Severe acute respiratory coronavirus virus 2 (SARS-CoV-2) surface contamination in staff common areas and impact on healthcare worker infection: Prospective surveillance during the coronavirus disease 2019 (COVID-19) pandemic.

We prospectively surveyed SARS-CoV-2 RNA contamination in staff common areas within an acute-care hospital. An increasing prevalence of surface contamination was detected over time. Adjusting for patient census or community incidence of coronavirus disease 2019 (COVID-19), the proportion of contaminated surfaces did not predict healthcare worker COVID-19 infection on study units.

Healthcare microenvironments define multidrug-resistant organism persistence.

BACKGROUND: Multidrug-resistant organisms (MDROs) colonizing the healthcare environment have been shown to contribute to risk for healthcare-associated infections (HAIs), with adverse effects on patient morbidity and mortality. We sought to determine how bacterial contamination and persistent MDRO colonization of the healthcare environment are related to the position of patients and wastewater sites. METHODS: We performed a prospective cohort study, enrolling 51 hospital rooms at the time of admitting a patient with an eligible MDRO in the prior 30 days. We performed systematic sampling and MDRO culture of rooms, as well as 16S rRNA sequencing to define the environmental microbiome in a subset of samples. RESULTS: The probability of detecting resistant gram-negative organisms, including Enterobacterales, Acinetobacter spp, and Pseudomonas spp, increased with distance from the patient. In contrast, Clostridioides difficile and methicillin-resistant Staphylococcus aureus were more likely to be detected close to the patient. Resistant Pseudomonas spp and S. aureus were enriched in these hot spots despite broad deposition of 16S rRNA gene sequences assigned to the same genera, suggesting modifiable factors that permit the persistence of these MDROs. CONCLUSIONS: MDRO hot spots can be defined by distance from the patient and from wastewater reservoirs. Evaluating how MDROs are enriched relative to bacterial DNA deposition helps to identify healthcare micro-environments and suggests how targeted environmental cleaning or design approaches could prevent MDRO persistence and reduce infection risk.

Comparability of clinical trials and spontaneous reporting data regarding COVID-19 vaccine safety.

Severe adverse events (AEs) after COVID-19 vaccination are not well studied in randomized controlled trials (RCTs) due to rarity and short follow-up. To monitor the safety of COVID-19 vaccines ("Pfizer" vaccine dose 1 and 2, "Moderna" vaccine dose 1 and 2, and "Janssen" vaccine single dose) in the U.S., especially regarding severe AEs, we compare the relative rankings of these vaccines using both RCT and the Vaccine Adverse Event Reporting System (VAERS) data. The risks of local and systemic AEs were assessed from the three pivotal COVID-19 vaccine trials and also calculated in the VAERS cohort consisting of 559,717 reports between December 14, 2020 and September 17, 2021. AE rankings of the five vaccine groups calculated separately by RCT and VAERS were consistent, especially for systemic AEs. For severe AEs reported in VAERS, the reported risks of thrombosis and GBS after Janssen vaccine were highest. The reported risk of shingles after the first dose of Moderna vaccine was highest, followed by the second dose of the Moderna vaccine. The reported risk of myocarditis was higher after the second dose of Pfizer and Moderna vaccines. The reported risk of anaphylaxis was higher after the first dose of Pfizer vaccine. Limitations of this study are the inherent biases of the spontaneous reporting system data, and only including three pivotal RCTs and no comparison with other active vaccine safety surveillance systems.

Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites.

Integrating real-world data (RWD) from several clinical sites offers great opportunities to improve estimation with a more general population compared to analyses based on a single clinical site. However, sharing patient-level data across sites is practically challenging due to concerns about maintaining patient privacy. We develop a distributed algorithm to integrate heterogeneous RWD from multiple clinical sites without sharing patient-level data. The proposed distributed conditional logistic regression (dCLR) algorithm can effectively account for between-site heterogeneity and requires only one round of communication. Our simulation study and data application with the data of 14,215 COVID-19 patients from 230 clinical sites in the UnitedHealth Group Clinical Research Database demonstrate that the proposed distributed algorithm provides an estimator that is robust to heterogeneity in event rates when efficiently integrating data from multiple clinical sites. Our algorithm is therefore a practical alternative to both meta-analysis and existing distributed algorithms for modeling heterogeneous multi-site binary outcomes.

Colonization with extended-spectrum cephalosporin-resistant Enterobacterales (ESCrE) and carbapenem-resistant Enterobacterales (CRE) in healthcare and community settings in Botswana: an antibiotic resistance in communities and hospitals (ARCH) study.

OBJECTIVES: Although extended-spectrum cephalosporin-resistant Enterobacterales (ESCrE) and carbapenem-resistant Enterobacterales (CRE) are a global challenge, data on these organisms in low- and middle-income countries are limited. In this study, we sought to characterize colonization data critical for greater antibiotic resistance surveillance efforts. METHODS: This study was conducted in three hospitals and six clinics in Botswana. We conducted ongoing surveillance of adult patients in hospitals and clinics and adults and children in the community. All participants underwent rectal swab sampling to identify ESCrE and CRE. RESULTS: Enrollment occurred from January 15, 2020, to September 4, 2020, but paused from April 2, 2020, to May 21, 2020, because of a countrywide COVID-19 lockdown. Of 5088 individuals approached, 2469 (49%) participated. ESCrE colonization prevalence was 30.7% overall (43% for hospital participants, 31% for clinic participants, 24% for adult community participants, and 26% for child community participants) (P <0.001). A total of 42 (1.7%) participants were colonized with CRE. CRE colonization prevalence was 1.7% overall (6.8% for hospital participants, 0.7% for clinic participants, 0.2% for adult community participants, and 0.5% for child community participants) (P <0.001). ESCrE and CRE prevalence varied substantially across regions and was significantly higher prelockdown versus postlockdown. CONCLUSIONS: ESCrE colonization was high in all settings in Botswana. CRE prevalence in hospitals was also considerable. Colonization prevalence varied by region and clinical setting and decreased after a countrywide lockdown.

Risk factors for antimicrobial resistance among Staphylococcus isolated from pets living with a patient diagnosed with methicillin-resistant Staphylococcus aureus infection.

It has been suggested that pets play a critical role in the maintenance of methicillin-resistant (MR) and multidrug-resistant (MDR) Staphylococcus spp. in the household. We examined risk factors for carriage of antimicrobial-resistant coagulase-positive staphylococci, with particular attention to Staphylococcus aureus and Staphylococcus pseudintermedius isolated from pets living in households of people diagnosed with methicillin-resistant S. aureus (MRSA) skin or soft-tissue infection. We analyzed data collected cross-sectionally from a study conducted in 2012 that evaluated the transmission of MRSA and other staphylococci from humans, their pets and the environment (Pets and Environmental Transmission of Staphylococci [PETS] study). We used unadjusted and adjusted stratified logistic regression analyses with household-clustered standard errors to evaluate the association between demographic, healthcare-related, contact-related and environmental risk factors and MDR Staphylococcus spp. isolated from dogs and cats. Staphylococcal isolates obtained from dogs (n = 63) and cats (n = 47) were included in these analyses. The use of oral or injectable antimicrobials by the pets during the prior year was the main risk factor of interest. Based on our results, 50% (12/24) of S. aureus, 3.3% (1/30) of S. pseudintermedius and 25% (14/56) of other coagulase-positive staphylococci (CPS) were determined to be MDR. S. aureus isolates were more likely to be MDR compared with S. pseudintermedius. We did not find a significant statistical association between the use of oral or injectable antimicrobials in the prior year and the presence of MDR bacteria. The results suggest that drivers of antimicrobial resistance in household staphylococci may vary by bacterial species, which could have implications for one health intervention strategies for staphylococci and inform the investigation of other reverse zoonoses, such as COVID-19.

DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models.

Linear mixed models are commonly used in healthcare-based association analyses for analyzing multi-site data with heterogeneous site-specific random effects. Due to regulations for protecting patients' privacy, sensitive individual patient data (IPD) typically cannot be shared across sites. We propose an algorithm for fitting distributed linear mixed models (DLMMs) without sharing IPD across sites. This algorithm achieves results identical to those achieved using pooled IPD from multiple sites (i.e., the same effect size and standard error estimates), hence demonstrating the lossless property. The algorithm requires each site to contribute minimal aggregated data in only one round of communication. We demonstrate the lossless property of the proposed DLMM algorithm by investigating the associations between demographic and clinical characteristics and length of hospital stay in COVID-19 patients using administrative claims from the UnitedHealth Group Clinical Discovery Database. We extend this association study by incorporating 120,609 COVID-19 patients from 11 collaborative data sources worldwide.

SARS-CoV-2 RNA persists on surfaces following terminal disinfection of COVID-19 hospital isolation rooms.

We evaluated the effect of terminal cleaning on SARS-CoV-2 RNA contamination of COVID-19 isolation rooms in an acute care hospital. SARS-CoV-2 RNA was detected on 32.1% of room surfaces after cleaning; the odds of contamination increased with month. The prevalence of elevated high-touch surface contamination was lower in terminally cleaned rooms than patient-occupied rooms.

Spatial and temporal effects on severe acute respiratory coronavirus virus 2 (SARS-CoV-2) contamination of the healthcare environment.

BACKGROUND: The spatial and temporal extent of severe acute respiratory coronavirus virus 2 (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% credible interval [CrI], 36.3-131) and high-touch elevated surfaces (OR, 7.39; 95% CrI, 4.31-13.1). Increased surface contamination was observed in room where patients required high-flow oxygen, positive airway pressure, or mechanical ventilation (OR, 1.6; 95% CrI, 1.03-2.53). The probability of elevated surface contamination decayed with prolonged hospitalization, but the probability of floor detection increased with the duration of the local pandemic wave. CONCLUSIONS: Distance from a 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.

Quantifying the Impact of Nasopharyngeal Specimen Quality on Severe Acute Respiratory Syndrome Coronavirus 2 Test Performance.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse-transcription polymerase chain reaction (RT-PCR) cycle threshold (Ct) has been used to estimate quantitative viral load, with the goal of targeting isolation precautions for individuals with coronavirus disease 2019 (COVID-19) and guiding public health interventions. However, variability in specimen quality can alter the Ct values obtained from SARS-CoV-2 clinical assays. We sought to define how variable nasopharyngeal (NP) swab quality impacts clinical SARS-CoV-2 test sensitivity. METHODS: We performed amplification of a human gene target (ß-actin) in parallel with a clinical RT-PCR targeting the SARS-CoV-2 ORF1ab gene for 1282 NP specimens collected from patients with clinical concern for COVID-19. We evaluated the relationship between NP specimen quality, characterized by late Ct values for the human gene target ß-actin Ct, and the probability of SARS-CoV-2 detection via logistic regression, as well as the linear relationship between SARS-CoV-2 and ß-actin Ct. RESULTS: Low-quality NP swabs are less likely to detect SARS-CoV-2 (odds ratio, 0.607 [95% credible interval {CrI}, .487-.753]). We observed a positive linear relationship between SARS-CoV-2 and ß-actin Ct values (slope, 0.181 [95% CrI, .097-.264]), consistent with a reduction in detection of 0.181 cycles for each additional cycle of the ß-actin target. COVID-19 disease severity was not associated with ß-actin Ct values. CONCLUSIONS: Variability in NP specimen quality significantly impacts the performance of clinical SARS-CoV-2 assays, and caution should be taken when interpreting quantitative SARS-CoV-2 Ct results. If unrecognized, low-quality NP specimens, which are characterized by a low level of amplifiable human DNA target, may limit the successful application of SARS-CoV-2 Ct values to direct infection control and public health interventions.