Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children <18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p<0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p<0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p<0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

Informing the State of Process Modeling and Automation of Blood Banking and Transfusion Services Through a Systematic Mapping Study

Shaima’ Abdallah Elhaj,1 Yousra Odeh,1,2 Dina Tbaishat,1,3,4 Anwar Rjoop,5,6 Asem Mansour,1 Mohammed Odeh1,7,8 1Cancer Care Informatics Research, King Hussain Cancer Center (KHCC), Amman, Jordan; 2Faculty of Information Technology, Philadelphia University, Amman, Jordan; 3College of Technological Innovation, Zayed University, Dubai, United Arab Emirates; 4Library and Information Science Department, University of Jordan, Amman, Jordan; 5Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan; 6Department of Pathology, Blood Bank, King Abdullah University Hospital, Irbid, Jordan; 7College of Arts, Technology and Environment, University of the West of England, Bristol, UK; 8Global Academy for Digital Health, Bristol, UKCorrespondence: Mohammed Odeh, University of the West of England, Frenchay Campus, Coldharbour Lane, Bristol, BS16 1QY, UK, Tel +44 (0)117 9656261, Email [email protected] Yousra Odeh, Philadelphia University, Jarash Road, P.O.Box 19392, Amman, Tel +96 (2) 64799000, Fax +96 (2) 64799040, Email [email protected]; [email protected]: The current state of the art in process modeling of blood banking and transfusion services is not well grounded; methodological reviews are lacking to bridge the gap between such blood banking and transfusion processes (and their models) and their automation. This research aims to fill this gap with a methodological review.Methods: A systematic mapping study was adopted, driven by five key research questions. Identified research studies were accepted based on fulfilling the following inclusion criteria: 1) research studies should focus on blood banking and transfusion process modeling since the late 1970s; and 2) research studies should focus on process automation in relation to workflow-based systems, with papers classified into categories in line with the analysis undertaken to answer each of the research questions.Results: The search identified 22 papers related to modeling and automation of blood banking and transfusion, published in the period 1979– 2022. The findings revealed that only four process modeling languages were reported to visualize process workflows. The preparation of blood components, serologic testing, blood distribution, apheresis, preparation for emergencies, maintaining blood banking and transfusion safety, and documentation have not been reported to have been modeled in the literature. This review revealed the lack of use of Business Process Modeling Notation (BPMN) as the industry standard process modeling language in the domain. The review also indicated a deficiency in modeling specialized processes in blood banking and transfusion, with the majority of reported processes being described as high level, but lacking elaboration. Automation was reported to improve transfusion safety, and to reduce cost, time cycle, and human errors.Conclusion: The work highlights the non-existence of a developed process architectural framework for blood banking and transfusion processes, which is needed to lay the groundwork for identifying and modeling strategic, managerial, and operational processes to bridge the gap with their enactment in healthcare systems. This paves the way for the development of a data-harvesting platform for blood banking and transfusion services.Keywords: blood banking, blood transfusion, blood banking process model, blood banking process automation, blood transfusion process model, blood transfusion process automation, Systematic Mapping Study, Blood Banking and Transfusion Service