Infection Control and Hospital Epidemiology
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Published By Cambridge University Press

1559-6834, 0899-823x

Author(s):  
Robert J. Clifford ◽  
Donna Newhart ◽  
Maryrose R. Laguio-Vila ◽  
Jennifer L. Gutowski ◽  
Melissa Z. Bronstein ◽  
...  

Abstract Objective: To quantitatively evaluate relationships between infection preventionists (IPs) staffing levels, nursing hours, and rates of 10 types of healthcare-associated infections (HAIs). Design and setting: An ambidirectional observation in a 528-bed teaching hospital. Patients: All inpatients from July 1, 2012, to February 1, 2021. Methods: Standardized US National Health Safety Network (NHSN) definitions were used for HAIs. Staffing levels were measured in full-time equivalents (FTE) for IPs and total monthly hours worked for nurses. A time-trend analysis using control charts, t tests, Poisson tests, and regression analysis was performed using Minitab and R computing programs on rates and standardized infection ratios (SIRs) of 10 types of HAIs. An additional analysis was performed on 3 stratifications: critically low (2–3 FTE), below recommended IP levels (4–6 FTE), and at recommended IP levels (7–8 FTE). Results: The observation covered 1.6 million patient days of surveillance. IP staffing levels fluctuated from ≤2 IP FTE (critically low) to 7–8 IP FTE (recommended levels). Periods of highest catheter-associated urinary tract infection SIRs, hospital-onset Clostridioides difficile and carbapenem-resistant Enterobacteriaceae infection rates, along with 4 of 5 types of surgical site SIRs coincided with the periods of lowest IP staffing levels and the absence of certified IPs and a healthcare epidemiologist. Central-line–associated bloodstream infections increased amid lower nursing levels despite the increased presence of an IP and a hospital epidemiologist. Conclusions: Of 10 HAIs, 8 had highest incidences during periods of lowest IP staffing and experience. Some HAI rates varied inversely with levels of IP staffing and experience and others appeared to be more influenced by nursing levels or other confounders.


Author(s):  
Nathanael R. Fillmore ◽  
Jennifer La ◽  
Chunlei Zheng ◽  
Shira Doron ◽  
Nhan Do ◽  
...  

Abstract Background: COVID-19 hospitalization definitions do not include a disease severity assessment. Thus, we sought to identify a simple and objective mechanism for identifying hospitalized severe cases and to measure the impact of vaccination on trends. Methods: All admissions to a Veterans Affairs (VA) hospital, where routine screening is recommended, between 3/1/2020-11/22/2021 with SARS-CoV-2 were included. Moderate-to-severe COVID-19 was defined as any oxygen supplementation or any SpO2 <94% between one day before and two weeks after the positive SARS-CoV-2 test. Admissions with moderate-to-severe disease were divided by the total number of admissions, and the proportion of admissions with moderate-to-severe COVID-19 was modelled using a penalized spline in a Poisson regression and stratified by vaccination status. Dexamethasone receipt and its correlation with moderate-to-severe cases was also assessed. Results: Among 67,025 admissions with SARS-CoV-2, the proportion with hypoxemia or supplemental oxygen fell from 64% prior to vaccine availability to 56% by November 2021, driven in part by lower rates in vaccinated patients (vaccinated, 52% versus unvaccinated, 58%). The proportion of cases of moderate-to-severe disease identified using SpO2 levels and oxygen supplementation was highly correlated with dexamethasone receipt (correlation coefficient, 0.95), and increased after 7/1/2021, concurrent with delta variant predominance. Conclusions: A simple and objective definition of COVID-19 hospitalizations using SpO2 levels and oxygen supplementation can be used to track pandemic severity. This metric could be used to identify risk factors for severe breakthrough infections, to guide clinical treatment algorithms, and to detect trends in changes in vaccine effectiveness over time and against new variants.


Author(s):  
Laura M. King ◽  
Michael Kusnetsov ◽  
Avgoustinos Filippoupolitis ◽  
Deniz Arik ◽  
Monina Bartoces ◽  
...  

Abstract Using a machine-learning model, we examined drivers of antibiotic prescribing for antibiotic-inappropriate acute respiratory illnesses in a large US claims data set. Antibiotics were prescribed in 11% of the 42 million visits in our sample. The model identified outpatient setting type, patient age mix, and state as top drivers of prescribing.


Author(s):  
Rachel Brown ◽  
Amanda M. Brown ◽  
Sharon Markman ◽  
Rukhshan Mian ◽  
Vineet M. Arora ◽  
...  

Abstract We surveyed healthcare workers at one urban academic hospital in the U.S. about their confidence in and knowledge of appropriate personal protective equipment use during the COVID-19 pandemic. Of 461 respondents, most were confident and knowledgeable about use; prescribers or nurses and those extremely confident about use were most knowledgeable.


Author(s):  
Vishal P. Shah ◽  
Laura E. Breeher ◽  
Julie M. Alleckson ◽  
David G. Rivers ◽  
Zhen Wang ◽  
...  

Abstract Objective: To assess the rate and factors associated with healthcare personnel (HCP) testing positive for SARS-CoV-2 after an occupational exposure Design: Retrospective cohort study Setting: Academic medical center with sites in Minnesota, Wisconsin, Arizona, and Florida Subjects: HCP with a high or medium risk occupational exposure to a patient or other HCP with SARS-CoV-2 Methods: We reviewed the records of HCP with significant occupational exposures from March 20th, 2020 through December 31st, 2020. We then performed regression analysis to assess the impact of demographic and occupational variables to assess their impact on the likelihood of testing positive for SARS-CoV-2 Results: A total of 2,253 confirmed occupational exposures occurred during the study period. Employees were the source for 57.1% of exposures. Overall, 101 (4.5%) HCP tested positive in the postexposure period. Of these, 80 had employee sources of exposure and 21 had patient sources of exposure. The post exposure infection rate was 6.2% when employees were the source, compared to 2.2% with patient sources. In a multivariate analysis, occupational exposure from an employee source had a higher risk of testing positive compared to a patient source (OR 3.22 95% CI (1.72-6.04)). Gender, age, high-risk exposure, and HCP role were not associated with increased risk of testing positive. Conclusions: The risk of acquiring COVID-19 following a significant occupational exposure is relatively low, even in the pre-vaccination era. Exposure to an infectious coworker carries a higher risk than exposure to a patient. Continued vigilance and precautions remain necessary in healthcare settings.


Author(s):  
Matthew J. Ziegler ◽  
Elizabeth Huang ◽  
Selamawit Bekele ◽  
Emily Reesey ◽  
Pam Tolomeo ◽  
...  

Abstract Background: The spatial and temporal extent of SARS-CoV-2 environmental contamination has not been precisely defined. We sought to elucidate contamination of different surface types and how contamination changes over time. Methods: We sampled surfaces longitudinally within COVID-19 patient rooms, performed quantitative RT-PCR for the detection of SARS-CoV-2 RNA, and modeled distance, time, and severity of illness on the probability of detecting SARS-CoV-2 using a mixed-effects binomial model. Results: The probability of detecting SARS-CoV-2 RNA in a patient room did not vary with distance. However, we found that surface type predicted probability of detection, with floors and high-touch surfaces having the highest probability of detection (floors odds ratio (OR) 67.8 (95% CrI 36.3 to 131); high-touch elevated OR 7.39 (95% CrI 4.31 to 13.1)). Increased surface contamination was observed in room where patients required high-flow oxygen, positive airway pressure, or mechanical ventilation (OR 1.6 (95% CrI 1.03 to 2.53)). The probability of elevated surface contamination decayed with prolonged hospitalization, but the probability of floor detection increased with duration of the local pandemic wave. Conclusions: Distance from patient’s bed did not predict SARS-CoV-2 RNA deposition in patient rooms, but surface type, severity of illness, and time from local pandemic wave predicted surface deposition.


Author(s):  
Charlesnika T. Evans ◽  
Benjamin J. DeYoung ◽  
Elizabeth L. Gray ◽  
Amisha Wallia ◽  
Joyce Ho ◽  
...  

Abstract Objective Healthcare workers (HCWs) are a high priority group for COVID-19 vaccination and serve as sources for information for the public. This analysis assessed vaccine intentions, factors associated with intentions, and change in uptake over time in HCWs. Methods A prospective cohort study of COVID-19 seroprevalence was conducted with HCWs in a large healthcare system in the Chicago area. Participants completed surveys (November 25, 2020-January 9, 2021 and April 24-July 12, 2021) on COVID-19 exposures, diagnosis and symptoms, demographics, and vaccination status. Results Of 4,180 HCWs who responded to a survey, 77.1% indicated they intended to get the vaccine; in this group, 23.2% had already received at least one dose of the vaccine (23.2%), 17.4% were unsure, and 5.5% reported that they would not get the vaccine. Factors associated with intention or vaccination were being exposed to clinical procedures (vs no procedures) and having a negative serology test for COVID-19 (vs no test) (adjusted odds ratio (AOR)=1.39, 95% Confidence Interval (CI) 1.16-1.65, AOR=1.46, 95% CI 1.24-1.73, respectively). Nurses (vs physicians, AOR=0.24 95% CI 0.17-0.33), non-Hispanic Black (vs Asians, AOR=0.35, 95% CI 0.21-0.59), and women (vs men, AOR=0.38, 95% CI 0.30-0.50) had lower odds of intention to get vaccinated. By 6-months follow-up, over 90% of those who had previously been unsure were vaccinated, while 59.7% of those who previously reported no intention of getting vaccinated, were vaccinated. Conclusions COVID-19 vaccination in HCWs was high, but variability in vaccination intention exists. Targeted messaging coupled with vaccine mandates can support uptake.


Author(s):  
Taha Gul Shaikh ◽  
Summaiyya Waseem ◽  
Syed Hassan Ahmed ◽  
Muhammad Sohaib Asghar ◽  
Muhammad Junaid Tahir

Author(s):  
Nutradee Narupaves ◽  
Purisha Kulworasreth ◽  
Nuchcha Manaanuntakul ◽  
David K. Warren ◽  
David J. Weber ◽  
...  

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