SARS-CoV-2 has been implicated in the largest recorded coronavirus outbreak to date. Initially, most COVID-19 cases were in China, but the virus has spread to more than 184 countries worldwide, and the United States currently has more cases than any other country.

With person-to-person spread expanding in the United States, we describe hospital preparedness for managing suspected and confirmed COVID-19 patients.

Cross-sectional survey focused on various elements of respiratory disease preparedness.

Critical access hospitals (CAHs) and acute-care hospitals (ACHs) in Idaho.

The electronic survey was sent to infection preventionists (IPs) and nurse administrators in 44 hospitals in Idaho.

Overall, 32 (73%) hospitals responded to the survey. Participating facilities reported their preparedness with respect to existing, formalized structures for managing infectious disease incidents—specifically COVID-19—as well as availability of resources, such as isolation rooms and personal protective equipment, for safely managing suspected and confirmed COVID-19 cases.

Hospitals covered by the survey had varying levels of preparedness for managing COVID-19 cases, with differences across the various categories of interest in this study. Although the study reveals strengths, including in application of emergency management and infection control frameworks, it also suggests that other areas, such as consistent implementation of federal guidelines and requirements for infection prevention, are potential areas for strengthening preparedness for SARS-CoV-2 and other respiratory pathogens with pandemic potential.

To determine whether ambient air pollutants and meteorological variables are associated with daily COVID-19 incidence.

A retrospective cohort from January 25 to February 29, 2020.

Cities of Wuhan, Xiaogan, and Huanggang, China.

The COVID-19 cases detected each day.

We collected daily data of COVID-19 incidence, 8 ambient air pollutants (particulate matter of ≤2.5 µm [PM2.5], particulate matter ≤10 µm [PM10], sulfur dioxide [SO2], carbon monoxide [CO], nitrogen dioxide [NO2], and maximum 8-h moving average concentrations for ozone [O3-8h]) and 3 meteorological variables (temperature, relative humidity, and wind) in China’s 3 worst COVID-19–stricken cities during the study period. The multivariate Poisson regression was performed to understand their correlation.

Daily COVID-19 incidence was positively associated with PM2.5 and humidity in all cities. Specifically, the relative risk (RR) of PM2.5 for daily COVID-19 incidences were 1.036 (95% confidence interval [CI], 1.032–1.039) in Wuhan, 1.059 (95% CI, 1.046–1.072) in Xiaogan, and 1.144 (95% CI, 1.12–1.169) in Huanggang. The RR of humidity for daily COVID-19 incidence was consistently lower than that of PM2.5, and this difference ranged from 0.027 to 0.111. Moreover, PM10 and temperature also exhibited a notable correlation with daily COVID-19 incidence, but in a negative pattern The RR of PM10 for daily COVID-19 incidence ranged from 0.915 (95% CI, 0.896–0.934) to 0.961 (95% CI, 0.95–0.972, while that of temperature ranged from 0.738 (95% CI, 0.717–0.759) to 0.969 (95% CI, 0.966–0.973).

Our data show that PM2.5 and humidity are substantially associated with an increased risk of COVID-19 and that PM10 and temperature are substantially associated with a decreased risk of COVID-19.

To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.

Multicenter retrospective cohort study.

The study included 43 hospitals using a common infection prevention surveillance system.

A space–time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods.

We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals’ surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work.

Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.

A significant proportion of inpatient antimicrobial prescriptions are inappropriate. Post-prescription review with feedback has been shown to be an effective means of reducing inappropriate antimicrobial use. However, implementation is resource intensive. Our aim was to evaluate the performance of traditional statistical models and machine-learning models designed to predict which patients receiving broad-spectrum antibiotics require a stewardship intervention.

We performed a single-center retrospective cohort study of inpatients who received an antimicrobial tracked by the antimicrobial stewardship program. Data were extracted from the electronic medical record and were used to develop logistic regression and boosted-tree models to predict whether antibiotic therapy required stewardship intervention on any given day as compared to the criterion standard of note left by the antimicrobial stewardship team in the patient’s chart. We measured the performance of these models using area under the receiver operating characteristic curves (AUROC), and we evaluated it using a hold-out validation cohort.

Both the logistic regression and boosted-tree models demonstrated fair discriminatory power with AUROCs of 0.73 (95% confidence interval [CI], 0.69–0.77) and 0.75 (95% CI, 0.72–0.79), respectively (P = .07). Both models demonstrated good calibration. The number of patients that would need to be reviewed to identify 1 patient who required stewardship intervention was high for both models (41.7–45.5 for models tuned to a sensitivity of 85%).

Complex models can be developed to predict which patients require a stewardship intervention. However, further work is required to develop models with adequate discriminatory power to be applicable to real-world antimicrobial stewardship practice.

Antibiotic use in nursing homes is often inappropriate, in terms of overuse and misuse, and it can be linked to adverse events and antimicrobial resistance. Antimicrobial stewardship programs (ASPs) can optimize antibiotic use by minimizing unnecessary prescriptions, treatment cost, and the overall spread of antimicrobial resistance. Nursing home providers and residents are candidates for ASP implementation, yet guidelines for implementation are limited.

To support nursing home providers with the selection and adoption of ASP interventions.

A multiphase modified Delphi method to assess 15 ASP interventions across criteria addressing scientific merit, feasibility, impact, accountability, and importance. This study included surveys supplemented with a 1-day consensus meeting.

A 16-member multidisciplinary panel of experts and resident representatives.

From highest to lowest, 6 interventions were prioritized by the panel: (1) guidelines for empiric prescribing, (2) audit and feedback, (3) communication tools, (4) short-course antibiotic therapy, (5) scheduled antibiotic reassessment, and (6) clinical decision support systems. Several interventions were not endorsed: antibiograms, educational interventions, formulary review, and automatic substitution. A lack of nursing home resources was noted, which could impede multifaceted interventions.

Nursing home providers should consider 6 key interventions for ASPs. Such interventions may be feasible for nursing home settings and impactful for improving antibiotic use; however, scientific merit supporting each is variable. A multifaceted approach may be necessary for long-term improvement but difficult to implement.

Antibiotics are commonly used in intensive care units (ICUs), yet differences in antibiotic use across ICUs are unknown. Herein, we studied antibiotic use across ICUs and examined factors that contributed to variation.

We conducted a retrospective cohort study using data from Ontario’s Critical Care Information System (CCIS), which included 201 adult ICUs and 2,013,397 patient days from January 2012 to June 2016. Antibiotic use was measured in days of therapy (DOT) per 1,000 patient days. ICU factors included ability to provide ventilator support (level 3) or not (level 2), ICU type (medical-surgical or other), and academic status. Patient factors included severity of illness using multiple-organ dysfunction score (MODS), ventilatory support, and central venous catheter (CVC) use. We analyzed the effect of these factors on variation in antibiotic use.

Overall, 269,351 patients (56%) received antibiotics during their ICU stay. The mean antibiotic use was 624 (range 3–1460) DOT per 1,000 patient days. Antibiotic use was significantly higher in medical-surgical ICUs compared to other ICUs (697 vs 410 DOT per 1,000 patient days; P < .0001) and in level 3 ICUs compared to level 2 ICUs (751 vs 513 DOT per 1,000 patient days; P < .0001). Higher antibiotic use was associated with higher severity of illness and intensity of treatment. ICU and patient factors explained 47% of the variation in antibiotic use across ICUs.

Antibiotic use varies widely across ICUs, which is partially associated with ICUs and patient characteristics. These differences highlight the importance of antimicrobial stewardship to ensure appropriate use of antibiotics in ICU patients.

To determine whether carbapenem consumption and Pseudomonas aeruginosa resistance rates can be used as benchmarks to compare and improve antimicrobial stewardship programs across multiple pediatric hospitals.

A prospective study.

Healthcare institutions in Japan with >100 pediatric beds.

An annual survey of the total days of therapy (DOT) per 1,000 patient days for carbapenem antibiotics (meropenem, imipenem-cilastatin, panipenem-betamipron, doripenem) and susceptibility rates of Pseudomonas aeruginosa to meropenem and imipenem-cilastatin from each institution was conducted over a 7-year period. Data were reported to the administration, as well as to the infection control team, of each institution annually.

Data were obtained from 32 facilities. The median total carbapenem DOT per 1,000 patient days was 16.6 and varied widely, with a range of 2.7 to 59.0. The median susceptibility to meropenem was 86.6%, ranging from 78.6% to 96.6%. We detected an inverse correlation between total carbapenem DOT versus susceptibility (r = – 0.36; P < .01). Over the 7-year period, the DOT per 1,000 patient days of carbapenem decreased by 27% from a median of 16.0 to 11.7 (P < .01). We also observed an improvement in susceptibility to meropenem from a median of 87% to 89.7% (P = .01) and to imipenem-cilastatin from 79% to 85% (P < .01). The decreases in the use of carbapenem were greater in institutions with antimicrobial stewardship programs led by pediatric infectious disease specialists.

Antimicrobial use and resistance, targeting carbapenems and P. aeruginosa, respectively, can serve as benchmarks that can be utilized to promote antimicrobial stewardship across pediatric healthcare institutions.

Given recent changes in the epidemiology of Clostridioides difficile infection (CDI) and prevention efforts, we investigated temporal changes over a period of 11 years (2006–2016) in incidence and risk factors for CDI.

Retrospective matched case-control study.

Pediatric and adult inpatients (n = 694,849) discharged from 3 hospitals (tertiary and quaternary care, community, and pediatric) in a large, academic health center in New York City.

Risk factors were identified in cases and controls matched by length of stay at a ratio of 1:4. A Cochran–Armitage or Mann-Kendall test was used to investigate trends of incidence and risk factors.

Of 694,849 inpatients, 6,038 (0.87%) had CDI: 44% of these cases were hospital acquired (HA-CDI) and 56% were community acquired (CA-CDI). We observed temporal downward trends in HA-CDI (−0.03% per year) and upward trends in CA-CDI (+0.04% per year). Over time, antibiotics were administered to more patients (+3% per year); the use of high-risk antibiotics declined (–1.2% per year); and antibiotic duration increased in patients with HA-CDI (+4.4% per year). Fewer proton-pump inhibitors and more histamine-2 blockers were used (−3.8% and +7.3% per year, respectively; all Ptrend <.05).

Although the incidence of HA-CDI decreased over time, CA-CDI simultaneously increased. Continued efforts to assure judicious use of antibiotics in inpatient and community settings is clearly vital. Measuring the actual the level of exposure of an antibiotic (incidence density) should be used for ongoing surveillance and assessment.

To institute facility-wide Kamishibai card (K-card) rounding for central venous catheter (CVC) maintenance bundle education and adherence and to evaluate its impact on bundle reliability and central-line–associated bloodstream infection (CLABSI) rates.

Quality improvement project.

Inpatient units at a large, academic freestanding children’s hospital.

Data for inpatients with a CVC in place for ≥1 day between November 1, 2017 and October 31, 2018 were included.

A K-card was developed based on 7 core elements in our CVC maintenance bundle. During monthly audits, auditors used the K-cards to ask bedside nurses standardized questions and to conduct medical record documentation reviews in real time. Adherence to every bundle element was required for the audit to be considered “adherent.” We recorded bundle reliability prospectively, and we compared reliability and CLABSI rates at baseline and 1 year after the intervention.

During the study period, 2,321 K-card audits were performed for 1,051 unique patients. Overall maintenance bundle reliability increased significantly from 43% at baseline to 78% at 12 months after implementation (P < .001). The hospital-wide CLABSI rate decreased from 1.35 during the 12-month baseline period to 1.17 during the 12-month intervention period, but the change was not statistically significant (incidence rate ratio [IRR], 0.87; 95% confidence interval [CI], 0.60–1.24; P = .41).

Hospital-wide CVC K-card rounding facilitated standardized data collection, discussion of reliability, and real-time feedback to nurses. Maintenance bundle reliability increased after implementation, accompanied by a nonsignificant decrease in the CLABSI rate.