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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.

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.

Background Housing insecurity resulting from multiple, involuntary residential moves is detrimental to the health and wellbeing of families with children. Policy makers seeking to mitigate these negative effects require a measure of risk of housing insecurity. Here we present the development of a novel risk index for England. Methods We undertook a literature review to select drivers of housing insecurity and identify relevant metrics. We recruited a practitioner panel to rank and weight these metrics using a Likert survey. The weighted metrics were summed for each small area (Lower Super Output Area) in England to produce the overall risk score. The score was then stratified into five levels, from very low to very high, linked to geographical units for data mapping. The final index (called the “Families at Risk of Housing Insecurity Index”) was made available on a public data platform. Results Eight drivers of housing insecurity were identified from the literature review as follows, (variable type and weight shown in brackets): primary school pupils eligible for free school meals (%, 0.5); income deprivation affecting children (%, 0.5); residential mobility (decile, 0.4); lone parent households (%, 0.3); pre-1919 dwellings (%, 0.3); households in fuel poverty (%, 0.3); households with dependent children in which the reference person is of Asian or Asian British, Black, Black British, or Caribbean ethnicity (%, 0.2); mental health (Small Area Mental Health Index; decile, 0.1). Analysis of the index indicated a highly varied distribution of risk across England. Two noteworthy findings were the greater proportion of very high risk areas in Greater London, possibly indicating the impact of higher living costs in the capital city region. The index also suggested there were areas at higher risk in generally more affluent settings, possibly due to a greater proportion of older housing stock in these locations. Conclusion The Families at Risk of Housing Insecurity Index (FRoHII) was composed of metrics from public datasets at the small area level. The index provides a public resource to help identify areas where families with children might be at risk of housing insecurity. The index constitutes a tool and resource for professionals seeking to provide support to families within their catchment areas.

“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.

ObjectiveArtificial intelligence (AI) will have a significant impact on healthcare over the coming decade. At the same time, health inequity remains one of the biggest challenges. Primary care is both a driver and a mitigator of health inequities and with AI gaining traction in primary care, there is a need for a holistic understanding of how AI affect health inequities, through the act of providing care and through potential system effects. This paper presents a systematic scoping review of the ways AI implementation in primary care may impact health inequity.DesignFollowing a systematic scoping review approach, we searched for literature related to AI, health inequity, and implementation challenges of AI in primary care. In addition, articles from primary exploratory searches were added, and through reference screening.The results were thematically summarised and used to produce both a narrative and conceptual model for the mechanisms by which social determinants of health and AI in primary care could interact to either improve or worsen health inequities.Two public advisors were involved in the review process.Eligibility criteriaPeer-reviewed publications and grey literature in English and Scandinavian languages.Information sourcesPubMed, SCOPUS and JSTOR.ResultsA total of 1529 publications were identified, of which 86 met the inclusion criteria. The findings were summarised under six different domains, covering both positive and negative effects: (1) access, (2) trust, (3) dehumanisation, (4) agency for self-care, (5) algorithmic bias and (6) external effects. The five first domains cover aspects of the interface between the patient and the primary care system, while the last domain covers care system-wide and societal effects of AI in primary care. A graphical model has been produced to illustrate this. Community involvement throughout the whole process of designing and implementing of AI in primary care was a common suggestion to mitigate the potential negative effects of AI.ConclusionAI has the potential to affect health inequities through a multitude of ways, both directly in the patient consultation and through transformative system effects. This review summarises these effects from a system tive and provides a base for future research into responsible implementation.

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.

Medication errors are common in primary care and are associated with considerable risk of patient harm. We tested whether a pharmacist-led, information technology-based intervention was more effective than simple feedback in reducing the number of patients at risk of measures related to hazardous prescribing and inadequate blood-test monitoring of medicines 6 months after the intervention. In this pragmatic, cluster randomised trial general practices in the UK were stratified by research site and list size, and randomly assigned by a web-based randomisation service in block sizes of two or four to one of two groups. The practices were allocated to either computer-generated simple feedback for at-risk patients (control) or a pharmacist-led information technology intervention (PINCER), composed of feedback, educational outreach, and dedicated support. The allocation was masked to general practices, patients, pharmacists, researchers, and statisticians. Primary outcomes were the proportions of patients at 6 months after the intervention who had had any of three clinically important errors: non-selective non-steroidal anti-inflammatory drugs (NSAIDs) prescribed to those with a history of peptic ulcer without co-prescription of a proton-pump inhibitor; β blockers prescribed to those with a history of asthma; long-term prescription of angiotensin converting enzyme (ACE) inhibitor or loop diuretics to those 75 years or older without assessment of urea and electrolytes in the preceding 15 months. The cost per error avoided was estimated by incremental cost-effectiveness analysis. This study is registered with Controlled-Trials.com, number ISRCTN21785299. 72 general practices with a combined list size of 480 942 patients were randomised. At 6 months' follow-up, patients in the PINCER group were significantly less likely to have been prescribed a non-selective NSAID if they had a history of peptic ulcer without gastroprotection (OR 0·58, 95% CI 0·38–0·89); a β blocker if they had asthma (0·73, 0·58–0·91); or an ACE inhibitor or loop diuretic without appropriate monitoring (0·51, 0·34–0·78). PINCER has a 95% probability of being cost effective if the decision-maker's ceiling willingness to pay reaches £75 per error avoided at 6 months. The PINCER intervention is an effective method for reducing a range of medication errors in general practices with computerised clinical records. Patient Safety Research Portfolio, Department of Health, England.

ObjectiveTo estimate the incidence of avoidable significant harm in primary care in England; describe and classify the associated patient safety incidents and generate suggestions to mitigate risks of ameliorable factors contributing to the incidents.DesignRetrospective case note review. Patients with significant health problems were identified and clinical judgements were made on avoidability and severity of harm. Factors contributing to avoidable harm were identified and recorded.SettingPrimary care.ParticipantsThirteen general practitioners (GPs) undertook a retrospective case note review of a sample of 14 407 primary care patients registered with 12 randomly selected general practices from three regions in England (total list size: 92 255 patients).Main outcome measuresThe incidence of significant harm considered at least ‘probably avoidable’ and the nature of the safety incidents.ResultsThe rate of significant harm considered at least probably avoidable was 35.6 (95% CI 23.3 to 48.0) per 100 000 patient-years (57.9, 95% CI 42.2 to 73.7, per 100 000 based on a sensitivity analysis). Overall, 74 cases of avoidable harm were detected, involving 72 patients. Three types of incident accounted for more than 90% of the problems: problems with diagnosis accounted for 45/74 (60.8%) primary incidents, followed by medication-related problems (n=19, 25.7%) and delayed referrals (n=8, 10.8%). In 59 (79.7%) cases, the significant harm could have been identified sooner (n=48) or prevented (n=11) if the GP had taken actions aligned with evidence-based guidelines.ConclusionThere is likely to be a substantial burden of avoidable significant harm attributable to primary care in England with diagnostic error accounting for most harms. Based on the contributory factors we found, improvements could be made through more effective implementation of existing information technology, enhanced team coordination and communication, and greater personal and informational continuity of care.

BackgroundExposure to green space can protect against poor health through a variety of mechanisms. However, there is heterogeneity in methodological approaches to exposure assessments which makes creating effective policy recommendations challenging.ObjectiveCritically evaluate the use of a satellite-derived exposure metric, the Enhanced Vegetation Index (EVI), for assessing access to different types of green space in epidemiological studies.MethodsWe used Landsat 5–8 (30 m resolution) to calculate average EVI for a 300 m radius surrounding 1.4 million households in Wales, UK for 2018. We calculated two additional measures using topographic vector data to represent access to green spaces within 300 m of household locations. The two topographic vector-based measures were total green space area stratified by type and average private garden size. We used linear regression models to test whether EVI could discriminate between publicly accessible and private green space and Pearson correlation to test associations between EVI and green space types.ResultsMean EVI for a 300 m radius surrounding households in Wales was 0.28 (IQR = 0.12). Total green space area and average private garden size were significantly positively associated with corresponding EVI measures (β = < 0.0001, 95% CI: 0.0000, 0.0000; β = 0.0001, 95% CI: 0.0001, 0.0001 respectively). In urban areas, as average garden size increases by 1 m2, EVI increases by 0.0002. Therefore, in urban areas, to see a 0.1 unit increase in EVI index score, garden size would need to increase by 500 m2. The very small β values represent no ‘measurable real-world’ associations. When stratified by type, we observed no strong associations between greenspace and EVI.ImpactIt is a widely implemented assumption in epidiological studies that an increase in EVI is equivalent to an increase in greenness and/or green space.We used linear regression models to test associations between EVI and potential sources of green reflectance at a neighbourhood level using satellite imagery from 2018.We compared EVI measures with a ‘gold standard’ vector-based dataset that defines publicly accessible and private green spaces.We found that EVI should be interpreted with care as a greater EVI score does not necessarily mean greater access to publicly available green spaces in the hyperlocal environment.