Ethical and Social Implications of Using Predictive Modeling for Alzheimer's Disease Prevention: A Systematic Literature Review.

Background: The therapeutic paradigm in Alzheimer's disease (AD) is shifting from symptoms management toward prevention goals. Secondary prevention requires the identification of individuals without clinical symptoms, yet "at-risk" of developing AD dementia in the future, and thus, the use of predictive modeling.

Objective: The objective of this study was to review the ethical concerns and social implications generated by this new approach.

Ethical and social implications of using predictive modeling for Alzheimer's disease prevention: a systematic literature review protocol.

Introduction: The therapeutic paradigm in Alzheimer's disease (AD) has shifted towards secondary prevention, defined as an intervention aiming to prevent or delay disease onset in pre-symptomatic individuals at risk of developing dementia due to AD. The key feature of AD prevention is the need to treat years or even decades before the onset of cognitive, behavioural or functional decline. Prediction of AD risk and evaluation of long-term treatment outcomes in this setting requires predictive modelling and is associated with ethical concerns and social implications.

Challenges for Optimizing Real-World Evidence in Alzheimer's Disease: The ROADMAP Project.

ROADMAP is a public-private advisory partnership to evaluate the usability of multiple data sources, including real-world evidence, in the decision-making process for new treatments in Alzheimer's disease, and to advance key concepts in disease and pharmacoeconomic modeling. ROADMAP identified key disease and patient outcomes for stakeholders to make informed funding and treatment decisions, provided advice on data integration methods and standards, and developed conceptual cost-effectiveness and disease models designed in part to assess whether early treatment provides long-term benefit.

Burden of Migraine in Europe Using Self-Reported Digital Diary Data from the Migraine Buddy© Application.

Introduction: Migraine is a neurological disease characterized by recurring attacks that can cause severe disabling pain. This study described the burden of migraine as reported by individuals with migraine in the real world using a mobile application.

Methods: A retrospective, cross-sectional analysis was conducted using data captured through the Migraine Buddy© smartphone application from adult, self-diagnosed individuals with migraine in 17 European countries.

Current issues and future research priorities for health economic modelling across the full continuum of Alzheimer's disease.

Available data and models for the health-economic evaluation of treatment in Alzheimer's disease (AD) have limitations causing uncertainty to decision makers. Forthcoming treatment strategies in preclinical or early AD warrant an update on the challenges associated with their economic evaluation. The perspectives of the co-authors were complemented with a targeted review of literature discussing methodological issues and data gaps in AD health-economic modelling.