Education, sex, and risk of stroke: a prospective cohort study

To determine whether the association between highest educational attainment and stroke differed by sex and age; and whether potential mediators of observed associations differ by sex.Prospective cohort study.Population based, New South Wales, Australia.253 657 stroke-free participants from the New South Wales 45 and Up Study.First-ever stroke events, identified through linkage to hospital and mortality records.During mean follow-up of 4.7 years, 2031 and 1528 strokes occurred among men and women, respectively. Age-standardised stroke rate was inversely associated with education level, with the absolute risk difference between the lowest and highest education group greater among women than men. In relative terms, stroke risk was slightly more pronounced in women than men when comparing low versus high education (age-adjusted HRs: 1.41, 95% CI 1.16 to 1.71 and 1.25, 95% CI 1.07 to 1.46, respectively), but there was no clear evidence of statistical interaction. This association persisted into older age, but attenuated. Much of the increased stroke risk was explained by modifiable lifestyle factors, in both men and women.Low education is associated with increased stroke risk in men and women, and may be marginally steeper in women than men. This disadvantage attenuates but persists into older age, particularly for women. Modifiable risk factors account for much of the excess risk from low education level. Public health policy and governmental decision-making should reflect the importance of education, for both men and women, for positive health throughout the life course

The Secure Anonymised Information Linkage databank Dementia e-cohort (SAIL-DeC)

Introduction: The rising burden of dementia is a global concern, and there is a need to study its causes, natural history and outcomes. The Secure Anonymised Information Linkage (SAIL) Databank contains anonymised, routinely-collected healthcare data for the population of Wales, UK. It has potential to be a valuable resource for dementia research owing to its size, long follow-up time and prospective collection of data during clinical care. Objectives:We aimed to apply reproducible methods to create the SAIL dementia e-cohort (SAIL-DeC). We created SAIL-DeC with a view to maximising its utility for a broad range of research questions whilst minimising duplication of effort for researchers. Methods:SAIL contains individual-level, linked primary care, hospital admission, mortality and demographic data. Data are currently available until 2018 and future updates will extend participant follow-up time. We included participants who were born between 1st January 1900 and 1st January 1958 and for whom primary care data were available. We applied algorithms consisting of International Classification of Diseases (versions 9 and 10) and Read (version 2) codes to identify participants with and without all-cause dementia and dementia subtypes. We also created derived variables for comorbidities and risk factors. Results:From 4.4 million unique participants in SAIL, 1.2 million met the cohort inclusion criteria, resulting in 18.8 million person-years of follow-up. Of these, 129,650 (10%) developed all-cause dementia, with 77,978 (60%) having dementia subtype codes. Alzheimer's disease was the most common subtype diagnosis (62%). Among the dementia cases, the median duration of observation time was 14 years. Conclusion:We have created a generalisable, national dementia e-cohort, aimed at facilitating epidemiological dementia research

Valuing Alzheimer's Disease drugs:A health technology assessment perspective on outcomes

ObjectivesDue to the nature of Alzheimer's disease (AD), health technology assessment (HTA) agencies might face considerable challenges in choosing appropriate outcomes and outcome measures for drugs that treat the condition. This study sought to understand which outcomes informed previous HTAs, to explore possible reasons for prioritizations, and derive potential implications for future assessments of AD drugs.MethodWe conducted a literature review of studies that analyzed decisions made in HTAs (across disease areas) in three European countries: England, Germany, and The Netherlands. We then conducted case studies of technology assessments conducted for AD drugs in these countries.ResultsOverall, outcomes measured using clinical scales dominated decisions or recommendations about whether to fund AD drugs, or price negotiations. HTA processes did not always allow the inclusion of outcomes relevant to people with AD, their carers, and families. Processes did not include early discussion and agreement on what would constitute appropriate outcome measures and cut-off points for effects.ConclusionsWe conclude that in order to ensure that future AD drugs are valued appropriately and timely, early agreement with various stakeholders about outcomes, outcome measures, and cut-offs is important

Formal and informal prediction of recurrent stroke and myocardial infarction after stroke:a systematic review and evaluation of clinical prediction models in a new cohort

BACKGROUND: The objective of this study was to: (1) systematically review the reporting and methods used in the development of clinical prediction models for recurrent stroke or myocardial infarction (MI) after ischemic stroke; (2) to meta-analyze their external performance; and (3) to compare clinical prediction models to informal clinicians’ prediction in the Edinburgh Stroke Study (ESS). METHODS: We searched Medline, EMBASE, reference lists and forward citations of relevant articles from 1980 to 19 April 2013. We included articles which developed multivariable clinical prediction models for the prediction of recurrent stroke and/or MI following ischemic stroke. We extracted information to assess aspects of model development as well as metrics of performance to determine predictive ability. Model quality was assessed against a pre-defined set of criteria. We used random-effects meta-analysis to pool performance metrics. RESULTS: We identified twelve model development studies and eleven evaluation studies. Investigators often did not report effective sample size, regression coefficients, handling of missing data; typically categorized continuous predictors; and used data dependent methods to build models. A meta-analysis of the area under the receiver operating characteristic curve (AUROCC) was possible for the Essen Stroke Risk Score (ESRS) and for the Stroke Prognosis Instrument II (SPI-II); the pooled AUROCCs were 0.60 (95% CI 0.59 to 0.62) and 0.62 (95% CI 0.60 to 0.64), respectively. An evaluation among minor stroke patients in the ESS demonstrated that clinicians discriminated poorly between those with and those without recurrent events and that this was similar to clinical prediction models. CONCLUSIONS: The available models for recurrent stroke discriminate poorly between patients with and without a recurrent stroke or MI after stroke. Models had a similar discrimination to informal clinicians' predictions. Formal prediction may be improved by addressing commonly encountered methodological problems

Hierarchical Complexity of the Adult Human Structural Connectome

The structural network of the human brain has a rich topology which many have sought to characterise using standard network science measures and concepts. However, this characterisation remains incomplete and the non-obvious features of this topology have largely confounded attempts towards comprehensive constructive modelling. This calls for new perspectives. Hierarchical complexity is an emerging paradigm of complex network topology based on the observation that complex systems are composed of hierarchies within which the roles of hierarchically equivalent nodes display highly variable connectivity patterns. Here we test the hierarchical complexity of the human structural connectomes of a group of seventy-nine healthy adults. Binary connectomes are found to be more hierarchically complex than three benchmark random network models. This provides a new key description of brain structure, revealing a rich diversity of connectivity patterns within hierarchically equivalent nodes. Dividing the connectomes into four tiers based on degree magnitudes indicates that the most complex nodes are neither those with the highest nor lowest degrees but are instead found in the middle tiers. Spatial mapping of the brain regions in each hierarchical tier reveals consistency with the current anatomical, functional and neuropsychological knowledge of the human brain. The most complex tier (Tier 3) involves regions believed to bridge high-order cognitive (Tier 1) and low-order sensorimotor processing (Tier 2). We then show that such diversity of connectivity patterns aligns with the diversity of functional roles played out across the brain, demonstrating that hierarchical complexity can characterise functional diversity strictly from the network topology