from water-associated pathogens.Funding: NoneDisclosures: None

Background: To provide a standardized, risk-adjusted method for summarizing antimicrobial use (AU), the Centers for Disease Control and Prevention developed the standardized antimicrobial administration ratio, an observed-to-predicted use ratio in which predicted use is estimated from a statistical model accounting for patient locations and hospital characteristics. The infection burden, which could drive AU, was not available for assessment. To inform AU risk adjustment, we evaluated the relationship between the burden of drug-resistant gram-positive infections and the use of anti-MRSA agents. Methods: We analyzed data from acute-care hospitals that reported ≥10 months of hospital-wide AU and microbiologic data to the National Healthcare Safety Network (NHSN) from January 2018 through June 2019. Hospital infection burden was estimated using the prevalence of deduplicated positive cultures per 1,000 admissions. Eligible cultures included blood and lower respiratory specimens that yielded oxacillin/cefoxitin–resistant Staphylococcus aureus (SA) and ampicillin-nonsusceptible enterococci, and cerebrospinal fluid that yielded SA. The anti-MRSA use rate is the total antimicrobial days of ceftaroline, dalbavancin, daptomycin, linezolid, oritavancin, quinupristin/dalfopristin, tedizolid, telavancin, and intravenous vancomycin per 1,000 days patients were present. AU rates were modeled using negative binomial regression assessing its association with infection burden and hospital characteristics. Results: Among 182 hospitals, the median (interquartile range, IQR) of anti-MRSA use rate was 86.3 (59.9–105.0), and the median (IQR) prevalence of drug-resistant gram-positive infections was 3.4 (2.1–4.8). Higher prevalence of drug-resistant gram-positive infections was associated with higher use of anti-MRSA agents after adjusting for facility type and percentage of beds in intensive care units (Table 1). Number of hospital beds, average length of stay, and medical school affiliation were nonsignificant. Conclusions: Prevalence of drug-resistant gram-positive infections was independently associated with the use of anti-MRSA agents. Infection burden should be used for risk adjustment in predicting the use of anti-MRSA agents. To make this possible, we recommend that hospitals reporting to NHSN’s AU Option also report microbiologic culture results. Funding: None Disclosures: None

Background: The CDC NHSN launched the Antimicrobial Use Option in 2011. The Antimicrobial Use Option allows users to implement risk-adjusted antimicrobial use benchmarking within- and between- facilities using the standardized antimicrobial administration ratio (SAAR) and to evaluate use over time. The SAAR can be used for public health surveillance and to guide an organization’s stewardship or quality improvement efforts. Methods: Antimicrobial Use Option enrollment grew through partner engagement, targeted education, and development of data benchmarking. We analyze enrollment over time and discuss key drivers of participation. Results: Initial 2011 Antimicrobial Use Option enrollment efforts awarded grant Funding: to 4 health departments. These health departments partnered with hospitals, which encouraged vendors to build infrastructure for electronic antimicrobial use reporting. CDC supported vendors through outreach and education. In 2012, with CDC support, Veterans’ Affairs (VA) Informatics, Decision-Enhancement, and Analytic Sciences Center and partners began implementation of Antimicrobial Use Option reporting and validation of submitted data. These early efforts led to enrollment of 64 facilities by 2014 (Fig. 1). As awareness of the antimicrobial use option grew, we focused on facility engagement and development of benchmark metrics. A second round of grant Funding: in 2015 supported submission to the Antimicrobial Use Option from additional facilities by Funding: a vendor, a healthcare system, and an antimicrobial stewardship network. In 2015, CMS recognized the Antimicrobial Use Option as a choice for public health registry reporting under Meaningful Use Stage 3, resulting in an increase in participating hospitals. Antimicrobial Use Option enrollment increased in 2015 (n = 120), coinciding with national prioritization of antimicrobial stewardship. In 2016, the SAAR, was released in NHSN. We leveraged the SAAR to encourage participation from additional facilities and began quarterly calls to encourage continued participation from existing users. In 2016, the Department of Defense began submitting data to the Antimicrobial Use Option, resulting in 207 facilities enrolled in 2016, which grew to 616 in 2017. As of November 2019, 12 vendors self-report submission capabilities and 1,470 facilities, of ~6,800 active NHSN participants, are enrolled in the Antimicrobial Use Option. Two states have passed requirements regulating Antimicrobial Use Option reporting with Tennessee’s requirement going into effect in 2021. Conclusions: The Antimicrobial Use Option offers evidence that collaboration with partners, and leveraging of benchmarking metrics available to a national surveillance system can lead to increased voluntary participation in surveillance of high-priority public health data. Moving forward, we will continue expanding analytic capabilities and partner engagement.Funding: NoneDisclosures: None

Background: The CDC National Healthcare Safety Network (NHSN) is the nation’s most widely used healthcare-associated infection (HAI) and antibiotic use and resistance (AUR) surveillance system. More than 22,000 healthcare facilities report data to the NHSN. The NHSN data are used by facilities, the CDC, health departments, the CMS, among other organizations and agencies. In 2017, the CDC updated the NHSN Agreement to Participate and Consent (Agreement), completed by facilities, broadening health department access to NHSN data and extending eligibility for data use agreements (DUAs) to local and territorial health departments. DUAs enable access to NHSN data reported by facilities in the health department’s jurisdiction and have been available to state health departments since 2011. The updated agreement also enables the CDC to provide NHSN data to health departments for targeted prevention projects outbreak investigations and responses. Methods: We reviewed the current NHSN DUA inventory to assess the extent to which health departments use the NHSN’s new data access provisions and used semistructured interviews with health department staff, conducted via emails, phone, and in person conversations, to identify and describe their NHSN data uses. Results: As of late 2019, the NHSN has DUAs with health departments in 17 states, 7 local health departments (including municipalities and counties), and 1 US territory. The NHSN also has received requests from 2 state health departments for data supporting HAI prevention projects. Health departments with DUAs described improved relationships with facilities in their jurisdictions because of new opportunities to offer NHSN data analysis assistance to facilities. One local health department analyzed their NHSN carbapenem-resistant Enterobacteriaceae (CRE) data to identify (1) facilities in its jurisdiction with comparatively high CRE infection burden and (2) geographic areas to target for a CRE isolate submission program. Outreach to facilities with high CRE burden led to enrollment of 15 clinical laboratories into a voluntary isolate submission program to analyze CRE isolates for additional characterization. Examples of health departments’ use of data for action include: notifying facilities with high standardized infection ratios (SIRs) and sharing Targeted Assessment for Prevention (TAP) reports. Conclusions: The NHSN’s role as a shared surveillance resource has expanded in multiple public health jurisdictions as a result of new data access provisions. Health departments are using NHSN data in their programmatic responses to HAI and AR challenges. New access to NHSN data is enabling public health jurisdictions to assess problems and opportunities, provide guidance for prevention projects, and support program evaluations. Funding: None Disclosures: None

Background: The CDC NHSN surveillance coverage includes central-line–associated bloodstream infections (CLABSIs) in acute-care hospital intensive care units (ICUs) and select patient-care wards across all 50 states. This surveillance enables the use of CLABSI data to measure time between events (TBE) as a potential metric to complement traditional incidence measures such as the standardized infection ratio and prevention progress. Methods: The TBEs were calculated using 37,705 CLABSI events reported to the NHSN during 2015–2018 from medical, medical-surgical, and surgical ICUs as well as patient-care wards. The CLABSI TBE data were combined into 2 separate pairs of consecutive years of data for comparison, namely, 2015–2016 (period 1) and 2017–2018 (period 2). To reduce the length bias, CLABSI TBEs were truncated for period 2 at the maximum for period 1; thereby, 1,292 CLABSI events were excluded. The medians of the CLABSI TBE distributions were compared over the 2 periods for each patient care location. Quantile regression models stratified by location were used to account for factors independently associated with CLABSI TBE, such as hospital bed size and average length of stay, and were used to measure the adjusted shift in median CLABSI TBE. Results: The unadjusted median CLABSI TBE shifted significantly from period 1 to period 2 for the patient care locations studied. The shift ranged from 20 to 75.5 days, all with 95% CIs ranging from 10.2 to 32.8, respectively, and P < .0001 (Fig. 1). Accounting for independent associations of CLABSI TBE with hospital bed size and average length of stay, the adjusted shift in median CLABSI TBE remained significant for each patient care location that was reduced by ∼15% (Table 1). Conclusions: Differences in the unadjusted median CLABSI TBE between period 1 and period 2 for all patient care locations demonstrate the feasibility of using TBE for setting benchmarks and tracking prevention progress. Furthermore, after adjusting for hospital bed size and average length of stay, a significant shift in the median CLABSI TBE persisted among all patient care locations, indicating that differences in patient populations alone likely do not account for differences in TBE. These findings regarding CLABSI TBEs warrant further exploration of potential shifts at additional quantiles, which would provide additional evidence that TBE is a metric that can be used for setting benchmarks and can serve as a signal of CLABSI prevention progress.Funding: NoneDisclosures: None

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. Published by Elsevier Inc. on behalf of Association for Professionals in Infection Control and Epidemiology, Inc.

Background:Staphylococcus aureus is frequently implicated in healthcare-associated infections in the United States, and a substantial proportion of these infections are attributed to methicillin-resistant Staphylococcus aureus (MRSA). Although MRSA infections have decreased in health care settings, accurate estimates of the rate of decline call for risk-adjusted methods for calculating the resistant proportion (%R), that is, the proportion of S. aureus resistant to cefoxitin or oxacillin. Risk-adjusted %R also enables more accurate interhospital comparisons and can serve as a quantitative guide and evaluation metric for prevention efforts. Methods: To develop a risk-adjusted %R for S. aureus, we analyzed the antimicrobial susceptibility test (AST) results for S. aureus isolates reported to the CDC NHSN Antimicrobial Resistance Option during 2017–2018. Isolates were reported for cerebrospinal fluid (CSF), blood, lower respiratory tract (LRT), and urine. Isolates without cefoxitin and oxacillin test results, or from the facilities that had >10% missing test results were excluded. Test results were differentiated between those associated with community-onset and hospital-onset (HO) infections by defining the latter group as test results for isolates obtained 3 days or more after hospital admission. Logistic regression was used to evaluate the factors associated with oxacillin/cefoxitin resistance. Hospital, patient and isolate-level variables from NHSN annual survey and AR option were assessed as covariates. Variable entry into the models is based on significance level P < .05. Results: Among 9,992 hospital-onset SA isolates from 9,019 patients in 315 facilities, 5,488 (54.9%) were MRSA. Logistic regression showed that a higher proportion of HO-MRSA was significantly associated with older age, female, particular sources of specimen (urine and LRT), and selected hospital characteristics: hospitals not serving as major teaching hospitals, hospitals with a higher proportion of MRSA among community-onset SA isolates, hospitals with lower percentage of beds in intensive care units, and hospitals outsourcing AST service (Table 1). Conclusions: HO-MRSA is independently associated with community burden of MRSA, older and female patient populations, and hospital teaching status and AST practices, which highlights the importance of public health engagement and regional collaborations to prevent MRSA. To provide a standardized MRSA proportion for public health surveillance, taking some of these factors into account in MRSA proportion standardization should be considered.Funding: NoneDisclosures: None