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Background: Estimating vaccine effectiveness (VE) against severe, acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among healthcare workers (HCWs) is necessary to demonstrate protection from the disease. Between 24 December 2020 and 15 June 2021, we determined the factors associated with vaccine coverage and estimated VE against SARS-CoV-2 infection in HCWs at a secondary hospital in Kuwait. Methods: We extracted sociodemographic, occupational, SARS-CoV-2 infection, and vaccination data for eligible HCWs from the hospital records. Vaccine coverage percentages were cross-tabulated with the HCW factors. Cox regression was used to estimate hazard ratios in vaccinated versus unvaccinated. Results: 3246 HCWs were included in the analysis, of which 82.1% received at least one vaccine dose (50.4% only one dose of ChAdOx1, 3.3% only one dose of BNT162b2, and 28.3% two doses of BNT162b2). However, 17.9% of HCWs were unvaccinated. A significantly lower vaccination coverage was reported amongst female HCWs, younger age group (20–30 years), and administrative/executive staff. The adjusted VE of fully vaccinated HCWs was 94.5% (95% CI = 89.4–97.2%), while it was 75.4% (95% CI = 67.2–81.6%) and 91.4% (95% CI = 65.1–97.9%) in partially vaccinated for ChAdOx1 and BNT162b2, respectively. Conclusions: BNT162b2 and ChAdOx1 vaccines prevented most symptomatic infections in HCWs across age groups, nationalities, and occupations.

Background: Stigma towards children with obesity can begin as early as 3 years old, leading to increased risk for poorer mental health outcomes and lower quality of life. This includes discriminatory language used by peers and adults, which may be compounded by use within the medical community and in published research.Objectives: Our primary objective was to investigate adherence to person-centred language (PCL) in childhood obesity-related medical publications.Methods: We searched PubMed for childhood obesity-related articles from 2018 through 2020, from journals frequently publishing childhood-obesity-related research. Articles were randomized and searched for a list of predetermined, stigmatizing terms.Results: Of the sample of 300 articles, only 21.7% were adherent to PCL guidelines.The most frequent labels found were 'obese' appearing in 70.33% of articles and 'overweight' in 63.7%. Labels such as 'chubby', 'large', and 'fat' were less common, but still appeared in the medical literature.Conclusions: A majority of childhood obesity-related articles did not adhere to PCL guidelines. Given the negative effects of stigma among children with obesity, it

BackgroundThe COVID-19 pandemic represents an unprecedented challenge to healthcare systems and nations across the world. Particularly challenging are the lack of agreed-upon management guidelines and variations in practice. Our hospital is a large, secondary-care government hospital in Kuwait, which has increased its capacity by approximately 28% to manage the care of patients with COVID-19. The surge in capacity has necessitated the redeployment of staff who are not well-trained to manage such conditions. There was a great need to develop a tool to help redeployed staff in decision-making for patients with COVID-19, a tool which could also be used for training.MethodsBased on the best available clinical knowledge and best practices, an eight member multidisciplinary group of clinical and quality experts undertook the development of a clinical algorithm-based toolkit to guide training and practice for the management of patients with COVID-19. The team followed Horabin and Lewis’ seven-step approach in developing the algorithms and a five-step method in writing them. Moreover, we applied Rosenfeld et al’s five points to each algorithm.ResultsA set of seven clinical algorithms and one illustrative layout diagram were developed. The algorithms were augmented with documentation forms, data-collection online forms and spreadsheets and an indicators’ reference sheet to guide implementation and performance measurement. The final version underwent several revisions and amendments prior to approval.ConclusionsA large volume of published literature on the topic of COVID-19 pandemic was translated into a user-friendly, algorithm-based toolkit for the management of patients with COVID-19. This toolkit can be used for training and decision-making to improve the quality of care provided to patients with COVID-19.