Antimicrobial-Resistant Pathogens Associated with Healthcare-Associated Infections Summary of Data Reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2009–2010

To describe the frequency of selected antimicrobial resistance patterns among pathogens causing device-associated and procedure-associated healthcare-associated infections (HAIs) reported by hospitals in the National Healthcare Safety Network (NHSN). Data are included on HAIs (ie, central line-associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated pneumonia, and surgical site infections) reported to the Patient Safety Component of the NHSN between January 2006 and October 2007. The results of antimicrobial susceptibility testing of up to 3 pathogenic isolates per HAI by a hospital were evaluated to define antimicrobial-resistance in the pathogenic isolates. The pooled mean proportions of pathogenic isolates interpreted as resistant to selected antimicrobial agents were calculated by type of HAI and overall. The incidence rates of specific device-associated infections were calculated for selected antimicrobial-resistant pathogens according to type of patient care area; the variability in the reported rates is described. Overall, 463 hospitals reported 1 or more HAIs: 412 (89%) were general acute care hospitals, and 309 (67%) had 200-1,000 beds. There were 28,502 HAIs reported among 25,384 patients. The 10 most common pathogens (accounting for 84% of any HAIs) were coagulase-negative staphylococci (15%), Staphylococcus aureus (15%), Enterococcus species (12%), Candida species (11%), Escherichia coli (10%), Pseudomonas aeruginosa (8%), Klebsiella pneumoniae (6%), Enterobacter species (5%), Acinetobacter baumannii (3%), and Klebsiella oxytoca (2%). The pooled mean proportion of pathogenic isolates resistant to antimicrobial agents varied significantly across types of HAI for some pathogen-antimicrobial combinations. As many as 16% of all HAIs were associated with the following multidrug-resistant pathogens: methicillin-resistant S. aureus (8% of HAIs), vancomycin-resistant Enterococcus faecium (4%), carbapenem-resistant P. aeruginosa (2%), extended-spectrum cephalosporin-resistant K. pneumoniae (1%), extended-spectrum cephalosporin-resistant E. coli (0.5%), and carbapenem-resistant A. baumannii, K. pneumoniae, K. oxytoca, and E. coli (0.5%). Nationwide, the majority of units reported no HAIs due to these antimicrobial-resistant pathogens.

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). 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 <18years old. Percentages of pathogens with nonsusceptibility (%NS) to selected antimicrobials were calculated by HAI type, location type, and surgical category. 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. 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.

Supplement: STRIVE1 October 2019The Centers for Disease Control and Prevention STRIVE Initiative: Construction of a National Program to Reduce Health Care–Associated Infections at the Local LevelFREEKyle J. Popovich, MD, MS, David P. Calfee, MD, Payal K. Patel, MD, MPH, Shelby Lassiter, BSN, RN, CPHQ, Andrew J. Rolle, MPH, Louella Hung, MPH, Sanjay Saint, MD, MPH, and Vineet Chopra, MD, MScKyle J. Popovich, MD, MSRush University Medical Center, Chicago, Illinois (K.J.P.), David P. Calfee, MDWeill Cornell Medicine, New York, New York (D.P.C.), Payal K. Patel, MD, MPHUniversity of Michigan Medical School and Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan (P.K.P., S.S., V.C.), Shelby Lassiter, BSN, RN, CPHQHealth Research & Educational Trust, American Hospital Association, Chicago, Illinois (S.L., A.J.R., L.H.), Andrew J. Rolle, MPHHealth Research & Educational Trust, American Hospital Association, Chicago, Illinois (S.L., A.J.R., L.H.), Louella Hung, MPHHealth Research & Educational Trust, American Hospital Association, Chicago, Illinois (S.L., A.J.R., L.H.), Sanjay Saint, MD, MPHUniversity of Michigan Medical School and Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan (P.K.P., S.S., V.C.), and Vineet Chopra, MD, MScUniversity of Michigan Medical School and Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan (P.K.P., S.S., V.C.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/M18-3529 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Health care–associated infection (HAI) remains an important problem in the United States (1, 2). Central line–associated bloodstream infection (CLABSI) and catheter-associated urinary tract infection (CAUTI) are among the most common device-associated infections, whereas Clostridioides difficile and methicillin-resistant Staphylococcus aureus (MRSA) are among the most prevalent pathogens causing HAI. In 2011, there were an estimated 721 800 HAIs in U.S. acute care hospitals, with C difficile, S aureus, Enterococcus species, and gram-negative bacilli being the most common pathogens (3). To address the burden of these infections, evidence-based infection prevention strategies, including "bundles" or combinations of interventions, have been developed and successfully implemented in many hospitals to prevent HAIs (4–8). For example, bundles have been created to decrease CLABSI (4), CAUTI (5, 9), and MRSA bloodstream infection (6, 7). In U.S. intensive care units, there has been a substantial reduction in CLABSIs, thought to be in large part due to implementation of bundles (4, 10).Many U.S. hospitals, unfortunately, continue to experience high rates of HAI (11) because of low compliance with infection prevention practices, poor organizational culture, financial limitations, limited engagement from front-line personnel, and limited leadership support (12). Of note, assistance from external sources, such as local, state, and national groups (including public health departments, quality improvement organizations, hospital associations, and academic medical centers), can help reduce HAI (13, 14). However, the ways and extent to which these entities engage with hospitals to improve HAI rates vary, resulting in heterogeneity of outcomes (12). Comprehensive solutions to this complex dynamic within and across hospitals, states, and the country have not been developed. In particular, strategies to help hospitals that continue to have high rates of HAI are needed.To reduce infections in hospitals with high rates of HAI, the Centers for Disease Control and Prevention (CDC) funded a prospective, interventional, nonrandomized, quality improvement program that spanned multiple hospitals and states. Development, implementation, and execution of the program was performed by the Health Research & Educational Trust (HRET), a not-for-profit research and education affiliate of the American Hospital Association, along with several partners, such as state hospital associations (SHAs), professional societies, and scientific experts from academic medical centers. Collectively, the program was titled CDC STRIVE (States Targeting Reduction in Infections via Engagement). This article provides a summary of how STRIVE constructed the building blocks for a national effort intended to reduce HAIs in participating hospitals.Program Goals and StructureThe STRIVE initiative focused on bringing national health care professional societies, subject-matter experts, and state-level health care organizations together with short-stay and long-term acute care hospitals to improve infection prevention and control practices. The overall objective of the program was to identify, partner with, and collaborate with hospitals struggling to reduce HAI by pairing national subject-matter experts with state, regional, and local organizations to effect sustainable change (Figure 1).Figure 1. Overall flow of the CDC STRIVE program.CDC = Centers for Disease Control and Prevention; STRIVE = States Targeting Reduction in Infections via Engagement. Download figure Download PowerPoint To deliver on this ambitious goal, the STRIVE initiative had 3 specific aims: 1) strengthen infection control practices through dissemination and implementation of CDC's Targeted Assessment for Prevention (TAP) strategy; 2) strengthen relationships among SHAs, state health departments, and other state HAI partners, such as the Centers for Medicare & Medicaid Services Quality Innovation Network–Quality Improvement Organizations, to create a structure to facilitate durable implementation of best infection control practices; and 3) provide technical assistance to facilities to improve implementation of infection control practices in existing and newly constructed health care facilities. Reductions in C difficile infection (CDI), CLABSI, CAUTI, and hospital-onset MRSA bloodstream infection in participating hospitals were chosen as measures to determine initiative success.Program planning for STRIVE began in September 2015. Subject-matter experts from multiple organizations were identified by CDC and HRET and brought together to form a national program team to provide oversight for the program and build educational content. Members of the national program team included representatives from CDC, HRET, Association for Professionals in Infection Control and Epidemiology, American Society for Health Care Engineering, Society of Hospital Medicine, and University of Michigan Health System.Stakeholder Considerations in Designing STRIVE InterventionsThe CDC outlined several objectives to increase alignment and coordination of HAI prevention efforts across stakeholders: First, identify strategies to improve infection control implementation activities on a state- and facility-level; second, identify indicators of capacity (infrastructure, staffing, partnerships, and training), ongoing regional collaboratives, and other contextual factors (such as state-level mandates) that may affect implementation of infection prevention efforts; and third, identify roles of state partners (state health departments, SHAs, Quality Innovation Network–Quality Improvement Organizations) in the coordination, integration, and alignment of infection prevention and control activities.Eligibility and Selection of Participating HospitalsThe CDC STRIVE initiative focused specifically on hospitals with a disproportionately high burden of HAI. To target these facilities, the CDC used National Healthcare Safety Network (NHSN) data from the first 2 quarters of 2015 to identify states with hospitals that had a high burden of CDI and a high burden of at least 1 of the following HAIs: CLABSI, CAUTI, or hospital-onset MRSA bloodstream infection. "High burden" was defined by examining the cumulative attributable difference (15) (using the U.S. Department of Health and Human Services' 2020 HAI goals as the standardized infection ratio target). Hospitals with a cumulative attributable difference above the first tertile (that is, the top one third) were designated as having a high burden of HAIs. Data for all 4 infection types were combined to identify hospitals with CDIs plus at least 1 other HAI with cumulative attributable differences above the first tertile.Three methods were used to identify eligible states. First, CDC identified states with the largest number of hospitals that met inclusion criteria. These states thus became the main focus of STRIVE efforts. Second, to include sites that may also benefit from STRIVE, HRET applied the CDC approach with publicly available Hospital Compare state-specific data to identify additional hospitals with a high burden of HAIs not included in the cumulative attributable difference first tertile. Finally, a few interested states not included in the above were allowed to volunteer to participate in STRIVE. Using these methods, 34 states and the District of Columbia were identified for possible inclusion in STRIVE.Rather than approach hospitals directly (and in keeping with the STRIVE goal to strengthen state and local partnerships to combat HAI), HRET shared the list of potentially eligible hospitals with SHAs and asked them to recruit sites. In this way, the CDC and HRET engaged SHAs to reach out to hospitals to inform them about the program, solicit their interest, and recruit them to participate. As word of the intervention and program spread, a few states that were not identified by the CDC also requested to participate in the STRIVE program, because they viewed this program as important to help improve hospital infection control practices.To better consolidate efforts and understand the impact of interventions, recruitment within STRIVE occurred within waves, leading to 4 cohorts of hospitals (Table): cohort 1 (June 2016 to April 2017), cohort 2 (November 2016 to October 2017), cohort 3 (April 2017 to March 2018), and cohort 4 (June 2017 to May 2018). Cohort 1 was identified as a pilot cohort in which interventions to reduce HAI were developed and pilot-tested in conjunction with key stakeholders. In total, 443 short-stay and long-term acute care hospitals from 28 states and the District of Columbia participated in 4 overlapping, 10- to 12-month cohorts (Appendix Figure). In 2015 (before the intervention), the median cumulative attributable difference values for cohorts 2, 3, and 4 were as follows: CAUTI, 0.67 (interquartile range [IQR], –0.62 to 4.22); CLABSI, 1.46 (IQR, –0.02 to 5.44); CDI, 5.04 (IQR, 0.16 to 17.48); and MRSA, 0.45 (IQR, –0.15 to 2.67).Table. Characteristics of Hospitals Participating in the STRIVE ProgramAppendix Figure. States that enrolled with the STRIVE program.In total, 443 hospitals from 28 states and the District of Columbia participated. Recruitment occurred as follows: cohort 1 (June 2016 to April 2017), cohort 2 (November 2016 to October 2017), cohort 3 (April 2017 to March 2018), and cohort 4 (June 2017 to May 2018). Hashing indicates states that participated in more than 1 cohort. STRIVE = States Targeting Reduction in Infections via Engagement. Download figure Download PowerPoint Informing Change—Designing InterventionsPractice Change AssessmentDuring STRIVE, participating hospitals were asked to complete a survey instrument to identify and address gaps in HAI prevention at the beginning of cohort enrollment (baseline) and at the end of the study wave (comparison) (Figure 2). This gap assessment could be done using either the CDC's Infection Control Assessment and Response (ICAR) survey (16) or the STRIVE Practice Change Assessment (PCA). The ICAR had been previously developed for state health departments to assess infection prevention practices in hospitals. The PCA, based on the ICAR, was modified to focus on 8 domains germane to the STRIVE program. Four of the domains focused on specific HAIs—CDI, CLABSI, CAUTI, and hospital-onset MRSA bloodstream infection—whereas the remaining 4 domains focused on hand hygiene, personal protective equipment, environmental cleaning, and antimicrobial stewardship.Figure 2. Education and engagement interventions implemented for participating hospitals.CDC = Centers for Disease Control and Prevention. Download figure Download PowerPoint Baseline surveys were administered by each participating hospital with support and (at times) a site visit by the state partners. If a hospital had completed an ICAR in the year before STRIVE, they were able to reuse that survey for their baseline assessment. A summary report from these assessments was provided to each site, highlighting opportunities for improvement and a list of STRIVE content and resources to assist in addressing these gaps.Education: Foundational and HAI-Specific Web-Based ModulesSubject-matter experts created educational materials for 12 different topics. Development of educational materials by experts occurred via in-person meetings and work group conference calls. Two primary topic domains were identified around which program education would be focused: foundational and HAI-specific elements.The foundational domain emphasized core infection control practices that are known to have variable compliance but are critical for success of any HAI prevention initiative (for example, hand hygiene, personal protective equipment use, and environmental cleaning). Many are considered "horizontal" infection control strategies in that they affect not one but many pathogens and HAIs. Eight elements for the foundational domain were identified: 1) competency-based training, auditing, and feedback; 2) hand hygiene; 3) personal protective equipment; 4) environmental cleaning; 5) antimicrobial stewardship; 6) making an effective infection prevention business case; 7) patient and family engagement; and 8) socioadaptive strategies for preventing infection.The HAI-specific domains were concentrated on best practices for preventing CDI, CLABSI, CAUTI, and hospital-onset MRSA bloodstream infection. In total, subject-matter experts created 51 short (10 to 20 minutes), Web-based, on-demand educational modules covering key topics in the 2 domains (Appendix Table).Appendix Table. Overview of the 51 Web-Based Learning Modules Developed for the STRIVE ProgramA 2-tiered intervention approach was developed for the HAIs targeted in STRIVE. Tier 1 interventions were defined as basic, evidence-based interventions that every hospital should have in place (for example, ensuring that central lines are placed aseptically). Foundational elements remained a critical aspect across tier 1 for the HAI-specific modules as these elements generally have demonstrated success, are economically efficient, and have multiplicative effects across HAIs. Foundational elements are also crucial to have in place before more complex technical and social interventions are introduced. Tier 2 interventions were generally considered more complex, "advanced" steps for hospitals to take once tier 1 interventions were reliably in place but not leading to a decline in a particular HAI. In general, tier 2 interventions were considered to require increased human and economic capital compared with tier 1.Engaging Sites: Learning Action ForumsIn conjunction with the Web-based modules, monthly learning action forums were hosted by HRET for all cohorts. These monthly, 1-hour webinars were discussion-based and interactive and were built on supporting the didactic content from the curriculum's on-demand courses. They provided hospitals with an opportunity to share their infection prevention strategies, challenges, and successes, thereby strengthening engagement and learning across member sites. The learning action forums also allowed national subject-matter experts to interact with hospitals and answer questions related to webinar content or materials. The lead for most learning action forums was often an infection preventionist or someone with a role in quality at the local hospital. The lead would distribute the webinar information to staff, which typically included nurse managers, environmental services, frontline clinicians, and other clinical and nonclinical staff, depending on the topic of the learning action forum.Education: TAP StrategyThe TAP strategy (15) developed by the CDC can be used not only to identify facilities and units with a high burden of HAIs, but also to highlight gaps in infection prevention. In this way, finite infection prevention resources can be directed to areas of greatest opportunity. The TAP strategy incorporates the TAP reports generated in the CDC's NHSN, along with standardized assessment tools and implementation strategies for CLABSI, CAUTI, and CDI.Feedback from the cohort 1 pilot revealed that additional, more intense education and training on how best to use TAP reports was needed. Although most hospital infection preventionists had heard of the TAP strategy, most lacked in-depth knowledge, and few organizations were actively using TAP resources. Therefore, many state-level in-person meetings incorporated TAP training, provided by their state health departments, to drive increased understanding of this strategy. In addition, from June 2017 to January 2018, the CDC collaborated with HRET to develop and deliver four 90-minute webinars on how to run and interpret TAP reports and use TAP strategies and resources to maximize HAI prevention. To further support state partner knowledge of this valuable resource, the CDC provided a webinar in December 2017 for state partners, providing additional education around how to use TAP reports and strategies at the state level to promote HAI prevention work.Strengthening Partnerships Through Coaching and CollaborationState health departments and SHAs collaborated to support hospitals in administering the PCA or ICAR, interpreting results, and finding resources to address identified gaps. In addition, state health departments were instrumental in educating hospitals on running and using TAP reports, utilizing STRIVE venues, such as in-person meetings and site visits in each state, along with the SHA. In addition, the SHA program lead (and often their health department partners) supported hospitals via monthly one-on-one calls, webinars, or office hours open to all STRIVE hospitals. These touch points were used for shared learning and coaching from the state mentors and experts around barriers and action planning to reach goals. Upon request, subject-matter experts from the national program team would also join such calls to add expertise. The state partners often acted in the role of encourager and cheerleader for teams to support momentum as well.State In-Person MeetingsOn the basis of feedback from cohort 1 pilot sites, state-level in-person meetings were implemented for all participating states in cohorts 2 to 4. Although the online and virtual materials were felt to be helpful, sites in cohort 1 felt that bringing hospitals and state partners together in person was necessary to support building relationships. Such meetings also provided protected time and space for hospital participants' learning and networking with peers as well as state and national experts.ImplementationIn contrast to single-unit interventions often found in infection control projects, the focus of this program was large-system transformation (17) to influence multiple hospitals, organizations, and health care providers. The national program team developed a full STRIVE implementation plan focused on leveraging content for both foundational and HAI-specific practices. The curriculum was divided into 3 phases: onboarding to the STRIVE program, foundational infection prevention strategies, and education targeted to the program's 4 HAIs.In May 2016, onboarding started for cohort 1, which included a general program overview, team formation, and education regarding ICAR/PCA assessments and TAP strategy. The rollout for Web-based modules then occurred for cohort 1 as follows: July to October 2016 (foundational elements modules), November 2016 to January 2017 (HAI-specific tier 1 modules), and February 2017 to March 2017 (HAI-specific tier 2 modules). These modules were available to all subsequent cohorts throughout their 12-month collaborative after their onboarding. Web modules for STRIVE can be found at www.cdc.gov/infectioncontrol/training/strive.html.ConclusionThe STRIVE initiative, coordinated by the HRET and funded by the CDC, brought together state-level organizations with short-stay and long-term acute care hospitals across the country to improve infection prevention and control practices for hospitals with a disproportionately high burden of HAIs. Federal funds for this initiative were in part in response to the lessons learned with Ebola and how stakeholders were interested in strengthening state partnerships and infection control measures in preparation for any future emerging infectious disease. Through the STRIVE initiative, the architecture of preventing HAI shifted from hospital-based to instead utilizing national efforts to effect local improvement efforts in hospitals across the United States.

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). 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. 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. This report provides an updated national summary of pathogen distributions and antimicrobial resistance among select HAIs and pathogens, stratified by several factors. These data underscore the importance of tracking antimicrobial resistance, particularly in vulnerable populations such as long-term acute-care hospitals and intensive care units.

Background Quality healthcare is dependent on continuous input and feedback. The California Department of Public Health (CDPH) requires that all general acute care hospital (GACH) report healthcare-associated infections (HAI) annually. Comparisons of current facility-specific standardized infection ratios (SIRs) to the National Healthcare Safety Network (NHSN) 2015 SIR baseline provide designations of whether a facility is statistically significantly better, worse, or average are displayed for public reporting purposes. Since then, the annual NHSN and CDPH SIR baselines have dropped below 1 for many HAIs; however, neither NHSN nor CDPH provide facility-specific comparisons to the most recent annual NHSN and CDPH SIRs. Facilities that are performing better or average using the 2015 baseline may be performing average or worse when compared the the most recent baselines (Figure A.). Methods The County of San Diego (COSD) HAI program began a collaborative effort with local GACHs in 2020 (Figure B). A work group comprised of Infection Preventionists and Epidemiologists convened to provide input into the format and content. Unique features include: 1) a display of an 18 month select HAI aggregate SIRS for the GACHs; 2) statistical analysis using a NHSN SAS 9.4 Macro to provide facility-specific comparative SIRs to the most recent annual NHSN and CDPH baselines and an 18-month aggregate COSD SIR baselines. Facilities were de-identified; the data were distributed only to Infection Prevention Departments for quality improvement and not used for regulatory purposes,; and met the intent of the NHSN Data Use Agreement. Results The first report (8/2023) included comparative SIRs on13 surgeries and central-line associated blood stream and catheter-associated urinary tract infections. The second report (4/2024) adds comparative standardized utilization ratios for central lines and urinary catheters (Figures C, D). Conclusion Through this effort, the COSD HAI program has strengthened communication between our local health department and the GACHs and also between GACHs. Future reports will be refined based on the input from the GACHs and could include comparative surveillance data on other HAIs being reported to NHSN and CDPH. Sharing best practices could result in continued decreases in HAIs. Disclosures All Authors: No reported disclosures

Introduction: Because the risk of health-care associated infections (HAIs) is high in intensive care units, and HAIs are one of the causes of morbidity and mortality and affects the overall quality of health care, the continuous monitoring of HAIs in intensive care patients is essential.Aim and objectives: This descriptive cross-sectional study was carried out over a period of five years in a tertiary-care teaching hospital. The aim of the study was to investigate the main and specific types of health-care associated Infections and determine the microbiological profile and antimicrobial susceptibility rates of isolates in patients with HAI.Methods: The active surveillance method was used to detect HAIs in patients who spent over 48 hr in a targeted ICU. Patients with blood stream infections (BSI), central line-associated bloodstream infection (CLABSI), catheter-associated urinary tract infections (CAUTI) and ventilator-associated events (VAE) were included in the study. HAI were diagnosed based on the Centre for Disease Control (CDC)’s National Healthcare Safety Network (NHSN) updated definitions of HAIs.Results: A total of 121,051 patient days, including 7,989 central line days, 64,557 urinary catheter days, and 18,443 ventilator days, were recorded in the study population and 832 HAIs were diagnosed (incidence rate 6.9%). The overall rates of BSI, CLABSI, CAUTI and possible ventilator-associated pneumonia (p-VAP) were 3.7, 10.6, 2.1 and 13.4/1,000 device days, respectively. The most common organism isolated from BSI was Acinetobacter baumanii (n=322, 29%), followed by Klebsiella pneumoniae 225 (n=225, 20.3%). 79.8% of Acinetobacter baumanii strains were resistant to imipenem, 77.1% to ciprofloxacin and 76.4% to ampicillin. The most common organisms isolated from CAUTI were non-albicans Candida species (n=38, 18%), followed by E. coli and Citrobacter spp. (each n=33, each 15.7%). Conclusions: A trend of increasing resistance of Acinetobacter baumannii to carbapenems was observed. Risk factor analysis showed invasive procedures during sepsis and organophosphorous poisoning as significant factors.

Purpose: In the present study, an attempt is made to understand the pattern of HAIs (Healthcare Associated Infections) [device associated infections such as Catheter Associated Urinary Tract Infection (CAUTI), Ventilator Associated Event (VAE), Central Line-Associated Bloodstream Infection (CLABSI) &amp; Surgical Site Infection (SSI) by analyzing statistical tool of quality indicators] and to establish a bench mark for HAIs in a single hospital for a period of 5 years. Methods: The Microbiologist &amp; ICN’s conduct rounds in ICU’s &amp; wards and collect data for active surveillance. The details of culture positive samples are collected by Microbiologist from the laboratory for passive surveillance. The surveillance forms (active &amp; passive) capture details of individual patients. The data collection forms are prepared and updated as per Centers for Disease Control and Prevention (CDC), National Healthcare Safety Network (NHSN) guidelines. The data is analyzed and presented in the meeting of Hospital Infection Control Committee meeting &amp; discussed with clinicians. Results: The cumulative (5 years) CAUTI rate is 0.45, VAE is 2.42, CLABSI is 1.35 &amp; SSI is 0.21. HAI rates were highest for VAE (2.42/1000 ventilator days), the next was CLABSI (1.35/1000 central line days), followed by CAUTI (0.45/1000 urinary catheter days). SSI rate was 0.21/ 100 surgeries. Conclusions: Before the study was started, the benchmark were 2 for CAUTI, 5.5 for VAE, 3 for CLABSI and 2 for SSI. We could able to reduce the baseline benchmark and established our new benchmark as 1 for CAUTI, 3 for VAE, 2 for CLABSI and 1 for SSI that can be used in developing HAI prevention policies by the institution.

Health care-associated infections (HAIs) are costly, and existing national cost estimates are out-of-date. We retrospectively analyzed the Agency for Healthcare Cost and Utilization Project's 2016 National Inpatient Sample, the largest all-payer U.S. inpatient database. We included all inpatient encounters with primary or secondary International Classification of Disease, 10th Revision Clinical Modification diagnosis codes corresponding to infection with catheter-associated urinary tract infections (T85.511), catheter- and line-associated blood stream infections (T80.211), surgical site infections (SSIs; T81.49), ventilator-associated pneumonias (J95.851), and Infection with Clostridioides difficile (CDI; A04.7). We combined HAI incidence data from the National Inpatient Sample with additional hospital inpatient HAI cost estimates to create national cost estimates for HAI individually and collectively. In 2016, 7.2 to 14.9 billion U.S. dollars were spent on HAIs in the United States. For admissions with any diagnosis of HAI, the frequencies of HAI in descending order were as follows: CDI (n = 356,754 [56%]), SSI (n = 196,215 [31%]), catheter- and line-associated blood stream infection (n = 42,811 [7%]), catheter-associated urinary tract infection (n = 23,546 [4%]), and ventilator-associated pneumonia (n = 16,767 [3%]). Collectively, CDI and SSI accounted for 79% of the cost of HAI in the United States. Health care-associated infections remain a significant economic burden for health care systems in the United States.

1. Julie Reagan, PhD, JD, MPH 1. Georgia Southern University, Jiann-Ping Hsu College of Public Health, Statesboro, GA 2. Rodney E. Rohde, PhD, MS, SV, SM (ASCP)CM, MBCM[⇑][1] 1. Clinical Laboratory Science Program, College of Health Professions, Texas State University, San Marcos, TX 3. Amber Hogan Mitchell, DrPH, MPH, CPH 1. The International Safety Center, Apopka, FL 4. Marilyn Felkner, DrPH, MT(ASCP) 1. Emerging and Acute Infectious Disease Branch (EAIDB), Infectious Disease Control Unit, Texas Department of State Health Services (DSHS), Austin, TX 5. Pat Tille, PhD, MT(ASCP) 1. Medical Laboratory Science, College of Pharmacy, South Dakota State University, SD <!-- --> 1. Address for Correspondence: Rodney E. Rohde, PhD, MS, SV, SM (ASCP)CM, MBCM, Professor & Chair, CLS Program; Associate Dean for Research, Clinical Laboratory Science Program, College of Health Professions, Texas State University, 601 University Drive, San Marcos, TX 78666, 512-245-2562, 512-245-7860, rrohde{at}txstate.edu 1. Discuss the role of federal influences on state-level HAI program initiatives and reporting activities. 2. Describe the progression of state-level initiatives to reduce HAIs from 2004 to the current date. 3. Describe core provisions of state HAI reporting laws: surveillance, collection system, healthcare settings subject to the laws, types of infections reported, public reporting requirements, and advisory committee structure. 4. Identify healthcare worker infection and illness reporting mandates. INTRODUCTION Since early 2000, there has been a “growing interest in the use of law as a tool to address” healthcare-associated infections (HAIs) in the U.S.1 All 50 states and two territories have HAI programs established within their public health agencies.2 Likewise, the majority of states have HAI public reporting laws.3-4 HAI data is being reported from hospitals in all 50 states, either voluntarily or under state or federal legal reporting mandates.5 Additionally, while the current national focus is on reporting HAIs in the patient population, requirements for reporting infections and illness in the healthcare worker population also exists. The purpose of this article is to provide a review of the federal and state-level legal environment applicable to HAI prevention in the context of the overall response to HAIs. Federal Influences To fully grasp state-level HAI program initiatives and reporting activities, it is important first to understand the many federal influences. Through the U.S. Department of Health and Human Services (HHS) and the Centers for Disease Control and Prevention (CDC), the federal government plays a central role in the control and prevention of HAIs. From 2004 to 2008, states began to recognize the need for state policies aimed at HAI prevention.4 Several states made significant strides toward HAI surveillance and prevention as the awareness of the public health impact of HAIs grew. However, due to budget constraints and the associated poor economic conditions starting in 2008, most states were unable to implement or further develop their HAI programs. Significant improvements… ABBREVIATIONS: ACA – The Patient Protection and Affordable Care Act, CDC – Centers for Disease Control and Prevention, CMS – Centers for Medicare and Medicaid Services, CLABSI – central line-associated bloodstream infection, CAUTI – catheter-associated urinary tract infection, CRE – Carbepenem-resistant Enterobacteriaceae, HAIs – healthcare-associated infections, HHS – U.S. Department of Health and Human Services, IQR – Inpatient Quality Reporting Program, MRSA -- Methicillin Resistant Staphylococcus aureus , NHSN – National Healthcare Safety Network, OSHA – Occupational Safety and Health Administration, VAP – ventilator-associated pneumonia 1. Discuss the role of federal influences on state-level HAI program initiatives and reporting activities. 2. Describe the progression of state-level initiatives to reduce HAIs from 2004 to the current date. 3. Describe core provisions of state HAI reporting laws: surveillance, collection system, healthcare settings subject to the laws, types of infections reported, public reporting requirements, and advisory committee structure. 4. Identify healthcare worker infection and illness reporting mandates. [1]: #corresp-1

Health care-associated infections in the USA

OBJECTIVE To describe antimicrobial resistance patterns for healthcare-associated infections (HAIs) that occurred in 2011-2014 and were reported to the Centers for Disease Control and Prevention's National Healthcare Safety Network. METHODS Data from central line-associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated pneumonias, and surgical site infections were analyzed. These HAIs were reported from acute care hospitals, long-term acute care hospitals, and inpatient rehabilitation facilities. Pooled mean proportions of pathogens that tested resistant (or nonsusceptible) to selected antimicrobials were calculated by year and HAI type. RESULTS Overall, 4,515 hospitals reported that at least 1 HAI occurred in 2011-2014. There were 408,151 pathogens from 365,490 HAIs reported to the National Healthcare Safety Network, most of which were reported from acute care hospitals with greater than 200 beds. Fifteen pathogen groups accounted for 87% of reported pathogens; the most common included Escherichia coli (15%), Staphylococcus aureus (12%), Klebsiella species (8%), and coagulase-negative staphylococci (8%). In general, the proportion of isolates with common resistance phenotypes was higher among device-associated HAIs compared with surgical site infections. Although the percent resistance for most phenotypes was similar to earlier reports, an increase in the magnitude of the resistance percentages among E. coli pathogens was noted, especially related to fluoroquinolone resistance. CONCLUSION This report represents a national summary of antimicrobial resistance among select HAIs and phenotypes. The distribution of frequent pathogens and some resistance patterns appear to have changed from 2009-2010, highlighting the need for continual, careful monitoring of these data across the spectrum of HAI types. Infect Control Hosp Epidemiol 2016;1-14.

Background: Global prevalence of healthcare associated infections (HAIs) ranges anywhere between 7% and 12% as per WHO estimates. This study was undertaken to understand the pattern and types of HAI at a selected healthcare facility and to determine the common causative agents and their antibiotic susceptibility profile. Methods: One hundred consecutive patients diagnosed with HAI were enrolled and monitored; the causative organisms isolated on culture were recorded and their sensitivity profiles were generated.Results: There were a total of 110 HAIs with 10 patients having two infections each. 69 patients had ventilator associated pneumonia (VAP), 21 patients had catheter associated urinary tract infection (CAUTI) patients, 20 patients had central line associated bloodstream infection (CLABSI), and 10 patients had both VAP and CAUTI. All of the HAIs were device associated. 76 pathogens were isolated on culture. No organism was isolated in 40 HAI. Majority (94.7%) of the organisms were gram-negative and all were multidrug resistant. Seventy-seven of the enrolled patients expired while 23 patients were discharged from the hospital.Conclusions: This study demonstrated that HAIs occur in patients of all age groups; younger patients were not spared. Majority of the HAIs were caused by multidrug resistant gram-negative bacteria and were associated with high mortality. Acinetobacter species was the most common organism associated with HAI.

Effect of chlorhexidine bathing in preventing infections and reducing skin burden and environmental contamination: A review of the literature.

Background: State Health Departments (SHDs) have systematically studied the validity of healthcare-associated infection (HAI) surveillance data submitted by healthcare facilities in their jurisdictions to the Centers for Disease Control and Prevention’s (CDC’s) National Healthcare Safety Network (NHSN) for central-line–associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), surgical site infections following colon and abdominal hysterectomy procedures (SSI COLO and HYST), methicillin-resistant Staphylococcus aureus and Clostridioides difficile laboratory identified (MRSA and CDI LabID respectively) events. These studies are a key source of information about data quality and completeness serving as an impetus and a guide for improving the caliber of NHSN’s HAI data. Methods: We contacted SHD HAI coordinators in all states for an inventory of HAI validation studies. We used data from these studies to calculate pooled mean sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for HAI case determinations. HAI case reporting “error rates” were computed as the proportion of mismatches (underreport and overreport) among the medical records reviewed by SHDs and reasons for misclassification were categorized. Results: SHD validation studies varied by HAI type (range, 4 studies for MRSA LabID and 23 for CLABSI). Pooled mean sensitivity of HAI reporting ranged from 73.1% (COLO SSI) to 92.7% (CDI LabID). Pooled mean specificity and PPV exceeded 90% for all HAIs. LabID event validations demonstrated the lowest NPV (58.8% for MRSA and 55.1% for CDI). Error rates of HAI reporting to NHSN ranged from 2.5% (HYST SSI) to 13.6% (MRSA LabID). Common errors identified during CLABSI and CAUTI validations were incorrect application of general NHSN and CLABSI- and CAUTI-specific definitions. Incorrect secondary BSI attribution was the most frequently identified reason by CLABSI SHD validations (64.7%). Of all operative procedure-associated misclassifications, inconsistent surveillance practices (66.6%), incorrect NHSN operative procedure category assignment (55.5%), and misapplication of general organ-space and/or site-specific infection criteria (44.4%) were identified as the most common shortcomings. Among MRSA and CDI LabID validations, missed case finding due to failure to review candidate events and gaps in understanding the 14-day reporting rule of LabID protocol were identified as predominant reasons for inaccurate reporting. Conclusions: SHD HAI data validations identified specific targets for additional surveillance training, especially CLABSI determinations and application of the protocol rules for MDRO/CDI LabID case determinations. Further work is also needed to assure that data sources in addition to wound cultures are used for SSI determinations and that postdischarge SSI surveillance is more vigorous and comprehensive.Funding: NoneDisclosures: None

Burden, spectrum, and impact of healthcare-associated infection at a South African children's hospital