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Current guidance states that asymptomatic screening for severe acute respiratory coronavirus virus 2 (SARS-CoV-2) prior to admission to an acute-care setting is at the facility’s discretion. This study’s objective was to estimate the number of undetected cases of SARS-CoV-2 admitted as inpatients under 4 testing approaches and varying assumptions.
Design and setting:
Individual-based microsimulation of 104 North Carolina acute-care hospitals
All simulated inpatient admissions to acute-care hospitals from December 15, 2021, to January 13, 2022 [ie, during the SARS-COV-2 ο (omicron) variant surge].
We simulated (1) only testing symptomatic patients, (2) 1-stage antigen testing with no confirmatory polymerase chain reaction (PCR) test, (3) 1-stage antigen testing with a confirmatory PCR for negative results, and (4) serial antigen screening (ie, repeat antigen test 2 days after a negative result).
Over 1 month, there were 77,980 admissions: 13.7% for COVID-19, 4.3% with but not for COVID-19, and 82.0% for non–COVID-19 indications without current infection. Without asymptomatic screening, 1,089 (credible interval [CI], 946–1,253) total SARS-CoV-2 infections (7.72%) went undetected. With 1-stage antigen screening, 734 (CI, 638–845) asymptomatic infections (67.4%) were detected, with 1,277 false positives. With combined antigen and PCR screening, 1,007 (CI, 875–1,159) asymptomatic infections (92.5%) were detected, with 5,578 false positives. A serial antigen testing policy detected 973 (CI, 845–1,120) asymptomatic infections (89.4%), with 2,529 false positives.
Serial antigen testing identified >85% of asymptomatic infections and resulted in fewer false positives with less cost per identified infection compared to combined antigen plus PCR testing.
This SHEA white paper identifies knowledge gaps and challenges in healthcare epidemiology research related to coronavirus disease 2019 (COVID-19) with a focus on core principles of healthcare epidemiology. These gaps, revealed during the worst phases of the COVID-19 pandemic, are described in 10 sections: epidemiology, outbreak investigation, surveillance, isolation precaution practices, personal protective equipment (PPE), environmental contamination and disinfection, drug and supply shortages, antimicrobial stewardship, healthcare personnel (HCP) occupational safety, and return to work policies. Each section highlights three critical healthcare epidemiology research questions with detailed description provided in supplementary materials. This research agenda calls for translational studies from laboratory-based basic science research to well-designed, large-scale studies and health outcomes research. Research gaps and challenges related to nursing homes and social disparities are included. Collaborations across various disciplines, expertise and across diverse geographic locations will be critical.
The modern healthcare system involves complex interactions among microbes, patients, providers, and the built environment. It represents a unique and challenging setting for control of the emergence and spread of infectious diseases. We examine an extension of the perspectives and methods from ecology (and especially urban ecology) to address these unique issues, and we outline 3 examples: (1) viewing patients as individual microbial ecosystems; (2) the altered ecology of infectious diseases specifically within hospitals; and (3) ecosystem management perspectives for infection surveillance and control. In each of these cases, we explore the accuracy and relevance of analogies to existing urban ecological perspectives, and we demonstrate a few of the potential direct uses of this perspective for altering research into the control of healthcare-associated infections.
Fecal microbiota transplantation (FMT) has been suggested as a new treatment to manage Clostridium difficile infection (CDI). With use of a mathematical model of C. difficile within an intensive care unit (ICU), we examined the potential impact of routine FMT.
Design, Setting, and Patients.
A mathematical model of C. difficile transmission, supplemented with prospective cohort, surveillance, and billing data from hospitals in the southeastern United States.
Cohort, surveillance, and billing data as well as data from the literature were used to construct a compartmental model of CDI within an ICU. Patients were defined as being in 1 of 6 potential health states: uncolonized and at low risk; uncolonized and at high risk; colonized and at low risk; colonized and at high risk; having CDI; or treated with FMT.
The use of FMT to treat patients after CDI was associated with a statistically significant reduction in recurrence but not with a reduction in incident cases. Treatment after administration of high-risk medications, such as antibiotics, did not result in a decrease in recurrence but did result in a statistically significant difference in incident cases across treatment groups, although whether this difference was clinically relevant was questionable.
Our study is a novel mathematical model that examines the effect of FMT on the prevention of recurrent and incident CDI. The routine use of FMT represents a promising approach to reduce complex recurrent cases, but a reduction in CDI incidence will require the use of other methods to prevent transmission.
Change from nonmolecular to molecular testing techniques is thought to contribute to the increasing trend in incidence of Clostridium difficile infection (CDI); however the degree of effect attributed to this versus other time-related epidemiologic factors is unclear.
We compared the relative change in incidence rate (IRR) of healthcare facility–associated (HCFA) CDI among hospitals in the Duke Infection Control Outreach Network before and after the date of switch from nonmolecular tests to polymerase chain reaction (PCR) using prospectively collected surveillance data from July 2009 to December 2011. Data from 10 hospitals that switched and 22 control hospitals were included. Individual hospital estimates were determined using Poisson regression. We used an interrupted time series approach to develop a Poisson mixed-effects model. Additional regression adjustments were made for clustering and proportion of intensive care unit patient-days. The variable for PCR was treated as a fixed effect; other modeled variables were random effects.
For those hospitals that switched to PCR, mean incidence rate of HCFA CDI before the switch was 6.0 CDIs per 10,000 patient-days compared with 9.6 CDIs per 10,000 patient-days after the switch. Estimates of hospital-specific IRR that compared after the switch with before the switch ranged from 0.89 (95% confidence interval [CI], 0.32–2.44) to 6.91 (95% CI, 1.12–42.54). After adjustment in the mixed-effects model, the overall IRR comparing CDI incidence after the switch to before the switch was 1.56 (95% CI, 1.28–1.90). Time-trend variables did not reach statistical significance.
Hospitals that switched from nonmolecular to molecular tests experienced an approximate 56% increase in the rate of HCFA CDI after testing change.
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