The seroprevalence of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) IgG antibody was evaluated among employees of a Veterans Affairs healthcare system to assess potential risk factors for transmission and infection.

All employees were invited to participate in a questionnaire and serological survey to detect antibodies to SARS-CoV-2 as part of a facility-wide quality improvement and infection prevention initiative regardless of clinical or nonclinical duties. The initiative was conducted from June 8 to July 8, 2020.

Of the 2,900 employees, 51% participated in the study, revealing a positive SARS-CoV-2 seroprevalence of 4.9% (72 of 1,476; 95% CI, 3.8%–6.1%). There were no statistically significant differences in the presence of antibody based on gender, age, frontline worker status, job title, performance of aerosol-generating procedures, or exposure to known patients with coronavirus infectious disease 2019 (COVID-19) within the hospital. Employees who reported exposure to a known COVID-19 case outside work had a significantly higher seroprevalence at 14.8% (23 of 155) compared to those who did not 3.7% (48 of 1,296; OR, 4.53; 95% CI, 2.67–7.68; P < .0001). Notably, 29% of seropositive employees reported no history of symptoms for SARS-CoV-2 infection.

The seroprevalence of SARS-CoV-2 among employees was not significantly different among those who provided direct patient care and those who did not, suggesting that facility-wide infection control measures were effective. Employees who reported direct personal contact with COVID-19–positive persons outside work were more likely to have SARS-CoV-2 antibodies. Employee exposure to SARS-CoV-2 outside work may introduce infection into hospitals.

The pressures exerted by the coronavirus disease 2019 (COVID-19) pandemic pose an unprecedented demand on healthcare services. Hospitals become rapidly overwhelmed when patients requiring life-saving support outpace available capacities.

We describe methods used by a university hospital to forecast case loads and time to peak incidence.

We developed a set of models to forecast incidence among the hospital catchment population and to describe the COVID-19 patient hospital-care pathway. The first forecast utilized data from antecedent allopatric epidemics and parameterized the care-pathway model according to expert opinion (ie, the static model). Once sufficient local data were available, trends for the time-dependent effective reproduction number were fitted, and the care pathway was reparameterized using hazards for real patient admission, referrals, and discharge (ie, the dynamic model).

The static model, deployed before the epidemic, exaggerated the bed occupancy for general wards (116 forecasted vs 66 observed), ICUs (47 forecasted vs 34 observed), and predicted the peak too late: general ward forecast April 9 and observed April 8 and ICU forecast April 19 and observed April 8. After April 5, the dynamic model could be run daily, and its precision improved with increasing availability of empirical local data.

The models provided data-based guidance for the preparation and allocation of critical resources of a university hospital well in advance of the epidemic surge, despite overestimating the service demand. Overestimates should resolve when the population contact pattern before and during restrictions can be taken into account, but for now they may provide an acceptable safety margin for preparing during times of uncertainty.

To determine risk factors for mortality among COVID-19 patients admitted to a system of community hospitals in the United States.

Retrospective analysis of patient data collected from the routine care of COVID-19 patients.

System of >180 acute-care facilities in the United States.

All admitted patients with positive identification of COVID-19 and a documented discharge as of May 12, 2020.

Determination of demographic characteristics, vital signs at admission, patient comorbidities and recorded discharge disposition in this population to construct a logistic regression estimating the odds of mortality, particular for those patients characterized as not being critically ill at admission.

In total, 6,180 COVID-19+ patients were identified as of May 12, 2020. Most COVID-19+ patients (4,808, 77.8%) were admitted directly to a medical-surgical unit with no documented critical care or mechanical ventilation within 8 hours of admission. After adjusting for demographic characteristics, comorbidities, and vital signs at admission in this subgroup, the largest driver of the odds of mortality was patient age (OR, 1.07; 95% CI, 1.06–1.08; P < .001). Decreased oxygen saturation at admission was associated with increased odds of mortality (OR, 1.09; 95% CI, 1.06–1.12; P < .001) as was diabetes (OR, 1.57; 95% CI, 1.21–2.03; P < .001).

The identification of factors observable at admission that are associated with mortality in COVID-19 patients who are initially admitted to non-critical care units may help care providers, hospital epidemiologists, and hospital safety experts better plan for the care of these patients.

To characterize associations between exposures within and outside the medical workplace with healthcare personnel (HCP) SARS-CoV-2 infection, including the effect of various forms of respiratory protection.

Case–control study.

We collected data from international participants via an online survey.

In total, 1,130 HCP (244 cases with laboratory-confirmed COVID-19, and 886 controls healthy throughout the pandemic) from 67 countries not meeting prespecified exclusion (ie, healthy but not working, missing workplace exposure data, COVID symptoms without lab confirmation) were included in this study.

Respondents were queried regarding workplace exposures, respiratory protection, and extra-occupational activities. Odds ratios for HCP infection were calculated using multivariable logistic regression and sensitivity analyses controlling for confounders and known biases.

HCP infection was associated with non–aerosol-generating contact with COVID-19 patients (adjusted OR, 1.4; 95% CI, 1.04–1.9; P = .03) and extra-occupational exposures including gatherings of ≥10 people, patronizing restaurants or bars, and public transportation (adjusted OR range, 3.1–16.2). Respirator use during aerosol-generating procedures (AGPs) was associated with lower odds of HCP infection (adjusted OR, 0.4; 95% CI, 0.2–0.8, P = .005), as was exposure to intensive care and dedicated COVID units, negative pressure rooms, and personal protective equipment (PPE) observers (adjusted OR range, 0.4–0.7).

COVID-19 transmission to HCP was associated with medical exposures currently considered lower-risk and multiple extra-occupational exposures, and exposures associated with proper use of appropriate PPE were protective. Closer scrutiny of infection control measures surrounding healthcare activities and medical settings considered lower risk, and continued awareness of the risks of public congregation, may reduce the incidence of HCP infection.

The coronavirus disease 2019 (COVID-19) pandemic has induced a reinforcement of infection control measures in the hospital setting. Here, we assess the impact of the COVID-19 pandemic on the incidence of nosocomial Clostridioides difficile infection (CDI).

We retrospectively compared the incidence density (cases per 10,000 patient days) of healthcare-facility–associated (HCFA) CDI in a tertiary-care hospital in Madrid, Spain, during the maximum incidence of COVID-19 (March 11 to May 11, 2020) with the same period of the previous year (control period). We also assessed the aggregate in-hospital antibiotic use (ie, defined daily doses [DDD] per 100 occupied bed days [BD]) and incidence density (ie, movements per 1,000 patient days) of patient mobility during both periods.

In total, 2,337 patients with reverse transcription-polymerase chain reaction–confirmed COVID-19 were admitted to the hospital during the COVID-19 period. Also, 12 HCFA CDI cases were reported at this time (incidence density, 2.68 per 10,000 patient days), whereas 34 HCFA CDI cases were identified during the control period (incidence density, 8.54 per 10,000 patient days) (P = .000257). Antibiotic consumption was slightly higher during the COVID-19 period (89.73 DDD per 100 BD) than during the control period (79.16 DDD per 100 BD). The incidence density of patient movements was 587.61 per 1,000 patient days during the control period and was significantly lower during the COVID-19 period (300.86 per 1,000 patient days) (P < .0001).

The observed reduction of ~70% in the incidence density of HCFA CDI in a context of no reduction in antibiotic use supports the importance of reducing nosocomial transmission by healthcare workers and asymptomatic colonized patients, reinforcing cleaning procedures and reducing patient mobility in the epidemiological control of CDI.