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Objectives: The objectives of this study were to examine post COVID-19 syndrome (PCS) among healthcare workers (HCWs) in England and explore risk factors for the condition., Methods: Data were collected by National Health Service (NHS) CHECK, a longitudinal study exploring HCWs' mental and physical well-being during and after the COVID-19 pandemic. NHS CHECK collected data at four timepoints: the baseline survey between April 2020 and January 2021, and then three follow-up surveys at approximately 6, 12 and 32 months post baseline. PCS data were collected at 12 and 32 months, while risk factor data were from baseline. HCWs were asked what COVID-19 symptoms they experienced and for how long and were classified as having PCS if they had any symptom for ≥12 weeks. Multilevel regressions were used to examine risk factors for PCS., Results: This study included 5248 HCWs. While 33.6% (n=1730) reported prolonged COVID-19 symptoms consistent with PCS, only 7.4% (n=385) reported a formal diagnosis of PCS. Fatigue, difficult concentrating, insomnia and anxiety or depression were the most common PCS symptoms. Baseline risk factors for reporting PCS included screening for common mental disorders, direct contact with COVID-19 patients, pre-existing respiratory illnesses, female sex and older age., Conclusions: While a third of HCWs reported prolonged COVID-19 symptoms consistent with PCS, a smaller percentage reported a formal diagnosis of the condition. We replicate findings that direct contact with COVID-19 patients, older age, female sex, pre-existing respiratory illness and symptoms of common mental disorders are associated with increased risk of PCS., Competing Interests: Competing interests: This report is independent research supported by the National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) North Thames and the National Institute for Health and Care Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response, a partnership between the UK Health Security Agency, King’s College London and the University of East Anglia. The views expressed are those of the author(s) and not necessarily those of the NIHR, UKHSA or the Department of Health and Social Care. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission. SAMS is supported by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and funded by the National Institute for Health and Care Research, NIHR Advanced Fellowship, Dr SAMS, NIHR300592. SAMS is also an independent scientific member of the Industrial Injuries Advisory Council. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health and Social Care, or the Industrial Injuries Advisory Council. SW is a member of the NHS England board., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Introduction: Early identification of patients with coronavirus disease 2019 (COVID-19) who are at risk for hospitalization may help to mitigate disease burden by allowing healthcare systems to conduct sufficient resource and logistical planning in the event of case surges. We sought to develop and validate a clinical risk score that uses readily accessible information at testing to predict individualized 30-day hospitalization risk following COVID-19 diagnosis., Methods: We assembled a retrospective cohort of U.S. Veterans Health Administration patients (age ≥ 18 years) diagnosed with COVID-19 between March 1, 2020, and December 31, 2020. We screened patient characteristics using Least Absolute Shrinkage and Selection Operator logistic regression and constructed the risk score using characteristics identified as most predictive for hospitalization. Patients diagnosed before November 1, 2020, comprised the development cohort, while those diagnosed on or after November 1, 2020, comprised the validation cohort. We assessed risk score discrimination by calculating the area under the receiver operating characteristic (AUROC) curve and calibration using the Hosmer-Lemeshow (HL) goodness-of-fit test. This study was approved by the Veteran's Institutional Review Board of Northern New England at the White River Junction Veterans Affairs Medical Center (Reference no.:1473972-1)., Results: The development and validation cohorts comprised 11,473 and 12,970 patients, of whom 4,465 (38.9%) and 3,669 (28.3%) were hospitalized, respectively. The independent predictors for hospitalization included in the risk score were increasing age, male sex, non-white race, Hispanic ethnicity, homelessness, nursing home/long-term care residence, unemployed or retired status, fever, fatigue, diarrhea, nausea, cough, diabetes, chronic kidney disease, hypertension, and chronic obstructive pulmonary disease. Model discrimination and calibration was good for the development (AUROC = 0.80; HL P-value = .05) and validation (AUROC = 0.80; HL P-value = .31) cohorts., Conclusions: The prediction tool developed in this study demonstrated that it could identify patients with COVID-19 who are at risk for hospitalization. This could potentially inform clinicians and policymakers of patients who may benefit most from early treatment interventions and help healthcare systems anticipate capacity surges., (Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Background: The COVID-19 pandemic has changed how maternity care services are provided worldwide. To contain the virus, many providers reduced the number of face-to-face visits for women. In addition, partner attendance was prohibited in many circumstances to protect staff, and other service users, from potential infection., Aims: To explore women's experience of pregnancy and birth in the Republic of Ireland during the COVID-19 pandemic., Methods: A qualitative study with 14 women was conducted using a grounded theory approach. Data were collected between April and July 2020, and in-depth interviews were conducted either in pregnancy or in the first 12 weeks after the birth., Results: Six categories emerged: loss of normality, navigating "new" maternity care systems, partners as bystanders, balancing information, uncertainty, and unexpected benefits of pregnancy during the pandemic. While benefits were reported (working from home and additional time spent with partners during the "fourth trimester"), in general, the themes were of increased anxiety and uncertainty., Conclusion: The pandemic caused additional anxiety for pregnant women. This was exacerbated by uncertainty about the effects of COVID-19 on pregnancy and unclear messaging about restrictions. More interactive and personalized communication is required to support women to cope with uncertainty during a pandemic. The birth partner plays an important role as an advocate for women and excluding them from pregnancy care caused additional anxiety for pregnant women. Containment strategies for a pandemic should be developed with this in mind, to view the family as a unit rather than the woman in isolation., (© 2021. The Author(s), under exclusive licence to Royal Academy of Medicine in Ireland.)

Introduction Long COVID (LC) occurs when people experience symptoms for weeks, months or even years after a COVID-19 infection. This review looks at research exploring the LC definitions, prevalence, symptoms, risk factors, and associated impacts in research on healthcare workers (HCWs). Data sources We systematically searched five electronic databases (CINAHL, EMBASE, Medline, PsycInfo and PubMed) and compiled a narrative literature review based on 56 relevant studies. Areas of agreement LC is prevalent among HCWs who become infected by COVID-19. Many of the most frequent symptoms associated with LC in the general population are also reported among HCWs. Some risk factors for LC are also similar to those in the general population, such as female sex, older age, and having a pre-existing respiratory illness. Areas of controversy The mechanism(s) responsible for LC remains unknown. A variety of terms, timeframes and symptoms are used to define LC, creating difficulties in comparing results across studies. Much of the research is cross-sectional and fails to explore the impacts that prolonged symptoms have on HCWs' personal and professional lives. Growing points The need to support HCWs with LC is clear. Identifying the mechanism(s) responsible for LC is a key priority, as this will inform treatments. Areas for developing research Future research should move towards a standard definition for LC. Greater attention should be paid to longitudinal and qualitative studies, which could give insights into prognosis, lived experience and work participation. Finally, studies evaluating treatments suitable for people with LC are timely. [ABSTRACT FROM AUTHOR]