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Anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV) is a chronic, relapsing-remitting condition associated with increased morbidity. Previous research has shown patients with AAV report high levels of fatigue, pain, depression and anxiety. Over recent years successful work has been carried out to improve clinical outcomes, resulting in reduced mortality and end stage kidney disease (ESKD). Despite this, little work has been done to better understand the role of the patient within this condition. The prevalence of AAV is increasing and to date, there is a shortage of specific tools that assess and measure key features relating to patient reported outcomes (PROs). This protocol details how we can better understand the lived experiences of those with AAV through the development of a disease specific, patient reported outcome measure (PROM), to be used in clinic practice. This will allow us to recognise and validate PROs and the impact the disease and its treatment has on patients' health related quality of life (HRQoL). In addition, we aim to identify potential differences in PRO's between demographics, organ involvement and treatment subgroups in AAV as well as outcomes relating to the patient experience. Patients from a single centre in the UK will be recruited to take part in the exploratory qualitative study which will include focus groups and semi-structured interviews. The inclusion criteria comprise anyone with a diagnosis of AAV and willing to participate, including those who have active or relapsing disease, those are economically active, unemployed, retired and patients receiving renal replacement therapy. The aim of the project is to identify key issues patients experience in relation to their disease and its management and how these can be better assessed in a new PROM developed for use in the clinic setting. This will enable better delivery of individualised care and inform shared decision making, while also serving as a platform for future research looking at PROs in other glomerulonephritides. [ABSTRACT FROM AUTHOR]

Post-partum haemorrhage is among the main causes of (preventable) mortality for women in low-resource settings (LRSs), where, in 2017, the mortality ratio was 462 out of every 100 000 live births, over 10 times higher than for high-resource settings. There are different treatments available for post-partum haemorrhage. The intrauterine balloon tamponade is a medical device that proved to be a simple and cost-effective approach. Currently, there are several balloon tamponades available, with different design and working principles. However, all these devices were designed for high-resource settings, presenting several aspects that could be inappropriate for many lower-income countries. This paper presents the results of a preclinical study aiming at informing the design, prototyping and validation of a 3D-printed intrauterine balloon tamponade concept, contributing towards the United Nation's Sustainable Development Goal 3: Good health and Well-being. Frugal engineering concepts and contextualised design techniques were applied throughout, to define the design requirements and specifications. The performance of the final prototype was validated against the requirements of the UK National Health System (NHS) technical guidelines and relevant literature, measuring the water leak and pressure drop over time, both open air and in a approximate uterus model. The resulting prototype is made up of six components, some of which are easy to retrieve, namely a water bottle, a silicone tube and an ordinary condom, while others can be manufactured locally using 3D printers, namely a modified bottle cap, a flow stopper and a valve for holding the condom in place. Validation testing bore promising results with no water or pressure leak open air, and minimal leaks in the approximate uterus model. This demonstrates that the 3D printed condom-based intrauterine balloon tamponade is performing well against the requirements and, when compared to the state of the art, it could be a more appropriate and more resilient solution to low-resource settings, as it bypasses the challenges in the supply of consumables and presents a greener option based on circular economy. [ABSTRACT FROM AUTHOR]

Background: Whilst the uniqueness of loneliness and social isolation is now recognised for members of the Armed Forces Community, there is currently a lack of evidence examining these experiences within the Military Widow/er population. Therefore, this scoping review aimed to search and synthesise the current evidence base exploring experiences of loneliness and social isolation in this community. Method: Six databases were searched; ASSIA; CINAHL; ProQuest Dissertation & Theses Global; PsycArticles; Medline; Web of Science. Any article type was included if they focused on UK or international Military Widows and loneliness and social isolation. In the absence of loneliness and social isolation, related aspects were included, for example, social support. Results: A thematic synthesis was completed on the nine eligible papers, where key findings were coded and generated into four themes; Experiences of Loneliness and Social Isolation, The Uniqueness of the Military, Access to Social Support, and The Importance of Peer Support. Conclusions: Evidence supports the need for military-specific support services with peers who recognise the individuals' unique experiences of loneliness and social isolation. None of the available evidence focused specifically on social isolation, however this was often prevalent in the results. All of the studies were carried out in the USA and Israel, with none including the views of widowers. Further evidence is required, particularly relating to a UK-context. [ABSTRACT FROM AUTHOR]

Objective: Despite calls to increase the 'cultural competence' of health care providers, racially minoritised people continue to experience a range of problems when it comes to health care, including discrimination. While relevant qualitative meta-syntheses have suggested better ways forward for health care for racialised minorities, many have lacked conceptual depth, and none have specifically investigated the relational dimensions involved in care. We set out to investigate the social and cultural influences on health care interventions, focusing on psychological approaches and/or cancer care to inform the trial of a new psychological therapy for those living with or beyond cancer. Method: A meta-ethnography approach was used to examine the relevant qualitative studies, following Noblit and Hare, and guided by patient involvement throughout. Papers were analysed between September 2018 and February 2023, with some interruptions caused by the Covid pandemic. The following databases were searched: Ovid MEDLINE, EBSCO CINAHL, Ovid Embase, EBSCO PsycINFO, Proquest Sociology Collection (including Applied Social Sciences Index & Abstracts (ASSIA), Sociological Abstracts and Sociology Database), EBSCO SocINDEX, Ovid AMED, and Web of Science. The systematic review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42018107695), and reporting follows the eMERGe Reporting Guidance for meta-ethnographies (France et al. 2019). Results: Twenty-nine journal papers were included in the final review. Themes (third-order constructs) developed in the paper include the centrality of the patient-practitioner relationship; how participants give meaning to their illness in connection to others; how families (rather than individuals) may make health decisions; how links with a higher power and spiritual/religious others can play a role in coping; and the ways in which a hierarchy of help-seeking develops, frequently with the first port of call being the resources of oneself. Participants in studies had a need to avoid being 'othered' in their care, valuing practitioners that connected with them, and who were able to recognise them as whole and complex (sometimes described in relational languages like 'love'). Complex family-based health decision-making and/or the importance of relations with non-human interactants (e.g. God, spiritual beings) were frequently uncovered, not to mention the profoundly emergent nature of stigma, whereby families could be relatively safe havens for containing and dealing with health challenges. A conceptual framework of 'animated via (frequently hidden) affective relationality' emerged in the final synthesis, bringing all themes together, and drawing attention to the emergent nature of the salient issues facing minoritised patients in health care interactions. Conclusion: Our analysis is important because it sheds light on the hitherto buried relational forces animating and producing the specific issues facing racially minoritised patients, which study participants thought were largely overlooked, but to which professionals can readily relate (given the universal nature of human relations). Thus, training around the affective relationality of consultations could be a fruitful avenue to explore to improve care of diverse patients. [ABSTRACT FROM AUTHOR]

Background: Women from Black, Asian and mixed ethnicity backgrounds in the UK experience higher rates of maternal and neonatal mortality and morbidity, and report poorer experiences of maternity care. Research is required to understand how to reduce these disparities, however, it is acknowledged these groups of women are under-represented in clinical research. Aim: To investigate factors which influence participation in maternity research for women from an ethnic minority background. Methods: A systematic review was conducted to examine influencing factors for research participation. MEDLINE/CINHAL/PsycInfo/EMBASE databases were systematically searched in March 2021 and updated in March 2022. Papers were eligible if they explored maternal research participation and identified a woman's ethnicity in the results. No restrictions were placed on methodology. A convergent integrated approach was used to synthesise findings. Findings: A total of 14 papers met the inclusion criteria. Results were divided into eight overarching themes. A personalised approach to recruitment and incorporating culturally sensitive communication and considerations enhanced research participation. Distrust around sharing data, a perception of risk to research participation, and research lacking in personal relevance adversely affected the decision to participate. Large variation existed in the quality of the studies reviewed. Conclusions: Consideration of a woman's culture and background in the design and the delivery of a maternity research study may facilitate participation, particularly when sampling from a specific population. Further research, informed by women from ethnic minority backgrounds is warranted to develop women-centred recommendations for conducting inclusive maternity research. Prospero registration:www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42021261686. [ABSTRACT FROM AUTHOR]

Mental health in the UK had deteriorated compared with pre-pandemic trends. Existing studies on heterogenous wellbeing changes associated COVID-19 tend to segment population based on isolated socio-economic and demographic indicators, notably gender, income and ethnicity, while a more holistic and contextual understanding of such heterogeneity among the workforce seems lacking. This study addresses this gap by 1) combining UK time use surveys collected before and during COVID-19, 2) identifying latent lifestyles within three working mode groups (commuter, homeworker and hybrid worker) using latent class model, and 3) quantifying nuanced experiential wellbeing (ExWB) changes across workers of distinct lifestyles. The direction and magnitude of ExWB changes were not uniform across activity types, time of day, and lifestyles. The direction of ExWB change during the daytime activities window varied in accordance with lifestyle classifications. Specifically, ExWB decreased for all homeworkers but increased significantly for certain hybrid workers. Magnitude of ExWB change correlated strongly with lifestyle. To understand the significant heterogeneity in ExWB outcomes, a spatial-temporal conceptualisation of working flexibility is developed to explicate the strong yet complex correlations between wellbeing and lifestyles. The implications to post-pandemic "back-to-work" policies are 1) continued expansion of hybrid working optionality, 2) provide wider support for lifestyle adaptation and transitions. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has had enormous implications for the world of work. However, there has been relatively little focus on the employment and workforce challenges of the virus in relation to workforce health, beyond the immediate management of the spread of the disease. There is an important gap in understanding the ongoing workforce issues created by the significant incidence of Long COVID in the population. Aim: This paper examines the effects of Long COVID on employment and workers' mental health to contribute to understanding of work-limiting health conditions and to offer policy implications for COVID-19 and similar health conditions on employment and the workforce. Methods: A large national panel study for the UK is used to estimate the likelihood of exiting employment as well as on changes in working hours and general mental health and happiness of those who remain in work. The sample includes individuals 16 years and older who were in employment in January/February 2020 and followed during the pandemic 2020–2021. Long COVID is self-reported in the data. Informed by conceptual consideration of employment protection in the UK, two groups of individuals with Long COVID are defined based on the duration of symptoms. Group 1 has Long COVID 5–28 weeks after an infection with COVID-19, which is up to the maximum length of Statutory Sick Pay in the UK. Group 2 has symptoms for 29+ weeks, which is beyond the statutory entitlement to sickness pay. Panel regression models are fitted both with fixed-effects and random-effects. Individual and job characteristics are used as controls Those with no COVID-19 symptoms are the reference group. Results: In between-person comparison, Group 2 is at higher risk of exiting employment compared to those with no COVID-19 symptoms. Between-person estimates of mental health and well-being show negative effects of Long COVID for both groups but these are greatest in Group 2. Within-person estimates suggest that factors associated with earnings mediate the negative Long COVID effects on mental health in Group 1 and that Group 2 adapts to working with Long COVID. Group 1 is at risk of working zero hours (i.e. being on sick leave) but neither Group 1 nor Group 2 have a higher probability of working fewer hours compared to those with no COVID-19 symptoms. The negative impact of Long COVID on working hours stems primarily from working zero hours (sickness leave) rather than working fewer hours, suggesting a lack of accommodation by employers of Long COVID at work. Policy implications: The extension of Statutory Sickness Pay and greater flexibility to manage partial (returns to) work would help preserve employment and mental health. Those with Long COVID for 12 months are likely to meet the definition of disability and so have a right to receive reasonable workplace adjustments. [ABSTRACT FROM AUTHOR]

Background: Stroke survivors with aphasia want to improve their everyday talking (discourse). In current UK practice, 90% of speech and language therapists believe discourse assessment and treatment is part of their role but are hampered by barriers in resources, time and expertise. There is a clinical need for well-articulated discourse assessment and treatments. LUNA is a multi-level treatment targeting words, sentences and discourse macrostructure in personal stories that addresses this clinical need. Objectives: This study aimed to assess the feasibility and acceptability of LUNA trial procedures in a randomised waitlist-controlled trial; and to evaluate preliminary efficacy. Methods: This paper reports a phase II, waitlist-controlled, proof-of-concept feasibility trial. Participants with chronic aphasia (n = 28) were recruited from the community and randomised to an Immediate (n = 14) or Delayed (n = 14) group. LUNA treatment was delivered twice weekly for 10 weeks via the videoconferencing technology, Zoom. Feasibility was assessed in terms of participant recruitment and retention, adherence, missing data, and treatment fidelity. Preliminary treatment efficacy was assessed in terms of between group differences in outcome measures relating to discourse, language, and psychosocial state. Results: The remote LUNA trial was feasible: 85% of those eligible consented to the trial; trial retention was 86%; 87% of treatment sessions were delivered as scheduled, and 79% of participants completed 80%+ of the treatment programme; data was missing only for participants who withdrew; treatment fidelity was high at 92% adherence; and only one clinical outcome measure demonstrated ceiling effects. ANCOVA analysis of the clinical outcome measures revealed group differences with medium and large effect sizes, indicating, improvements in the production of words, sentences, discourse macrostructure, overall language functioning (WAB-R), and psychosocial state (VAMS) following LUNA treatment. For most outcomes measured, similar treatment benefits were suggested in a secondary, non-parametric analysis. Conclusions: Large-scale evaluation of the clinical efficacy and cost-effectiveness of LUNA is warranted and supported by these findings. Trial registration: Clinical trials registration: NCT05847023 (clinical trials.gov). [ABSTRACT FROM AUTHOR]

International students' mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wide range of challenges while studying abroad that have an impact on their psychological well-being. These challenges can include language obstacles, cultural differences, homesickness, financial issues and other elements that could severely impact the mental health of international students. Given the limited research on the demographic, cultural, and psychosocial variables that influence international students' mental health, and the scarcity of studies on the use of machine learning algorithms in this area, this study aimed to analyse data to understand the demographic, cultural factors, and psychosocial factors that impact mental health of international students. Additionally, this paper aimed to build a machine learning-based model for predicting depression among international students in the United Kingdom. This study utilized both primary data gathered through an online survey questionnaire targeted at international students and secondary data was sourced from the 'A Dataset of Students' Mental Health and Help-Seeking Behaviors in a Multicultural Environment,' focusing exclusively on international student data within this dataset. We conducted data analysis on the primary data and constructed models using the secondary data for predicting depression among international students. The secondary dataset is divided into training (70%) and testing (30%) sets for analysis, employing four machine learning models: Logistic Regression, Decision Tree, Random Forest, and K Nearest Neighbor. To assess each algorithm's performance, we considered metrics such as Accuracy, Sensitivity, Specificity, Precision and AU-ROC curve. This study identifies significant demographic variables (e.g., loan status, gender, age, marital status) and psychosocial factors (financial difficulties, academic stress, homesickness, loneliness) contributing to international students' mental health. Among the machine learning models, the Random Forest model demonstrated the highest accuracy, achieving an 80% accuracy rate in predicting depression. [ABSTRACT FROM AUTHOR]

Background: Research into canine health and welfare is supported by Government, charitable and private UK funding organisations. However, there is no current overall visibility or coordination of these funding activities, potentially compromising optimal distribution of limited resources. This study aimed to survey UK canine health and welfare funding by not-for-profit funders between 2012 and 2022, providing a novel baseline analysis to inform future sector stakeholder priorities. Results: Funding data were collected from 10 wide-scope funders (UK Government funding councils and medical charities), 18 animal-directed funders (organisations specifically concerned with animal health and welfare) and 81 breed community groups. These 109 UK funders together provided traceable canine-relevant funding of £57.8 million during the surveyed period, comprising 684 individual grant awards supporting over 500 separate research projects. Wide-scope funders contributed £41.2 million (71.2% of total funding); animal-directed organisations, £16.3 million (28.1% of total funding); and breed-specific groups, £370K (0.6% of total funding). Individual grants ranged from £2.3 million to £300. Funding patterns varied between sectors. Animal-directed funders provided £14.7 million of canine-relevant research funding that foregrounded the dog, 73% of all such funding; wide-scope funders provided £17.5 million of canine-relevant One Health research funding, 97% of all such funding. Customised metrics developed for this study assessed the 'benefit to the dog' and 'pathway to impact' of individual research projects. Overall, studies supported by animal-directed funders achieved significantly higher 'benefit to the dog' scores (Mann-Whitney U = 45235, p<0.001) and 'pathway to impact' scores (Mann-Whitney U = 43506.5, p<0.001) than those supported by wide-scope funders. Conclusion: The landscape of UK not-for-profit funding of canine health and welfare research is complex, with considerable variation between providers. Although wide-scope funders provide the majority of overall canine-relevant research funding, animal-directed funders provide the majority of canine-focused funding and support research with greater direct impact on canine welfare. Visibility of past funding patterns will enable stakeholders in this sector to make more informed decisions about future research. Definitions: To increase clarity, certain words and phrases are used in specific ways within the context of this paper. Animal-directed funders—Charities and other funding organisations whose remit primarily concerns animals or veterinary work Canine-focused research—Investigations where the primary purpose is to advance understandings of canine health and/or welfare Canine-relevant research—All research that is framed as advancing understandings of canine health and/or welfare as a primary or subsidiary purpose Institution—Refers to universities and other centres where research is carried out Organisation—Refers to funding bodies, including research councils, charities and other groups Research grant—A single funding event originating from one or more funders Research project—A cohesive piece of research concerning a particular topic; may involve multiple researchers and/or multiple research grants, in series or in parallel Wide-scope funders—Large organisations whose remit does not primarily concern animals, i.e. (in this dataset) UKRI councils and the Wellcome Trust [ABSTRACT FROM AUTHOR]

Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy. Author summary: Biological pleiotropy refers to a SNP or gene that has a direct biological influence on more than one phenotypic trait, which can offer significant insights in understanding the complex genotype-phenotype relationships. Network analyses provide an integrative approach to characterize complex genomic associations by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). Jointly analyzing multiple phenotypes and incorporating the genetic information into the phenotype clustering may increase the statistical power to discover the cross-phenotype association and pleiotropy. We evaluate our proposed multiple phenotype association tests based on network modules detected by GPN for 72 EHR-derived phenotypes in the diseases of the musculoskeletal system and connective tissue in the UK Biobank. From the post-GWAS analyses, we observe that the test based on GPN can identify more significantly enriched biological pathways than that without considering the network modules. Meanwhile, some of the uniquely identified SNPs by the test based on GPN are also colocalized in the eQTL study of the gene expression in the Muscle Skeletal tissue. [ABSTRACT FROM AUTHOR]

This paper introduces three new scales to measure left- and right-wing radical as well as general extremist attitudes that can be applied across Western European countries. We therefore propose a thorough conceptualization of extremist attitudes that consists of two dimensions: general extremism, by which we understand attitudes that oppose the constitutional democratic state, and another dimension that differentiates between right- and left-wing radicalism by which we understand people who take far-reaching but often one-sided positions on political issues (e.g., on nationalism or anti-imperialism) by advocating fundamental socio-political change. Based on data from Germany, Great Britain, and the Netherlands (n = 6,201) we created short indices for general extremism and left- and right-wing radicalism. We check for convergence validity by assessing the psychometric properties of the extracted indices, i.e. their internal coherence and the degree to which a scale is able to distinguish strongly extremist and non-extremist individuals. Finally, we correlate the scales with various constructs that are likely related to extremist attitudes in order to assure external or construct validity. The results indicate that the three scales are highly valid and applicable across three Western European countries. Overall, we find that about two to four percent of citizens in each country hold left-wing or right-wing extremist attitudes. [ABSTRACT FROM AUTHOR]

Researchers commonly perform sentiment analysis on large collections of short texts like tweets, Reddit posts or newspaper headlines that are all focused on a specific topic, theme or event. Usually, general-purpose sentiment analysis methods are used. These perform well on average but miss the variation in meaning that happens across different contexts, for example, the word "active" has a very different intention and valence in the phrase "active lifestyle" versus "active volcano". This work presents a new approach, CIDER (Context Informed Dictionary and sEmantic Reasoner), which performs context-sensitive linguistic analysis, where the valence of sentiment-laden terms is inferred from the whole corpus before being used to score the individual texts. In this paper, we detail the CIDER algorithm and demonstrate that it outperforms state-of-the-art generalist unsupervised sentiment analysis techniques on a large collection of tweets about the weather. CIDER is also applicable to alternative (non-sentiment) linguistic scales. A case study on gender in the UK is presented, with the identification of highly gendered and sentiment-laden days. We have made our implementation of CIDER available as a Python package: https://pypi.org/project/ciderpolarity/. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this paper is to investigate what factors are associated to cardiovascular disease and what differences exists between Asians living in the UK (from the English Longitudinal Study of Ageing) and the Asians living in India (from the Longitudinal Ageing Study in India). Methods: Logistic regression was used to investigate how demographic and physical performance factors were associated with cardiovascular disease using data from Wave 6 of the English Longitudinal Study of Ageing and Wave 1 of the Longitudinal Study of Ageing in India, with the main variable of interest being country of residence, Asians in England or Asians in India. Results: A total of 83,997 participants were included in the analyses. In the primary analysis, 73,396 participants from LASI were compared to 171 Asians in ELSA. After adjusting for age, blood pressure, resting heart rate, sex, waist circumference, gait, handgrip strength and standing balance, there was a statistically significant difference for the outcome of CVD between Whites ELSA (reference) and the participants of LASI (odds ratio = 0.77; 95% confidence interval = 0.60 to 0.99). There were no significant differences in CVD between the LASI participants, Asian ELSA, and the Non-White but not Asian ELSA groups. Discussion: No difference was found between Asians that live in India compared to ethnic minorities living in England, including Asians, after adjusting for confounders, but was found between Whites in ELSA compared to LASI participants. A key limitation was the massive disparity in sample sizes between the ELSA subgroups and LASI. Further work is required where comparable sample sizes and longitudinal analyses allow trends to be identified and to investigate the factors associated with the difference in CVD between two similar ethnicities living in distinct locations. Conclusion: After adjusting for risk factors, there was no difference in CVD between localised Asians and the ethnic minorities in the UK, but there was a difference between the majority ethnicities in the respective countries. [ABSTRACT FROM AUTHOR]

Introduction: HIV transmission continues among gay and bisexual men who have sex with men (GBMSM), with those who are younger, or recent migrants, or of minority ethnicity or who are gender diverse remaining at increased risk. We aimed to identify and describe recent studies evaluating the effectiveness of HIV prevention interventions for GBMSM in high income countries. Methods: We searched ten electronic databases for randomized controlled trials (RCTs), conducted in high income settings, and published since 2013 to update a previous systematic review (Stromdahl et al, 2015). We predefined four outcome measures of interest: 1) HIV incidence 2) STI incidence 3) condomless anal intercourse (CLAI) (or measure of CLAI) and 4) number of sexual partners. We used the National Institute for Health and Care Excellence (UK) Quality Appraisal of Intervention Studies tool to assess the quality of papers included in the review. As the trials contained a range of effect measures (e.g. odds ratio, risk difference) comparing the arms in the RCTs, we converted them into standardized effect sizes (SES) with 95% confidence intervals (CI). Results: We identified 39 original papers reporting 37 studies. Five intervention types were identified: one-to-one counselling (15 papers), group interventions (7 papers), online interventions (9 papers), Contingency Management for substance use (2 papers) and Pre-exposure Prophylaxis (PrEP) (6 papers). The quality of the studies was mixed with over a third of studies rated as high quality and 11% rated as poor quality. There was some evidence that one-to-one counselling, group interventions (4–10 participants per group) and online (individual) interventions could be effective for reducing HIV transmission risk behaviours such as condomless anal intercourse. PrEP was the only intervention that was consistently effective at reducing HIV incidence. Conclusions: Our systematic review of the recent evidence that we were able to analyse indicates that PrEP is the most effective intervention for reducing HIV acquisition among GBMSM. Targeted and culturally tailored behavioural interventions for sub-populations of GBMSM vulnerable to HIV infection and other STIs should also be considered, particularly for GBMSM who cannot access or decline to use PrEP. [ABSTRACT FROM AUTHOR]

Background: Autism is a normal part of cognitive diversity, resulting in communication and sensory processing differences, which can become disabling in a neurotypical world. Autistic people have an increased likelihood of physical and mental co-occurring conditions and die earlier than neurotypical peers. Inaccessible healthcare may contribute to this. Autism Health Passports (AHPs) are paper-based or digital tools which can be used to describe healthcare accessibility needs; they are recommended in UK clinical guidance. However, questions remained as to the theoretical underpinnings and effectiveness of AHPs. Methods: We undertook a systematic literature search identifying studies focused on AHPs for adults (aged over 16 years) from five databases. Included literature was subjected to realist evaluation. Data were extracted using a standardised form, developed by the research team, which considered research design, study quality for realist review and the Context, Mechanisms and Outcomes (CMOs) associated with each AHP tool. Findings: 162 unique records were identified, and 13 items were included in the review. Only one item was considered high quality. Contextual factors focused on the inaccessibility of healthcare to Autistic patients and staff lack of confidence and training in supporting Autistic needs. Interventions were heterogeneous, with most sources reporting few details as to how they had been developed. The most frequently included contents were communication preferences. Mechanisms were often not stated or were inferred by the reviewers and lacked specificity. Outcomes were included in four studies and were primarily focused on AHP uptake, rather than Outcomes which measured impact. Conclusion: There is insufficient evidence to conclude that AHPs reduce the health inequalities experienced by Autistic people. Using an AHP tool alone in a healthcare Context that does not meet Autistic needs, without the inclusion of the local Autistic community developing the tool, and a wider intervention to reduce known barriers to health inequality, may mean that AHPs do not trigger any Mechanisms, and thus cannot affect Outcomes. [ABSTRACT FROM AUTHOR]

This paper examines the moral and legal underpinnings of corporate tax avoidance. Cast in terms of a totemic symbol that brand tax avoidance as within the purview of the law, the paper invokes the attributional frames of the new sociology of morality to examine the position of both the moral advocates and the amoral critics of aggressive tax avoidance. The paper uses the United Kingdom as a jurisdiction where complex tax planning by tax advisors serves as a measure of protection for corporations who may have already conceived that they are paying too much tax. Data for the paper came from semi-structured interviews conducted with tax accountants, consultants, parliamentarians, and government officials. To supplement the interviews, data from the Parliamentary Commission on Banking Standards were collected and analyzed to provide useful insights. The findings reveal that through effective tax planning, companies can reduce the present values of future tax payments. Given the singular justification of their actions within the contours of the tax rules, the moral culpability of organized tax avoidance is minimized, with very little liability attached. Tax avoidance is a morally charged area that is slowly drifting away from conventional social norms of what is right or wrong. It is hard not to see those in charge of tax regulation not using the findings of this paper to provide a more nuanced understanding of the intractable problems associated with corporate tax avoidance and use it as a reference point for regulatory reforms. [ABSTRACT FROM AUTHOR]

Although research has demonstrated that transport infrastructure development can have positive and negative health-related impacts, most of this research has not considered mental health and wellbeing separately from physical health. There is also limited understanding of whether and how any effects might be experienced differently across population groups, whether this differs according to the stage of development (e.g. planning, construction), and how changes to planned infrastructure may affect mental health and wellbeing. This paper presents a protocol for the Wellbeing Impact Study of HS2 (WISH2), which seeks to address these questions using a high-speed rail development in the UK as an applied example. WISH2 is a 10-year, integrated, longitudinal, mixed-methods project using general practices (primary medical care providers in the UK) as an avenue for participant recruitment and for providing a geographically defined population for which aggregated data on mental health indicators are available. The research comprises: (i) a combined longitudinal and repeated cross-sectional cohort study involving multiple waves of survey data collection and data from medical records; (ii) longitudinal, semi-structured interviews and focus groups with residents and community stakeholders from exposed areas; (iii) analysis of administrative data aggregated at the general practice population level; and (iv) health economic analysis of mental health and wellbeing impacts. The study findings will support the development of strategies to reduce negative impacts and/or enhance positive mental health and wellbeing impacts of high-speed rail developments and other large-scale infrastructure projects. [ABSTRACT FROM AUTHOR]

Using qualitative interview data (n = 142 interviews) generated with 50 nurses, over the course of the COVID-19 pandemic, this paper traces the trajectories of nurses in the UK and attempts to unpick the interplay between structure and agency in their narratives. Interviews were inductively analysed for themes and an additional narrative analysis was undertaken to preserve the form of each participant's narrative. We argue that nurses' pandemic trajectories occurred within the 'psychological vulnerability-stigma nexus' which operates within health and social care providers in the UK and whilst constraining nurses' agency at times it could also provide an impetus to act agentically. We found that the nurses' COVID-19 trajectories were characterised by: getting by, getting out (job-hopping) getting needs met and getting organised. We call for more considered systemic support to be generated and consistently provided to nurses to ensure retention of nurses and the security of society to avoid exacerbating existing workforce shortages. [ABSTRACT FROM AUTHOR]

Understanding lay theories on the causes of economic inequality is the first step to comprehending why people tolerate, justify, or react against it. Accordingly, this paper aims to develop and validate with two cross-sectional studies the Attributions for Cross-Country Inequality Scale (ACIS), which assesses how people explain cross-country economic inequality–namely, the uneven distribution of income and wealth between poor and rich countries. After selecting and adapting items from existing scales of attributions for poverty and wealth, in Study 1, we tested the factorial structure of this initial pool of items in three countries with different levels of economic development and inequality, namely, Italy (n = 246), the UK (n = 248), and South Africa (n = 228). Three causal dimensions emerged from the Exploratory Factor Analysis: "rich countries" (blaming the systematic advantage of and exploitation by rich countries), "poor countries" (blaming the dispositional inadequacy and faults of poor countries), and "fate" (blaming destiny and luck). The retained items were administered in Study 2 to three new samples from Italy (n = 239), the UK (n = 249), and South Africa (n = 248). Confirmatory Factor Analysis (CFA) corroborated the factorial structure of the ACIS, and Multi-Group CFA supported configural and metric invariances of the scale across countries. In addition, we show internal consistency and construct validity of the scale: the scale correlates with relevant constructs (e.g., beliefs about cross-country inequality and ideological orientation) and attitudes toward relevant policies related to international redistribution and migration. Overall, the scale is a valid instrument to assess causal attribution for cross-national inequality and is reliable across countries. By focusing on resource distribution from an international perspective, this scale will allow researchers to broaden the discussion on economic inequality to a global level. [ABSTRACT FROM AUTHOR]

Background: Over a third of pregnant women (around 250,000) each year in the United Kingdom have experienced trauma such as domestic abuse, childhood trauma or sexual assault. These experiences can have a long-term impact on women's mental and physical health. This global qualitative evidence synthesis explores the views of women and maternity care professionals on routine discussion of previous trauma in the perinatal period. Methods: Systematic database searches (MEDLINE, EMBASE, CINAHL Plus, APA PsycINFO and Global Index Medicus) were conducted in July 2021 and updated in April 2022. The quality of each study was assessed using the Critical Appraisal Skills Programme. We thematically synthesised the data and assessed confidence in findings using GRADE-CERQual. Results: We included 25 papers, from five countries, published between 2001 and 2022. All the studies were conducted in high-income countries; therefore findings cannot be applied to low- or middle-income countries. Confidence in most of the review findings was moderate or high. The findings are presented in six themes. These themes described how women and clinicians felt trauma discussions were valuable and worthwhile, provided there was adequate time and appropriate referral pathways. However, women often found being asked about previous trauma to be unexpected and intrusive, and women with limited English faced additional challenges. Many pregnant women were unaware of the extent of the trauma they have suffered, or its impact on their lives. Before disclosing trauma, women needed to have a trusting relationship with a clinician; even so, some women chose not to share their histories. Hearing trauma disclosures could be distressing for clinicians. Conclusion: Discussions of previous trauma should be undertaken when women want to have the discussion, when there is time to understand and respond to the needs and concerns of each individual, and when there are effective resources available for follow up if needed. Continuity of carer should be considered a key feature of routine trauma discussion, as many women will not disclose their histories to a stranger. All women should be provided with information about the impact of trauma and how to independently access support in the event of non-disclosures. Care providers need support to carry out these discussions. [ABSTRACT FROM AUTHOR]

Background: Evidence regarding the presence and persistence of ethnic inequalities in mental healthcare is well established. The reasons for these inequalities and lack of progress in diminishing them are less understood. This meta-ethnography aims to provide a new conceptual understanding of how ethnic inequalities are created and sustained; this is essential to develop effective interventions. Specifically, we sought to understand why people from ethnic minority groups are underrepresented in primary care mental health service provision and overrepresented in crisis pathways and detention. Methods and findings: Following eMERGe guidelines for meta-ethnographies, we searched OpenGrey, Kings Fund, CINAHL, Medline, PsycINFO, and Social Care Online databases for qualitative articles published from database inception until October 2, 2022, using broad categories of search terms relating to "ethnicity AND (mental illness/mental health/emotional distress) AND (help-seeking/service utilisation/experience/perception/view)." We included all conceptually rich articles that used qualitative methods of data collection and analysis and excluded non-UK studies and those that focused solely on causation of mental illness. Our patient, public, and practitioner lived experience advisory group provided feedback and input on key stages of the project including search terms, research questions, data analysis, and dissemination. A total of 14,142 articles were identified; 66 met the inclusion criteria. We used reciprocal, refutational, and line of argument analytical approaches to identify convergence and divergence between studies. The synthesis showed that current models of statutory mental healthcare are experienced as a major barrier to the delivery of person-centred care to those in ethnic minority groups due to the perceived dominance of monocultural and reductionist frameworks of assessment and treatment (described as "medical" and "Eurocentric") and direct experiences of racist practice. The lack of socially oriented and holistic frameworks of knowledge and understanding in medical training and services is experienced as epistemic injustice, particularly among those who attribute their mental illness to experiences of migration, systemic racism, and complex trauma. Fear of harm, concerns about treatment suitability, and negative experiences with health providers such as racist care and medical neglect/injury contribute to avoidance of, and disengagement from, mainstream healthcare. The lack of progress in tackling ethnic inequalities is attributed to failures in coproduction and insufficient adoption of existing recommendations within services. Study limitations include insufficient recording of participant characteristics relating to generational status and social class in primary studies, which prevented exploration of these intersections. Conclusions: In this study, we found that the delivery of safe and equitable person-centred care requires a model of mental health that is responsive to the lived experiences of people in ethnic minority groups. For the people considered in this review, this requires better alignment of mental health services with social and anti-racist models of care. Our findings suggest that intersections related to experiences of racism, migration, religion, and complex trauma might be more relevant than crude ethnic group classifications. Strategies to tackle ethnic inequalities in mental healthcare require an evaluation of individual, systemic, and structural obstacles to authentic and meaningful coproduction and implementation of existing community recommendations in services. In a meta-ethnography, Dr Narinder Bansal and colleagues report insights into ethnic inequalities in mental healthcare in the UK. Author summary: Why was this study done?: People from ethnic minority groups in the UK have poorer mental health and access to mental healthcare, and more negative experiences and outcomes compared to the majority white British group. These inequalities have been reported for over 50 years. There remains some debate regarding their causes and the interventions required to tackle these. In order to develop effective interventions, we need to consider a wide range of perspectives from communities, service users, carers, and mental health professionals in a way that allows interrogation of commonalities and variations in experience across a diverse range of ethnic and lay/professional backgrounds. What did the researchers do and find?: We conducted a comprehensive literature search and identified 66 relevant studies that explored the experiences and perspectives of people in different ethnic minority groups and mental health professionals in relation to access to and experience of mental healthcare. We identified key themes across papers and explored similarities and differences in experiences across ethnic groups and between communities and mental health professionals. We found that current statutory approaches to the assessment and treatment of mental illness are experienced as a major barrier to the delivery of appropriate and person-centred care to those in ethnic minority groups. These are perceived to exclude the everyday realities of people's lives including racism, migration stress, complex trauma, and spirituality and the ways in which these may contribute to (or mitigate) mental illness. The lack of progress in tackling ethnic inequalities is attributed to superficial attempts at coproduction and inadequate adoption of existing community recommendations within services. What do these findings mean?: The delivery of safe and equitable person-centred care requires a model of mental healthcare that recognises, and is responsive to, the lived experiences of people in ethnic minority groups. This includes paying attention to how experiences of racism and migration affect mental health during assessment and treatment and tackling institutional racism in mental healthcare. Strategies to tackle ethnic inequalities in mental healthcare require an evaluation of existing barriers and obstacles to authentic and meaningful inclusion of people with lived experience from ethnic minority groups in the development and delivery of mental healthcare services. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, digital contact-tracing has been employed in many countries to monitor and manage the spread of the disease. However, to be effective such a system must be adopted by a substantial proportion of the population; therefore, public trust plays a key role. This paper examines the NHS COVID-19 smartphone app, the digital contact-tracing solution in the UK. A series of interviews were carried out prior to the app's release (n = 12) and a large scale survey examining attitudes towards the app (n = 1,001) was carried out after release. Extending previous work reporting high level attitudes towards the app, this paper shows that prevailing negative attitudes prior to release persisted, and affected the subsequent use of the app. They also show significant relationships between trust, app features, and the wider social and societal context. There is lower trust amongst non-users of the app and trust correlates to many other aspects of the app, a lack of trust could hinder adoption and effectiveness of digital contact-tracing. The design of technology requiring wide uptake, e.g., for public health, should embed considerations of the complexities of trust and the context in which the technology will be used. [ABSTRACT FROM AUTHOR]

Objective: To determine the extent to which various factors are associated with greater or lesser odds of reporting non-specific low back pain (NS-LBP) in field hockey. Methods: To meet the objective of the study, a cross-sectional study design was used with a purposive sampling strategy. A total of 194 responses (~18% of those accessible) from Premier and Division One players within the UK were received using a UK-based online survey. Data collected included information on NS-LBP, participant characteristics, injury history, training related factors, and work and personal factors. The overall and category-specific prevalence of NS-LBP was calculated. Univariable and multivariable logistic regression was used in conjunction with clinical value to identify associations. Results: The overall prevalence of NS-LBP was 44.0%, with this varying from 23.5 to 70.0% for categories with responses of "yes" and "no" to experiencing NS-LBP. A total of ten individual factors associated with a greater odds ratio (OR) of reporting NS-LBP (OR = 1.43–7.39) were identified in Premier and Division One players. Five individual factors were associated with reduced odds (OR = 0.11–0.60) of reporting NS-LBP. Seven factors (age, stature, playing position, playing internationally, performing a drag flick, low back stiffness/tightness and occupational factors) were deemed particularly pertinent to those working in field hockey given the magnitude of association and clinical value to clinicians. Conclusions: Clinicians working in field hockey can consider the key risk factors identified in this study that are associated with NS-LBP when assessing injury risk, movement screening approaches, and overall athlete management. [ABSTRACT FROM AUTHOR]

Background: Cervical cerclage, cervical pessary, and vaginal progesterone have each been shown to reduce preterm birth (PTB) in high-risk women, but to our knowledge, there has been no randomised comparison of the 3 interventions. The SuPPoRT "Stitch, Pessary, or Progesterone Randomised Trial" was designed to compare the rate of PTB <37 weeks between each intervention in women who develop a short cervix in pregnancy. Methods and findings: SuPPoRT was a multicentre, open label 3-arm randomised controlled trial designed to demonstrate equivalence (equivalence margin 20%) conducted from 1 July 2015 to 1 July 2021 in 19 obstetric units in the United Kingdom. Asymptomatic women with singleton pregnancies with transvaginal ultrasound cervical lengths measuring <25 mm between 14+0 and 23+6 weeks' gestation were eligible for randomisation (1:1:1) to receive either vaginal cervical cerclage (n = 128), cervical pessary (n = 126), or vaginal progesterone (n = 132). Minimisation variables were gestation at recruitment, body mass index (BMI), and risk factor for PTB. The primary outcome was PTB <37 weeks' gestation. Secondary outcomes included PTB <34 weeks', <30 weeks', and adverse perinatal outcome. Analysis was by intention to treat. A total of 386 pregnant women between 14+0 and 23+6 weeks' gestation with a cervical length <25 mm were randomised to one of the 3 interventions. About 67% were of white ethnicity, 18% black ethnicity, and 7.5% Asian ethnicity. Mean BMI was 25.6. Around 85% of women had prior risk factors for PTB; 39.1% had experienced a spontaneous PTB or midtrimester loss (>14 weeks gestation); and 45.8% had prior cervical surgery. Data from 381 women were available for outcome analysis. Using binary regression, randomised therapies (cerclage versus pessary versus vaginal progesterone) were found to have similar effects on the primary outcome PTB <37 weeks (39/127 versus 38/122 versus 32/132, p = 0.4, cerclage versus pessary risk difference (RD) −0.7% [−12.1 to 10.7], cerclage versus progesterone RD 6.2% [−5.0 to 17.0], and progesterone versus pessary RD −6.9% [−17.9 to 4.1]). Similarly, no difference was seen for PTB <34 and 30 weeks, nor adverse perinatal outcome. There were some differences in the mild side effect profile between interventions (vaginal discharge and bleeding) and women randomised to progesterone reported more severe abdominal pain. A small proportion of women did not receive the intervention as per protocol; however, per-protocol and as-treated analyses showed similar results. The main study limitation was that the trial was underpowered for neonatal outcomes and was stopped early due to the COVID-19 pandemic. Conclusions: In this study, we found that for women who develop a short cervix, cerclage, pessary, and vaginal progesterone were equally efficacious at preventing PTB, as judged with a 20% equivalence margin. Commencing with any of the therapies would be reasonable clinical management. These results can be used as a counselling tool for clinicians when managing women with a short cervix. Trial registration: EU Clinical Trials register. EudraCT Number: 2015-000456-15, clinicaltrialsregister.eu., ISRCTN Registry: ISRCTN13364447, isrctn.com. In a randomised controlled trial, Natasha Hezelgrave and co-workers compare therapies for prevention of preterm birth in women with a short cervix. Author summary: Why was this study done?: We evaluated whether commonly used treatments (cervical cerclage, cervical pessary, and vaginal progesterone) are equally effective for preventing preterm birth (PTB) before 37 weeks' gestation in women who are found to have a short cervix on ultrasound scan What did the researchers do and find?: We randomised 386 at-risk women with a short cervix to one of 3 treatments (cervical cerclage, cervical pessary, and vaginal progesterone). Each group had a similar rate of PTB before 37 weeks of gestation. What do these findings mean?: For women with a short cervix, commencing any of the 3 therapies would be reasonable management for PTB prevention. However, as a small number of women received alternative or additional therapies to the randomised treatment, continued monitoring of the cervix is advised. Further research should evaluate the benefit of combination therapies. [ABSTRACT FROM AUTHOR]

Introduction: Postoperative complications affect up to 15% of surgical patients constituting a major part of the overall disease burden in a modern healthcare system. While several surgical risk calculators have been developed, none have so far been shown to decrease the associated mortality and morbidity. Combining deep neural networks and genomics with the already established clinical predictors may hold promise for improvement. Methods: The UK Biobank was utilized to build linear and deep learning models for the prediction of surgery relevant outcomes. An initial GWAS for the relevant outcomes was initially conducted to select the Single Nucleotide Polymorphisms for inclusion in the models. Model performance was assessed with Receiver Operator Characteristics of the Area Under the Curve and optimum precision and recall. Feature importance was assessed with SHapley Additive exPlanations. Results: Models were generated for atrial fibrillation, venous thromboembolism and pneumonia as genetics only, clinical features only and a combined model. For venous thromboembolism, the ROC-AUCs were 60.1% [59.6%-60.4%], 63.4% [63.2%-63.4%] and 66.6% [66.2%-66.9%] for the linear models and 51.5% [49.4%-53.4%], 63.2% [61.2%-65.0%] and 62.6% [60.7%-64.5%] for the deep learning SNP, clinical and combined models, respectively. For atrial fibrillation, the ROC-AUCs were 60.3% [60.0%-60.4%], 78.7% [78.7%-78.7%] and 80.0% [79.9%-80.0%] for the linear models and 59.4% [58.2%-60.9%], 78.8% [77.8%-79.8%] and 79.8% [78.8%-80.9%] for the deep learning SNP, clinical and combined models, respectively. For pneumonia, the ROC-AUCs were 50.1% [49.6%-50.6%], 69.2% [69.1%-69.2%] and 68.4% [68.0%-68.5%] for the linear models and 51.0% [49.7%-52.4%], 69.7% [.5%-70.8%] and 69.7% [68.6%-70.8%] for the deep learning SNP, clinical and combined models, respectively. Conclusion: In this report we presented linear and deep learning predictive models for surgery relevant outcomes. Overall, predictability was similar between linear and deep learning models and inclusion of genetics seemed to improve accuracy. [ABSTRACT FROM AUTHOR]

Background: The Implicit Learning in Stroke study was a pilot cluster randomised controlled trial, investigating the use of different motor learning strategies in acute stroke rehabilitation. Participating Stroke Units (n = 8) were from the South East/West regions of the UK, with the experimental intervention (implicit learning) being delivered by clinical teams. It required therapists to change how they gave instructions and feedback to patients during rehabilitation. This paper reports the processes underpinning implementation of the implicit learning intervention. The evaluation aimed to i) understand how therapists made sense of, engaged with and interpreted the effects of the intervention; ii) compare this to the experience reported by patients; iii) extrapolate learning of broader relevance to the design and conduct of research involving complex interventions in stroke rehabilitation. Methods: Qualitative evaluation, with data collected through focus groups with clinical staff (n = 20) and semi structured interviews with people with stroke (n = 19). Mixed inductive and theory driven analysis, underpinned by Normalisation Process Theory. Results: How therapists made sense of and experienced the intervention impacted how it was implemented. The intervention was delivered by individual therapists, and was influenced by their individual values, beliefs and concerns. However, how teams worked together to build a shared (team) understanding, also played a key role. Teams with a more "flexible" interpretation, reported the view that the intervention could have benefits in a wide range of scenarios. Those with a more fixed, "rule based" interpretation, found it harder to implement, and perceived the benefits to be more limited. Therapists' concerns that the intervention may impair therapeutic relationships and patient learning were not reflected in how patients experienced it. Conclusions: Changing practice, whether in a research study or in the "real world", is complex. Understanding the process of implementation is crucial to effective research delivery. Implementation frameworks facilitate understanding, and subsequently the systematic and iterative development of strategies for this to be addressed. How teams (rather than individuals) work together is central to how complex interventions are understood and implemented. It is possible that new complex interventions work best in contexts where there are 'flexible' cultures. Researchers should consider, and potentially measure this, before they can effectively implement and evaluate an intervention. Trial registration: Clinical Trials - NCT03792126. [ABSTRACT FROM AUTHOR]

Background: Youth adversity (e.g., abuse and bullying victimisation) is robust risk factor for later mental health problems (e.g., depression and anxiety). Research shows the prevalence of youth adversity and rates of mental health problems vary by individual characteristics, identity or social groups (e.g., gender and ethnicity). However, little is known about whether the impact of youth adversity on mental health problems differ across the intersections of these characteristics (e.g., white females). This paper reports on a component of the ATTUNE research programme (work package 2) which aims to investigate the impact and mechanisms of youth adversity on depressive and anxiety symptoms in young people by intersectionality profiles. Methods: The data are from 4 UK adolescent cohorts: HeadStart Cornwall, Oxwell, REACH, and DASH. These cohorts were assembled for adolescents living in distinct geographical locations representing coastal, suburban and urban places in the UK. Youth adversity was assessed using a series of self-report questionnaires and official records. Validated self-report instruments measured depressive and anxiety symptoms. A range of different variables were classified as possible social and cognitive mechanisms. Results and analysis: Structural equation modelling (e.g., multiple group models, latent growth models) and multilevel modelling will be used, with adaptation of methods to suit the specific available data, in accord with statistical and epidemiological conventions. Discussion: The results from this research programme will broaden our understanding of the association between youth adversity and mental health, including new information about intersectionality and related mechanisms in young people in the UK. The findings will inform future research, clinical guidance, and policy to protect and promote the mental health of those most vulnerable to the negative consequences of youth adversity. [ABSTRACT FROM AUTHOR]

Objective: Assess knowledge, attitudes, and practices (KAPs) of a diverse population. Identify barriers and facilitators that inform routine vitamin D supplementation and self-care in the community setting. Design: Cross-sectional online voluntary survey. Electronic survey link published on college Qualtrics platform and advertised widely. Study information provided with Participant Information Sheet. Setting and participants: 556 community dwelling adults across the UK. Methods: The overarching study included two phases, incorporating quantitative and qualitative methodologies. This paper reports findings from the first phase of the FABCOM-D (Facilitators and Barriers to Community (Healthy) Vitamin D status) study. Online survey questions were iteratively developed after background literature searches and piloted to ensure clarity and ease of understanding. Survey responses summarised using frequencies and percentages, and univariable and multivariable logistic regression models explored for any association. A p-value less than 0.05 was considered statistically significant. The Checklist for Reporting Results of Internet E-Surveys guided reporting. Statistical analysis performed using IBM SPSS software. Main outcome measures: Awareness of vitamin D information sources, health benefits and testing. Attitudes to supplementation, sun exposure and fortification. Results: Three quarters of the community had some awareness of vitamin D and around half were taking supplements. The two most trusted sources of information included health professionals and the NHS website. Participants were willing to pay for supplements, supporting a self-care agenda. With increasing age, there was significant reduced intake of vitamin D supplements. This aspect needs to be explored further as this could be a concern in deficiency status in the elderly. There was acceptance of food fortification but uncertainty on how to balance food intake with supplementation. Conclusion: We were successful in eliciting views on KAPs around vitamin D from a community population including a large proportion of Black and Minority Ethnic individuals. The community wanted information and guidance to help manage individual vitamin D status, especially for high-risk groups, and on balancing supplementation, food fortification and sun exposure. [ABSTRACT FROM AUTHOR]

Introduction: It is well established that the actions and behaviour of dementia care workers are fundamental to the wellbeing of the people they care for. Not only do they deal with basic healthcare needs, but they also perform a vital psycho-social function by providing–through their regular presence–an underlying continuity for residents. This has been shown to improve well-being, particularly for those in the advanced stages of dementia. It has also been suggested that there are additional psycho-social benefits of such contact which can directly influence the need for anti-psychotic medication. However, unlike most other healthcare and medical settings, the specialised and often difficult interactions that dementia care workers handle every day have not yet been the subject of detailed micro-level analysis. This is particularly significant because much of the impact that care-workers have relates to the way in which they interact with the people they care for. Not having a clear understanding of how their interactions 'work' at the micro-level–particularly ones that are specific to dementia care settings, and that care workers report to be difficult or challenging–means that any training interventions that are developed may not resonate with their real-world experience, and ultimately run the risk of failing. This video-based observational study aims to provide a detailed micro-exploration of problematic and challenging interactions involving care-workers and people living with dementia. Setting and methods: The study is based in the UK and will involve up to 20 dementia care staff and 60 people living with dementia. Fieldwork will be conducted in 5 dementia care home and community-based dementia day care settings using naturalistic observational methods (primarily video-ethnography). Data will be analysed using Conversation Analysis (CA). [ABSTRACT FROM AUTHOR]

Background: In April 2018, the UK government implemented a levy on soft drinks importers and manufacturers, tiered according to the amount of sugar in drinks. The stated aim was to encourage manufacturers to reduce sugar and portion sizes. Previous evidence suggests that the policy has been successful in reducing sugar in drinks in the short-term since implementation, but their sustained effects have not been explored. This study aimed to assess the impact of the soft drink industry levy (SDIL) on sugar levels, price, portion size and use of non-sugar sweeteners in the medium-term. Methods and findings: Product data from 30 November 2017 to 14 March 2020 from one major UK retail supermarket were analysed (112,452 observations, 126 weekly time points). We used interrupted time series analysis, to assess the impact of the soft-drink industry levy (SDIL) on levy-eligible soft drinks, with exempt drinks (i.e. 100% fruit juices, milks, flavoured milks) acting as a comparator series. At the point of implementation of the SDIL (April 2018) there was a step change in the proportion of eligible drinks with sugar content below the SDIL levy threshold (5g per 100ml) (+0.08, 95%CI: +0.04, +0.12), with a similar sized decrease in the proportion in the highest levy category (> = 8g sugar per 100ml) (-0.06, 95%CI: -0.10, -0.03). Between April 2018 and March 2020, the proportion of eligible drinks below the SDIL levy threshold continued to gradually increase (p = 0.003), while those in the highest levy category decreased (p = 0.007). There was a step change in price of eligible drinks in the higher levy category at the point of implementation of +£0.049 (95%CI: +£0.034, +£0.065) per 100mL (for comparison, the levy is set at £0.024 per 100mL for this group). Trends in price for the high levy category were not altered by the SDIL. In the no levy category, there was a step change in price at the implementation (+£0.012 per 100mL, 95%CI: +£0.008, +£0.023), followed by a second step change in October 2018 (-£0.018p per 100mL, 95%CI: -£0.033, -£0.001p). The volume of products in the higher levy group decreased at the time of the implementation (-305mL on average including multipacks, 95%CI: -511, -99). The change in trend for the product volume of drinks in the higher levy group between April 2018 and March 2020 was in the increasing direction (+704mL per year, 95%CI: -95, 1504), but it did not meet our threshold for statistical significance (p = 0.084). There were no changes observed in the volume of lower levy drinks or no levy drinks. There was a step change in the proportion of drinks with non-sugar sweeteners at the implementation of the SDIL (+0.04, 95%CI: +0.02, +0.06). Conclusion: These results suggest that the SDIL was successful in [1] producing reductions in sugar levels that were maintained over the medium term up to March 2020 and [2] a reduction in product volume for higher tier drinks that may be diminishing over time. Our results also show that the SDIL was associated with a maintained price differential between high and low sugar drinks. [ABSTRACT FROM AUTHOR]

Obesity, along with its associated health issues, is closely tied to lifestyle habits. While certain elements affecting childhood health, such as genetics and ethnicity, are beyond individuals' control, there exists modifiable lifestyle behaviours that can facilitate healthier living. This study employed multiple regression analysis to investigate the relationship between specific modifiable lifestyle behaviours and self-reported health. The independent variables considered included days of moderate to vigorous physical activity (MVPA), fruit and vegetable consumption, breakfast frequency, school night sleep duration, and non-school night sleep duration. These variables were chosen for their practical modifiability within participants' daily lives. The analysis revealed a highly significant overall model (F(13,11363) = 191.117, p <.001), explaining 17.9% of the variance in self-reported health. Notably, higher MVPA levels were associated with improved self-reported health (B = 0.136 to 0.730, p <.001). Additionally, regular breakfast consumption and increased fruit and vegetable intake exhibited positive associations with self-reported health (B = 0.113 to 0.377, p <.001), while girls reported lower self-reported health (B = -0.079, p <.001). School night sleep duration was positively linked to self-reported health (B = 0.071, p <.001). Furthermore, a dose-response relationship between MVPA, dietary habits, and health was identified. These findings hold substantial potential for public health campaigns to promote healthy behaviours and prevent chronic diseases in young individuals. It is imperative to emphasise that all the variables considered in this study are readily modifiable aspects of individuals' lives, offering a promising avenue for personal health and well-being enhancement. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has highlighted an era in hearing health care that necessitates a comprehensive rethinking of audiology service delivery. There has been a significant increase in the number of individuals with hearing loss who seek information online. An estimated 430 million individuals worldwide suffer from hearing loss, including 11 million in the United Kingdom. The objective of this study was to identify National Health Service (NHS) audiology service social media posts and understand how they were used to communicate service changes within audiology departments at the onset of the Covid-19 pandemic. Facebook and Twitter posts relating to audiology were extracted over a six week period (March 23 to April 30 2020) from the United Kingdom. We manually filtered the posts to remove those not directly linked to NHS audiology service communication. The extracted data was then geospatially mapped, and themes of interest were identified via a manual review. We also calculated interactions (likes, shares, comments) per post to determine the posts' efficacy. A total of 981 Facebook and 291 Twitter posts were initially mined using our keywords, and following filtration, 174 posts related to NHS audiology change of service were included for analysis. The results were then analysed geographically, along with an assessment of the interactions and sentiment analysis within the included posts. NHS Trusts and Boards should consider incorporating and promoting social media to communicate service changes. Users would be notified of service modifications in real-time, and different modalities could be used (e.g. videos), resulting in a more efficient service. [ABSTRACT FROM AUTHOR]

Breast cancer claims 11,400 lives on average every year in the UK, making it one of the deadliest diseases. Mammography is the gold standard for detecting early signs of breast cancer, which can help cure the disease during its early stages. However, incorrect mammography diagnoses are common and may harm patients through unnecessary treatments and operations (or a lack of treatment). Therefore, systems that can learn to detect breast cancer on their own could help reduce the number of incorrect interpretations and missed cases. Various deep learning techniques, which can be used to implement a system that learns how to detect instances of breast cancer in mammograms, are explored throughout this paper. Convolution Neural Networks (CNNs) are used as part of a pipeline based on deep learning techniques. A divide and conquer approach is followed to analyse the effects on performance and efficiency when utilising diverse deep learning techniques such as varying network architectures (VGG19, ResNet50, InceptionV3, DenseNet121, MobileNetV2), class weights, input sizes, image ratios, pre-processing techniques, transfer learning, dropout rates, and types of mammogram projections. This approach serves as a starting point for model development of mammography classification tasks. Practitioners can benefit from this work by using the divide and conquer results to select the most suitable deep learning techniques for their case out-of-the-box, thus reducing the need for extensive exploratory experimentation. Multiple techniques are found to provide accuracy gains relative to a general baseline (VGG19 model using uncropped 512 × 512 pixels input images with a dropout rate of 0.2 and a learning rate of 1 × 10−3) on the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) dataset. These techniques involve transfer learning pre-trained ImagetNet weights to a MobileNetV2 architecture, with pre-trained weights from a binarised version of the mini Mammography Image Analysis Society (mini-MIAS) dataset applied to the fully connected layers of the model, coupled with using weights to alleviate class imbalance, and splitting CBIS-DDSM samples between images of masses and calcifications. Using these techniques, a 5.6% gain in accuracy over the baseline model was accomplished. Other deep learning techniques from the divide and conquer approach, such as larger image sizes, do not yield increased accuracies without the use of image pre-processing techniques such as Gaussian filtering, histogram equalisation and input cropping. [ABSTRACT FROM AUTHOR]

Machine learning tools have proven useful across biological disciplines, allowing researchers to draw conclusions from large datasets, and opening up new opportunities for interpreting complex and heterogeneous biological data. Alongside the rapid growth of machine learning, there have also been growing pains: some models that appear to perform well have later been revealed to rely on features of the data that are artifactual or biased; this feeds into the general criticism that machine learning models are designed to optimize model performance over the creation of new biological insights. A natural question arises: how do we develop machine learning models that are inherently interpretable or explainable? In this manuscript, we describe the SWIF(r) reliability score (SRS), a method building on the SWIF(r) generative framework that reflects the trustworthiness of the classification of a specific instance. The concept of the reliability score has the potential to generalize to other machine learning methods. We demonstrate the utility of the SRS when faced with common challenges in machine learning including: 1) an unknown class present in testing data that was not present in training data, 2) systemic mismatch between training and testing data, and 3) instances of testing data that have missing values for some attributes. We explore these applications of the SRS using a range of biological datasets, from agricultural data on seed morphology, to 22 quantitative traits in the UK Biobank, and population genetic simulations and 1000 Genomes Project data. With each of these examples, we demonstrate how the SRS can allow researchers to interrogate their data and training approach thoroughly, and to pair their domain-specific knowledge with powerful machine-learning frameworks. We also compare the SRS to related tools for outlier and novelty detection, and find that it has comparable performance, with the advantage of being able to operate when some data are missing. The SRS, and the broader discussion of interpretable scientific machine learning, will aid researchers in the biological machine learning space as they seek to harness the power of machine learning without sacrificing rigor and biological insight. Author summary: Machine learning methods are incredibly powerful at performing tasks such as classification and clustering, but they also pose unique problems that can limit new insights. Complex machine learning models may reach conclusions that are difficult or impossible for researchers to understand after-the-fact, sometimes producing biased or meaningless results. It is therefore essential that researchers have tools that allow them to understand how machine learning tools reach their conclusions, so that they can effectively design models. This paper builds on the machine learning method SWIF(r), originally designed to detect regions in the genome targeted by natural selection. Our new method, the SWIF(r) Reliability Score (SRS), can help researchers evaluate how trustworthy the prediction of a SWIF(r) model is when classifying a specific instance of data. We also show how SWIF(r) and the SRS can be used for biological problems outside the original scope of SWIF(r). We show that the SRS is helpful in situations where the data used to train the machine learning model fails to represent the testing data in some way. The SRS can be used across many different disciplines, and has unique properties for scientific machine learning research. [ABSTRACT FROM AUTHOR]

Induction of labour, or starting labour artificially, is offered when the risks of continuing pregnancy are believed to outweigh the risks of the baby being born. In the United Kingdom, cervical ripening is recommended as the first stage of induction. Increasingly, maternity services are offering this outpatient or 'at home', despite limited evidence on its acceptability and how different approaches to cervical ripening work in practice. There is also a paucity of literature on clinicians' experiences of providing induction care in general, despite their central role in developing local guidelines and delivering this care. This paper explores induction, specifically cervical ripening and the option to return home during that process, from the perspective of midwives, obstetricians and other maternity staff. As part of a process evaluation involving five case studies undertaken in British maternity services, interviews and focus groups were conducted with clinicians who provide induction of labour care. The thematic findings were generated through in-depth analysis and are grouped to reflect key points within the process of cervical ripening care: 'Implementing home cervical ripening', 'Putting local policy into practice', 'Giving information about induction' and 'Providing cervical ripening'. A range of practices and views regarding induction were recorded, showing how the integration of home cervical ripening is not always straightforward. Findings demonstrate that providing induction of labour care is complex and represents a significant workload. Home cervical ripening was seen as a solution to managing this workload; however, findings highlighted ways in which this expectation might not be borne out in practice. More comprehensive research is needed on workload impacts and possible lateral effects within other areas of maternity services. [ABSTRACT FROM AUTHOR]

The mathematical interpretation of interventions for the mitigation of epidemics in the literature often involves finding the optimal time to initiate an intervention and/or the use of the number of infections to manage impact. Whilst these methods may work in theory, in order to implement effectively they may require information which is not likely to be available in the midst of an epidemic, or they may require impeccable data about infection levels in the community. In reality, testing and cases data can only be as good as the policy of implementation and the compliance of the individuals, which implies that accurately estimating the levels of infections becomes difficult or complicated from the data that is provided. In this paper, we demonstrate a different approach to the mathematical modelling of interventions, not based on optimality or cases, but based on demand and capacity of hospitals who have to deal with the epidemic on a day to day basis. In particular, we use data-driven modelling to calibrate a susceptible-exposed-infectious-recovered-died type model to infer parameters that depict the dynamics of the epidemic in several regions of the UK. We use the calibrated parameters for forecasting scenarios and understand, given a maximum capacity of hospital healthcare services, how the timing of interventions, severity of interventions, and conditions for the releasing of interventions affect the overall epidemic-picture. We provide an optimisation method to capture when, in terms of healthcare demand, an intervention should be put into place given a maximum capacity on the service. By using an equivalent agent-based approach, we demonstrate uncertainty quantification on the likelihood that capacity is not breached, by how much if it does, and the limit on demand that almost guarantees capacity is not breached. [ABSTRACT FROM AUTHOR]

Studies show especially for the UK and the US that physicians experience more occupational stress than nurses. It has also been shown that a higher status within the medical and nursing hierarchy is associated with less occupational stress. Our study's aim is to examine whether these results also can be found in the context of the German university hospital sector. Thus, we test the stress of higher status hypothesis in and between the occupational groups of nurses and physicians at a German university hospital. Based on two cross-section surveys in the years of 2016 and 2019 this paper compares the perceived level of occupational stress between physicians (n = 588) and nurses (n = 735). Perceived levels of occupational stress–measured via the effort-reward imbalance model and the job demand-control model–are differentiated by status positions within and between both occupational groups. Descriptive as well as inferential statistics (Mann-Whitney U test, Kruskal-Wallis H test) are used to test the stress of higher status hypothesis. Contrary to the stress of higher status hypothesis, our main result is that physicians and nurses perceive similar levels of occupational stress. Furthermore, within each hierarchy the perceived degree of work stress decreases with increasing status for both groups. Our main conclusion is that the stress of higher status hypothesis must be rejected in the German university hospital context and the competing resources of higher status hypothesis must be assumed. The findings can be explained by the unique relationship between physicians and nurses and the role of New Public Management in the German hospital sector. [ABSTRACT FROM AUTHOR]

It is widely acknowledged that social network support plays an important role in the quality of life and illness management of breast cancer survivors. However, the factors and processes that enable and sustain such support are less well understood. This paper reports baseline findings from a prospective UK national cohort of 1,202 women with breast cancer (aged <50 years at diagnosis), recruited before starting treatment, conducted in 2016–2019. Descriptive, univariate and multivariate regression analyses explored associations between the individual, and network member characteristics, and the type of support provided. Social network members provided a substantial level of illness-related, practical and emotional support. Highest contribution was provided by friends, followed by close family members. The social network members of women who did not have a partner provided a higher level of support than those in networks with a partner. Women without higher education were more reliant on close family members than those with higher education, and this was more so for women without a partner. Women with higher education without a partner were more reliant on friends and were overall best supported. Women without higher education who did not have a partner were overall least well supported. They had much smaller networks, were highly reliant on close family members, and on high level contributions from all network members. There is a need to develop network-based interventions to support people with a cancer diagnosis, prioritising support for the groups identified as most at risk. Interventions that support engagement with existing network members during treatment, and those that help extend such networks after treatment, are likely to be of benefit. A network perspective can help to develop tailored support and interventions by recognising the interactions between network and individual level processes. [ABSTRACT FROM AUTHOR]

In this paper we review the methodological underpinnings of the general pharmacogenetic approach for uncovering genetically-driven treatment effect heterogeneity. This typically utilises only individuals who are treated and relies on fairly strong baseline assumptions to estimate what we term the 'genetically moderated treatment effect' (GMTE). When these assumptions are seriously violated, we show that a robust but less efficient estimate of the GMTE that incorporates information on the population of untreated individuals can instead be used. In cases of partial violation, we clarify when Mendelian randomization and a modified confounder adjustment method can also yield consistent estimates for the GMTE. A decision framework is then described to decide when a particular estimation strategy is most appropriate and how specific estimators can be combined to further improve efficiency. Triangulation of evidence from different data sources, each with their inherent biases and limitations, is becoming a well established principle for strengthening causal analysis. We call our framework 'Triangulation WIthin a STudy' (TWIST)' in order to emphasise that an analysis in this spirit is also possible within a single data set, using causal estimates that are approximately uncorrelated, but reliant on different sets of assumptions. We illustrate these approaches by re-analysing primary-care-linked UK Biobank data relating to CYP2C19 genetic variants, Clopidogrel use and stroke risk, and data relating to APOE genetic variants, statin use and Coronary Artery Disease. Author summary: Understanding how much a specific treatment's effect is moderated by common genetic variation is an important public health question. If a person's genetics means they will experience a much reduced treatment effect, as measured with respect to a particular health outcome, then they could be switched to an alternative therapy. When assessing the impact of such a switch at the population level, it is typical to only use data on those who are treated with the said drug. However, this analysis is compromised if genetic variants exist which moderate the treatment's effect and affect the outcome through alternative pathways. In this paper we describe an extended analysis framework to estimating the 'genetically moderated treatment effect' (GMTE) that incorporates information on both treated and untreated individuals. With this larger set of information we show that four analysis approaches for estimating the GMTE are possible. Each one relies on a different set of assumptions to work correctly and provides estimates that are largely uncorrelated with one another. Our paper describes a decision framework for triangulating the findings from these four approaches in order to provide a more robust basis for decision making in public health. [ABSTRACT FROM AUTHOR]

Marine citizenship is a relatively new field of enquiry and research to date has focused on individual pro-environmental behaviour change as an expression of responsibility towards the ocean. The field is underpinned by knowledge-deficit and technocratic approaches to behaviour change such as awareness raising, ocean literacy, and environmental attitudes research. In this paper we develop an interdisciplinary and inclusive conceptualisation of marine citizenship. We use mixed methods to study the views and experiences of active marine citizens in the United Kingdom to broaden understandings of marine citizens' characterisation of marine citizenship, and their perceptions of its importance in policy- and decision-making. Our study shows that marine citizenship entails more than individual pro-environmental behaviours, and includes public-facing and socially collective political actions. We contextualise the role of knowledge, finding more complexity than normative knowledge-deficit approaches permit. We illustrate the importance of a rights-based framing of marine citizenship which incorporates political and civic rights to participate in the transformation of the human-ocean relationship for sustainability. Recognising this more inclusive approach to marine citizenship, we propose an expanded definition to support further exploration of the multiple dimensions and complexities of marine citizenship and to enhance its benefits for marine policy and management. [ABSTRACT FROM AUTHOR]

In recent years, researchers have begun to explore the use of Distributed Ledger Technologies (DLT), also known as blockchain, in health data sharing contexts. However, there is a significant lack of research that examines public attitudes towards the use of this technology. In this paper, we begin to address this issue and present results from a series of focus groups which explored public views and concerns about engaging with new models of personal health data sharing in the UK. We found that participants were broadly in favour of a shift towards new decentralised models of data sharing. Retaining 'proof' of health information stored about patients and the capacity to provide permanent audit trails, enabled by immutable and transparent properties of DLT, were regarded as particularly valuable for our participants and prospective data custodians. Participants also identified other potential benefits such as supporting people to become more health data literate and enabling patients to make informed decisions about how their data was shared and with whom. However, participants also voiced concerns about the potential to further exacerbate existing health and digital inequalities. Participants were also apprehensive about the removal of intermediaries in the design of personal health informatics systems. [ABSTRACT FROM AUTHOR]

Background: Population mental health in the United Kingdom (UK) has deteriorated, alongside worsening socioeconomic conditions, over the last decade. Policies such as Universal Basic Income (UBI) have been suggested as an alternative economic approach to improve population mental health and reduce health inequalities. UBI may improve mental health (MH), but to our knowledge, no studies have trialled or modelled UBI in whole populations. We aimed to estimate the short-term effects of introducing UBI on mental health in the UK working-age population. Methods and findings: Adults aged 25 to 64 years were simulated across a 4-year period from 2022 to 2026 with the SimPaths microsimulation model, which models the effects of UK tax/benefit policies on mental health via income, poverty, and employment transitions. Data from the nationally representative UK Household Longitudinal Study were used to generate the simulated population (n = 25,000) and causal effect estimates. Three counterfactual UBI scenarios were modelled from 2023: "Partial" (value equivalent to existing benefits), "Full" (equivalent to the UK Minimum Income Standard), and "Full+" (retaining means-tested benefits for disability, housing, and childcare). Likely common mental disorder (CMD) was measured using the General Health Questionnaire (GHQ-12, score ≥4). Relative and slope indices of inequality were calculated, and outcomes stratified by gender, age, education, and household structure. Simulations were run 1,000 times to generate 95% uncertainty intervals (UIs). Sensitivity analyses relaxed SimPaths assumptions about reduced employment resulting from Full/Full+ UBI. Partial UBI had little impact on poverty, employment, or mental health. Full UBI scenarios practically eradicated poverty but decreased employment (for Full+ from 78.9% [95% UI 77.9, 79.9] to 74.1% [95% UI 72.6, 75.4]). Full+ UBI increased absolute CMD prevalence by 0.38% (percentage points; 95% UI 0.13, 0.69) in 2023, equivalent to 157,951 additional CMD cases (95% UI 54,036, 286,805); effects were largest for men (0.63% [95% UI 0.31, 1.01]) and those with children (0.64% [95% UI 0.18, 1.14]). In our sensitivity analysis assuming minimal UBI-related employment impacts, CMD prevalence instead fell by 0.27% (95% UI −0.49, −0.05), a reduction of 112,228 cases (95% UI 20,783, 203,673); effects were largest for women (−0.32% [95% UI −0.65, 0.00]), those without children (−0.40% [95% UI −0.68, −0.15]), and those with least education (−0.42% [95% UI −0.97, 0.15]). There was no effect on educational mental health inequalities in any scenario, and effects waned by 2026. The main limitations of our methods are the model's short time horizon and focus on pathways from UBI to mental health solely via income, poverty, and employment, as well as the inability to integrate macroeconomic consequences of UBI; future iterations of the model will address these limitations. Conclusions: UBI has potential to improve short-term population mental health by reducing poverty, particularly for women, but impacts are highly dependent on whether individuals choose to remain in employment following its introduction. Future research modelling additional causal pathways between UBI and mental health would be beneficial. Rachel M Thomson and colleagues used a policy model to simulate how a Universal Basic Income might influence mental health for working-age adults in the UK. Author summary: Why was this study done?: Universal Basic Income (UBI) is a radical social security policy proposal where everyone in a society would receive a regular, unconditional cash payment. It has been suggested that UBI might be beneficial for mental health. However, there has never been a trial of a true UBI in a high-income country to know what its potential mental health impacts might be. What did the researchers do and find?: We used a policy model to simulate how a UBI might influence mental health for working-age adults in the United Kingdom. We found that the effects of UBI were very sensitive to assumptions we made about how people's employment choices might respond to receiving the money—if people choose to stay in work, UBI may have small benefits for population mental health, but if people are more likely to leave work, population mental health may actually worsen. The groups most likely to experience positive mental health effects of UBI were women and those with the lowest educational qualifications. What do these findings mean?: UBI may have potential to improve population mental health in high-income countries, but only if people do not choose to leave work in response to the policy. More real-world research is needed to know how people are likely to respond to receiving a UBI in reality. The main limitation of our modelling study is that it looks at how UBI would influence mental health only through income and employment, and other pathways might also be important to include in future research. [ABSTRACT FROM AUTHOR]

Background: Endometrial hyperplasia (EH) is a precusor lesion for endometrial cancer (EC), the commonest gynaecological malignancy in high-income countries. EH is a proliferation of glandular tissue, classified as either non-atypical endometrial hyperplasia (NEH) or, if the cytological features are abnormal, atypical endometrial hyperplasia (AEH). The clinical significance of AEH is that patients face both a high risk of having occult EC and a high risk of progression to EC if untreated. Recommendations on the care of women with EH were introduced by United Kingdom–wide guidance (Green-top Guide No. 67, 2016). National adherence to guidance is unknown. We aimed to describe the care of patients with EH; to compare the patterns of care for those with EH with national guidance to identify opportunities for quality improvement; and to compare patterns of care prior to and following the introduction of national guidance to understand its impact. Methods and findings: In this UK-wide patient-level clinical audit, we included 3,307 women who received a new histological diagnosis of EH through a gynaecology service between 1 January 2012 and 30 June 2020. We described first-line management, management at 2 years, and surgical characteristics prior to and following national guidance for EH using proportions and 95% confidence intervals (CIs) and compared process measures between time periods using multilevel Poisson regression. Of the 3,307 patients, 1,570 had NEH and 1,511 had AEH between 2012 and 2019. An additional 85 patients had NEH and 141 had AEH during 2020. Prior to national guidance, 9% (95% CI [6%, 15%]) received no initial treatment for NEH compared with 3% (95% CI [1%, 5%]) post-guidance; 31% (95% CI [26%, 36%]) and 48% (95% CI [43% 53%]) received an intrauterine progestogen, respectively, in the same periods. The predominant management of women with AEH did not differ, with 68% (95% CI [61%, 74%]) and 67% (95 CI [63%, 71%]) receiving first-line hysterectomy, respectively. By 2 years, follow-up to histological regression without hysterectomy increased from 38% (95% CI [33%, 43%]) to 52% (95% CI [47%, 58%]) for those with NEH (rate ratio (RR) 1.38, 95% CI [1.18, 1.63] p < 0.001). We observed an increase in the use of total laparoscopic hysterectomy among those with AEH (RR 1.26, 95% CI [1.04, 1.52]). In the later period, 37% (95% CI [29%, 44%]) of women initially diagnosed with AEH who underwent a first-line hysterectomy, received an upgraded diagnosis of EC. Study limitations included retrospective data collection from routine clinical documentation and the inability to comprehensively understand the shared decision-making process where care differed from guidance. Conclusions: The care of patients with EH has changed in accordance with national guidance. More women received first-line medical management of NEH and were followed up to histological regression. The follow-up of those with AEH who do not undergo hysterectomy must be improved, given their very high risk of coexistent cancer and high risk of developing cancer. Author summary: Why was this study done?: New national guidance was introduced in the United Kingdom with recommendations for the care and surveillance of people with endometrial hyperplasia (EH). Comparing patterns of care with these recommendations has identified opportunities for improvement. What did the researchers do and find?: After the guidance, medical treatment of non-atypical hyperplasia increased and more patients achieved histological regression, avoiding hysterectomy. Surveillance of hyperplasia for those who do not undergo hysterectomy could be improved. A greater proportion of women with atypia diagnosed in 2020 commenced medical management and fewer underwent hysterectomy; the impact of the pandemic on care must be considered as a contributory factor towards this. What do these findings mean?: This work has identified where the care of patients with EH diverged from recommended guidance. Clinicians may use these findings to review their local care pathways and quality assurance processes so that they can improve the care of women with EH. The main limitation was the retrospective collection of data from routine clinical documentation. [ABSTRACT FROM AUTHOR]

The Extended Crosswise Model (ECWM) is a randomized response model with neutral response categories, relatively simple instructions, and the availability of a goodness-of-fit test. This paper refines this model with a number sequence randomizer that virtually precludes the possibility to give evasive responses. The motivation for developing this model stems from a strategic priority of WADA (World Anti-Doping Agency) to monitor the prevalence of doping use by elite athletes. For this model we derived a maximum likelihood estimator that allows for binary logistic regression analysis. Three studies were conducted on online platforms with a total of over 6, 000 respondents; two on controlled substance use and one on compliance with COVID-19 regulations in the UK during the first lockdown. The results of these studies are promising. The goodness-of-fit tests showed little to no evidence for response biases, and the ECWM yielded higher prevalence estimates than direct questions for sensitive questions, and similar ones for non-sensitive questions. Furthermore, the randomizer with the shortest number sequences yielded the smallest response error rates on a control question with known prevalence. [ABSTRACT FROM AUTHOR]

Healthcare accounts for approximately 5% of emissions in developed nations, and the public healthcare provider in the United Kingdom (UK), the National Health Service (NHS), has set a target to reach net-zero emissions by 2040 without detriment to its quality of patient care. With Uncrewed Aerial Vehicles (UAVs; a.k.a. drones, UAS, or RPAS) starting to be used in healthcare systems outside the UK, there is interest in how they could be integrated into NHS operations to transport diagnostic specimens. Reflecting on a business-as-usual analysis of current NHS diagnostic specimen logistics across the Solent region (southern UK), this paper critically evaluates the practical reality of integrating UAV deliveries of this commodity, identifying the benefits and challenges that must be addressed to realise commercial services, including dangerous goods legislation, cargo stability, routing, and weather. In the analysis, 14 out of 79 surgeries could be realistically served by a 5m wingspan vertical take-off/landing (VTOL) UAV: seven directly, and seven via ground-based transfers. The results suggested that an average of 1,628 samples could be served by UAV each week, resulting in 42 flights/week with 10 taxi services to cover periods where weather limited flying. This equated to an approximate total service cost of £2,964/week if regulations develop to relax UAV personnel constraints. The introduction of UAVs reduced the marginal external costs (greenhouse gas emissions, congestion, and air pollution) by £196 per week and cut travel times to UAV served sites by 72% (weather permitting). Tailpipe emissions (excl. taxis), vehicle-kilometres travelled, and van costs were reduced by 20%, 20%, and 23% (respectively), but the overall system cost increased by 56%. Whilst this increase is likely to make the introduction of UAV services financially challenging, the benefits in terms of emissions and journey time savings may offset some of the additional cost and warrant further investigation. [ABSTRACT FROM AUTHOR]

Background: Numbers of urgent short stay admissions (SSAs) of children to UK hospitals are rising rapidly. This paper reports on experiences of SSAs from the perspective of parents accessing urgent care for their acutely unwell child and of health professionals referring, caring for, or admitting children. Methods: A qualitative interview study was conducted by a multi-disciplinary team with patient and public involvement (PPI) to explore contextual factors relating to SSAs and better understand pre-hospital urgent care pathways. Purposive sampling of Health Board areas in Scotland, health professionals with experience of paediatric urgent care pathways and parents with experience of a SSA for their acutely unwell child was undertaken to ensure maximal variation in characteristics such as deprivation, urban-rural and hospital structure. Interviews took place between Dec 2019 and Mar 2021 and thematic framework analysis was applied. Results: Twenty-one parents and forty-eight health professionals were interviewed. In the context of an urgent SSA, the themes were centred around shared outcomes of care that matter. The main outcome which was common to both parents and health professionals was the importance of preserving the child's safety. Additional shared outcomes by parents and health professionals were a desire to reduce worries and uncertainty about the illness trajectory, and provide reassurance with sufficient time, space and personnel to undertake a period of skilled observation to assess and manage the acutely unwell child. Parents wanted easy access to urgent care and, preferably, with input from paediatric-trained staff. Healthcare professionals considered that it was important to reduce the number of children admitted to hospital where safe and appropriate to do so. Conclusions: The shared outcomes of care between parents and health professionals emphasises the potential merit of adopting a partnership approach in identifying, developing and testing interventions to improve the acceptability, safety, efficiency, and cost-effectiveness of urgent care pathways between home and hospital. [ABSTRACT FROM AUTHOR]

Nurse, Midwives and Allied Health Professionals (AHPs), along with other health and social care colleagues are the backbone of healthcare services. They have played a key role in responding to the increased demands on healthcare during the COVID-19 pandemic. This paper compares cross-sectional data on quality of working life, wellbeing, coping and burnout of nurses, midwives and AHPs in the United Kingdom (UK) at two time points during the COVID-19 pandemic. An anonymous online repeated cross-sectional survey was conducted at two timepoints, Phase 1 (7th May 2020-3rd July 2020); Phase 2 (17th November 2020-1st February 2021). The survey consisted of the Short Warwick-Edinburgh Mental Wellbeing Scale, the Work-Related Quality of Life Scale, and the Copenhagen Burnout Inventory (Phase 2 only) to measure wellbeing, quality of working life and burnout. The Brief COPE scale and Strategies for Coping with Work and Family Stressors scale assessed coping strategies. Descriptive statistics and multiple linear regressions examined the effects of coping strategies and demographic and work-related variables on wellbeing and quality of working life. A total of 1839 nurses, midwives and AHPs responded to the first or second survey, with a final sample of 1410 respondents -586 from Phase 1; 824 from Phase 2, (422 nurses, 192 midwives and 796 AHPs). Wellbeing and quality of working life scores were significantly lower in the Phase 2 sample compared to respondents in Phase 1 (p<0.001). The COVID-19 pandemic had a significant effect on psychological wellbeing and quality of working life which decreased while the use of negative coping and burnout of these healthcare professionals increased. Health services are now trying to respond to the needs of patients with COVID-19 variants while rebuilding services and tackling the backlog of normal care provision. This workforce would benefit from additional support/services to prevent further deterioration in mental health and wellbeing and optimise workforce retention. [ABSTRACT FROM AUTHOR]

Background: The population prevalence of multimorbidity (the existence of at least 2 or more long-term conditions [LTCs] in an individual) is increasing among young adults, particularly in minority ethnic groups and individuals living in socioeconomically deprived areas. In this study, we applied a data-driven approach to identify clusters of individuals who had an early onset multimorbidity in an ethnically and socioeconomically diverse population. We identified associations between clusters and a range of health outcomes. Methods and findings: Using linked primary and secondary care data from the Clinical Practice Research Datalink GOLD (CPRD GOLD), we conducted a cross-sectional study of 837,869 individuals with early onset multimorbidity (aged between 16 and 39 years old when the second LTC was recorded) registered with an English general practice between 2010 and 2020. The study population included 777,906 people of White ethnicity (93%), 33,915 people of South Asian ethnicity (4%), and 26,048 people of Black African/Caribbean ethnicity (3%). A total of 204 LTCs were considered. Latent class analysis stratified by ethnicity identified 4 clusters of multimorbidity in White groups and 3 clusters in South Asian and Black groups. We found that early onset multimorbidity was more common among South Asian (59%, 33,915) and Black (56% 26,048) groups compared to the White population (42%, 777,906). Latent class analysis revealed physical and mental health conditions that were common across all ethnic groups (i.e., hypertension, depression, and painful conditions). However, each ethnic group also presented exclusive LTCs and different sociodemographic profiles: In White groups, the cluster with the highest rates/odds of the outcomes was predominantly male (54%, 44,150) and more socioeconomically deprived than the cluster with the lowest rates/odds of the outcomes. On the other hand, South Asian and Black groups were more socioeconomically deprived than White groups, with a consistent deprivation gradient across all multimorbidity clusters. At the end of the study, 4% (34,922) of the White early onset multimorbidity population had died compared to 2% of the South Asian and Black early onset multimorbidity populations (535 and 570, respectively); however, the latter groups died younger and lost more years of life. The 3 ethnic groups each displayed a cluster of individuals with increased rates of primary care consultations, hospitalisations, long-term prescribing, and odds of mortality. Study limitations include the exclusion of individuals with missing ethnicity information, the age of diagnosis not reflecting the actual age of onset, and the exclusion of people from Mixed, Chinese, and other ethnic groups due to insufficient power to investigate associations between multimorbidity and health-related outcomes in these groups. Conclusions: These findings emphasise the need to identify, prevent, and manage multimorbidity early in the life course. Our work provides additional insights into the excess burden of early onset multimorbidity in those from socioeconomically deprived and diverse groups who are disproportionately and more severely affected by multimorbidity and highlights the need to ensure healthcare improvements are equitable. Fabiola Eto and co-workers study variations in comorbidity, service use and health outcomes by ethnicity and socioeconomic position, among people aged 16-39 years. Author summary: Why was this study done?: Most studies of multimorbidity focus on older adults, and only a few have investigated multimorbidity in younger populations. The prevalence of multimorbidity is increasing among young adults, particularly in minority ethnic groups and individuals living in socioeconomically deprived areas. There is evidence showing that individuals with socioeconomic vulnerability experience poorer health outcomes, such as lower quality healthcare provision, premature death, and higher mortality rates. The association between early onset multimorbidity and poor health outcomes in ethnically and socially diverse populations in England remains underinvestigated. What did the researchers do and find?: We used primary and secondary healthcare electronic health records from 837,869 individuals of White, South Asian, and Black African/Caribbean ethnicity in England with early onset of multimorbidity who were registered with an English general practice between 2010 and 2020. We found that the early onset of multimorbidity was more common among minority ethnic groups (59% and 56%, in the South Asian and Black populations, respectively) than in the White population (42%) living in the UK. South Asians and Black individuals with early onset multimorbidity died earlier than White individuals with early onset multimorbidity (52 and 48 years old in the median, respectively, versus 61 years old). South Asian and Black groups were more socioeconomically deprived than White groups, with a consistent deprivation gradient across all multimorbidity clusters. In White groups, the cluster of individuals with the highest rates/odds of the outcomes was more socioeconomically deprived than the cluster with the lowest rates/odds of the outcomes. What do these findings mean?: Our findings emphasise the need to identify, prevent, and manage multimorbidity early in the life course. Our work highlights the need to ensure that public health policies are equitable and reach those living in socioeconomic deprivation and minority ethnic groups who are disproportionately and more severely affected by early onset multimorbidity. [ABSTRACT FROM AUTHOR]

Background: Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening. Methods and findings: For model development, we used data from 216,714 ever-smokers recruited between 2006 and 2010 to the UK Biobank prospective cohort and 26,616 high-risk ever-smokers recruited between 2002 and 2004 to the control arm of the US National Lung Screening (NLST) randomised controlled trial. The NLST trial randomised high-risk smokers from 33 US centres with at least a 30 pack-year smoking history and fewer than 15 quit-years to annual CT or chest radiography screening for lung cancer. We externally validated our models among 49,593 participants in the chest radiography arm and all 80,659 ever-smoking participants in the US Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial. The PLCO trial, recruiting from 1993 to 2001, analysed the impact of chest radiography or no chest radiography for lung cancer screening. We primarily validated in the PLCO chest radiography arm such that we could benchmark against comparator models developed within the PLCO control arm. Models were developed to predict the risk of 2 outcomes within 5 years from baseline: diagnosis of lung cancer and death from lung cancer. We assessed model discrimination (area under the receiver operating curve, AUC), calibration (calibration curves and expected/observed ratio), overall performance (Brier scores), and net benefit with decision curve analysis. Models predicting lung cancer death (UCL-D) and incidence (UCL-I) using 3 variables—age, smoking duration, and pack-years—achieved or exceeded parity in discrimination, overall performance, and net benefit with comparators currently in use, despite requiring only one-quarter of the predictors. In external validation in the PLCO trial, UCL-D had an AUC of 0.803 (95% CI: 0.783, 0.824) and was well calibrated with an expected/observed (E/O) ratio of 1.05 (95% CI: 0.95, 1.19). UCL-I had an AUC of 0.787 (95% CI: 0.771, 0.802), an E/O ratio of 1.0 (95% CI: 0.92, 1.07). The sensitivity of UCL-D was 85.5% and UCL-I was 83.9%, at 5-year risk thresholds of 0.68% and 1.17%, respectively, 7.9% and 6.2% higher than the USPSTF-2021 criteria at the same specificity. The main limitation of this study is that the models have not been validated outside of UK and US cohorts. Conclusions: We present parsimonious ensemble machine learning models to predict the risk of lung cancer in ever-smokers, demonstrating a novel approach that could simplify the implementation of risk-based lung cancer screening in multiple settings. Thomas Callender and colleagues present a development and validation study using parsimonious ensemble machine learning models to assess eligibility for lung cancer screening. Author summary: Why was this study done?: Screening and disease prevention programmes are increasingly bespoke; however, their simultaneous delivery at a population-scale presents considerable challenges. Lung cancer is the most common cause of cancer death worldwide, with poor survival in the absence of early detection. Screening for lung cancer among those at high-risk could reduce lung cancer-specific mortality by 20% to 24% among those screened, but the ideal way to determine if someone is high-risk remains uncertain and existing approaches are resource intensive. What did the researchers do and find?: We used data from the UK Biobank and US National Lung Screening Trial to develop novel, parsimonious models, to simplify the prediction of lung cancer risk and selection to lung cancer screening programmes. Using ensemble machine learning and 3 predictors—age, smoking duration, and pack-years—we found our models achieved or exceeded parity in performance with leading comparators despite requiring one-third of the variables. Our models were externally validated in the US Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial and benchmarked against models that are either in use or have performed strongly in previous analyses. What do these findings mean?: Risk assessment for lung cancer screening can be simplified without reducing performance, potentially improving the uptake and effectiveness of national lung cancer screening programmes, and therefore contributing to reducing deaths from lung cancer. Future research should focus on the application of this modelling approach to other conditions such as cardiovascular disease, diabetes, and chronic kidney disease to support the implementation at scale of multiple concurrent risk-stratified prevention and early detection programmes for major causes of morbidity and mortality. This study has key limitations as it is based on past data from the US and UK, so prospective evaluation in different countries and regions should be considered. [ABSTRACT FROM AUTHOR]