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Burden of RSV infections among young children in primary care: a prospective cohort study in five European countries (2021–23)
Background: The majority of respiratory syncytial virus (RSV) infections in young children are managed in primary care, however, the disease burden in this setting remains poorly defined. Methods: We did a prospective cohort study in primary care settings in Belgium, Italy, Spain, the Netherlands, and the UK during the RSV seasons of 2020–21 (UK only; from Jan 1, 2021), 2021–22, and 2022–23. Children aged younger than 5 years presenting to their general practitioner or primary care paediatrician with symptoms of an acute respiratory tract infection were eligible for RSV testing. Children who tested positive for RSV were consented and followed up for 30 days via a physician clinical report (initial primary care visit on day 1) and two parent-report questionnaires (days 14 and 30). We assessed the burden of RSV in terms of clinical course (symptoms, illness duration, and complications), health-care resource utilisation (primary care visits, emergency department visits, hospitalisation rate, and medication use), and societal impact (daycare or school absence and parental work absence) for the 30-day follow-up period. Findings: Among 3414 tested children, 1124 (32·9%; 95% CI 31·3–34·5) tested positive for RSV. Among children with data on age, RSV positivity rate was 38·9% (36·1–41·7; n=466 of 1198) in children younger than 1 year and 25·9% (24·0–27·9; n=513 of 1979) in those aged 1 to <5 years. Of the 1124 RSV-positive children, 878 (78·1%) were enrolled and had day 1 data collected (median age 11·1 months [IQR 6·0–22·0]; 446 [50·9%] boys and 431 [49·1%] girls [N=877]). RSV illness lasted a mean of 11·7 days (95% CI 11·2–12·2; n=794). At day 14 and day 30, any remaining symptoms were reported in 451 of 804 (56·1% [95% CI 52·6–59·6]) and 261 of 724 (36·0% [32·6–39·7]) children. The mean number of primary care visits per child ranged from 1·4 (95% CI 1·2–1·6; the Netherlands) to 3·0 (2·8–3·3; Spain), and was higher in children younger than 1 year (2·7 visits [2·4–2·9]) than in those aged 1 to <5 years (2·1 [1·9–2·2]). Prescribed medication use varied, from 25 of 96 children (26·0% [95% CI 17·6–36·0]; the UK) to 228 of 297 children (76·8% [71·5–81·5]; Italy), with bronchodilators and antibiotics being the most commonly prescribed medicines across all countries. Prescribed medication use was reported in 258 of 418 children aged 1 to <5 years (61·7% [56·9–66·4]) and 196 of 394 children younger than 1 year (49·7% [44·7–54·8]). Missed working days by parents due to their child's RSV illness were reported in 340 of 744 cases (45·7% [42·1–49·4]); the mean number of missed workdays ranged from 1·3 days (95% CI 0·5–2·2) in Spain to 4·1 days (3·3–5·0) in Belgium. Interpretation: RSV infections in children younger than 5 years in primary care are associated with substantial symptomatology, health-care utilisation, and parental work absence. Notable differences in RSV burden existed across countries, likely due to differences in primary health-care systems, clinical practice, and health-care-seeking behaviour. This study emphasises the importance of considering country-specific primary care burden estimates when considering the implementation of RSV immunisations programmes. Funding: Sanofi and AstraZeneca.
Developmental Insights from Modelling the Digital Maturity of the Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC).
The Oxford-RCGP Research and Surveillance Centre (RSC) is one of the largest health surveillance systems in Europe, using real-world primary care data to inform public health policy and clinical practice. As UK general practice has evolved digitally and the demand for strong information governance (IG) has increased, the RSC has expanded its technological capabilities. This paper applies a Digital Maturity Model (DMM) to assess the RSC's progress across five dimensions and ten key informatics and IG elements. The analysis highlights the RSC's strengths in meeting public health needs and identifies areas for improvement. This review identifies predictive analytics, cloud-based services, multi-dimensional modelling, and advanced visualisations as areas for future development.
Experiences of integrating social prescribing link workers into primary care in England — bolting on, fitting in, or belonging: a realist evaluation
Background Following the 2019 NHS Long Term Plan, link workers have been employed across primary care in England to deliver social prescribing. Aim To understand and explain how the link worker role is being implemented in primary care in England. Design and setting This was a realist evaluation undertaken in England, focusing on link workers based in primary care. Method The study used focused ethnographies around seven link workers from different parts of England. As part of this, we interviewed 61 patients and 93 professionals from health care and the voluntary, community, and social enterprise sector. We reinterviewed 41 patients, seven link workers, and a link worker manager 9–12 months after their first interview. Results We developed four concepts from the codes developed during the project on the topic around how link workers are integrated (or not) within primary care: (or not) within primary care: centralising or diffusing power; forging an identity in general practice; demonstrating effect; and building a facilitative infrastructure. These concepts informed the development of a programme theory around a continuum of integration of link workers into primary care — from being ‘bolted on’ to existing provision, without much consideration, to ‘fitting in’, shaping what is delivered to be accommodating, through to ‘belonging’, whereby they are accepted as a legitimate source of support, making a valued contribution to patients’ broader wellbeing. Conclusion Social prescribing was introduced into primary care to promote greater attention to the full range of factors affecting patients’ health and wellbeing, beyond biomedicine. For that to happen, our analysis highlights the need for a whole-system approach to defining, delivering, and maintaining this new part of practice.
The consequences of micro-discretions and boundaries in the social prescribing link worker role in England: a realist evaluation.
BACKGROUND: Social prescribing addresses non-medical factors affecting health and well-being. Link workers are key to its delivery by connecting people to relevant support, often in the voluntary, community and social enterprise sector. Funding from the National Health Service means that link workers are becoming a common part of primary care in England. OBJECTIVE: To explore and understand the implementation of link workers in primary care in England. DESIGN: A realist evaluation addressed the question - When implementing link workers in primary care to sustain outcomes - what works, for whom, why and in what circumstances? SETTING: Link workers and staff associated with seven primary care sites across England. METHODS: Researchers spent 3 weeks with each link worker, going to meetings with them, watching them interact with patients, with healthcare staff and with voluntary, community and social enterprise organisations. In addition, interviews were conducted with 61 patients and 93 professionals (voluntary, community and social enterprise representatives and healthcare staff, including link workers). Follow-up interviews were conducted with 41 patients and with link workers 9-12 months later. Data were coded and developed into statements to identify how context around the link worker triggers mechanisms that lead to intended and unintended outcomes. RESULTS: We found that link workers exercise micro-discretions in their role - actions and advice-giving based on personal judgement of a situation, which may not always reflect explicit guidance or protocols. Our analysis highlighted that micro-discretions engender positive connections (with patients, healthcare staff, the voluntary, community and social enterprise sector) and promote buy-in to the link worker role in primary care. Micro-discretions supported delivery of person-centred care and enhanced job satisfaction. Data also highlighted that lack of boundaries could place link workers at risk of overstepping their remit. LIMITATIONS: Our research focused on link workers attached to primary care; findings may not be applicable to those working in other settings. Data were collected around seven link worker cases, who were selected purposively for variation in terms of geographical spread and how/by whom link workers were employed. However, these link workers were predominately white females. CONCLUSIONS: Enabling link workers to exercise micro-discretions allows for responsiveness to individual patient needs but can result in uncertainty and to link workers feeling overstretched. FUTURE WORK: Poor link worker retention may, in part, be associated with a lack of clarity around their role. Research to explore how this shapes intention to leave their job is being conducted by authors of this paper. FUNDING: This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR130247.
Experiential caring and the mobilisation of peerhood in group clinics
The concept of ‘peer support’ has generated much interest in mainstream health services. In policy discourse, peer-based initiatives are often described as ‘empowering’ and seen as contributing to more ‘democratic’ and ‘holistic’ forms of care. Focusing on group clinics as one such example, this article challenges the assumption that peer-based initiatives represent a straightforward and unequivocal ‘good’ when embedded in clinical care. We draw on qualitative data from three studies (2016–2025), including 118 interviews and ethnographic observation in 59 in-person, remote, and hybrid group clinics for diabetes and menopause at 5 primary and secondary care sites in England. Adopting a sociomaterial lens, we uncover how different forms and practices of peerhood emerge (or not) in the circumstances through which these clinics are materialised. We show how biomedical artefacts (e.g. diabetes test results, menopause symptom lists) used as part of consulting play a key role in constituting forms of affiliation and differentiation between patients, in turn determining whether and what forms of peer ‘support’ (e.g. disciplinary, affirmative) are accomplished. We go on to explore how being presented as a peer as part of clinical consulting brings about new roles and responsibilities for patients, and introduce the term ‘experiential caring’ to denote a new mode of consulting that mobilises roles, practices, and subjectivities associated with peerhood.
Using AI for Mental Health Analysis and Prediction in School Surveys
Abstract Background Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper-based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data-driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models. Methods For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases. Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning can then be deployed to classify specific behaviour and mental health patterns. Results We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project. Conclusions This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns of unknown effects. Key messages This model uses artificial intelligence to improve mental health surveillance and evaluation in school settings. Artificial intelligence can be applied more broadly in public health to harness the potential of predictive models.
Human centered AI design for clinical monitoring and data management
Abstract Background In clinical settings, significant resources are spent on data collection and monitoring patients' health parameters to improve decision-making and provide better care. With increased digitization, the healthcare sector is shifting towards implementing digital technologies for data management and in administration. New technologies offer better treatment opportunities and streamline clinical workflow, but the complexity can cause ineffectiveness, frustration, and errors. To address this, we believe digital solutions alone are not sufficient. Therefore, we take a human-centred design approach for AI development, and apply systems engineering methods to identify system leverage points. We demonstrate how automation enables monitoring clinical parameters, using existing non-intrusive sensor technology, resulting in more resources toward patient care. Furthermore, we provide a framework on digitization of clinical data for integration with data management. Methods Activities of Daily Living (ADLs) are essential parameters, necessary for evaluating patients in mental health wards. Ideally logging the parameters should take place at hourly intervals; however, time constraints and lack of resources restrict the nursing staff to consolidating the overall impression during the day, relying on what they recall. Using design methods, sensors (e.g. infrared, proximity, pressure) are used to automate the acquisition of data for machine learning that correspond to the ADLs, considering privacy and other medical requirements. Results We present a concept of a room with sensors that can be deployed in clinical settings. Sensor data log ADLs, and provide machine learning data. A theoretical framework demonstrates how collected data can be used in electronic/medical health records. Conclusions Data acquisition of the ADLs with automation enable variable specificity and sensitivity on-demand. It further facilitates interoperability and provides data for machine learning. Key messages Our research demonstrates automated data acquisition techniques for clinical monitoring. Human centered AI design approach enables on-demand analysis of ADLs for mental health treatment.
The Comprehensive Community Engagement Framework for Health and Well-being
Abstract Background Community engagement (CE) and empowerment are required to support the sustainability and effectiveness of actions to reach Agenda 2030. There is a need to guide CE for health and well-being to take action on important societal challenges such as the growing burden of non-communicable diseases (NCDs) and health inequities. The framework proposed in this study has been designed to assist professionals, practitioners and communities to effectively engage. Methods A narrative review of existing grey literature, policy papers and models related to CE was performed. This guided the development of a systematic search strategy, performed by two researchers, which reviewed CE approaches and key influencing factors. The search strategy captured different terms used for CE. Results A total of 27 studies of different types, from around the world, were identified for inclusion into the review. The study compiled a set of widely-used theories and approaches to CE. Key factors such as governance, trust, accessibility and sociocultural contextualisation were also identified as important for the success of CE initiatives. Subsequently, the Comprehensive Community Engagement Framework (CCEF) was developed. It combines theoretical and empirical principles, proven participatory actions and key factors to produce evidence-based health and well-being outcomes across different sectors and levels of society. Conclusions This study has formed the basis of a forthcoming WHO report on CE. The CCEF enables the operationalisation of CE to guide for possible practical approaches to planning, initiating, sustaining and evaluating CE processes alongside the community. It can be used by the health sector as well as the non-health sectors to address health, well-being and broader societal challenges. Key messages The CCEF can be used to engage health and non-health stakeholders to tailor CE processes, increase impact of interventions and policies, building capacity and empowering communities. The proposed framework provides the first comprehensive guidance to conduct community engagement.
Implementation framework for AI deployment at scale in healthcare systems
Artificial intelligence (AI) and digital health technologies are increasingly used in the medical field. Despite promises of leading the future of personalized medicine and better clinical outcomes, implementation of AI faces barriers for deployment at scale. We introduce a novel implementation framework that can facilitate digital health designers, developers, patient groups, policymakers, and other stakeholders, to co-create and solve issues throughout the life cycle of designing, developing, deploying, monitoring, and maintaining algorithmic models. This framework targets health systems that integrate multiple machine learning (ML) models with various modalities. This design thinking approach promotes clinical utility beyond model prediction, combining privacy preservation with clinical parameters to establish a reward function for reinforcement learning, ranking competing models. This allows leveraging explainable AI (xAI) methods for clinical interpretability. Governance mechanisms and orchestration platforms can be integrated to monitor and manage models. The proposed framework guides users toward human-centered AI design and developing AI-enhanced health system solutions.
Nurse-delivered sleep restriction therapy to improve insomnia disorder in primary care: the HABIT RCT
Background: Insomnia is a prevalent and distressing sleep disorder. Multicomponent cognitive– behavioural therapy is the recommended first-line treatment, but access remains extremely limited, particularly in primary care where insomnia is managed. One principal component of cognitive– behavioural therapy is a behavioural treatment called sleep restriction therapy, which could potentially be delivered as a brief single-component intervention by generalists in primary care. Objectives: The primary objective of the Health-professional Administered Brief Insomnia Therapy trial was to establish whether nurse-delivered sleep restriction therapy in primary care improves insomnia relative to sleep hygiene. Secondary objectives were to establish whether nurse-delivered sleep restriction therapy was cost-effective, and to undertake a process evaluation to understand intervention delivery, fidelity and acceptability. Design: Pragmatic, multicentre, individually randomised, parallel-group, superiority trial with embedded process evaluation. Setting: National Health Service general practice in three regions of England. Participants: Adults aged ≥ 18 years with insomnia disorder were randomised using a validated webbased randomisation programme. Interventions: Participants in the intervention group were offered a brief four-session nurse-delivered behavioural treatment involving two in-person sessions and two by phone. Participants were supported to follow a prescribed sleep schedule with the aim of restricting and standardising time in bed. Participants were also provided with a sleep hygiene leaflet. The control group received the same sleep hygiene leaflet by e-mail or post. There was no restriction on usual care. Main outcome measures: Outcomes were assessed at 3, 6 and 12 months. Participants were included in the primary analysis if they contributed at least one post-randomisation outcome. The primary end point was self-reported insomnia severity with the Insomnia Severity Index at 6 months. Secondary outcomes were health-related and sleep-related quality of life, depressive symptoms, work productivity and activity impairment, self-reported and actigraphy-defined sleep, and hypnotic medication use. Costeffectiveness was evaluated using the incremental cost per quality-adjusted life-year. For the process evaluation, semistructured interviews were carried out with participants, nurses and practice managers or general practitioners. Due to the nature of the intervention, both participants and nurses were aware of group allocation. Results: We recruited 642 participants (n = 321 for sleep restriction therapy; n = 321 for sleep hygiene) between 29 August 2018 and 23 March 2020. Five hundred and eighty participants (90.3%) provided data at a minimum of one follow-up time point; 257 (80.1%) participants in the sleep restriction therapy arm and 291 (90.7%) participants in the sleep hygiene arm provided primary outcome data at 6 months. The estimated adjusted mean difference on the Insomnia Severity Index was −3.05 (95% confidence interval −3.83 to −2.28; p < 0.001, Cohen’s d = −0.74), indicating that participants in the sleep restriction therapy arm [mean (standard deviation) Insomnia Severity Index = 10.9 (5.5)] reported lower insomnia severity compared to sleep hygiene [mean (standard deviation) Insomnia Severity Index = 13.9 (5.2)]. Large treatment effects were also found at 3 (d = –0.95) and 12 months (d = −0.72). Superiority of sleep restriction therapy over sleep hygiene was evident at 3, 6 and 12 months for self-reported sleep, mental health-related quality of life, depressive symptoms, work productivity impairment and sleep-related quality of life. Eight participants in each group experienced serious adverse events but none were judged to be related to the intervention. The incremental cost per quality-adjusted life-year gained was £2075.71, giving a 95.3% probability that the intervention is cost-effective at a cost-effectiveness threshold of £20,000. The process evaluation found that sleep restriction therapy was acceptable to both nurses and patients, and delivered with high fidelity. Limitations: While we recruited a clinical sample, 97% were of white ethnic background and 50% had a university degree, which may limit generalisability to the insomnia population in England. Conclusions: Brief nurse-delivered sleep restriction therapy in primary care is clinically effective for insomnia disorder, safe, and likely to be cost-effective. Future work: Future work should examine the place of sleep restriction therapy in the insomnia treatment pathway, assess generalisability across diverse primary care patients with insomnia, and consider additional methods to enhance patient engagement with treatment. Trial registration: This trial is registered as ISRCTN42499563.
Nurse-delivered sleep restriction therapy in primary care for adults with insomnia disorder: a mixed-methods process evaluation
Background Sleep restriction therapy (SRT) is a behavioural therapy for insomnia. Aim To conduct a process evaluation of a randomised controlled trial comparing SRT delivered by primary care nurses plus a sleep hygiene booklet with the sleep hygiene booklet only for adults with insomnia disorder. Design and setting A mixed-methods process evaluation in a general practice setting. Method Semi-structured interviews were conducted in a purposive sample of patients receiving SRT, the practice nurses who delivered the therapy, and also GPs or practice managers at the participating practices. Qualitative data were explored using framework analysis, and integrated with nurse comments and quantitative data, including baseline Insomnia Severity Index score and serial sleep efficiency outcomes to investigate the relationships between these. Results In total, 16 patients, 13 nurses, six practice managers, and one GP were interviewed. Patients had no previous experience of behavioural therapy, needed flexible appointment times, and preferred face-to-face consultations; nurses felt prepared to deliver SRT, accommodating patient concerns, tailoring therapy, and negotiating sleep timings despite treatment complexity and delays between training and intervention delivery. How the intervention produced change was explored, including patient and nurse interactions and patient responses to SRT. Difficulties maintaining SRT, negative attitudes towards treatment, and low self-efficacy were highlighted. Contextual factors, including freeing GP time, time constraints, and conflicting priorities for nurses, with suggestions for alternative delivery options, were raised. Participants who found SRT a positive process showed improvements in sleep efficiency, whereas those who struggled did not. Conclusion SRT was successfully delivered by practice nurses and was generally well received by patients, despite some difficulties delivering and applying the intervention in practice.
Sleep and motor learning in stroke (SMiLES): A longitudinal study investigating sleep-dependent consolidation of motor sequence learning in the context of recovery after stroke
Introduction There is growing evidence that sleep is disrupted after stroke, with worse sleep relating to poorer motor outcomes. It is also widely acknowledged that consolidation of motor learning, a critical component of poststroke recovery, is sleep-dependent. However, whether the relationship between disrupted sleep and poor outcomes after stroke is related to direct interference of sleep-dependent motor consolidation processes, is currently unknown. Therefore, the aim of the present study is to understand whether measures of motor consolidation mediate the relationship between sleep and clinical motor outcomes post stroke. Methods and analysis We will conduct a longitudinal observational study of up to 150 participants diagnosed with stroke affecting the upper limb. Participants will be recruited and assessed within 7 days of their stroke and followed up at approximately 1 and 6 months. The primary objective of the study is to determine whether sleep in the subacute phase of recovery explains the variability in upper limb motor outcomes after stroke (over and above predicted recovery potential from the Predict Recovery Potential algorithm) and whether this relationship is dependent on consolidation of motor learning. We will also test whether motor consolidation mediates the relationship between sleep and whole-body clinical motor outcomes, whether motor consolidation is associated with specific electrophysiological sleep signals and sleep alterations during subacute recovery. Ethics and dissemination This trial has received both Health Research Authority, Health and Care Research Wales and National Research Ethics Service approval (IRAS: 304135; REC: 22/LO/0353). The results of this trial will help to enhance our understanding of the role of sleep in recovery of motor function after stroke and will be disseminated via presentations at scientific conferences, peer-reviewed publication, public engagement events, stakeholder organisations and other forms of media where appropriate. Trial registration number ClinicalTrials.gov: NCT05746260, registered on 27 February 2023.