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To characterize and compare severe acute respiratory coronavirus virus 2 (SARS-CoV-2)–specific immune responses in plasma and gingival crevicular fluid (GCF) from nursing home residents during and after natural infection.
SARS-CoV-2–infected nursing home residents.
A convenience sample of 14 SARS-CoV-2–infected nursing home residents, enrolled 4–13 days after real-time reverse transcription polymerase chain reaction diagnosis, were followed for 42 days. After diagnosis, plasma SARS-CoV-2–specific pan-Immunoglobulin (Ig), IgG, IgA, IgM, and neutralizing antibodies were measured at 5 time points, and GCF SARS-CoV-2–specific IgG and IgA were measured at 4 time points.
All participants demonstrated immune responses to SARS-CoV-2 infection. Among 12 phlebotomized participants, plasma was positive for pan-Ig and IgG in all 12 participants. Neutralizing antibodies were positive in 11 participants; IgM was positive in 10 participants, and IgA was positive in 9 participants. Among 14 participants with GCF specimens, GCF was positive for IgG in 13 participants and for IgA in 12 participants. Immunoglobulin responses in plasma and GCF had similar kinetics; median times to peak antibody response were similar across specimen types (4 weeks for IgG; 3 weeks for IgA). Participants with pan-Ig, IgG, and IgA detected in plasma and GCF IgG remained positive throughout this evaluation, 46–55 days after diagnosis. All participants were viral-culture negative by the first detection of antibodies.
Nursing home residents had detectable SARS-CoV-2 antibodies in plasma and GCF after infection. Kinetics of antibodies detected in GCF mirrored those from plasma. Noninvasive GCF may be useful for detecting and monitoring immunologic responses in populations unable or unwilling to be phlebotomized.
Repeated antigen testing of 12 severe acute respiratory coronavirus virus 2 (SARS-CoV-2)–positive nursing home residents using Abbott BinaxNOW identified 9 of 9 (100%) culture-positive specimens up to 6 days after initial positive test. Antigen positivity lasted 2–24 days. Antigen positivity might last beyond the infectious period, but it was reliable in residents with evidence of early infection.
Previously reported associations between hospital-level antibiotic use and hospital-onset Clostridioides difficile infection (HO-CDI) were reexamined using 2012–2018 data from a new cohort of US acute-care hospitals. This analysis revealed significant positive associations between total, third-generation, and fourth-generation cephalosporin, fluoroquinolone, carbapenem, and piperacillin-tazobactam use and HO-CDI rates, confirming previous findings.
Antibiotics are widely used by all specialties in the hospital setting. We evaluated previously defined high-risk antibiotic use in relation to Clostridioides difficile infections (CDIs).
We analyzed 2016–2017 data from 171 hospitals. High-risk antibiotics included second-, third-, and fourth-generation cephalosporins, fluoroquinolones, carbapenems, and lincosamides. A CDI case was a positive stool C. difficile toxin or molecular assay result from a patient without a positive result in the previous 8 weeks. Hospital-associated (HA) CDI cases included specimens collected >3 calendar days after admission or ≤3 calendar days from a patient with a prior same-hospital discharge within 28 days. We used the multivariable Poisson regression model to estimate the relative risk (RR) of high-risk antibiotic use on HA CDI, controlling for confounders.
The median days of therapy for high-risk antibiotic use was 241.2 (interquartile range [IQR], 192.6–295.2) per 1,000 days present; the overall HA CDI rate was 33 (IQR, 24–43) per 10,000 admissions. The overall correlation of high-risk antibiotic use and HA CDI was 0.22 (P = .003), and higher correlation was observed in teaching hospitals (0.38; P = .002). For every 100-day (per 1,000 days present) increase in high-risk antibiotic therapy, there was a 12% increase in HA CDI (RR, 1.12; 95% CI, 1.04–1.21; P = .002) after adjusting for confounders.
High-risk antibiotic use is an independent predictor of HA CDI. This assessment of poststewardship implementation in the United States highlights the importance of tracking trends of antimicrobial use over time as it relates to CDI.
A nationwide survey indicated that screening for asymptomatic carriers of C. difficile is an uncommon practice in US healthcare settings. Better understanding of the role of asymptomatic carriage in C. difficile transmission, and of the measures available to reduce that risk, are needed to inform best practices regarding the management of carriers.
To test the hypothesis that long-term care facility (LTCF) residents with Clostridium difficile infection (CDI) or asymptomatic carriage of toxigenic strains are an important source of transmission in the LTCF and in the hospital during acute-care admissions.
A 6-month cohort study with identification of transmission events was conducted based on tracking of patient movement combined with restriction endonuclease analysis (REA) and whole-genome sequencing (WGS).
Veterans Affairs hospital and affiliated LTCF.
The study included 29 LTCF residents identified as asymptomatic carriers of toxigenic C. difficile based on every other week perirectal screening and 37 healthcare facility-associated CDI cases (ie, diagnosis >3 days after admission or within 4 weeks of discharge to the community), including 26 hospital-associated and 11 LTCF-associated cases.
Of the 37 CDI cases, 7 (18·9%) were linked to LTCF residents with LTCF-associated CDI or asymptomatic carriage, including 3 of 26 hospital-associated CDI cases (11·5%) and 4 of 11 LTCF-associated cases (36·4%). Of the 7 transmissions linked to LTCF residents, 5 (71·4%) were linked to asymptomatic carriers versus 2 (28·6%) to CDI cases, and all involved transmission of epidemic BI/NAP1/027 strains. No incident hospital-associated CDI cases were linked to other hospital-associated CDI cases.
Our findings suggest that LTCF residents with asymptomatic carriage of C. difficile or CDI contribute to transmission both in the LTCF and in the affiliated hospital during acute-care admissions. Greater emphasis on infection control measures and antimicrobial stewardship in LTCFs is needed, and these efforts should focus on LTCF residents during hospital admissions.
To determine the typical microbial bioburden (overall bacterial and multidrug-resistant organisms [MDROs]) on high-touch healthcare environmental surfaces after routine or terminal cleaning.
Prospective 2.5-year microbiological survey of large surface areas (>1,000 cm2).
MDRO contact-precaution rooms from 9 acute-care hospitals and 2 long-term care facilities in 4 states.
Samples from 166 rooms (113 routine cleaned and 53 terminal cleaned rooms).
Using a standard sponge-wipe sampling protocol, 2 composite samples were collected from each room; a third sample was collected from each Clostridium difficile room. Composite 1 included the TV remote, telephone, call button, and bed rails. Composite 2 included the room door handle, IV pole, and overbed table. Composite 3 included toileting surfaces. Total bacteria and MDROs (ie, methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci [VRE], Acinetobacter baumannii, Klebsiella pneumoniae, and C. difficile) were quantified, confirmed, and tested for drug resistance.
The mean microbial bioburden and range from routine cleaned room composites were higher (2,700 colony-forming units [CFU]/100 cm2; ≤1–130,000 CFU/100 cm2) than from terminal cleaned room composites (353 CFU/100 cm2; ≤1–4,300 CFU/100 cm2). MDROs were recovered from 34% of routine cleaned room composites (range ≤1–13,000 CFU/100 cm2) and 17% of terminal cleaned room composites (≤1–524 CFU/100 cm2). MDROs were recovered from 40% of rooms; VRE was the most common (19%).
This multicenter bioburden summary provides a first step to determining microbial bioburden on healthcare surfaces, which may help provide a basis for developing standards to evaluate cleaning and disinfection as well as a framework for studies using an evidentiary hierarchy for environmental infection control.
To determine the potential epidemiologic and economic value of the implementation of a multifaceted Clostridium difficile infection (CDI) control program at US acute care hospitals
Markov model with a 5-year time horizon
Patients whose data were used in our simulations were limited to hospitalized Medicare beneficiaries ≥65 years old.
CDI is an important public health problem with substantial associated morbidity, mortality, and cost. Multifaceted national prevention efforts in the United Kingdom, including antimicrobial stewardship, patient isolation, hand hygiene, environmental cleaning and disinfection, and audit, resulted in a 59% reduction in CDI cases reported from 2008 to 2012.
Our analysis was conducted from the federal perspective. The intervention we modeled included the following components: antimicrobial stewardship utilizing the Antimicrobial Use and Resistance module of the National Healthcare Safety Network (NHSN), use of contact precautions, and enhanced environmental cleaning. We parameterized our model using data from CDC surveillance systems, the AHRQ Healthcare Cost and Utilization Project, and literature reviews. To address uncertainty in our parameter estimates, we conducted sensitivity analyses for intervention effectiveness and cost, expenditures by other federal partners, and discount rate. Each simulation represented a cohort of 1,000 hospitalized patients over 1,000 trials.
In our base case scenario with 50% intervention effectiveness, we estimated that 509,000 CDI cases and 82,000 CDI-attributable deaths would be prevented over a 5-year time horizon. Nationally, the cost savings across all hospitalizations would be $2.5 billion (95% credible interval: $1.2 billion to $4.0 billion).
The potential benefits of a multifaceted national CDI prevention program are sizeable from the federal perspective.
To predict the likelihood of hospital-onset Clostridium difficile infection (HO-CDI) based on patient clinical presentations at admission
Retrospective data analysis
Six US acute care hospitals
We used clinical data collected at the time of admission in electronic health record (EHR) systems to develop and validate a HO-CDI predictive model. The outcome measure was HO-CDI cases identified by a nonduplicate positive C. difficile toxin assay result with stool specimens collected >48 hours after inpatient admission. We fit a logistic regression model to predict the risk of HO-CDI. We validated the model using 1,000 bootstrap simulations.
Among 78,080 adult admissions, 323 HO-CDI cases were identified (ie, a rate of 4.1 per 1,000 admissions). The logistic regression model yielded 14 independent predictors, including hospital community onset CDI pressure, patient age ≥65, previous healthcare exposures, CDI in previous admission, admission to the intensive care unit, albumin ≤3 g/dL, creatinine >2.0 mg/dL, bands >32%, platelets ≤150 or >420 109/L, and white blood cell count >11,000 mm3. The model had a c-statistic of 0.78 (95% confidence interval [CI], 0.76–0.81) with good calibration. Among 79% of patients with risk scores of 0–7, 19 HO-CDIs occurred per 10,000 admissions; for patients with risk scores >20, 623 HO-CDIs occurred per 10,000 admissions (P<.0001).
Using clinical parameters available at the time of admission, this HO-CDI model demonstrated good predictive ability, and it may have utility as an early risk identification tool for HO-CDI preventive interventions and outcome comparisons.
Previously published guidelines are available that provide comprehensive recommendations for detecting and preventing healthcare-associated infections (HAIs). The intent of this document is to highlight practical recommendations in a concise format designed to assist acute care hospitals in implementing and prioritizing their Clostridium difficile infection (CDI) prevention efforts. This document updates “Strategies to Prevent Clostridium difficile Infections in Acute Care Hospitals,” published in 2008. This expert guidance document is sponsored by the Society for Healthcare Epidemiology of America (SHEA) and is the product of a collaborative effort led by SHEA, the Infectious Diseases Society of America (IDSA), the American Hospital Association (AHA), the Association for Professionals in Infection Control and Epidemiology (APIC), and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise. The list of endorsing and supporting organizations is presented in the introduction to the 2014 updates.
Recent studies have demonstrated that central line-associated bloodstream infections (CLABSIs) are preventable through implementation of evidence-based prevention practices. Hospitals have reported CLABSI data to the Centers for Disease Control and Prevention (CDC) since the 1970s, providing an opportunity to characterize the national impact of CLABSIs over time. Our objective was to describe changes in the annual number of CLABSIs in critical care patients in the United States.
Monte Carlo simulation.
US acute care hospitals.
Nonneonatal critical care patients.
We obtained administrative data on patient-days for nearly all US hospitals and applied CLABSI rates from the National Nosocomial Infections Surveillance and the National Healthcare Safety Network systems to estimate the annual number of CLABSIs in critical care patients nationally during the period 1990–2010 and the number of CLABSIs prevented since 1990.
We estimated that there were between 462,000 and 636,000 CLABSIs in nonneonatal critical care patients in the United States during 1990–2010. CLABSI rate reductions led to between 104,000 and 198,000 fewer CLABSIs than would have occurred if rates had remained unchanged since 1990. There were 15,000 hospital-onset CLABSIs in nonneonatal critical care patients in 2010; 70% occurred in medium and large teaching hospitals.
Substantial progress has been made in reducing the occurrence of CLABSIs in US critical care patients over the past 2 decades. The concentration of critical care CLABSIs in medium and large teaching hospitals suggests that a targeted approach may be warranted to continue achieving reductions in critical care CLABSIs nationally.
To determine the attributable in-hospital mortality, length of stay (LOS), and cost of hospital-onset Clostridium difficile infection (HO-CDI).
Propensity score matching.
Six Pennsylvania hospitals (2 academic centers, 1 community teaching facility, and 3 community nonteaching facilities) contributing data to a clinical research database.
Adult inpatients between 2007 and 2008.
We defined HO-CDI in adult inpatients as a positive C. difficile toxin assay result from a specimen collected more than 48 hours after admission and more than 8 weeks following any previous positive result. We developed an HO-CDI propensity model and matched cases with noncases by propensity score at a 1 : 3 ratio. We further restricted matching within the same hospital, within the same principal disease group, and within a similar length of lead time from admission to onset of HO-CDI.
Among 77,257 discharges, 282 HO-CDI cases were identified. The propensity score-matched rate was 90%. Compared with matched noncases, HO-CDI patients had higher mortality (11.8% vs 7.3%; P<.05), longer LOS (median [interquartile range (IQR)], 12 [9–21] vs 11 [8–17] days; P< .01), and higher cost (median [IQR], $20,804 [$ll,059-$38,429] vs $16,634 [$9,413–$30,319]; P< .01). The attributable effect of HO-CDI was 4.5% (95% confidence interval [CI], 0.2%–8.7%; P<.05) for mortality, 2.3 days (95% CI, 0.9–3.8; P<.01) for LOS, and $6,117 (95% CI, $1,659–$10,574; P<.01) for cost.
Patients with HO-CDI incur additional attributable mortality, LOS, and cost burden compared with patients with similar primary clinical condition, exposure risk, lead time of hospitalization, and baseline characteristics.
Long-term care facility (LTCF) residents are at increased risk of Clostridium difficile infection (CDI). However, little is known about the incidence, recurrence, and severity of CDI in LTCFs or the extent to which acute care exposure contributes to CDI in LTCFs. We describe the epidemiology of CDI in a cohort of LTCF residents in Monroe County, New York, where recent estimates suggest a CDI incidence in hospitals of 9.2 cases per 10,000 patient-days.
Population-based surveillance study.
Monroe County, New York.
LTCF residents with onset of CDI while in the LTCF or less than 4 calendar-days after hospital admission from the LTCF from January 1 through December 31, 2010.
We conducted surveillance for CDI in residents of 33 LTCFs. A CDI case was defined as a stool specimen positive for C. difficile obtained from a patient without a C. difficile-positive specimen in the previous 8 weeks; recurrence was defined as a stool specimen positive for C. difficile obtained between 2 and 8 weeks after the last C. difficile-positive stool specimen.
There were 425 LTCF-onset cases and 184 recurrences, which yielded an incidence of 2.3 cases per 10,000 resident-days (interquartile range [IQR], 1.2–3.3) and a recurrence rate of 1.0 case per 10,000 resident-days (IQR, 0.3–1.4). The cases occurred in 394 LTCF residents, and 52% of these residents developed CDI within 4 weeks after hospital discharge. Hospitalization for CDI occurred in 70 cases (16%). Of those cases that involved hospitalization for CDI, 70% were severe CDI, and 23% ended in death within 30 days after hospital admission.
CDI incidence in Monroe County LTCFs is one-fourth the incidence among hospitalized patients. Approximately 50% of LTCF-onset cases occurred more than 4 weeks after hospital discharge, which emphasizes that prevention of CDI transmission should go beyond acute care settings.
Little is known about how hospital organizational and cultural factors associated with implementation of quality initiatives such as the Institute for Healthcare Improvement's (IHI) 100,000 Lives Campaign differ among levels of healthcare staff.
Evaluation of a mixed qualitative and quantitative methodology (“trilogic evaluation model”).
Six hospitals that joined the campaign before June 2006.
Three strata of staff (executive leadership, midlevel, and frontline) at each hospital.
Surveys were completed in 2008 by 135 hospital personnel (midlevel, 43.7%; frontline, 38.5%; executive, 17.8%) who also participated in 20 focus groups. Overall, 93% of participants were aware of the IHI campaign in their hospital and perceived that 58% (standard deviation, 22.7%) of improvements in quality at their hospital were a direct result of the campaign. There were significant differences between staff levels on the organizational culture (OC) items, with executive-level staff having higher scores than midlevel and frontline staff. All 20 focus groups perceived that the campaign interventions were sustainable and that data feedback, buy-in, hardwiring (into daily activities), and leadership support were essential to sustainability.
The trilogic model demonstrated that the 3 levels of staff had markedly different perceptions regarding the IHI campaign and OC. A framework in which frontline, midlevel, and leadership staff are simultaneously assessed may be a useful tool for future evaluations of OC and quality initiatives such as the IHI campaign.
To assess healthcare personnel (HCP) perceptions regarding implementation of sensor-based electronic systems for automated hand hygiene adherence monitoring.
Using a mixed-methods approach, structured focus groups were designed to elicit quantitative and qualitative responses on familiarity, comfort level, and perceived impact of sensor-based hand hygiene adherence monitoring
A university hospital, a Veterans Affairs hospital, and a community hospital in the Midwest.
Focus groups were homogenous by HCP type, with separate groups held for leadership, midlevel management, and frontline personnel at each hospital.
Overall, 89 HCP participated in 10 focus groups. Levels of familiarity and comfort with electronic oversight technology varied by HCP type; when compared with frontline HCP, those in leadership positions were significantly more familiar with (P<.01) and more comfortable with (P<.01) the technology. The most common concerns cited by participants across groups included lack of accuracy in the data produced, such as the inability of the technology to assess the situational context of hand hygiene opportunities, and the potential punitive use of data produced. Across groups, HCP had decreased tolerance for electronic collection of spatial-temporal data, describing such oversight as Big Brother.
While substantial concerns were expressed by all types of HCP, participants' recommendations for effective implementation of electronic oversight technologies for hand hygiene monitoring included addressing accuracy issues before implementation and transparent communication with frontline HCP about the intended use of the data.
Expanding hospitalized patients' risk stratification for Clostridium difficile infection (CDI) is important for improving patient safety. We applied definitions for hospital-onset (HO) and community-onset (CO) CDI to electronic data from 85 hospitals between January 2007 and June 2008 to identify factors associated with higher HO CDI rates.
Nonrecurrent CDI cases were identified among adult (≥18-year-old) inpatients by a positive C. difficile toxin assay result more than 8 weeks after any previous positive result. Case categories included HO, CO-hospital associated (CO-HA), CO-indeterminate hospital association (CO-IN), and CO–non–hospital associated (CO-NHA). C. difficile testing intensity (CDTI) was defined as the total number of C. difficile tests performed, normalized to the number of patients with at least 1 C. difficile toxin test recorded. We calculated both the incidence density and the prevalence of CDI where appropriate. We fitted a multivariable Poisson model to identify factors associated with higher HO CDI rates.
Among 1,351,156 unique patients with 2,022,213 admissions, 9,803 cases of CDI were identified; of these, 50.6% were HO, 17.4% were CO-HA, 9.0% were CO-IN, and 23.0% were CO-NHA. The incidence density of HO was 6.3 per 10,000 patient-days. The prevalence of CO CDI on admission was, per 10,000 admissions, 8.4 for CO-HA, 4.4 for CO-IN, and 11.1 for CO-NHA. Factors associated (P< .0001) with higher HO CDI rates included older age, higher CO-NHA prevalence on admission, and increased CDTI.
Electronic health information can be leveraged to risk-stratify HO CDI rates by patient age and CO-NHA prevalence on admission. Hospitals should optimize diagnostic testing to improve patient care and measured CDI rates.
At a major referral hospital in the Southern Hemisphere, the 2009 influenza A (H1N1) pandemic brought increased critical care demand and more unscheduled nursing absences. Because of careful preparedness planning, including rapid expansion and redistribution of the numbers of available beds and staff, hospital surge capacity was not exceeded.
To determine the feasibility of using electronic laboratory and admission-discharge-transfer data from BioSense, a national automated surveillance system, to apply new modified Clostridium difficile infection (CDI) surveillance definitions and calculate overall and facility-specific rates of disease.
Retrospective, multicenter cohort study.
Thirty-four hospitals sending inpatient, emergency department, and /or outpatient data to BioSense.
Laboratory codes and text-parsing methods were used to extract C. difficile-positive toxin assay results from laboratory data sent to BioSense during the period from January 1, 2007, through June 30, 2008; these were merged with administrative records to determine whether cases were community associated or healthcare onset, as well as patient-day data for rate calculations. A patient was classified as having hospital-onset CDI if he or she had a C. difficile toxin-positive result on a stool sample collected 3 or more days after admission and community-onset CDI if the specimen was collected less than 3 days after admission or the patient was not hospitalized.
A total of 4,585 patients from 34 hospitals in 12 states had C. difficile-positive assay results. More than half (53.0%) of the cases were community-onset, and 30.8% of these occurred in patients who were recently hospitalized. The overall rate of healthcare-onset CDI was 7.8 cases per 10,000 patient-days, with a range among facilities of 1.5-27.8 cases per 10,000 patient-days.
Electronic laboratory data sent to the BioSense surveillance system were successfully used to produce disease rates of CDI comparable to those of other studies, which shows the feasibility of using electronic laboratory data to track a disease of public health importance.