Citations

AbstractBackground. The field of implementation research has recently seen considerable, and long overdue, interest in equity, with a strong emphasis on recognizing and responding to disparities in care. The COMPLETE (COMPLEx inTErventions and service innovation)framework is intended to support inclusive intervention and service design. The aim is the development of a patient-centered and justice-informed framework for the design of complex healthcare interventions and innovations in service design.Methods. We undertook three studies. (1) We operationalized constructs from sociological theories of inequality, implementation processes, social roles and expectations, to build a Matrix Model of mechanisms that drive the embedding of disparities in complex healthcare interventions and service innovations. (2) We developed patient-centered and justice-informed principles for design that form a Theory of Change that can be applied in intervention and service design. (3) We then linked the Matrix Model and the Theory of Change together with core elements of theories of prefigurative design and research justice, to build a Transformational Design Cycle for complex interventions and service innovations around a set of critical questions about the implications.Results. The COMPLETE framework focuses on the ways health inequalities and inequities are brought into being by systemic mechanisms within health services; are then propagated and normalized through implementation mechanisms; and are then realized and normalized in practice through institutionalized expectations of patients and caregivers. The framework provides tools to resist the naturalization of inequities and inequalities.Conclusion. We developed a framework for intervention and service design that consists of three components: a conceptual model of the translation of social determinants of health through organizational mechanisms that shape patient and caregivers’ lived experience of disparities in health and healthcare; a transformational theory of change; and a set of principles for design that seeks to place restitution for disparities at its center.

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 percentage points) compared to the full longitudinal dataset. The submission system remains open via the website https://tadpole.grand-challenge.org, while TADPOLE SHARE (https://tadpole-share.github.io/) collates code for submissions. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.

Objectives Treatment burden is the workload of healthcare for people with long-term conditions (LTC) and its impact on well-being. A method of measurement is required to identify those experiencing high burden and to measure intervention efficacy. Our aim was to identify, examine and appraise validated patient-reported measures (PRMs) of treatment burden in stroke. Here, stroke serves as an exemplar LTC of older adults. Design A systematic review of published studies that describe the development and validation of PRMs measuring treatment burden in stroke survivors. Data sources We searched MEDLINE, Embase, CINAHL and PsycINFO electronic databases. Eligibility criteria Studies published between January 2000 and 12 April 2019 inclusive, in English language. No restrictions were set based on clinical setting or geographical location. Data extraction and synthesis Screening, data extraction and quality appraisal were conducted by two independent reviewers. Content of the PRMs was compared with a published taxonomy of treatment burden. Quality appraisal was conducted using International Society for Quality of Life Research standards. Results From 3993 articles, 6 relevant PRMs were identified: 3 were stroke specific: The Satisfaction with Stroke Care questionnaire; The Stroke Patient-Reported Outcome Measure and The Barriers to Physical Activity after Stroke scale. Three were generic but validated in stroke: The WHO Quality of Life-100; The Patient’s Questionnaire on Participation in Discharge Planning and The Chao Perception of Continuity scale. None comprehensively measured treatment burden. Examples of omitted burdens included developing coping strategies, managing finances and returning to driving. The most notable issue regarding quality appraisal was that three PRMs lacked any underpinning qualitative research relevant to the sample. Conclusion There is a need to develop a comprehensive PRM of treatment burden for use in stroke, with potential for use in other older populations.

AbstractBackgroundLate‐onset Alzheimer's disease (AD) is heritable with 20 genes showing genome‐wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease, we extended these genetic data in a pathway analysis.MethodsThe ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain.ResultsALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (P = 3.27 × 10−12 after multiple testing correction for pathways), regulation of endocytosis (P = 1.31 × 10−11), cholesterol transport (P = 2.96 × 10−9), and proteasome‐ubiquitin activity (P = 1.34 × 10−6). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected P = .002–.05).ConclusionsThe immune response, regulation of endocytosis, cholesterol transport, and protein ubiquitination represent prime targets for AD therapeutics.