Background: The identification and understanding of the discrepancy between caregivers’ reports of people with dementia’s (PwD) performance of activities of daily living (ADLs) and observed performance, could clarify what kind of support a PwD effectively needs when completing tasks. Strategies used by caregivers have not been included in the investigation of this discrepancy. Objective: To (1) investigate if caregivers’ report of PwD’s ADL performance are consistent with PwD’s observed performance; (2) explore if caregiver management styles, depression, and anxiety, contribute to this discrepancy. Methods: PwD (n = 64) were assessed with standardized performance-based (Assessment of Motor and Process Skills, AMPS) and informant-based (Disability Assessment for Dementia, DAD) ADL assessments. Caregivers completed depression (PHQ-9), anxiety (GAD-7), and dementia management style (DMSS: criticism, active-management, and encouragement) questionnaires. Cohen’s kappa determined agreement/disagreement in ADL performance. To investigate the potential discrepancy between the DAD and AMPS, a continuous variable was generated: comparative ADL score. Multiple linear regression analysis explored whether caregivers’ management styles, depression or anxiety could explain the ADL discrepancy. Results: Poor level of agreement between observed and reported ADL performance [k = –0.025 (95% CI –0.123 –0.073)] was identified, with most caregivers underestimating ADL performance. The combined model explained 18% (R2 = 0.18, F (5,55)  = 2.52, p≤0.05) of the variance of the comparative ADL score. Active-management (β= –0.037, t (60)  = –3.363, p = 0.001) and encouragement (β= 0.025, t  (60)  = 2.018, p = 0.05) styles made the largest and statistically significant contribution to the model. Conclusion: Encouragement style could be advised for caregivers who underestimate ADL performance, while active management style for those who overestimate it. Findings have scope to increase caregivers’ abilities to support PwD activity engagement in daily life.

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Background Studies show ethnic inequalities in rates of involuntary admission and types of clinical care (such as psychological therapies). However, few studies have investigated if there is a relationship between clinical care practices and ethnic inequalities in involuntary admission. Aims This study investigated the impact of ethnicity and clinical care on involuntary admission and the potential mediation effects of prior clinical care. Method In this retrospective cohort study, we used data from the electronic records of the South London and Maudsley NHS Foundation Trust and identified patients with a first hospital admission between January 2008 and May 2021. Logistic regression and mediation analyses were used to investigate the association between ethnicity and involuntary admission, and whether clinical care, in the 12 months preceding admission, mediates the association. Results Compared with White British people, higher odds of involuntary admission were observed among 10 of 14 minority ethnic groups; with more than twice the odds observed among people of Asian Chinese, of Asian Bangladeshi and of any Black background. There were some ethnic differences in clinical care prior to admission, but these had a minimal impact on the inequalities in involuntary admission. More out-patient appointments and home treatment were associated with higher odds of involuntary admission, whereas psychological therapies and having a care plan were associated with reduced odds of involuntary admission. Conclusions Ethnic inequalities in involuntary admission persist after accounting for potential mediating effects of several types and frequencies of clinical care. Promoting access to psychological therapies and ensuring that care plans are in place may reduce involuntary admissions.

IntroductionThe increasingly ageing population is associated with greater numbers of people living with dementia (PLwD) and mild cognitive impairment (MCI). There are an estimated 55 million PLwD and approximately 6% of people over 60 years of age are living with MCI, with the figure rising to 25% for those aged between 80 and 84 years. Sleep disturbances are common for this population, but there is currently no standardised approach within UK primary care to manage this. Coined as a ‘wicked design problem’, sleep disturbances in this population are complex, with interventions supporting best management in context.Methods and analysisThe aim of this realist review is to deepen our understanding of what is considered ‘sleep disturbance’ in PLwD or MCI within primary care. Specifically, we endeavour to better understand how sleep disturbance is assessed, diagnosed and managed. To co-produce this protocol and review, we have recruited a stakeholder group comprising individuals with lived experience of dementia or MCI, primary healthcare staff and sleep experts. This review will be conducted in line with Pawson’s five stages including the development of our initial programme theory, literature searches and the refinement of theory. The Realist and Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) quality and reporting standards will also be followed. The realist review will be an iterative process and our initial realist programme theory will be tested and refined in response to our data searches and stakeholder discussions.Ethics and disseminationEthical approval is not required for this review. We will follow the RAMESES standards to ensure we produce a complete and transparent report. Our final programme theory will help us to devise a tailored sleep management tool for primary healthcare professionals, PLwD and their carers. Our dissemination strategy will include lay summaries via email and our research website, peer-reviewed publications and social media posts.PROSPERO registration numberCRD42022304679.

Background Health care professionals seek information about effectiveness of treatments in patients who would be offered them in routine clinical practice. Electronic medical records (EMRs) and randomized controlled trials (RCTs) can both provide data on treatment effects; however, each data source has limitations when considered in isolation. Methods A novel modelling methodology which incorporates RCT estimates in the analysis of EMR data via informative prior distributions is proposed. A Bayesian mixed modelling approach is used to model outcome trajectories among patients in the EMR dataset receiving the treatment of interest. This model incorporates an estimate of treatment effect based on a meta-analysis of RCTs as an informative prior distribution. This provides a combined estimate of treatment effect based on both data sources. Results The superior performance of the novel combined estimator is demonstrated via a simulation study. The new approach is applied to estimate the effectiveness at 12 months after treatment initiation of acetylcholinesterase inhibitors in the management of the cognitive symptoms of dementia in terms of Mini-Mental State Examination scores. This demonstrated that estimates based on either trials data only (1.10, SE = 0.316) or cohort data only (1.56, SE = 0.240) overestimated this compared with the estimate using data from both sources (0.86, SE = 0.327). Conclusions It is possible to combine data from EMRs and RCTs in order to provide better estimates of treatment effectiveness.