Black Americans are disproportionately affected by dementia. To expand our understanding of mechanisms of this disparity, we look to Alzheimer's disease (AD) biomarkers. In this review, we summarize current data, comparing the few studies presenting these findings. Further, we contextualize the data using two influential frameworks: the National Institute on Aging–Alzheimer's Association (NIA‐AA) Research Framework and NIA's Health Disparities Research Framework. The NIA‐AA Research Framework provides a biological definition of AD that can be measured in vivo. However, current cut‐points for determining pathological versus non‐pathological status were developed using predominantly White cohorts—a serious limitation. The NIA's Health Disparities Research Framework is used to contextualize findings from studies identifying racial differences in biomarker levels, because studying biomakers in isolation cannot explain or reduce inequities. We offer recommendations to expand study beyond initial reports of racial differences. Specifically, life course experiences associated with racialization and commonly used study enrollment practices may better account for observations than exclusively biological explanations.

BACKGROUND/OBJECTIVES Aligning healthcare decisions with patients’ priorities may improve care for older adults with multiple chronic conditions (MCCs). We conducted a pilot study to assess the feasibility of identifying patient priorities in routine geriatrics care and to compare clinicians’ recommendations for patients who did or did not have their priorities identified. DESIGN Retrospective chart review. SETTING Veterans Administration Medical Center Geriatrics Clinic. PARTICIPANTS Older adults with MCCs receiving Patient Priorities Care (PPC; n = 35) were matched with patients receiving usual care (UC; n = 35). Both PPC and UC patients were cared for by three primary care providers (PCPs) in an ambulatory geriatric clinic. INTERVENTION In the PPC group, a clinician facilitator met with each patient to identify their healthcare priorities and transmitted patients’ priorities in the electronic health record (EHR). Trained PCPs then sought to align healthcare decisions with patients’ priorities. In the UC group, patients received usual care from the same PCPs. MEASUREMENTS We matched patients by clinician seen, patient’s age, number of active conditions, medications, hospitalizations, functional status, and prior hospitalizations. EHRs were reviewed to identify care decisions including medications added or stopped, referrals and consults added or avoided, referrals to community services and supports, self‐management activities added or avoided, and total number of changes to care. Mean differences in recommended care between PPC and UC patients from the same PCPs were examined. RESULTS Clinician facilitators could identify patient priorities during routine clinic encounters. Compared with patients in the UC group, those in the PPC group had, on average, fewer medications added (P = .05), more referrals to community services and supports (P = .03), and more priorities‐aligned self‐management tasks added (P = .005). CONCLUSION These findings support the feasibility of identifying and documenting patient priorities during routine encounters. Results also suggest that clinicians use patient priorities in recommending care.

Background: To fully characterize the risk for dementia associated with cigarette smoking, studies must consider competing risks that hinder the observation of dementia or modify the chance that dementia occurs (i.e., death). Extant research examining the competing risks fails to account for the occurrence of death following dementia, limiting our understanding of the relation between smoking and dementia. Objective: Examine the impact of smoking status, lifetime smoking exposure, and duration of abstinence on incident dementia, death following dementia, and death without dementia. Methods: Multi-state models estimated hazard ratios (HR) for 95% confidence interval (CI) of 10,681 cognitively healthy adults for transition from baseline to dementia, baseline to death, and dementia to death based on smoking status, lifetime cigarette exposure, and abstinence duration. Results: Compared to never smokers, current smokers had increased risk of dementia (HR = 1.66; 95% CI 1.18– 2.32; p = 0.004), and death from baseline (HR = 2.98; 95% CI 2.24– 3.98; p <  0.001) and incident dementia (HR = 1.88; 95% CI 1.08– 3.27; p = 0.03). Pack years increased risk of death from baseline (HR = 1.01; 95% CI 1.00– 1.01; p <  0.001), but not dementia risk (HR = 1.00; 95% CI 1.00– 1.00; p = 0.78) or death following dementia (HR = 1.01; 95% CI 1.00– 1.01; p = 0.05). Recent quitters (quit <  10 years), compared to never smokers, had increased risk of death after baseline (HR = 2.31; 95% CI 1.55– 3.43; p <  0.001), but not dementia (HR = 1.17; 95% CI 0.73– 1.88; p = 0.52) or death following dementia (HR = 1.01; 95% CI 0.42– 2.41; p = 0.99). Conclusion: Current smoking increases the risk for dementia and death, but dementia is better attributed to smoking recency than lifetime exposure. Smoking cessation at any age might reduce these risks for cognitively healthy individuals.

No abstract