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

"I Want to Know Everything ... ": The Return of Research Results and the Importance of Transparency in the Acceptability of Lumbar Punctures for African American Older Adults

BACKGROUND

Although African Americans experience the highest risk of Alzheimer's disease (AD), they are dramatically underrepresented in preclinical biomarker research. This is especially true for studies involving lumbar puncture as it may involve more perceived risk even for those participants who are otherwise supportive of research.

OBJECTIVE

To understand the unique concerns of African American participants regarding biomarker studies involving lumbar puncture who demonstrate support for AD research.

METHODS

Study participants were African American adults contacted through an AD research registry. We employed a novel method used to create hypothetical research studies varying on a set number of factors. The method is designed to collect potential patterns in decision making regarding research participation but differs from experimental vignette design in that the survey is administered with an accompanying qualitive interview to determine the meaning participants ascribe to factors independently and in conjunction with one another.

RESULTS

Sixty-one participants each reviewed three randomly selected research scenarios and created their "ideal" study involving lumbar puncture. Scenario variables included: disclosure of research results, racial and ethnic identity of the researcher, recruitment method, and amount of incentive.

CONCLUSION

Findings indicate that transparency in the return of AD research results to be the strongest driver of participation, followed by race of the researcher and amount of incentive. Recruitment method had limited impact on hypothetical decision making.

Dementia Risk Score for a Population in Southern Europe Calculated Using Competing Risk Models

Background:

Dementia prevention can be addressed if the intervention is applied early.

Objective:

The objective of this study was to develop and validate competing risk models to predict the late risk of dementia based on variables assessed in middle age in a southern European population.

Methods:

We conducted a prospective observational study of the EPIC-Spain cohort that included 25,015 participants. Dementia cases were identified from electronic health records and validated by neurologists. Data were gathered on sociodemographic characteristics and cardiovascular risk factors. To stratify dementia risk, Fine and Gray competing risk prediction models were constructed for the entire sample and for over-55-year-olds. Risk scores were calculated for low (the 30% of the sample with the lowest risk), moderate (> 30% –60%), and high (> 60% –100%) risk.

Results:

The 755 cases of dementia identified represented a cumulative incidence of 3.1% throughout the study period. The AUC of the model for over-55-year-olds was much higher (80.8%) than the overall AUC (68.5%) in the first 15 years of follow-up and remained that way in the subsequent follow-up. The weight of the competing risk of death was greater than that of dementia and especially when the entire population was included.

Conclusion:

This study presents the first dementia risk score calculated in a southern European population in mid-life and followed up for 20 years. The score makes it feasible to achieve the early identification of individuals in a southern European population who could be targeted for the prevention of dementia based on the intensive control of risk factors.

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.