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. METHODS: We conducted a systematic literature review in Medline, Embase, PsycInfo, and Scopus, and complemented it with a gray literature search between March and July 2018. Then we analyzed data qualitatively using a thematic analysis technique. RESULTS: We identified thirty-one ethical issues and social concerns corresponding to eight ethical principles: (i) respect for autonomy, (ii) beneficence, (iii) non-maleficence, (iv) equality, justice, and diversity, (v) identity and stigma, (vi) privacy, (vii) accountability, transparency, and professionalism, and (viii) uncertainty avoidance. Much of the literature sees the discovery of disease-modifying treatment as a necessary and sufficient condition to justify AD risk assessment, overlooking future challenges in providing equitable access to it, establishing long-term treatment outcomes and social consequences of this approach, e.g., medicalization. The ethical/social issues associated specifically with predictive models, such as the adequate predictive power and reliability, infrastructural requirements, data privacy, potential for personalized medicine in AD, and limiting access to future AD treatment based on risk stratification, were covered scarcely. CONCLUSION: The ethical discussion needs to advance to reflect recent scientific developments and guide clinical practice now and in the future, so that necessary safeguards are implemented for large-scale AD secondary prevention.

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. The objective of this review is to identify and elucidate them, as presented in the literature. METHODS AND ANALYSIS: A systematic literature review was conducted in Medline, Embase, PsycInfo and Scopus, and was complemented with a grey literature search. All searches were conducted between March and July 2018. Two reviewers independently assessed each study for inclusion and disagreements were adjudicated by a third reviewer. Data are now being extracted using an extraction sheet developed within the group of reviewers, based on an initial sample of three manuscripts, but allowing for inclusion of newly identified data items (ethical arguments). Data will be analysed qualitatively using a thematic analysis technique. Potential biases in selection and interpretation of extracted data are mitigated by the fact that reviewers come from a range of different scientific backgrounds and represent different types of stakeholders in this ethical discussion (academia, industry, patient advocacy groups). ETHICS AND DISSEMINATION: The study does not require ethical approval. The findings of the review will be disseminated in a peer-reviewed journal and presented at conferences. They will also be reported through the Innovative Medicine Initiative project: Real World Outcomes Across the AD Spectrum for Better Care: Multi-modal Data Access Platform (IMI: ROADMAP). TRIAL REGISTRATION NUMBER: CRD42018092205.

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. Data were analyzed descriptively for the most recent 28-day period reported by users (n = 3900) during the study period (June 2015-July 2016) who were randomly selected on the basis of data completeness (completion rates > 70%) and stratified by migraine headache days/month: 4-7 episodic migraine (EM; n = 1500), 8-14 EM (n = 1500), and chronic migraine (≥ 15; CM; n = 900). RESULTS: More than 95% of users reported that migraine negatively affected their daily activities during at least one migraine attack. Attacks affected 50.5% (184.4 days/year), 26.9% (98 days/year), and 14.5% (53 days/year) of the year among CM, 8-14 EM, and 4-7 EM groups, respectively. On average, 44.8% CM, 40.9% 8-14 EM, and 34.7% of 4-7 EM sufferers, respectively, reported anxiety and/or depression symptoms during migraine attacks. Social or home activities, productivity, and sleep were highly affected, regardless of migraine frequency. Employed respondents (n = 3106) reported an average of 2.3 workdays missed per month and that at least one in four migraines led to work absenteeism; these migraines were commonly reported to have at least moderate to severe levels of pain, corresponding to the inability of persons to perform some or even any activities. Triptans (68%), opioids (46%), and nonsteroidal anti-inflammatory drugs (45%) were self-reported as the most common medicines used. CONCLUSIONS: This study, leveraging patient-reported data collected through a mobile application, demonstrates the high burden and impact of migraine on health-related quality of life, work productivity, and overall well-being of individuals suffering from migraines. FUNDING: Novartis Pharma AG, Switzerland.

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. The methods and data available to translate treatment efficacy in early disease into long-term outcomes of relevance to policy makers and payers are limited. Current long-term large-scale data accurately representing the continuous, multifaceted, and heterogeneous disease process are missing. The potential effect of disease-modifying treatment on key long-term outcomes such as institutionalization and death is uncertain but may have great effect on cost-effectiveness. Future research should give priority to collaborative efforts to access better data on the natural progression of AD and its association with key long-term outcomes.