Factors affecting the delivery of community-based salon interventions to prevent cardiovascular disease and breast cancer among ethnically diverse women in South London: a concept-mapping approach.

BACKGROUND: In the UK, women from ethnically diverse and socioeconomically deprived communities are at increased risk of underdiagnosis of cardiovascular disease (CVD) and breast cancer. Promoting CVD prevention and awareness of breast cancer screening via community salons and primary health care partnerships can improve uptake of screening services and early detection. METHODS: Concept mapping is a multistage mixed methods participatory approach comprised of six stages: preparation, brainstorming, structuring of statements, representing statements, interpretation and utilisation of maps using Group wisdom software. A target of 20 salons, excluding male-only salons were approached. Salons included Salons included hairdressing or hairdressing and beauty salons. Purposeful and convenience sampling (online and face to face) among UK salons (hair and beauty) was conducted. Participants were given a focus prompt "What would be some factors that can influence the ability of salons to deliver this service?" and required to generate statements, which were sorted into categories based on similarity and rated for importance and feasibility. Concept maps using multidimensional scaling and hierarchical cluster analyses were produced. FINDINGS: Of 35 participants invited, 25 (71%) consented and agreed to take part in concept mapping. Reported ages were 26-35 years (n=5, 20%), 36-45 years (n=12, 48%), 46-55 years (n=3, 12%), 56-65 years (n=5, 20%), and no age reported (n=10, 40%). Around 36% (n=9) of participants were from non-White ethnic groups, with 12% (n=3) being male and 88% (n=22) female. Seven clusters emerged. Salon staff capabilities and capacities and engaging in health conversations in community salons scored average bridging values of 0·09 and 0·2 respectively, indicating good cluster homogeneity (similar meaning statements were closely sorted). Facilitating health-care access with GP practices was rated highly important to effectively promote the intervention. Engaging in health conversations in community salons and salon incentives for participation were examples of factors that were highly feasible to address. The r correlation coefficient was 0·68 between importance and feasibility to address factors affecting community health interventions. INTERPRETATION: Salons are well positioned to support health promotion interventions. Actionable priorities were identified for a salon-GP surgery partnership to promote CVD prevention through lifestyle changes and health check uptake, raising breast cancer screening awareness and address issue of equity. FUNDING: National Institute of Health and Care Research (NIHR), Research for Patient Benefit (RfPB) Programme NIHR202769.

Effectiveness and Safety of Non-Vitamin K Oral Anticoagulants versus Warfarin in Patients with Atrial Fibrillation and Previous Stroke: A Systematic Review and Meta-Analysis.

INTRODUCTION: Current evidence regarding the clinical outcomes of non-vitamin K oral anticoagulants (NOACs) versus warfarin in patients with atrial fibrillation (AF) and previous stroke is inconclusive, especially in patients with previous intracranial haemorrhage (ICrH). We aim to undertake a systematic review and meta-analysis assessing the effectiveness and safety of NOACs versus warfarin in AF patients with a history of stroke. METHODS: We searched studies published up to December 10, 2022, on PubMed, Medline, Embase, and Cochrane Central Register of Controlled Trials. Studies on adults with AF and previous ischaemic stroke (IS) or IrCH receiving either NOACs or warfarin and capturing outcome events (thromboembolic events, ICrH, and all-cause mortality) were eligible for inclusion. RESULTS: Six randomized controlled trials (RCTs) (including 19,489 patients with previous IS) and fifteen observational studies (including 132,575 patients with previous IS and 13,068 patients with previous ICrH) were included. RCT data showed that compared with warfarin, NOACs were associated with a significant reduction in thromboembolic events (odds ratio [OR]: 0.85, 95% confidence interval [CI]: 0.75-0.96), ICrH (OR: 0.57, 95% CI: 0.36-0.90), and all-cause mortality (OR: 0.88, 95% CI: 0.80-0.98). In analysing observational studies, similar results were retrieved. Moreover, patients with previous ICrH had a lower OR on thromboembolic events than those with IS (OR: 0.66, 95% CI: 0.46-0.95 vs. OR: 0.80, 95% CI: 0.70-0.93) in the comparison between NOACs and warfarin. CONCLUSIONS: Observational data showed that in AF patients with previous stroke, NOACs showed better clinical performance compared to warfarin and the benefits of NOACs were more pronounced in patients with previous IrCH versus those with IS. RCT data also showed NOACs are superior to warfarin. However, current RCTs only included AF patients who survived an IS, and further large RCTs focused on patients with previous ICrH are warranted.

Risk prediction of 30-day mortality after stroke using machine learning: a nationwide registry-based cohort study.

BACKGROUNDS: We aimed to develop and validate machine learning (ML) models for 30-day stroke mortality for mortality risk stratification and as benchmarking models for quality improvement in stroke care. METHODS: Data from the UK Sentinel Stroke National Audit Program between 2013 to 2019 were used. Models were developed using XGBoost, Logistic Regression (LR), LR with elastic net with/without interaction terms using 80% randomly selected admissions from 2013 to 2018, validated on the 20% remaining admissions, and temporally validated on 2019 admissions. The models were developed with 30 variables. A reference model was developed using LR and 4 variables. Performances of all models was evaluated in terms of discrimination, calibration, reclassification, Brier scores and Decision-curves. RESULTS: In total, 488,497 stroke patients with a 12.3% 30-day mortality rate were included in the analysis. In 2019 temporal validation set, XGBoost model obtained the lowest Brier score (0.069 (95% CI: 0.068-0.071)) and the highest area under the ROC curve (AUC) (0.895 (95% CI: 0.891-0.900)) which outperformed LR reference model by 0.04 AUC (p < 0.001) and LR with elastic net and interaction term model by 0.003 AUC (p < 0.001). All models were perfectly calibrated for low (< 5%) and moderate risk groups (5-15%) and ≈1% underestimation for high-risk groups (> 15%). The XGBoost model reclassified 1648 (8.1%) low-risk cases by the LR reference model as being moderate or high-risk and gained the most net benefit in decision curve analysis. CONCLUSIONS: All models with 30 variables are potentially useful as benchmarking models in stroke-care quality improvement with ML slightly outperforming others.

Real-world effectiveness of steroids in severe COVID-19: a retrospective cohort study.

INTRODUCTION: Randomised controlled trials have shown that steroids reduce the risk of dying in patients with severe Coronavirus disease 2019 (COVID-19), whilst many real-world studies have failed to replicate this result. We aim to investigate real-world effectiveness of steroids in severe COVID-19. METHODS: Clinical, demographic, and viral genome data extracted from electronic patient record (EPR) was analysed from all SARS-CoV-2 RNA positive patients admitted with severe COVID-19, defined by hypoxia at presentation, between March 13th 2020 and May 27th 2021. Steroid treatment was measured by the number of prescription-days with dexamethasone, hydrocortisone, prednisolone or methylprednisolone. The association between steroid > 3 days treatment and disease outcome was explored using multivariable cox proportional hazards models with adjustment for confounders (including age, gender, ethnicity, co-morbidities and SARS-CoV-2 variant). The outcome was in-hospital mortality. RESULTS: 1100 severe COVID-19 cases were identified having crude hospital mortality of 15.3%. 793/1100 (72.1%) individuals were treated with steroids and 513/1100 (46.6%) received steroid ≤ 3 days. From the multivariate model, steroid > 3 days was associated with decreased hazard of in-hospital mortality (HR: 0.47 (95% CI: 0.31-0.72)). CONCLUSION: The protective effect of steroid treatment for severe COVID-19 reported in randomised clinical trials was replicated in this retrospective study of a large real-world cohort.

Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study.

BACKGROUND: The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification. METHODS: Training cohorts comprised 1276 patients admitted to King's College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy's and St Thomas' Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models. RESULTS: A baseline model of 'NEWS2 + age' had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI 0.715, 0.757), and these improvements were replicated across seven UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites. CONCLUSIONS: NEWS2 score had poor-to-moderate discrimination for medium-term COVID-19 outcome which raises questions about its use as a screening tool at hospital admission. Risk stratification was improved by including readily available blood and physiological parameters measured at hospital admission, but there was evidence of miscalibration in external sites. This highlights the need for a better understanding of the use of early warning scores for COVID.

Challenges to implementing electronic trial data collection in primary care: a qualitative study.

BACKGROUND: Within-consultation recruitment to primary care trials is challenging. Ensuring procedures are efficient and self-explanatory is the key to optimising recruitment. Trial recruitment software that integrates with the electronic health record to support and partially automate procedures is becoming more common. If it works well, such software can support greater participation and more efficient trial designs. An innovative electronic trial recruitment and outcomes software was designed to support recruitment to the Runny Ear randomised controlled trial, comparing topical, oral and delayed antibiotic treatment for acute otitis media with discharge in children. A qualitative evaluation investigated the views and experiences of primary care staff using this trial software. METHODS: Staff were purposively sampled in relation to site, role and whether the practice successfully recruited patients. In-depth interviews were conducted using a flexible topic guide, audio recorded and transcribed. Data were analysed thematically. RESULTS: Sixteen staff were interviewed, including GPs, practice managers, information technology (IT) leads and research staff. GPs wanted trial software that automatically captures patient data. However, the experience of getting the software to work within the limited and complex IT infrastructure of primary care was frustrating and time consuming. Installation was reliant on practice level IT expertise, which varied between practices. Although most had external IT support, this rarely included supported for research IT. Arrangements for approving new software varied across practices and often, but not always, required authorisation from Clinical Commissioning Groups. CONCLUSIONS: Primary care IT systems are not solely under the control of individual practices or CCGs or the National Health Service. Rather they are part of a complex system that spans all three and is influenced by semi-autonomous stakeholders operating at different levels. This led to time consuming and sometimes insurmountable barriers to installation at the practice level. These need to be addressed if software supporting efficient research in primary care is to become a reality.

Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners.

BACKGROUND: Well-established electronic data capture in UK general practice means that algorithms, developed on patient data, can be used for automated clinical decision support systems (CDSSs). These can predict patient risk, help with prescribing safety, improve diagnosis and prompt clinicians to record extra data. However, there is persistent evidence of low uptake of CDSSs in the clinic. We interviewed UK General Practitioners (GPs) to understand what features of CDSSs, and the contexts of their use, facilitate or present barriers to their use. METHODS: We interviewed 11 practicing GPs in London and South England using a semi-structured interview schedule and discussed a hypothetical CDSS that could detect early signs of dementia. We applied thematic analysis to the anonymised interview transcripts. RESULTS: We identified three overarching themes: trust in individual CDSSs; usability of individual CDSSs; and usability of CDSSs in the broader practice context, to which nine subthemes contributed. Trust was affected by CDSS provenance, perceived threat to autonomy and clear management guidance. Usability was influenced by sensitivity to the patient context, CDSS flexibility, ease of control, and non-intrusiveness. CDSSs were more likely to be used by GPs if they did not contribute to alert proliferation and subsequent fatigue, or if GPs were provided with training in their use. CONCLUSIONS: Building on these findings we make a number of recommendations for CDSS developers to consider when bringing a new CDSS into GP patient records systems. These include co-producing CDSS with GPs to improve fit within clinic workflow and wider practice systems, ensuring a high level of accuracy and a clear clinical pathway, and providing CDSS training for practice staff. These recommendations may reduce the proliferation of unhelpful alerts that can result in important decision-support being ignored.

Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse.

BACKGROUND: Enormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. OBJECTIVE: In this paper, we discuss whether data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of data. METHODS: This paper is based on the authors' experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records. RESULTS: We first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature. CONCLUSIONS: There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven health care will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.

Templates as a method for implementing data provenance in decision support systems.

Decision support systems are used as a method of promoting consistent guideline-based diagnosis supporting clinical reasoning at point of care. However, despite the availability of numerous commercial products, the wider acceptance of these systems has been hampered by concerns about diagnostic performance and a perceived lack of transparency in the process of generating clinical recommendations. This resonates with the Learning Health System paradigm that promotes data-driven medicine relying on routine data capture and transformation, which also stresses the need for trust in an evidence-based system. Data provenance is a way of automatically capturing the trace of a research task and its resulting data, thereby facilitating trust and the principles of reproducible research. While computational domains have started to embrace this technology through provenance-enabled execution middlewares, traditionally non-computational disciplines, such as medical research, that do not rely on a single software platform, are still struggling with its adoption. In order to address these issues, we introduce provenance templates - abstract provenance fragments representing meaningful domain actions. Templates can be used to generate a model-driven service interface for domain software tools to routinely capture the provenance of their data and tasks. This paper specifies the requirements for a Decision Support tool based on the Learning Health System, introduces the theoretical model for provenance templates and demonstrates the resulting architecture. Our methods were tested and validated on the provenance infrastructure for a Diagnostic Decision Support System that was developed as part of the EU FP7 TRANSFoRm project.

Researching Mental Health Disorders in the Era of Social Media: Systematic Review.

BACKGROUND: Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose. OBJECTIVE: The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research. METHODS: We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals. RESULTS: The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis. CONCLUSIONS: Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques.

Effectiveness of the influenza vaccine in preventing admission to hospital and death in people with type 2 diabetes.

BACKGROUND: The health burden caused by seasonal influenza is substantial. We sought to examine the effectiveness of influenza vaccination against admission to hospital for acute cardiovascular and respiratory conditions and all-cause death in people with type 2 diabetes. METHODS: We conducted a retrospective cohort study using primary and secondary care data from the Clinical Practice Research Datalink in England, over a 7-year period between 2003/04 and 2009/10. We enrolled 124 503 adults with type 2 diabetes. Outcome measures included admission to hospital for acute myocardial infarction (MI), stroke, heart failure or pneumonia/influenza, and death. We fitted Poisson regression models for influenza and off-season periods to estimate incidence rate ratios (IRR) for cohorts who had and had not received the vaccine. We used estimates for the summer, when influenza activity is low, to adjust for residual confounding. RESULTS: Study participants contributed to 623 591 person-years of observation during the 7-year study period. Vaccine recipients were older and had more comorbid conditions compared with nonrecipients. After we adjusted for covariates and residual confounding, vaccination was associated with significantly lower admission rates for stroke (IRR 0.70, 95% confidence interval [CI] 0.53-0.91), heart failure (IRR 0.78, 95% CI 0.65-0.92) and pneumonia or influenza (IRR 0.85, 95% CI 0.74-0.99), as well as all-cause death (IRR 0.76, 95% CI 0.65-0.83), and a nonsignificant change for acute MI (IRR 0.81, 95% CI 0.62-1.04) during the influenza seasons. INTERPRETATION: In this cohort of patients with type 2 diabetes, influenza vaccination was associated with reductions in rates of admission to hospital for specific cardiovascular events. Efforts should be focused on improvements in vaccine uptake in this important target group as part of comprehensive secondary prevention.

Feasibility and acceptability of TRANSFoRm to improve clinical trial recruitment in primary care.

BACKGROUND: Recruitment of study participants is a challenging process for health professionals and patients. The Translational Medicine and Patient Safety in Europe (TRANSFoRm) clinical trial tools enable automated identification, recruitment and follow-up in clinical trials, potentially saving time, effort and costs for all parties involved. OBJECTIVES: This study evaluates the acceptability and feasibility of TRANSFoRm to improve clinical trial recruitment in primary care. METHODS: A feasibility study was conducted in three general practices in Poland. Participants were physicians and patients with gastro-oesophageal reflux disease. Semi-structured interviews were held to obtain feedback about the usefulness, ease of use and overall experience with the TRANSFoRm tools and to identify potential usability issues. Data were analysed thematically. RESULTS: A total of 5 physicians and 10 patients participated in the study. Physicians were satisfied with the usefulness of the system, as it enabled easier and faster identification, recruitment and follow-up of patients compared with existing methods. Patients found the TRANSFoRm apps easy to use to report patient outcomes. However, they also felt that the apps may not be useful for patients with limited exposure to smartphone and web technologies. Two main usability issues were identified: physicians could not access the result of the randomization at the end of each visit, and participants could not locate the follow-up reminder email. CONCLUSIONS: This study provides new evidence on the acceptability and feasibility of TRANSFoRm to enable automated identification, recruitment and follow-up of study participants in primary care trials. It also helps to better understand and address users' requirements in eHealth-supported clinical research.

The impact of timing and duration of thiopurine treatment on first intestinal resection in Crohn's disease: national UK population-based study 1989-2010.

OBJECTIVES: The efficacy of thiopurines (TPs) in altering the risk of surgery in Crohn's disease (CD) remains controversial. We evaluated the impact of TP therapy, optimal timing, and duration of TP therapy on first intestinal resection rates using a population-based cohort. METHODS: We constructed a population-based cohort of incident cases of CD between 1989 and 2005. We used the Kaplan-Meier analysis to calculate time trends in TP use and first intestinal resection in three groups defined by time period of diagnosis: 1989-1993, 1994-1999, and 2000-2005 groups A, B, and C, respectively. We quantified impact of duration and timing of TP treatment on likelihood of surgery using Cox regression and propensity score matching. RESULTS: We identified 5,640 eligible patients with CD. The 5-year cumulative probability of TP use increased from 12, 18, to 25% ( P<0.0001) while probability of first intestinal resection decreased from 15, 12 to 9% (P<0.001) in groups A, B, and C, respectively. Patients treated with at least 6 months of TP therapy had a 44% reduction in the risk of surgery (hazards ratio (HR): 0.56; 95% confidence interval (CI): 0.37-0.85) and those receiving at least 12 months of TP therapy had a 69% reduction in the risk of surgery (HR: 0.31; 95% CI: 0.22-0.44). Early treatment (<12 months from diagnosis) vs. late treatment with TP showed no additional benefit in reducing risk of surgery (HR: 0.41; 95% CI: 0.27-0.61 vs. 0.21; 95% CI: 0.13-0.34). CONCLUSIONS: Over the past 20 years, TP use has doubled, whereas intestinal surgery has fallen by one-third among the UK population of Crohn's patients. Prolonged exposure is associated with a reduced likelihood of surgery whereby more than 12 months TP therapy reduces the risk of first intestinal surgery two-fold; however, early initiation of TP treatment offered no apparent additional benefit.

Model-driven approach to data collection and reporting for quality improvement.

Continuous data collection and analysis have been shown essential to achieving improvement in healthcare. However, the data required for local improvement initiatives are often not readily available from hospital Electronic Health Record (EHR) systems or not routinely collected. Furthermore, improvement teams are often restricted in time and funding thus requiring inexpensive and rapid tools to support their work. Hence, the informatics challenge in healthcare local improvement initiatives consists of providing a mechanism for rapid modelling of the local domain by non-informatics experts, including performance metric definitions, and grounded in established improvement techniques. We investigate the feasibility of a model-driven software approach to address this challenge, whereby an improvement model designed by a team is used to automatically generate required electronic data collection instruments and reporting tools. To that goal, we have designed a generic Improvement Data Model (IDM) to capture the data items and quality measures relevant to the project, and constructed Web Improvement Support in Healthcare (WISH), a prototype tool that takes user-generated IDM models and creates a data schema, data collection web interfaces, and a set of live reports, based on Statistical Process Control (SPC) for use by improvement teams. The software has been successfully used in over 50 improvement projects, with more than 700 users. We present in detail the experiences of one of those initiatives, Chronic Obstructive Pulmonary Disease project in Northwest London hospitals. The specific challenges of improvement in healthcare are analysed and the benefits and limitations of the approach are discussed.

Social media for palliative and end-of-life care research: a systematic review.

BACKGROUND: Social media with real-time content and a wide-reaching user network opens up more possibilities for palliative and end-of-life care (PEoLC) researchers who have begun to embrace it as a complementary research tool. This review aims to identify the uses of social media in PEoLC studies and to examine the ethical considerations and data collection approaches raised by this research approach. METHODS: Nine online databases were searched for PEoLC research using social media published before December 2022. Thematic analysis and narrative synthesis approach were used to categorise social media applications. RESULTS: 21 studies were included. 16 studies used social media to conduct secondary analysis and five studies used social media as a platform for information sharing. Ethical considerations relevant to social media studies varied while 15 studies discussed ethical considerations, only 6 studies obtained ethical approval and 5 studies confirmed participant consent. Among studies that used social media data, most of them manually collected social media data, and other studies relied on Twitter application programming interface or third-party analytical tools. A total of 1 520 329 posts, 325 videos and 33 articles related to PEoLC from 2008 to 2022 were collected and analysed. CONCLUSIONS: Social media has emerged as a promising complementary research tool with demonstrated feasibility in various applications. However, we identified the absence of standardised ethical handling and data collection approaches which pose an ongoing challenge. We provided practical recommendations to bridge these pressing gaps for researchers wishing to use social media in future PEoLC-related studies.

Co-development of breast cancer health promotion educational materials for ethnically diverse women working with hairdressing and beauty salons: BELONG study.

BACKGROUND: Improved screening uptake is essential for early breast cancer detection, women's health and reducing health disparities. However, minority ethnic and deprived communities often face lower breast cancer screening rates and limited access to culturally tailored educational materials. A recent review found limited culturally tailored materials for breast cancer education. AIM: To investigate the culturally appropriate interfaces and preferences of salon staff in educating their clients about breast cancer METHOD: We used a two-stage approach, following the Double Diamond framework; discover and define phases. Relevant breast cancer materials (i.e., based on cultural appropriateness, English language presentation, and alignment with the UK context) were assessed using the Suitability Assessment of Materials (SAM) toolkit. Interviews with ethnically diverse salon staff provided insights into their needs and preferences for client education materials. Thematic analysis was applied to interview transcripts. RESULTS: Cultural appropriateness was evident in 9/14 (64%) of the materials identified (e.g., targeting black ethnicities with positive representations). Of those, six of them demonstrated an overall SAM rating of 76% ("Superior"). Thematic analysis of interviews identified seven key themes, including the importance of engagement strategies, education and awareness for health promotion, salon staff's role, preferred training methods, supportive materials, inclusivity, representation, and participant satisfaction. CONCLUSION: This study highlights the SAM toolkit's role in selecting suitable educational materials for breast cancer prevention. The research offers prospects for improving breast cancer awareness in ethnically diverse communities and addressing healthcare access disparities, with salon hairdressers emerging as crucial advocates for health promotion.

ROAD2H: Development and evaluation of an open-source explainable artificial intelligence approach for managing co-morbidity and clinical guidelines.

Introduction: Clinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. In this work, we develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle-income country in West Balkans. Methods: We used an ontological framework, the Transition-based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR-based implementation of the Assumptions-Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists. Results: Pulmonologists agreed 97% of the time with the GOLD-based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co-morbidities was suggested in the future along with customisation of the level of explanation with expertise. Conclusion: An ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Extension to other guidelines and multiple co-morbidities is needed to test the approach further.

Effect of on-demand vs continuous prescription of proton pump inhibitors on symptom burden and quality of life: results of a real-world randomized controlled trial in primary care patients with gastroesophageal reflux disease.

OBJECTIVES: This study aimed to assess the impact of on-demand versus continuous prescribing of proton pump inhibitors (PPIs) on symptom burden and health-related quality of life in patients with gastroesophageal reflux disease (GERD) presenting to primary care. METHODS: Thirty-six primary care centres across Europe enrolled adult GERD patients from electronic health records. Participants were randomised to on-demand or continuous PPI prescriptions and were followed for 8 weeks. PPI intake, symptom burden, and quality of life were compared between the two groups using mixed-effect regression analyses. Spearman's correlation was used to assess the association between changes in PPI dose and patient-reported outcomes. RESULTS: A total of 488 patients (median age 51 years, 58% women) completed the initial visit, with 360 attending the follow-up visit. There was no significant difference in PPI use between the continuous and on-demand prescription groups (b=.57, 95%CI:0.40-1.53), although PPI use increased in both groups (b = 1.33, 95%CI:0.65 - 2.01). Advice on prescribing strategy did not significantly affect patient-reported outcomes. Both symptom burden (Reflux Disease Questionnaire, b=-0.61, 95%CI:-0.73 - -0.49) and quality of life (12-item Short Form Survey physical score b = 3.31, 95%CI:2.17 - 4.45) improved from baseline to follow-up in both groups. Increased PPI intake correlated with reduced reflux symptoms (n = 347, ρ=-0.12, p = 0.02) and improved quality of life (n = 217, ρ = 0.16, p = 0.02). CONCLUSION: In real-world settings, both continuous and on-demand PPI prescriptions resulted in similar increases in PPI consumption with no difference in treatment effects. Achieving an adequate PPI dose to alleviate reflux symptom burden improves quality of life in GERD patients. EudraCT number 2014-001314-25.

Continuous and on-demand prescription increase in proton pump inhibitor consumption equally in real-world settings and did not result in different outcomes.Reaching a sufficient dose of proton pump inhibitor to reduce reflux symptom burden improves quality of life in patients with gastroesophageal reflux disease.

Mechanisms for Integrating Real Data into Search Game Simulations: An Application to Winter Health Service Pressures and Preventative Policies.

While modelling and simulation are powerful techniques for exploring complex phenomena, if they are not coupled with suitable real-world data any results obtained are likely to require extensive validation. We consider this problem in the context of search game modelling, and suggest that both demographic and behaviour data are used to configure certain model parameters. We show this integration in practice by using a combined dataset of over 150,000 individuals to configure a specific search game model that captures the environment, population, interventions and individual behaviours relating to winter health service pressures. The presence of this data enables us to more accurately explore the potential impact of service pressure interventions, which we do across 33,000 simulations using a computational version of the model. We find government advice to be the best-performing intervention in simulation, in respect of improved health, reduced health inequalities, and thus reduced pressure on health service utilisation.