A Snapshot of COVID-19 Vaccine Discourse Related to Ethnic Minority Communities in the United Kingdom Between January and April 2022: Mixed Methods Analysis.

BACKGROUND: Existing literature highlights the role of social media as a key source of information for the public during the COVID-19 pandemic and its influence on vaccination attempts. Yet there is little research exploring its role in the public discourse specifically among ethnic minority communities, who have the highest rates of vaccine hesitancy (delay or refusal of vaccination despite availability of services). OBJECTIVE: This study aims to understand the discourse related to minority communities on social media platforms Twitter and YouTube. METHODS: Social media data from the United Kingdom was extracted from Twitter and YouTube using the software Netlytics and YouTube Data Tools to provide a "snapshot" of the discourse between January and April 2022. A mixed method approach was used where qualitative data were contextualized into codes. Network analysis was applied to provide insight into the most frequent and weighted keywords and topics of conversations. RESULTS: A total of 260 tweets and 156 comments from 4 YouTube videos were included in our analysis. Our data suggests that the most popular topics of conversation during the period sampled were related to communication strategies adopted during the booster vaccine rollout. These were noted to be divisive in nature and linked to wider conversations around racism and historical mistrust toward institutions. CONCLUSIONS: Our study suggests a shift in narrative from concerns about the COVID-19 vaccine itself, toward the strategies used in vaccination implementation, in particular the targeting of ethnic minority groups through vaccination campaigns. The implications for public health communication during crisis management in a pandemic context include acknowledging wider experiences of discrimination when addressing ethnic minority communities.

Machine learning algorithms for the prognostication of abdominal aortic aneurysm progression: a systematic review.

INTRODUCTION: Abdominal aortic aneurysm (AAA), often characterized by an abdominal aortic diameter over 3.0 cm, is managed through screening, surveillance, and surgical intervention. AAA growth can be heterogeneous and rupture carries a high mortality rate, with size and certain risk factors influencing rupture risk. Research is ongoing to accurately predict individual AAA growth rates for personalized management. Machine learning, a subset of artificial intelligence, has shown promise in various medical fields, including endoleak detection post-EVAR. However, its application for predicting AAA growth remains insufficiently explored, thus necessitating further investigation. Subsequently, this paper aims to summarize the current status of machine learning in predicting AAA growth. EVIDENCE ACQUISITION: A systematic database search of Embase, MEDLINE, Cochrane, PubMed and Google Scholar from inception till December 2022 was conducted of original articles that discussed the use of machine learning in predicting AAA growth using the aforementioned databases. EVIDENCE SYNTHESIS: Overall, 2742 articles were extracted, of which seven retrospective studies involving 410 patients were included using a predetermined criteria. Six out of seven studies applied a supervised learning approach for their machine learning (ML) models, with considerable diversity observed within specific ML models. The majority of the studies concluded that machine learning models perform better in predicting AAA growth in comparison to reference models. All studies focused on predicting AAA growth over specified durations. Maximal luminal diameter was the most frequently used indicator, with alternative predictors being AAA volume, ILT (intraluminal thrombus) and flow-medicated diameter (FMD). CONCLUSIONS: The nascent field of applying machine learning (ML) for Abdominal Aortic Aneurysm (AAA) expansion prediction exhibits potential to enhance predictive accuracy across diverse parameters. Future studies must emphasize evidencing clinical utility in a healthcare system context, thereby ensuring patient outcome improvement. This will necessitate addressing key ethical implications in establishing prospective studies related to this topic and collaboration among pivotal stakeholders within the AI field.

Simulation training for obstetric emergencies in low- and lower-middle income countries: A systematic review.

BACKGROUND: This review aims to systematically evaluate the currently available evidence investigating the effectiveness of simulation-based training (SBT) in emergency obstetrics care (EmOC) in Low- and Lower-Middle Income Countries (LMIC). Furthermore, based on the challenges identified we aim to provide a series of recommendations and a knowledge base for future research in the field. METHODS: A systematic database search was conducted of original articles that explored the use of simulation-based training for EmOC in LMIC in EMBASE, MEDLINE, Cochrane database and Google Scholar, from inception to January 2022. RESULTS: The literature search identified 1,957 articles of which a total of 15 studies were included in this review, featuring 8,900 healthcare professionals from 18 countries. The SBT programmes varied in the reviewed studies. The most common training programme consisted of the PRONTO programme implemented by four studies, comprising of 970 participants across four different countries. In general, programmes consisted of lectures, workshops and simulations of emergency obstetric scenarios followed by a debrief of participants. There were thirteen studies, comprising of 8,332 participants, which tested for improvements in clinical knowledge in post-partum haemorrhage, neonatal resuscitation, pre-eclampsia, shoulder dystocia and sepsis. All the included studies reported improvements in clinical knowledge following the simulation of scenarios. Changes in teamwork, improvement in leadership and in communication skills were also widely reported. CONCLUSION: The use of SBT programmes is not only sustainable, feasible and acceptable in LMIC, but could also improve clinical knowledge, communication, and teamwork among healthcare providers, thus directly addressing the UN Sustainable Development Goals.

Mental health of women and children experiencing family violence in conflict settings: a mixed methods systematic review.

BACKGROUND: Armed conflict has significant impacts on individuals and families living in conflict-affected settings globally. Scholars working to prevent violence within families have hypothesised that experiencing armed conflict leads to an increase in family violence and mental health problems. In this review, we assessed the prevalence of family violence in conflict settings, its association with the mental health of survivors, moderating factors, and the importance of gender relations. METHODS: Following PRISMA guidelines, we systematically reviewed quantitative and qualitative studies that assessed the prevalence of family violence and the association between family violence and mental health problems, within conflict settings (PROSPERO reference CRD42018114443). RESULTS: We identified 2605 records, from which 174 full text articles were screened. Twenty-nine studies that reported family violence during or up to 10 years after conflict were eligible for inclusion. Twenty one studies were quantitative, measuring prevalence and association between family violence and mental health problems. The studies were generally of high quality and all reported high prevalence of violence. The prevalence of violence against women was mostly in the range of 30-40%, the highest reported prevalence of physical abuse being 78.9% in Bosnia and Herzegovina. For violence against children, over three-quarters had ever experienced violence, the highest prevalence being 95.6% in Sri Lanka. Associations were found with a number of mental health problems, particularly post-traumatic stress disorder. The risk varied in different locations. Eight qualitative studies showed how men's experience of conflict, including financial stresses, contributes to their perpetration of family violence. CONCLUSIONS: Family violence was common in conflict settings and was associated with mental health outcomes, but the studies were too heterogenous to determine whether prevalence or risk was greater than in non-conflict settings. The review highlights an urgent need for more robust data on perpetrators, forms of family violence, and mental health outcomes in conflict-affected settings in order to help understand the magnitude of the problem and identify potential solutions to address it.