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Presenteeism Among Health Care Personnel With COVID-19
Get PDFIMPORTANCE: Presenteeism-defined as continuing to work during an illness-poses a public health risk in the workplace and is especially hazardous within health care institutions where vulnerable patients may be exposed to nosocomial infections. Understanding the frequency and characteristics of health care personnel (HCP) who report presenteeism while ill with COVID-19 may help mitigate SARS-CoV-2 spread in hospitals and other health care institutions.
OBJECTIVES: To determine the frequency of presenteeism among HCP with symptomatic COVID-19, and to evaluate the demographic, occupational, and clinical factors associated with it.
DESIGN, SETTING, AND PARTICIPANTS: This is an observational cohort study that uses data from the Preventing Emerging Infections Through Vaccine Effectiveness Testing (PREVENT) project: a test-negative, case-control vaccine effectiveness study that enrolled HCP who had COVID-19 symptoms at 24 academic medical centers from December 2020 through April 2024.
EXPOSURE: Exposures include demographic, occupational, and clinical characteristics of participants.
MAIN OUTCOMES AND MEASURES: Having confirmed symptomatic COVID-19 infection and reporting presenteeism; overall frequency of presenteeism through the study period and the association of the exposure characteristics with presenteeism, adjusting for confounders using 3 multivariable models. Presenteeism was defined as HCP who did not stop working during their illness, but the study did not differentiate whether they continued working remotely.
RESULTS: A total of 3721 HCP were included in the analysis (2842 [76.4%] aged 18-49 years; 2993 [80.4%] female; 278 [7.5%] Asian, 406 [10.9%] Black, and 2912 [78.3%] White). Overall, 293 (7.9%) reported presenteeism during the study period, and the frequency of presenteeism increased each year of the study period (from 1 of 73 [1.4%] in 2020 to 16 of 105 [15.2%] in 2024). Presenteeism was associated with HCP who have minimal patient contact (adjusted odds ratio [aOR], 3.73; 95% CI, 2.39-4.37), a graduate or professional degree (aOR, 1.90; 95% CI, 1.45-2.50), and income over $100 000 (aOR, 1.74; 95% CI, 1.12-2.69).
CONCLUSION AND RELEVANCE: In this observational cohort study of 3721 HCP, there was an increasing frequency of presenteeism from 2020 through 2024, and job role and socioeconomic factors were associated. More studies are needed to understand the rationale behind the decision to continue working and the exact causes of presenteeism's rising incidence among HCP with COVID-19.No embarg
A Randomized Pilot Cognitive Behavioral Sleep Health Trial for Young Adults with Type 1 Diabetes
No full textObjectives: The purpose of this randomized controlled trial was to determine whether a cognitive-behavioral sleep health self-management intervention (CB-Sleep Health) would be more effective than a time-balanced attention control (AC) condition in improving multiple dimensions of sleep health (self-reported and objectively derived).
Methods: Young adults with T1D (ages 18-26 years) were randomly assigned to a 12-week CB-Sleep Health (n = 21) or AC condition (n = 18). They wore concurrent continuous glucose monitors and actigraphy devices and completed daily sleep surveys for 14 days at baseline, post-intervention, and 3-month follow-up.
Results: Of the randomized participants, 31 (79.5%) completed the post-intervention, while 33 (84.6%) completed the 3-month follow-up. The CB-Sleep Health intervention had a significant effect on alertness and duration compared to the control group. The changes from baseline were -3.21 s vs. +0.71, p = .005 and +18 min vs. -25.8 min, p = .01, respectively. These effects were sustained at the 3-month follow-up.
Conclusions: Longer sleep duration, higher daytime alertness, and sustained sleep efficiency are possible with this CB-Sleep Health intervention in young adults managing a complex condition.No embarg
Comprehensive Segmentation of Deep Grey Nuclei From Structural MRI Data
No full textThere is a lack of tools for comprehensive and complete segmentation of deep grey nuclei using a single software for reproducibility and repeatability. We present a fast, accurate, and robust method for segmentation of deep grey nuclei (thalamic nuclei, basal ganglia, amygdala, claustrum, and red nucleus) from structural T MRI data at conventional field strengths. We leveraged the improved contrast of white-matter-nulled imaging by using the recently proposed Histogram-based Polynomial Synthesis (HIPS) to synthesize white-matter nulled images from standard T and then use a multi-atlas segmentation with joint label fusion to segment deep grey nuclei. The method worked robustly on all field strengths (1.5/3/7T) and Dice coefficients ≥ 0.7 were achieved for all structures compared against manual segmentation ground truth. In conclusion, this method facilitates careful investigation of deep grey nuclei by enabling the use of conventional T data from large public databases, which has not been possible hitherto due to lack of robust reproducible segmentation tools.No embarg
Scaffolding Case Studies: Advancing Competency-Based Nurse Practitioner Education
No full textBackground: Nursing education is transitioning to competency-based education (CBE). A key principle of CBE is scaffolding curricula from simple to complex to facilitate student learning.
Problem: There is no standardized method of scaffolding nurse practitioner (NP) curricula.
Approach: A workgroup of NP educators analyzed visit types-such as wellness, acute, chronic, and transition of care visits-to determine the appropriate placement of case scenarios for early, middle, or late-stage learners. Each case was then aligned with the educational preparation requirements for specific NP populations to identify which populations would benefit most from the scenario.
Outcomes: A structured classification system, using predefined criteria, was developed to scaffold case scenarios in NP education. This systematic arrangement supports the implementation of CBE and facilitates learning.
Conclusions: The scaffolding model can be used with a variety of learning resources and provides a standardized method for scaling complexity within NP curricula.No embarg
Uncovering Structurally Differential Care: Pediatric Oncology Nurses' Perspectives Caring for African American Families
No full textBackground: Psychosocial intervention studies aimed at reducing stress among parents of children with cancer have historically included insufficient samples of African American parents. Pediatric oncology nurses are uniquely positioned to identify and address parent psychosocial needs. However, research exploring their perspectives to serve as psychosocial interventionists specifically for African American families of children with cancer is limited.
Objective: To explore the perspectives of pediatric oncology nurses on their role as psychosocial interventionists for African American families navigating childhood cancer.
Methods: We conducted 32 remote individual interviews and 2 focus groups (n = 4 each) with 40 pediatric oncology nurses from three pediatric cancer centers and a large pediatric oncology nursing organization. Using Corbin and Strauss' Grounded Theory methodology, we used constant comparative analysis to generate a theory based on the nurses' perspectives.
Results: Our emergent theory - Structurally Differential Care - had two major themes (psychosocial resource facilitators and psychosocial resource obstructors). Psychosocial resource facilitators: 1) appreciating families' experiences, 2) longitudinal presence, 3) open communication, 4) holistic care, and 5) safe spaces mitigated structurally differential care. Nurses also identified: 1) difficulty with serious illness conversation, 2) lack of nursing experience, and 3) competing work demands as psychosocial resource obstructors that intensify structurally differential care.
Conclusions: This sample of pediatric oncology nurses described experiences that either bolstered or obstructed their psychosocial care provision, signaling potential opportunities for nurse-targeted interventions that may reduce factors contributing to disparities in the psychosocial care for African American families of children with cancer.No embarg
Predicting Traumatic Brain Injury Post-Trauma Using Temporal Attention on Sleep-Wake Data
No full textBackground: Traumatic Brain Injury (TBI) is a major public health concern, and accurate classification is essential for effective treatment and improved patient outcomes. Sleep/wake behavior has emerged as a potential biomarker for TBI classification, yet the optimal time window in which to identify sleep/wake changes after TBI remains unclear. Methods: We evaluated daily longitudinal sleep/wake data from a prospective cohort of more than 2,000 emergency department patients with and without blood biomarker-documented TBI (Glial Fibrillary Acidic Protein - GFAP >268pgml). We utilized a deep learning model to identify the impact of time from trauma and duration of data collection on the model's ability to distinguish between TBI-positive (TBI+) and TBI-negative (TBI-) cases. Results: Our analysis showed that sleep/wake data from the first 7 days after TBI most accurately identified TBI. Sleep-wake data from the first 7, 14, and 21 days after trauma achieved sensitivity/specificity of 81%/25%, 40%/66%, and 45%/58%, respectively. F1 scores of deep learning models developed from the first 7, 14, and 21 days were 22%, 21%, and 20%, respectively. Conclusions: The results suggest that early sleep/wake data has promise for assisting with TBI identification. Significance: In the future, the incorporation of sleep/wake derived biomarkers into TBI identification tools could assist in the identification of individuals with potential TBI for further screening and intervention.No embarg
Using machine learning models to predict vaccine hesitancy: a showcase of COVID-19 vaccine hesitancy in rural populations during the pandemic
No full textUnderstanding vaccine hesitancy is a critical public health challenge, yet traditional statistical methods often fail to capture the complex drivers behind it. This study uses COVID-19 vaccine hesitancy in a rural population as a case study to demonstrate a more powerful and interpretable machine learning workflow. We compared seven models and found that non-linear approaches significantly outperformed logistic regression in predictive accuracy. Interpretation of the best-performing model identified vaccine safety perceptions as the most important predictor. This approach revealed nuanced, non-linear relationships with feature importance and partial dependence plots. This work serves as a practical guide for researchers, showing how a machine learning framework provides not only more accurate predictions but also a richer, more actionable understanding of complex human behaviors to better inform public policy.No embarg
The HDL-transporting scavenger receptor B1 promotes viral infection through endolysosomal acidification
No full textVirus infections pose a continuous threat to human health and can result in millions of deaths per year. SARS-CoV-2 infection has been linked to the high-affinity high-density lipoprotein (HDL) receptor scavenger receptor class B, type 1 (SR-B1). Mechanisms by which SR-B1 supports SARS-CoV-2 infection and replication, as well as the breadth of viruses that exploit this receptor, are incompletely defined. In evaluating the role of SR-B1 in the biology of infection with SARS-CoV-2, influenza A virus, and vesicular stomatitis virus, we show that SR-B1 chemical inhibition or knockout adversely affects infection for these viruses. Inhibiting SR-B1 results in lack of acidification in the endolysosomal compartment and entrapment of SARS-CoV-2 in endosomal-lysosomal vesicles. These findings together indicate that SR-B1, and possibly HDL, is critical for successful SARS-CoV-2 trafficking through a pH-dependent vesicular entry pathway. Our work provides insights into how SR-B1 can impact viral infection in human lung cells.No embarg
Pre-trauma insomnia and posttraumatic alcohol and cannabis use in the AURORA observational cohort study of trauma survivors
No full textBackground and aims: Insomnia symptoms are a potential risk factor for alcohol and cannabis use, particularly in trauma-exposed populations. The initial weeks and months after trauma are a period of risk for problematic substance use, however prior research has not examined whether insomnia symptoms predict alcohol or cannabis use after trauma.
Design: Using a large-scale, multi-site, prospective study of trauma survivors presenting to emergency departments (EDs), the current study tested direct and indirect associations between pre-trauma insomnia symptoms, two-week posttraumatic stress disorder (PTSD) symptoms, and eight-week post-trauma heavy alcohol and cannabis use and binge drinking.
Setting: Participants were recruited from 23 EDs in the United States and followed up using remote assessments.
Participants/cases: Participants were from the AURORA study (n = 2449). A slight majority were women (63.8 %) and were an average of 37 years old. Participants were racially and ethnically diverse (50.5 % Black, 11.2 % Hispanic).
Measurements: Participants completed self-report measures during their ED visit, and two- and eight-weeks post-trauma.
Findings: Pre-trauma insomnia symptoms significantly predicted eight-week post-trauma heavy alcohol and cannabis use, as well as binge drinking. Associations persisted after covarying for pre-trauma substance use, demographic variables, and trauma severity at the time of emergency care. Further, the association between pre-trauma insomnia symptoms and heavy alcohol and cannabis use at eight-weeks post-trauma was significantly mediated by two-week PTSD symptoms.
Conclusions: Insomnia symptoms may be an important malleable risk factor for heavy alcohol and cannabis use and binge drinking after trauma. Further research is needed to explore the effectiveness of insomnia interventions to mitigate post-trauma substance use and to better understand the complex relationships between sleep, trauma, PTSD, and substance use.No embarg
Association between inactivated COVID-19 vaccine and semen quality among males recovered from omicron infection: a retrospective cohort study
No full textBackground: The protective effects of inactivated COVID-19 vaccines against SARS-CoV-2-associated semen impairment remain underexplored. We investigated associations between BBIBP-CorV vaccination and the semen quality in males recovering from SARS-CoV-2 infection.
Research design and methods: This single-center retrospective cohort study included 1,496 males recovering from SARS-CoV-2 infection at a tertiary hospital in Urumqi, China (February-May 2023). Participants were categorized into long-term and short-term effects groups based on the interval between semen collection and their most recent SARS-CoV-2 infection. The study assessed the association between the different doses of BBIBP-CorV vaccination and semen quality in both groups.
Results: A total of 1496 participants were recruited for the short-term (n = 307) and long-term effect groups (n = 1189). Participants had a median age of 32 (IQR: 30, 35). Compared to unvaccinated controls, 2-dose and 3-dose recipients showed reduced short-term semen quality impairment risks, with adjusted RR of 0.945 (95% CI 0.918, 0.973) and 0.965 (95% CI 0.937, 0.993), respectively. No significant results were found for long-term effect groups.
Conclusion: Inactivated COVID-19 vaccination may protect against semen quality impairment among males recovering from SARS-CoV-2 Omicron infection within 90 days, especially in terms of semen volume and sperm progressive motility.No embarg