Hospital employees are at risk of severe acute respiratory coronavirus 2 (SARS-CoV-2) infection from patient, coworker, and community interactions. Understanding employees’ perspectives on transmission risks may inform hospital pandemic management strategies.

Qualitative interviews were conducted with 23 employees to assess factors contributing to perceived transmission risks during patient, coworker, and community interactions and to elicit recommendations. Using a deductive approach, transcripts were coded to identify recurring themes.

Tertiary hospital in Boston, Massachusetts.

Employees with a positive SARS-CoV-2 PCR test between March 2020 and January 2021, a period before widespread vaccine availability.

Employees generally reported low concern about transmission risks during patient care. Most patient-related risks, including limited inpatient testing and personal protective equipment availability, were only reported during the early weeks of the pandemic, except for suboptimal masking adherence by patients. Participants reported greater perceived transmission risks from coworkers, due to limited breakroom space, suboptimal coworker masking, and perceptions of inadequate contact tracing. Perceived community risks were related to social gatherings and to household members who also had high SARS-CoV-2 infection risk because they were essential workers. Recommendations included increasing well-ventilated workspaces and breakrooms, increasing support for sick employees, and stronger hospital communication about risks from non-patient-care activities, including the importance of masking adherence with coworkers and in the community.

To reduce transmission during future pandemics, hospitals may consider improving communication on risk reduction during coworker and community interactions. Societal investments are needed to improve hospital infrastructure (eg, better ventilation and breakroom space) and increase support for sick employees.

Disasters exacerbate inequities in health care. Health systems use the Hospital Incident Command System (HICS) to plan and coordinate their disaster response. This study examines how 2 health systems prioritized equity in implementing the Hospital Incident Command System (HICS) during the coronavirus disease 2019 (COVID-19) pandemic and identifies factors that influenced implementation.

This is a qualitative case comparison study, involving semi-structured interviews with 29 individuals from 2 US academic health systems. Strategies for promoting health equity were categorized by social determinants of health. The Consolidated Framework for Implementation Research (CFIR) guided analysis using a hybrid inductive-deductive approach.

The health systems used various strategies to incorporate health equity throughout implementation, addressing all 5 social determinants of health domains. Facilitators included HICS principles, external partnerships, community relationships, senior leadership, health equity experts and networks, champions, equity-stratified data, teaming, and a culture of health equity. Barriers encompassed clarity of the equity representative role, role ambiguity for equity representatives, tokenism, competing priorities, insufficient resource allocation, and lack of preparedness.

These findings elucidate how health systems centered equity during HICS implementation. Health systems and regulatory bodies can use these findings as a foundation to revise the HICS and move toward a more equitable disaster response.

We investigated concurrent outbreaks of Pseudomonas aeruginosa carrying blaVIM (VIM-CRPA) and Enterobacterales carrying blaKPC (KPC-CRE) at a long-term acute-care hospital (LTACH A).

We defined an incident case as the first detection of blaKPC or blaVIM from a patient’s clinical cultures or colonization screening test. We reviewed medical records and performed infection control assessments, colonization screening, environmental sampling, and molecular characterization of carbapenemase-producing organisms from clinical and environmental sources by pulsed-field gel electrophoresis (PFGE) and whole-genome sequencing.

From July 2017 to December 2018, 76 incident cases were identified from 69 case patients: 51 had blaKPC, 11 had blaVIM, and 7 had blaVIM and blaKPC. Also, blaKPC were identified from 7 Enterobacterales, and all blaVIM were P. aeruginosa. We observed gaps in hand hygiene, and we recovered KPC-CRE and VIM-CRPA from drains and toilets. We identified 4 KPC alleles and 2 VIM alleles; 2 KPC alleles were located on plasmids that were identified across multiple Enterobacterales and in both clinical and environmental isolates.

Our response to a single patient colonized with VIM-CRPA and KPC-CRE identified concurrent CPO outbreaks at LTACH A. Epidemiologic and genomic investigations indicated that the observed diversity was due to a combination of multiple introductions of VIM-CRPA and KPC-CRE and to the transfer of carbapenemase genes across different bacteria species and strains. Improved infection control, including interventions that minimized potential spread from wastewater premise plumbing, stopped transmission.

Clinical trials, which are mainly conducted in urban medical centers, may be less accessible to rural residents. Our aims were to assess participation and the factors associated with participation of rural residents in clinical trials.

Using geocoding, the residential address of participants enrolled into clinical trials at Mayo Clinic locations in Arizona, Florida, and the Midwest between January 1, 2016, and December 31, 2017, was categorized as urban or rural. The distance travelled by participants and trial characteristics was compared between urban and rural participants. Ordinal logistic regression analyses were used to evaluate whether study location and risks were associated with rural participation in trials.

Among 292 trials, including 136 (47%) cancer trials, there were 2313 participants. Of these, 731 (32%) were rural participants, which is greater than the rural population in these 9 states (19%, P < 0.001). Compared to urban participants, rural participants were older (65 ± 12 years vs 64 ± 12 years, P = 0.004) and travelled further to the medical center (103 ± 104 vs 68 ± 88 miles, P < 0.001). The proportion of urban and rural participants who were remunerated was comparable. In the multivariable analysis, the proportion of rural participants was lower (P < 0.001) in Arizona (10%) and Florida (18%) than the Midwest (38%) but not significantly associated with the study-related risks.

Approximately one in three clinical trial participants were rural residents versus one in five in the population. Rural residents travelled further to access clinical trials. The study-associated risks were not associated with the distribution of rural and urban participants in trials.

The rapid spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) throughout key regions of the United States in early 2020 placed a premium on timely, national surveillance of hospital patient censuses. To meet that need, the Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN), the nation’s largest hospital surveillance system, launched a module for collecting hospital coronavirus disease 2019 (COVID-19) data. We present time-series estimates of the critical hospital capacity indicators from April 1 to July 14, 2020.

From March 27 to July 14, 2020, the NHSN collected daily data on hospital bed occupancy, number of hospitalized patients with COVID-19, and the availability and/or use of mechanical ventilators. Time series were constructed using multiple imputation and survey weighting to allow near–real-time daily national and state estimates to be computed.

During the pandemic’s April peak in the United States, among an estimated 431,000 total inpatients, 84,000 (19%) had COVID-19. Although the number of inpatients with COVID-19 decreased from April to July, the proportion of occupied inpatient beds increased steadily. COVID-19 hospitalizations increased from mid-June in the South and Southwest regions after stay-at-home restrictions were eased. The proportion of inpatients with COVID-19 on ventilators decreased from April to July.

The NHSN hospital capacity estimates served as important, near–real-time indicators of the pandemic’s magnitude, spread, and impact, providing quantitative guidance for the public health response. Use of the estimates detected the rise of hospitalizations in specific geographic regions in June after they declined from a peak in April. Patient outcomes appeared to improve from early April to mid-July.

Shared patient–clinician decision-making is central to choosing between medical treatments. Decision support tools can have an important role to play in these decisions. We developed a decision support tool for deciding between nonsurgical treatment and surgical total knee replacement for patients with severe knee osteoarthritis. The tool aims to provide likely outcomes of alternative treatments based on predictive models using patient-specific characteristics. To make those models relevant to patients with knee osteoarthritis and their clinicians, we involved patients, family members, patient advocates, clinicians, and researchers as stakeholders in creating the models.

Stakeholders were recruited through local arthritis research, advocacy, and clinical organizations. After being provided with brief methodological education sessions, stakeholder views were solicited through quarterly patient or clinician stakeholder panel meetings and incorporated into all aspects of the project.

Participating in each aspect of the research from determining the outcomes of interest to providing input on the design of the user interface displaying outcome predications, 86% (12/14) of stakeholders remained engaged throughout the project. Stakeholder engagement ensured that the prediction models that form the basis of the Knee Osteoarthritis Mathematical Equipoise Tool and its user interface were relevant for patient–clinician shared decision-making.

Methodological research has the opportunity to benefit from stakeholder engagement by ensuring that the perspectives of those most impacted by the results are involved in study design and conduct. While additional planning and investments in maintaining stakeholder knowledge and trust may be needed, they are offset by the valuable insights gained.

To enhance enrollment into randomized clinical trials (RCTs), we proposed electronic health record-based clinical decision support for patient–clinician shared decision-making about care and RCT enrollment, based on “mathematical equipoise.”

As an example, we created the Knee Osteoarthritis Mathematical Equipoise Tool (KOMET) to determine the presence of patient-specific equipoise between treatments for the choice between total knee replacement (TKR) and nonsurgical treatment of advanced knee osteoarthritis.

With input from patients and clinicians about important pain and physical function treatment outcomes, we created a database from non-RCT sources of knee osteoarthritis outcomes. We then developed multivariable linear regression models that predict 1-year individual-patient knee pain and physical function outcomes for TKR and for nonsurgical treatment. These predictions allowed detecting mathematical equipoise between these two options for patients eligible for TKR. Decision support software was developed to graphically illustrate, for a given patient, the degree of overlap of pain and functional outcomes between the treatments and was pilot tested for usability, responsiveness, and as support for shared decision-making.

The KOMET predictive regression model for knee pain had four patient-specific variables, and an r2 value of 0.32, and the model for physical functioning included six patient-specific variables, and an r2 of 0.34. These models were incorporated into prototype KOMET decision support software and pilot tested in clinics, and were generally well received.

Use of predictive models and mathematical equipoise may help discern patient-specific equipoise to support shared decision-making for selecting between alternative treatments and considering enrollment into an RCT.

The success of central line-associated bloodstream infection (CLABSI) prevention programs in intensive care units (ICUs) has led to the expansion of surveillance at many hospitals. We sought to compare non-ICU CLABSI (nCLABSI) rates with national reports and describe methods of surveillance at several participating US institutions.

An electronic survey of several medical centers about infection surveillance practices and rate data for non-ICU Patients.

Ten tertiary care hospitals.

In March 2011, a survey was sent to 10 medical centers. The survey consisted of 12 questions regarding demographics and CLABSI surveillance methodology for non-ICU patients at each center. Participants were also asked to provide available rate and device utilization data.

Hospitals ranged in size from 238 to 1,400 total beds (median, 815). All hospitals reported using Centers for Disease Control and Prevention (CDC) definitions. Denominators were collected by different means: counting patients with central lines every day (5 hospitals), indirectly estimating on the basis of electronic orders (n = 4), or another automated method (n = 1). Rates of nCLABSI ranged from 0.2 to 4.2 infections per 1,000 catheter-days (median, 2.5). The national rate reported by the CDC using 2009 data from the National Healthcare Surveillance Network was 1.14 infections per 1,000 catheter-days.

Only 2 hospitals were below the pooled CLABSI rate for inpatient wards; all others exceeded this rate. Possible explanations include differences in average central line utilization or hospital size in the impact of certain clinical risk factors notably absent from the definition and in interpretation and reporting practices. Further investigation is necessary to determine whether the national benchmarks are low or whether the hospitals surveyed here represent a selection of outliers.

To evaluate the impact of postprescription review of broad-spectrum antimicrobial (study-ABX) agents on rates of antimicrobial use.

Quasi-experimental before-after study.

Five academic medical centers.

Adults receiving at least 48 hours of study-ABX.

The baseline, intervention, and follow-up periods were 6 months each in 2 units at each of 5 sites. Adults receiving at least 48 hours of study-ABX entered the cohort as case-patients. During the intervention, infectious-diseases physicians reviewed the cases after 48 hours of study-ABX. The provider was contacted with alternative recommendations if antimicrobial use was considered to be unjustified on the basis of predetermined criteria. Acceptance rates were assessed 48 hours later. The primary outcome measure was days of study-ABX per 1,000 study-patient-days in the baseline and intervention periods.

There were 1,265 patients in the baseline period and 1,163 patients in the intervention period. Study-ABX use decreased significantly during the intervention period at 2 sites: from 574.4 to 533.8 study-ABX days/1,000 patient-days (incidence rate ratio [IRR], 0.93; 95% confidence interval [CI], 0.88-0.97; P = .002) at hospital В and from 615.6 to 514.4 study-ABX days/1,000 patient-days (IRR, 0.83; 95% CI, 0.79-0.88; P < .001) at hospital D. Both had established antimicrobial stewardship programs (ASP). Study-ABX use increased at 2 sites and stayed the same at 1 site. At all institutions combined, 390 of 1,429 (27.3%) study-ABX courses were assessed as unjustified; recommendations to modify or stop therapy were accepted for 260 (66.7%) of these courses.

Postprescription review of study-ABX decreased antimicrobial utilization in some of the study hospitals and may be more effective when performed as part of an established ASP.