Despite infection control guidance, sporadic nosocomial coronavirus disease 2019 (COVID-19) outbreaks occur. We describe a complex severe acute respiratory coronavirus virus 2 (SARS-CoV-2) cluster with interfacility spread during the SARS-CoV-2 δ (delta) pandemic surge in the Midwest.

This study was conducted in (1) a hematology-oncology ward in a regional academic medical center and (2) a geographically distant acute rehabilitation hospital.

We conducted contact tracing for each COVID-19 case to identify healthcare exposures within 14 days prior to diagnosis. Liberal testing was performed for asymptomatic carriage for patients and staff. Whole-genome sequencing was conducted for all available clinical isolates from patients and healthcare workers (HCWs) to identify transmission clusters.

In the immunosuppressed ward, 19 cases (4 patients, 15 HCWs) shared a genetically related SARS-CoV-2 isolate. Of these 4 patients, 3 died in the hospital or within 1 week of discharge. The suspected index case was a patient with new dyspnea, diagnosed during preprocedure screening. In the rehabilitation hospital, 20 cases (5 patients and 15 HCWs) positive for COVID-19, of whom 2 patients and 3 HCWs had an isolate genetically related to the above cluster. The suspected index case was a patient from the immune suppressed ward whose positive status was not detected at admission to the rehabilitation facility. Our response to this cluster included the following interventions in both settings: restricting visitors, restricting learners, restricting overflow admissions, enforcing strict compliance with escalated PPE, access to on-site free and frequent testing for staff, and testing all patients prior to hospital discharge and transfer to other facilities.

Stringent infection control measures can prevent nosocomial COVID-19 transmission in healthcare facilities with high-risk patients during pandemic surges. These interventions were successful in ending these outbreaks.

The National Pediatric Cardiology Quality Improvement Collaborative (NPC-QIC) lacks a rigorous enrollment audit process, unlike other collaborative networks. Most centers require individual families to consent to participate. It is unknown whether there is variation across centers or biases in enrollment.

We used the Pediatric Cardiac Critical Care Consortium (PC4) registry to assess enrollment rates in NPC-QIC for those centers participating in both registries using indirect identifiers (date of birth, date of admission, gender, and center) to match patient records. All infants born 1/1/2018–12/31/2020 and admitted 30 days of life were eligible. In PC4, all infants with a fundamental diagnosis of hypoplastic left heart or variant or who underwent a surgical or hybrid Norwood or variant were eligible. Standard descriptive statistics were used to describe the cohort and center match rates were plotted on a funnel chart.

Of 898 eligible NPC-QIC patients, 841 were linked to 1,114 eligible PC4 patients (match rate 75.5%) in 32 centers. Match rates were lower in patients of Hispanic/Latino ethnicity (66.1%, p = 0.005), and those with any specified chromosomal abnormality (57.4%, p = 0.002), noncardiac abnormality (67.8%, p = 0.005), or any specified syndrome (66.5%, p = 0.001). Match rates were lower for patients who transferred to another hospital or died prior to discharge. Match rates varied from 0 to 100% across centers.

It is feasible to match patients between the NPC-QIC and PC4 registries. Variation in match rates suggests opportunities for improvement in NPC-QIC patient enrollment.

To estimate population-based rates and to describe clinical characteristics of hospital-acquired (HA) influenza.

Cross-sectional study.

US Influenza Hospitalization Surveillance Network (FluSurv-NET) during 2011–2012 through 2018–2019 seasons.

Patients were identified through provider-initiated or facility-based testing. HA influenza was defined as a positive influenza test date and respiratory symptom onset >3 days after admission. Patients with positive test date >3 days after admission but missing respiratory symptom onset date were classified as possible HA influenza.

Among 94,158 influenza-associated hospitalizations, 353 (0.4%) had HA influenza. The overall adjusted rate of HA influenza was 0.4 per 100,000 persons. Among HA influenza cases, 50.7% were 65 years of age or older, and 52.0% of children and 95.7% of adults had underlying conditions; 44.9% overall had received influenza vaccine prior to hospitalization. Overall, 34.5% of HA cases received ICU care during hospitalization, 19.8% required mechanical ventilation, and 6.7% died. After including possible HA cases, prevalence among all influenza-associated hospitalizations increased to 1.3% and the adjusted rate increased to 1.5 per 100,000 persons.

Over 8 seasons, rates of HA influenza were low but were likely underestimated because testing was not systematic. A high proportion of patients with HA influenza were unvaccinated and had severe outcomes. Annual influenza vaccination and implementation of robust hospital infection control measures may help to prevent HA influenza and its impacts on patient outcomes and the healthcare system.

Many women experience both vasomotor menopausal symptoms (VMS) and depressed mood at midlife, but little is known regarding the prospective bi-directional relationships between VMS and depressed mood and the role of sleep difficulties in both directions.

A pooled analysis was conducted using data from 21 312 women (median: 50 years, interquartile range 49−51) in eight studies from the InterLACE consortium. The degree of VMS, sleep difficulties, and depressed mood was self-reported and categorised as never, rarely, sometimes, and often (if reporting frequency) or never, mild, moderate, and severe (if reporting severity). Multivariable logistic regression models were used to examine the bi-directional associations adjusted for within-study correlation.

At baseline, the prevalence of VMS (40%, range 13–62%) and depressed mood (26%, 8–41%) varied substantially across studies, and a strong dose-dependent association between VMS and likelihood of depressed mood was found. Over 3 years of follow-up, women with often/severe VMS at baseline were more likely to have subsequent depressed mood compared with those without VMS (odds ratios (OR) 1.56, 1.27–1.92). Women with often/severe depressed mood at baseline were also more likely to have subsequent VMS than those without depressed mood (OR 1.89, 1.47–2.44). With further adjustment for the degree of sleep difficulties at baseline, the OR of having a subsequent depressed mood associated with often/severe VMS was attenuated and no longer significant (OR 1.13, 0.90–1.40). Conversely, often/severe depressed mood remained significantly associated with subsequent VMS (OR 1.80, 1.38–2.34).

Difficulty in sleeping largely explained the relationship between VMS and subsequent depressed mood, but it had little impact on the relationship between depressed mood and subsequent VMS.

This article discusses the process of defining competencies and development of a best practices training course for investigators and clinical research coordinators who conduct social and behavioral research.

The first project phase established recommendations for training in Good Clinical Practice (GCP) and was done in conjunction with representatives from 62 Clinical and Translational Science Award (CTSA) hubs. Diversity in behavioral clinical trials and differences in regulation of behavioral trials compared with clinical trials involving drugs, devices, or biologics necessitated a separate Social and Behavioral Work Group. This group worked with CTSA representatives to tailor competencies and fundamental GCP principles into best practices for social and behavioral research.

Although concepts underlying GCP were deemed similar across all clinical trials, not all areas were equally applicable and the ways in which GCP would be enacted differ for behavioral trials. It was determined that suitable training in best practices for social and behavioral research was lacking.

Based on the training need, an e-learning course for best practices is available to all CTSA sites. Each institution is able to track outcomes for its employees to help achieve standardized competency-based best practices for social and behavioral investigators and staff.