scholarly journals Trends in Hospital Onset Clostridioides difficile Infection Incidence, National Healthcare Safety Network, 2010–2018

2020 ◽  
Vol 41 (S1) ◽  
pp. s53-s54
Author(s):  
Yi Mu ◽  
Margaret Dudeck ◽  
Karen Jones ◽  
Qunna Li ◽  
Minn Soe ◽  
...  
Keyword(s):  
Average Length ◽  
Rate Of Change ◽  
Observation Unit ◽  
Test Type ◽  
Quality Reporting ◽  
National Healthcare ◽  
Clostridioides Difficile ◽  
National Healthcare Safety Network ◽  
National Priority ◽  
Over Time

Background:Clostridioides difficile infection (CDI) is one of the most common laboratory-identified (LabID) healthcare-associated events reported to the National Healthcare Safety Network (NHSN). CDI prevention remains a national priority, and efforts to reduce infection burden and improve antibiotic stewardship continue to expand across the healthcare spectrum. Beginning in 2013, the Centers for Medicare and Medicaid Services (CMS) required acute-care hospitals participating in CMS’ Inpatient Quality Reporting program to report CDI LabID data to NHSN and, in 2015, extended this reporting requirement to emergency departments (ED) and 24-hour observation units. To assess national progress, we evaluated changes in hospital onset CDI (HO-CDI) incidence during 2010–2018. Methods: Cases of HO-CDI were reported to NHSN by hospitals using the NHSN’s LabID criteria. Generalized linear mixed-effects modeling was used to assess trends of HO-CDI by treating the hospital as a random intercept to account for the correlation of the repeated responses over time. The data were summarized at the quarterly level, the main effect was time, and the covariates of interest were the following: CDI test type, inpatient community-onset (CO) infection rate, hospital type, average length of stay, medical school affiliation, number of beds, number of ICU beds, number of infection control professionals, presence of an ED or observation unit , and an indicator for 2015 to account for CDI protocol changes that required hospitals to conduct surveillance in both inpatient and ED or observation unit setting. Results: During 2010–2013, the number of hospitals reporting CDI increased and then stabilized after 2013 (Table 1). Crude HO-CDI rates decreased over time, except for an increase in 2015 and steeper reduction thereafter. (Table 2). During 2010–2014, the adjusted quarterly rate of change was −0.45% (95% CI, −0.57% to −0.33%; P < .0001). The rate of reduction was smaller in 2010–2014 compared to those of 2015–2018 (−2.82%; 95% CI, −3.10% to −2.54%; P < .0001). Compared to 2014, the adjusted rate in 2015 increased by 79.14% (95% CI, 72.42%–86.11%; P < .0001). Conclusions: The number of hospitals reporting CDI LabID data grew substantially in 2013 as a result of the CMS requirement for reporting. Adjusted HO-CDI rates decreased over time, with a rate hike in the year of 2015 and a rapid decrease thereafter. The increase in 2015 may be explained by changes in the NHSN CDI surveillance protocol and better test type classification in later years. Overall decreases in HO-CDI rates may be influenced by prevention strategies.Funding: NoneDisclosures: None

scholarly journals Analysis of National Healthcare Safety Network Clostridioides difficile Infection Standardized Infection Ratio by Test Type

2020 ◽  
Vol 41 (S1) ◽  
pp. s87-s89
Author(s):  
Qunna Li ◽  
Andrea Benin ◽  
Alice Guh ◽  
Margaret Dudeck ◽  
Katherine Allen-Bridson ◽  
...  
Keyword(s):  
Risk Adjustment ◽  
Healthcare Facility ◽  
Directional Pattern ◽  
Incidence Rates ◽  
Nucleic Acid Amplification ◽  
Test Type ◽  
National Healthcare ◽  
Clostridioides Difficile ◽  
National Healthcare Safety Network ◽  
The Mean

Background: The National Healthcare Safety Network (NHSN) has used positive laboratory tests for surveillance of Clostridioides difficile infection (CDI) LabID events since 2009. Typically, CDIs are detected using enzyme immunoassays (EIAs), nucleic acid amplification tests (NAATs), or various test combinations. The NHSN uses a risk-adjusted, standardized infection ratio (SIR) to assess healthcare facility-onset (HO) CDI. Despite including test type in the risk adjustment, some hospital personnel and other stakeholders are concerned that NAAT use is associated with higher SIRs than EIA use. To investigate this issue, we analyzed NHSN data from acute-care hospitals for July 1, 2017, through June 30, 2018. Methods: Calendar quarters where CDI test type was reported as NAAT (includes NAAT, glutamate dehydrogenase (GDH)+NAAT and GDH+EIA followed by NAAT if discrepant) or EIA (includes EIA and GDH+EIA) were selected. HO-CDI SIRs were calculated for facility-wide inpatient locations. We conducted the following 2 analyses: (1) Among hospitals that did not switch their test type, we compared the distribution of HO incident rates and SIRs by those reporting NAAT versus EIA. (2) Among hospitals that switched their test type, we selected quarters with a stable switch pattern of 2 consecutive quarters of each of EIA and NAAT (categorized as EIA-to-NAAT or NAAT-to-EIA). Pooled semiannual SIRs for EIA and NAAT were calculated, and a paired t test was used to evaluate the difference in SIRs by switch pattern. Results: Most hospitals did not switch test types (3,242, 89%), and 2,872 (89%) reported sufficient data to calculate an SIR, with 2,444 (85%) using NAAT. The crude pooled HO CDI incidence rates for hospitals using EIAs clustered at the lower end of the histogram versus rates for NAATs (Fig. 1). The SIR distributions, both NAATs and EIAs, overlapped substantially and covered a similar range of SIR values (Fig. 1). Among hospitals with a switch pattern, hospitals were equally likely to have an increase or decrease in their SIRs (Fig. 2). The mean SIR difference for the 42 hospitals switching from EIA to NAAT was 0.048 (95% CI, −0.189 to 0.284; P = .688). The mean SIR difference for the 26 hospitals switching from NAAT to EIA was 0.162 (95% CI, −0.048 to 0.371; P = .124). Conclusions: The pattern of SIR distribution for both NAAT and EIA substantiate the soundness of the NHSN’s risk adjustment for CDI test types. Switching test type did not produce a consistent directional pattern in SIR that was statistically significant.Funding: NoneDisclosures: None

scholarly journals 1153. National Trend of Urinary Catheter Device Utilization by Hospital Type, National Healthcare Safety Network (2015–2019)

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S411-S412
Author(s):  
Minn M Soe
Keyword(s):  
Acute Care ◽  
Negative Binomial ◽  
Urinary Catheter ◽  
Inpatient Rehabilitation ◽  
Acute Care Hospitals ◽  
Equation Modeling ◽  
Quality Reporting ◽  
Factors Associated ◽  
National Healthcare ◽  
National Healthcare Safety Network

Abstract Background Reducing unnecessary urinary catheter use and optimizing insertion techniques and catheter maintenance and care practices are the most important urinary tract infection (CAUTI) prevention strategies. To monitor device use (DU) as quality improvement activity, the Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN) developed the risk adjusted, standardized urinary catheter device utilization ratio in 2015. This study aims to assess national trends of DU from the baseline year 2015 through 2019. Methods For our trend analysis, we analyzed DU data (catheter days per 100 inpatient-days) that acute care hospitals (ACHs), long-term acute care hospitals (LTACHs), inpatient rehabilitation facilities (IRFs), and critical access hospitals (CAHs) reported to NHSN from 2015Q1 through 2019Q1. The ward and intensive care unit patient care locations included in our analysis are those that ACHs, LTACHs, IRFs and CAHs are required to report to CMS to comply with CMS Inpatient Quality Reporting program requirements. We regressed DU by quarterly period using generalized estimating equation modeling with the negative-binomial distribution, after adjusting for factors associated with corresponding SUR models of 2015 baseline and accounting for autocorrelation of error terms within a location. For graphic display, we also computed quarterly DU using marginal predictive models. Results The DU decreased over time (P ≤ 0.05, average percent change per quarter (%change): −0.54 [95% CI: −0.54, −0.53]) among ACHs (Table 1, Figure 1), and −0.54 [95% CI: −0.58, −0.49] among LTACHs (Table 1, Figure 2). Among IRFs, quarterly DU in 2015Q2–2016Q3 were similar relative to 2015Q1, but decreased from 2016Q4 onward (P ≤ 0.05, % change: −0.51 [95% CI: −0.61, −0.40]) (Table 1, Figure 3). Among CAHs, quarterly DU in 2015Q2–2016Q4 were similar relative to 2015Q1, but decreased from 2017Q1 onward (P ≤ 0.05, % change: −0.22 [95% CI: −0.39, −0.04]) (Table 1, Figure 4). Conclusion There was a statistically significant decrease in National DU of urinary catheter during 2015–2019 across NHSN, although the magnitude of change per quarter was not large. Further research is needed to explore causal factors associated with such reduction. Disclosures All authors: No reported disclosures.

scholarly journals Potential for the current National Healthcare Safety Network (NHSN) >3 days after admission definition of laboratory-identified, healthcare-facility–onset, Clostridioides difficile infection (HO-CDI) to overestimate rates

2020 ◽  
Vol 41 (4) ◽  
pp. 467-468
Author(s):  
Shruti Puri ◽  
Heather Y. Hughes ◽  
Monica D. McCrackin ◽  
Robert Williford ◽  
Mulugeta Gebregziabher ◽  
...  
Keyword(s):  
Healthcare Facility ◽  
National Healthcare ◽  
Clostridioides Difficile ◽  
National Healthcare Safety Network ◽  
Positive Stool ◽  
Clostridioides Difficile Infection ◽  
Stool Samples ◽  
Definition Of

AbstractHealthcare-facility–onset C.difficile LabID events are defined as positive stool samples collected >3 days after hospitalization. Using a definition of >72 hours, we found that 84 of 1013 cases (8.3%) identified as C. difficile LabID events were collected between 48 and 72 hours after admission.

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

2020 ◽  
Vol 71 (10) ◽  
pp. e702-e709 ◽  
Author(s):  
Erin N O’Leary ◽  
Jonathan R Edwards ◽  
Arjun Srinivasan ◽  
Melinda M Neuhauser ◽  
Amy K Webb ◽  
...  
Keyword(s):  
Patient Care ◽  
Negative Binomial ◽  
Eligible Patient ◽  
Negative Binomial Regression ◽  
Risk Modeling ◽  
National Healthcare ◽  
Clostridioides Difficile ◽  
National Healthcare Safety Network ◽  
Using Data ◽  
Antimicrobial Administration

Abstract 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.

Reductions in positive Clostridioides difficile events reportable to National Healthcare Safety Network (NHSN) with adoption of reflex enzyme immunoassay (EIA) testing in 13 Atlanta hospitals

2021 ◽  
pp. 1-4
Author(s):  
Dana Goodenough ◽  
Samantha Sefton ◽  
Elizabeth Overton ◽  
Elizabeth Smith ◽  
Colleen S. Kraft ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Enzyme Immunoassay ◽  
Infection Prevention ◽  
Nucleic Acid Amplification Test ◽  
Nucleic Acid Amplification ◽  
National Healthcare ◽  
Clostridioides Difficile ◽  
Performance Metric ◽  
National Healthcare Safety Network ◽  
Testing Practice

Abstract In total, 13 facilities changed C. difficile testing to reflexive testing by enzyme immunoassay (EIA) only after a positive nucleic acid-amplification test (NAAT); the standardized infection ratio (SIR) decreased by 46% (range, −12% to −71% per hospital). Changing testing practice greatly influenced a performance metric without changing C. difficile infection prevention practice.

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

2020 ◽  
Vol 41 (3) ◽  
pp. 313-319 ◽  
Author(s):  
Shannon A. Novosad ◽  
Lucy Fike ◽  
Margaret A. Dudeck ◽  
Katherine Allen-Bridson ◽  
Jonathan R. Edwards ◽  
...  
Keyword(s):  
Acute Care ◽  
Bloodstream Infections ◽  
Central Line ◽  
Incidence Density ◽  
Targeted Prevention ◽  
Prevention Strategies ◽  
National Healthcare ◽  
National Healthcare Safety Network ◽  
Control And Prevention ◽  
Over Time

AbstractObjective:To describe pathogen distribution and rates for central-line–associated bloodstream infections (CLABSIs) from different acute-care locations during 2011–2017 to inform prevention efforts.Methods:CLABSI data from the Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN) were analyzed. Percentages and pooled mean incidence density rates were calculated for a variety of pathogens and stratified by acute-care location groups (adult intensive care units [ICUs], pediatric ICUs [PICUs], adult wards, pediatric wards, and oncology wards).Results:From 2011 to 2017, 136,264 CLABSIs were reported to the NHSN by adult and pediatric acute-care locations; adult ICUs and wards reported the most CLABSIs: 59,461 (44%) and 40,763 (30%), respectively. In 2017, the most common pathogens were Candida spp/yeast in adult ICUs (27%) and Enterobacteriaceae in adult wards, pediatric wards, oncology wards, and PICUs (23%–31%). Most pathogen-specific CLABSI rates decreased over time, excepting Candida spp/yeast in adult ICUs and Enterobacteriaceae in oncology wards, which increased, and Staphylococcus aureus rates in pediatric locations, which did not change.Conclusions:The pathogens associated with CLABSIs differ across acute-care location groups. Learning how pathogen-targeted prevention efforts could augment current prevention strategies, such as strategies aimed at preventing Candida spp/yeast and Enterobacteriaceae CLABSIs, might further reduce national rates.

scholarly journals 875. Pseudo-outbreak of Clostridioides (Clostridium) difficile amongst post-operative Oncology Patients

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S475-S475
Author(s):  
Daniel Kagedan ◽  
Roderich Schwarz ◽  
Jillianna Wasiura ◽  
Nikolaos Almyroudis ◽  
Robin Patel ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Healthcare Facility ◽  
Whole Genome ◽  
Research Support ◽  
National Healthcare ◽  
Clostridioides Difficile ◽  
National Healthcare Safety Network ◽  
Allelic Differences ◽  
Inpatient Units

Abstract Background Clostridioides difficile infection rates are subject to infection prevention surveillance as a quality measure within the hospital setting. A large spike in Clostridioides difficile infections in post-operative patients, the majority of whom were gastrointestinal surgery (GIS) patients, was noted within a six month period (June through November 2019) at our comprehensive cancer center. These patients had been housed in one of two inpatient units and there was appropriate concern that this represented a C. difficile outbreak possibly related some type of infection control breach. Methods In an effort to query case relatedness, whole genome sequencing was performed using Illumina MiSeq instrumentation and chemistry with Illumina Nextera XT library chemistry. Assembly and core genome multilocus sequence typing analysis were performed with Ridom SeqSphere+ software. Cases were classified as community or hospital acquired based on the National Healthcare Safety Network (NHSN) definitions. Results There were 23 samples submitted for possible whole genome sequencing (WGS). 5 samples were unable to be grown therefore WGS was not completed; 16 were found to be unrelated (51 or more allelic differences); 2 of the 18 isolates were found to be possibly related (7 to 50 allelic differences). There were no isolates found to be definitively related (zero to 6 allelic differences). Conclusion Given the overwhelming unrelatedness of the isolates via whole genome sequencing, this increase of C. difficile cases, identified by routine surveillance within two inpatient units, was determined to be representative of a pseudo-outbreak rather than an outbreak. This study has implications on public health reporting. National Healthcare Safety Network definitions are used to identify healthcare facility-onset C. difficile infections (CDI). The majority of cases in this study met the definition of healthcare facility-onset, and thus were reported as such, despite being genetically unrelated. This raises the concern that a significant percentage of C. difficile infections may be currently misclassified as hospital-associated and this may have negative, unfair consequences for hospitals, such as implications on reimbursement. Disclosures Robin Patel, MD, Accelerate Diagnostics (Grant/Research Support)CD Diagnostics (Grant/Research Support)Contrafect (Grant/Research Support)Curetis (Consultant)GenMark Diagnostics (Consultant)Heraeus Medical (Consultant)Hutchison Biofilm Medical Solutions (Grant/Research Support)Merck (Grant/Research Support)Next Gen Diagnostics (Consultant)PathoQuest (Consultant)Qvella (Consultant)Samsung (Other Financial or Material Support, Dr. Patel has a patent on Bordetella pertussis/parapertussis PCR issued, a patent on a device/method for sonication with royalties paid by Samsung to Mayo Clinic, and a patent on an anti-biofilm substance issued.)Selux Dx (Consultant)Shionogi (Grant/Research Support)Specific Technologies (Consultant)

scholarly journals Factors associated with professional identity formation within psychiatry residency training: A longitudinal study

2021 ◽  
Author(s):  
Qian Hui Chew ◽  
Yvonne Steinert ◽  
Kang Sim
Keyword(s):  
Professional Identity ◽  
Identity Formation ◽  
Psychiatry Residency ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Developmental Stages ◽  
Rate Of Change ◽  
Psychiatry Residents ◽  
Factors Associated ◽  
Over Time

Abstract Introduction Conceptual frameworks for professional identity (PI) formation highlight the importance of developmental stages and socialization as the learner progresses from legitimate peripheral to full participation. Based on extant literature and clinical impressions, the authors aimed to explore factors associated with PI formation in psychiatry residents over time, and hypothesized that time in training, seniority status, and duration of exposure to psychiatry prior to residency would be associated with PI formation. Methods Eighty out of 96 psychiatry residents (response rate, 83.3%) from the National Psychiatry Residency Program in Singapore participated and rated their PI development using the Professional Self Identity Questionnaire (PSIQ) across four timepoints from January 2016–December 2019. The residents were classified as junior (first 3 years) or senior residents (years 4–5). Linear mixed model analyses were conducted, with time in training, seniority status (junior versus senior residents), duration of psychiatry postings prior to residency, and their interaction as associated factors with PI over time. Results Time in training, seniority, and duration of psychiatry postings before residency (all p < 0.01) were significantly associated with higher PSIQ scores at baseline. Over time, although all residents had increases in PSIQ scores, this rate of change did not differ significantly between junior and senior residents. Discussion Exposure to psychiatry postings before residency, time in learning, and seniority are factors which influence PI development in residents. This has implications for psychiatry residency selection and training, adequate clinical exposure during training rotations, and continual support for new and senior residents to foster PI formation over time.

scholarly journals Analysis of National Healthcare Safety Network Clostridioides difficile Infection Standardized Infection Ratio by Test Type

2020 ◽  
Vol 41 (S1) ◽  
pp. s116-s118
Author(s):  
Qunna Li ◽  
Andrea Benin ◽  
Alice Guh ◽  
Margaret A. Dudeck ◽  
Katherine Allen-Bridson ◽  
...  
Keyword(s):  
Risk Adjustment ◽  
Healthcare Facility ◽  
Directional Pattern ◽  
Incidence Rates ◽  
Nucleic Acid Amplification ◽  
Test Type ◽  
Clostridioides Difficile ◽  
Clostridioides Difficile Infection ◽  
The Mean ◽  
The Difference

Background: The NHSN has used positive laboratory tests for surveillance of Clostridioides difficile infection (CDI) LabID events since 2009. Typically, CDIs are detected using enzyme immunoassays (EIAs), nucleic acid amplification tests (NAATs), or various test combinations. The NHSN uses a risk-adjusted, standardized infection ratio (SIR) to assess healthcare facility-onset (HO) CDI. Despite including test type in the risk adjustment, some hospital personnel and other stakeholders are concerned that NAAT use is associated with higher SIRs than are EIAs. To investigate this issue, we analyzed NHSN data from acute-care hospitals for July 1, 2017 through June 30, 2018. Methods: Calendar quarters for which CDI test type was reported as NAAT (includes NAAT, glutamate dehydrogenase (GDH)+NAAT and GDH+EIA followed by NAAT if discrepant) or EIA (includes EIA and GDH+EIA) were selected. HO CDI SIRs were calculated for facility-wide inpatient locations. We conducted the following analyses: (1) Among hospitals that did not switch their test type, we compared the distribution of HO incident rates and SIRs by those reporting NAAT vs EIA. (2) Among hospitals that switched their test type, we selected quarters with a stable switch pattern of 2 consecutive quarters of each of EIA and NAAT (categorized as pattern EIA-to-NAAT or NAAT-to-EIA). Pooled semiannual SIRs for EIA and NAAT were calculated, and a paired t test was used to evaluate the difference of SIRs by switch pattern. Results: Most hospitals did not switch test types (3,242, 89%), and 2,872 (89%) reported sufficient data to calculate SIRs, with 2,444 (85%) using NAAT. The crude pooled HO CDI incidence rates for hospitals using EIA clustered at the lower end of the histogram versus rates for NAAT (Fig. 1). The SIR distributions of both NAAT and EIA overlapped substantially and covered a similar range of SIR values (Fig. 1). Among hospitals with a switch pattern, hospitals were equally likely to have an increase or decrease in their SIR (Fig. 2). The mean SIR difference for the 42 hospitals switching from EIA to NAAT was 0.048 (95% CI, −0.189 to 0.284; P = .688). The mean SIR difference for the 26 hospitals switching from NAAT to EIA was 0.162 (95% CI, −0.048 to 0.371; P = .124). Conclusions: The pattern of SIR distributions of both NAAT and EIA substantiate the soundness of NHSN risk adjustment for CDI test types. Switching test type did not produce a consistent directional pattern in SIR that was statistically significant.Disclosures: NoneFunding: None

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