Supported self-management in community stroke rehabilitation: what is it and how does it work? A protocol for a realist evaluation study.

INTRODUCTION: A growing evidence base demonstrates the effectiveness of supported self-management in stroke for stroke survivors and their families. However, there is significant variation in its implementation in community stroke care and little understanding about how supported self-management works and is delivered across different settings, models used and contexts of community stroke rehabilitation. METHODS AND ANALYSIS: Using a mixed method, realist approach across two phases, this protocol describes a study on community-based supported self-management. The aim is to identify the mechanisms and outcomes of supported self-management in stroke and to understand how supported self-management is implemented in different contexts of community stroke rehabilitation. Phase 1 involves (1) a realist synthesis, (2) a scoping and mapping of current community rehabilitation settings and (3) a Q-methodology study to develop initial programme theories about how community-based supported self-management works, for whom and in what contexts. Phase 2 involves realist informed interviews/focus groups with stroke survivors, community rehabilitation practitioners and team managers from across Scotland to test and refine programme theories and an explanatory model for how supported self-management works across different contexts of community-based stroke rehabilitation. ETHICS AND DISSEMINATION: Ethical approval and R&D approvals have been granted from East of Scotland Research Ethics Committee (REC reference number: 19/ES/0055) and participating NHS boards. An understanding of how, for whom and in what contexts community-based supported self-management works will help to strengthen its delivery in practice. Such an understanding will enable the design of context-specific recommendations for policy and practice that genuinely reflect the challenges in implementing supported self-management in community stroke care. Results will be disseminated to clinical partners working in community stroke rehabilitation, stroke survivors and families and to policymakers and third sector partners involved in the provision of long-term support for people affected by stroke. PROSPERO REGISTRATION NUMBER: CRD42020166208

The contribution of risk factors to socioeconomic inequalities in multimorbidity across the lifecourse: a longitudinal analysis of the Twenty-07 cohort

Background: Multimorbidity is a major challenge to health systems globally and disproportionately affects socioeconomically disadvantaged populations. We examined socioeconomic inequalities in developing multimorbidity across the lifecourse and investigated the contribution of five behaviour-related risk factors. Methods: The Twenty-07 study recruited participants aged approximately 15, 35, and 55 years in 1987 and followed them up over 20 years. The primary outcome was development of multimorbidity (2+ health conditions). The relationship between five different risk factors (smoking, alcohol consumption, diet, body mass index (BMI), physical activity) and the development of multimorbidity was assessed. Social patterning in the development of multimorbidity based on two measures of socioeconomic status (area-based deprivation and household income) was then determined, followed by investigation of potential mediation by the five risk factors. Multilevel logistic regression models and predictive margins were used for statistical analyses. Socioeconomic inequalities in multimorbidity were quantified using relative indices of inequality and attenuation assessed through addition of risk factors. Results: Multimorbidity prevalence increased markedly in all cohorts over the 20 years. Socioeconomic disadvantage was associated with increased risk of developing multimorbidity (most vs least deprived areas: odds ratio (OR) 1.46, 95% confidence interval (CI) 1.26–1.68), and the risk was at least as great when assessed by income (OR 1.53, 95% CI 1.25–1.87) or when defining multimorbidity as 3+ conditions. Smoking (current vs never OR 1.56, 1.36–1.78), diet (no fruit/vegetable consumption in previous week vs consumption every day OR 1.57, 95% CI 1.33–1.84), and BMI (morbidly obese vs healthy weight OR 1.88, 95% CI 1.42–2.49) were strong independent predictors of developing multimorbidity. A dose–response relationship was observed with number of risk factors and subsequent multimorbidity (3+ risk factors vs none OR 1.91, 95% CI 1.57–2.33). However, the five risk factors combined explained only 40.8% of socioeconomic inequalities in multimorbidity development. Conclusions: Preventive measures addressing known risk factors, particularly obesity and smoking, could reduce the future multimorbidity burden. However, major socioeconomic inequalities in the development of multimorbidity exist even after taking account of known risk factors. Tackling social determinants of health, including holistic health and social care, is necessary if the rising burden of multimorbidity in disadvantaged populations is to be redressed

Ethnic differences in the association between depression and chronic pain:cross sectional results from UK Biobank

<b>Background</b> Comorbid chronic pain and depression is a challenging dyad of conditions to manage in primary care and reporting has shown to vary by ethnic group. Whether the relationship between depression and chronic pain varies by ethnicity is unclear. This study aims to explore chronic pain and depression reporting across ethnic groups and examine whether this association differs, independently of potential confounding factors. <p></p> <b>Methods</b> Cross-sectional study of UK Biobank participants with complete data on chronic pain and probable lifetime history of depression, who reported their ethnic group as White, Asian/Asian British or Black/Black British. Chronic pain classification: present if participants had ≥ 1 site of body pain (up to seven sites or “pain all over the body” could be selected) that lasted ≥ 3 months; extent of chronic pain categories: 0, 1, 2–3, 4–7 sites or pain all over the body. Probable depression classification: an algorithm of low mood, anhedonia and help-seeking behaviour. Relationship between depression and presence/extent of chronic pain assessed using logistic/multinomial regression models (odds ratio (OR); relative risk ratio (RRR), 95 % confidence intervals), adjusted for sociodemographic, lifestyle, and morbidity factors; and a final adjustment for current depressive symptoms. <p></p> <b>Results</b> The number of participants eligible for inclusion was 144,139: 35,703 (94 %) White, 4539 (3 %) Asian, and 3897 (3 %) Black. Chronic pain was less (40.5 %, 45.8 %, 45.0 %, respectively) and depression more (22.1 %, 12.9 %, 13.8 %, respectively) commonly reported in White participants than Asian and Black participants. Statistically significant associations between depression and presence/extent of chronic pain persisted following adjustment for potential confounding variables; this relationship was strongest for Black participants (presence of chronic pain: OR 1.86 (1.52, 2.27); RRR 1 site 1.49 (1.16, 1.91), 2–3 sites 1.98 (1.53, 2.56), 4–7 sites 3.23 (2.09, 4.99), pain all over the body 3.31 (2.05, 5.33). When current depressive symptoms were considered these relationships were attenuated. <p></p> <b>Conclusions</b> Chronic pain and depression reporting varies across ethnic groups. Differences in health seeking behaviour between ethnic groups may impact on the results reported. Clinicians, particularly in primary care, need to be aware of the cultural barriers within certain ethic groups to expressing concern over mood and to consider their approach accordingly

The Dementias Platform UK (DPUK) Data Portal

Abstract: The Dementias Platform UK Data Portal is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data. The Data Portal uses UK Secure eResearch Platform infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure ‘lab’ using VMware which is accessed using two factor authentication. Over the last 2 years, 160 project proposals involving 579 individual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Projects are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee