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Socioeconomic deprivation is known to be associated with worse outcomes in asthma, but there is a lack of population-based evidence of its impact across all stages of patient care. We investigated the association of socioeconomic deprivation with asthma-related care and outcomes across primary and secondary care and with asthma-related death in Wales. We constructed a national cohort, identified from 76% (2.4 million) of the Welsh population, of continuously treated asthma patients between 2013 and 2017 using anonymised, person-level, linked, routinely collected primary and secondary care data in the Secure Anonymised Information Linkage (SAIL) Databank. We investigated the association between asthma-related health service utilisation, prescribing, and deaths with the 2011 Welsh Index of Multiple Deprivation (WIMD) and its domains. We studied 106,926 patients (534,630 person-years), 56.3% were female, with mean age of 47.5 years (SD = 20.3). Compared to the least deprived patients, the most deprived patients had slightly fewer total asthma-related primary care consultations per patient (incidence rate ratio [IRR] = 0.98, 95% CI 0.97-0.99, p-value < 0.001), slightly fewer routine asthma reviews (IRR = 0.98, 0.97-0.99, p-value < 0.001), lower controller-to-total asthma medication ratios (AMRs; 0.50 versus 0.56, p-value < 0.001), more asthma-related accident and emergency (A&E) attendances (IRR = 1.27, 1.10-1.46, p-value = 0.001), more asthma emergency admissions (IRR = 1.56, 1.39-1.76, p-value < 0.001), longer asthma-related hospital stay (IRR = 1.64, 1.39-1.94, p-value < 0.001), and were at higher risk of asthma-related death (risk ratio of deaths with any mention of asthma 1.56, 1.18-2.07, p-value = 0.002). Study limitations include the deprivation index being area based and the potential for residual confounders and mediators. In this study, we observed that the most deprived asthma patients in Wales had different prescribing patterns, more A&E attendances, more emergency hospital admissions, and substantially higher risk of death. Interventions specifically designed to improve treatment and outcomes for these disadvantaged groups are urgently needed.

“Data Saves Lives” is a public engagement campaign that highlights the benefits of big data research and aims to establish public trust for this emerging research area. This study explores how the hashtag #DataSavesLives is used on Twitter. We focused on the period when the UK government and its agencies adopted #DataSavesLives in an attempt to support their plans to set up a new database holding National Health Service (NHS) users’ medical data. Public tweets published between April 19 and July 15, 2021, using the hashtag #DataSavesLives were saved using NCapture for NVivo 12. All tweets were coded twice. First, each tweet was assigned a positive, neutral, or negative attitude toward the campaign. Second, inductive thematic analysis was conducted. The results of the thematic analysis were mapped under 3 models of public engagement: deficit, dialogue, and participatory. Of 1026 unique tweets available for qualitative analysis, discussion around #DataSavesLives was largely positive (n=716, 69.8%) or neutral (n=276, 26.9%) toward the campaign with limited negative attitudes (n=34, 3.3%). Themes derived from the #DataSavesLives debate included ethical sharing, proactively engaging the public, coproducing knowledge with the public, harnessing potential, and gaining an understanding of big data research. The Twitter discourse was largely positive toward the campaign. The hashtag is predominantly used by similar-minded Twitter users to share information about big data projects and to spread positive messages about big data research when there are public controversies. The hashtag is generally used by organizations and people supportive of big data research. Tweet authors recognize that the public should be proactively engaged and involved in big data projects. The campaign remains UK centric. The results indicate that the communication around big data research is driven by the professional community and remains 1-way as members of the public rarely use the hashtag. The results demonstrate the potential of social media but draws attention to hashtag usage being generally confined to “Twitter bubbles”: groups of similar-minded Twitter users.

Background Big data research requires public support. It has been argued that this can be achieved by public involvement and engagement to ensure that public views are at the centre of research projects. Researchers should aim to include diverse communities, including seldom‐heard voices, to ensure that a range of voices are heard and that research is meaningful to them. Objective We explored how researchers involve and engage seldom‐heard communities around big data research. Methods This is a qualitative study. Researchers who had experience of involving or engaging seldom‐heard communities in big data research were recruited. They were based in England (n = 5), Scotland (n = 4), Belgium (n = 2) and Canada (n = 1). Twelve semistructured interviews were conducted on Zoom. All interviews were audio‐recorded and transcribed, and we used reflexive thematic analysis to analyse participants' experiences. Results The analysis highlighted the complexity of involving and engaging seldom‐heard communities around big data research. Four themes were developed to represent participants' experiences: (1) ion and complexity of big data, (2) one size does not fit all, (3) working in partnership and (4) empowering the public contribution. Conclusion The study offers researchers a better understanding of how to involve and engage seldom‐heard communities in a meaningful way around big data research. There is no one right approach, with involvement and engagement activities required to be project‐specific and dependent on the public contributors, researchers' needs, resources and time available. Patient and Public Involvement Two public contributors are authors of the paper and they were involved in the study design, analysis and writing.

Natural environments can promote well-being through multiple mechanisms. Many studies have investigated relationships between residential green/blue space (GBS) and well-being, fewer explore relationships with actual use of GBS. We used a nationally representative survey, the National Survey for Wales, anonymously linked with spatial GBS data to investigate associations of well-being with both residential GBS and time in nature (N = 7631). Both residential GBS and time spent in nature were associated with subjective well-being. Higher green-ness was associated with lower well-being, counter to hypotheses (predicting the Warwick and Edinburgh Mental Well-Being Scale (WEMWBS): Enhanced vegetation index β =  − 1.84, 95% confidence interval (CI) − 3.63, − 0.05) but time spent in nature was associated with higher well-being (four hours a week in nature vs. none β = 3.57, 95% CI 3.02, 4.13). There was no clear association between nearest GBS proximity and well-being. In support of the equigenesis theory, time spent in nature was associated with smaller socioeconomic inequalities in well-being. The difference in WEMWBS (possible range 14–70) between those who did and did not live in material deprivation was 7.7 points for those spending no time in nature, and less at 4.5 points for those spending time in nature up to 1 h per week. Facilitating access and making it easier for people to spend time in nature may be one way to reduce socioeconomic inequalities in well-being.

Background Research suggests that living in fuel poverty and cold homes contributes to poor physical and mental health, and that interventions targeted at those living in poor quality housing may lead to health improvements. However, little is known about the socio-economic intermediaries and processes that contribute to better health. This study examined the relationship between energy efficiency investments to homes in low-income areas and mental and physical health of residents, as well as a number of psychosocial outcomes likely to be part of the complex relationship between energy efficiency measures and health outcomes. Methods A quasi-experimental field study with a controlled pretest-posttest design was conducted (intervention n  = 364; control n  = 418) to investigate the short-term health and psychosocial impacts of a domestic energy efficiency programme that took place across Wales between 2013 and 2015. Survey data were collected in the winters before and after installation of energy efficiency measures, including external wall insulation. The study used a multilevel modelling repeated measures approach to analyse the data. Results The energy efficiency programme was not associated with improvements in physical and mental health (using the SF-12v2 physical and mental health composite scales) or reductions in self-reported respiratory and asthma symptoms. However, the programme was associated with improved subjective wellbeing (B = 0.38, 95% CI 0.12 to 0.65), as well as improvements in a number of psychosocial outcomes, including increased thermal satisfaction (OR = 3.83, 95% CI 2.40 to 5.90), reduced reports of putting up with feeling cold to save heating costs (OR = 0.49, CI = 0.25 to 0.94), fewer financial difficulties (B = −0.15, 95% CI -0.25 to -0.05), and reduced social isolation (OR = 0.32, 95% CI 0.13 to 0.77). Conclusion The study showed that investing in energy efficiency in low-income communities does not lead to self-reported health improvements in the short term. However, investments increased subjective wellbeing and were linked to a number of psychosocial intermediaries that are conducive to better health. It is likely that better living conditions contribute to improvements in health outcomes in the longer term. Better understanding of the impacts on recipients of energy efficiency schemes, could improve targeting of future fuel poverty policies.

Health is largely determined by the interactions between social, economic, and physical environments and inherited characteristics, which together affect exposures and behaviours.1,2 Despite an improving evidence base showing the cost-effectiveness of disease prevention with non-health-care interventions, most investments into improving health continue to focus on health service treatment of acute and chronic illnesses, with little mainstreaming of effective preventive interventions other than vaccination or screening.3 Even within the prevention model there is a tendency towards investment in medical models of individual behaviour change, despite growing evidence showing that environmentally-based interventions often provide better value for money.4 Resource-constrained health organisations are generally disinclined to invest in non-health-care interventions, unless the quality of supporting evidence approximates that from randomised trials. The evidence base for many broad non-health-care interventions is often less extensive--eg, evidence for the health and societal effects of improved housing is largely based on findings from logic, extrapolation, and observational studies, albeit supported by a small but growing amount of interventional research.5 Assessment of non-health-care interventions needs recognition of the complexity of real-life settings, and hence adoption of socioecological and life-course perspectives in which multiple exposures and interventions interact at different levels over long periods.6 This scenario is in stark contrast with that used for the testing of pharmaceutical agents, which generally involves well funded, short-term trials of simple interventions in atypical disease-specific populations that are free of comorbidities.

IntroductionPrevious systematic reviews investigating the effects of green and blue space (GBS) on maternal and neonatal health have mainly focused on cross-sectional evidence, limiting potential causal inferences. The last review on the topic was published in January 2024. This review focused on residential greenness effects and neonatal health only but did not include other green/blue space measures, or maternal health outcomes. This review also only included papers published up to June 2023; discounting the 15 studies that have been published since. Thus, this study will capture the growing number of studies that generate causal evidence and aims to investigate the association between GBS and maternal and/or neonatal health.Methods and analysisThe study protocol was developed with reference to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This review will include study designs such as experiments, quasi-experiments, longitudinal studies and more. The study independent variable must be a GBS, green space and/or blue space measure. Eligible maternal health outcomes are those reported during pregnancy and up to 1 year after pregnancy. Neonatal health outcomes are limited to neonates no older than 28 days. A total of seven online databases will be searched: Medline, Scopus, Web of Science, PsycInfo, Embase, Environment Complete, and Maternity and Infant Care Database. Abstract and full-text screenings will be undertaken by three reviewers. Risk of bias assessment will be conducted based on the Risk of Bias in Non-randomized Studies-of Exposure framework.A narrative synthesis will be undertaken. If sufficiently comparable studies are identified, meta-analyses using random effects models will be conducted. We will explore heterogeneity using the I2 test.Ethics and disseminationEthical approval is not required as all the data will be derived from published primary studies that have already obtained ethical permissions. The findings will be disseminated through relevant conferences and peer-reviewed publications.PROSPERO registration numberCRD42023396372.

The success of big data initiatives depends on public support. Public involvement and engagement could be a way of establishing public support for big data research. This review aims to synthesize the evidence on public involvement and engagement in big data research. This scoping review mapped the current evidence on public involvement and engagement activities in big data research. We searched 5 electronic databases, followed by additional manual searches of Google Scholar and gray literature. In total, 2 public contributors were involved at all stages of the review. A total of 53 papers were included in the scoping review. The review showed the ways in which the public could be involved and engaged in big data research. The papers discussed a broad range of involvement activities, who could be involved or engaged, and the importance of the context in which public involvement and engagement occur. The findings show how public involvement, engagement, and consultation could be delivered in big data research. Furthermore, the review provides examples of potential outcomes that were produced by involving and engaging the public in big data research. This review provides an overview of the current evidence on public involvement and engagement in big data research. While the evidence is mostly derived from discussion papers, it is still valuable in illustrating how public involvement and engagement in big data research can be implemented and what outcomes they may yield. Further research and evaluation of public involvement and engagement in big data research are needed to better understand how to effectively involve and engage the public in big data research. RR2-https://doi.org/10.1136/bmjopen-2021-050167.

Abstract Background and Objectives A systematic review was conducted to explore the use of smartphones and tablet computers as cognitive and memory aids by older adults with and without cognitive impairment, specifically the effects of smartphone and tablet use on participants’ cognition and memory, and the barriers and facilitators to smartphone and tablet use for cognitive and memory support. Research Design and Methods A systematic search of 6 key databases found 11,895 citations published between 2010 and 2021. Studies were included if they involved community-dwelling older adults with or without cognitive impairment arising from acquired brain injury, mild cognitive impairment, or dementia, and if they evaluated everyday smartphone or tablet device use for cognition, memory, or activities of daily living. Results A total of 28 papers were included in the narrative synthesis. There was some evidence that the use of smartphones and tablets could aid cognitive function in older adults without cognitive impairment, particularly executive function and processing speed. There was modest evidence that smartphone and tablet use could support memory in both older adults without cognitive impairment and those with acquired brain injury and dementia. Discussion and Implications Smartphones and tablets were seen by users as acceptable, enjoyable, and nonstigmatizing alternatives to conventional assistive technology devices; however, current use of smartphone and tablet devices is hindered by the digital literacy of older adults, a lack of accommodation for older adult users’ motor and sensory impairments, and a lack of input from clinicians and researchers. Much of the evidence presented in this review derives from case studies and small-scale trials of smartphone and tablet training interventions. Further research is needed into older adults’ use of smartphones and tablets for cognitive support before and after the onset of cognitive impairment in order to develop effective evidence-based smart technology cognition and memory aids.

Previous studies have mainly examined the independent effects of green or blue space on health from a perspective of spatial homogeneity, which neglects their interactive or spatially varying effects. Here, we examined the spatially varying and interactive effects of green and blue space on health using open access data in Northern Ireland (NI). Aggregate health data was collected from 2017 Northern Ireland Multiple Deprivation Measure at the Super Output Area (SOA) level. Green and blue spaces were extracted from Land Cover Map data. The proportion of grassland and the proportion of woodland for each SOA were calculated as proxies for green space, while the proportion of water bodies was calculated for measuring blue space. Spatially varying effects of green and blue space were modelled using multiscale Geographic Weighted Regression (MGWR). Interaction terms between green and blue spaces were added into the MGWR models to test the interactive association of green and blue space on different health outcomes (e.g., preventable death ratio and cancer registrations). Results indicate that associations were distributed zonally, with green and blue spaces in eastern areas of NI more strongly associated with health outcomes than in western areas. Within these large regional zones, further spatially varying effects of different green and blue spaces were observed. Grassland was generally positively associated with some health outcomes (e.g., less preventable death ratio, cancer registrations ratio, multiple prescriptions ratio, and long-term health problem or disability ratio), while the results of woodland and water body were mixed. Water bodies were found to strengthen the effect of woodland and grassland. The above results indicate that green and blue space have independently and interactive spatially varying associations with different health outcomes in NI. It is also important to combine both green and blue space elements to enhance health impacts in future interventions.