2022 – Moving toward best practices for research on racial and ethnic inequalities in cognitive aging and dementia

Schedule and Presentations 2022

How can I prepare for ΨMCA 2022

Workgroup themes 2022

Overall goal: Health equity research in the area of Alzheimer’s Disease and related disorders requires careful consideration of theoretical framework, study design, expertise in measurement across different cultural and language populations, and use of advanced psychometric and statistical techniques to ensure proper analysis of data. Both between group comparisons and within group studies are needed to understand shared and specific risk and protective factors for cognitive decline and dementia and to target interventions to promote cognitive health. Approaches to this research must incorporate a framework that measures and models the structures and racialized systems that influence brain health over the lifecourse. The ΨMCA 2022 workshop will focus on conceptual frameworks and analytic strategies for ADRD research in minoritized populations that will add novel and actionable knowledge to this area. This meeting will be chaired by Jennifer Manly.

Datasets: Datasets for this meeting will come from coordinated longitudinal cohort studies of racially/ethnically diverse older adults in northern California that have harmonized cognitive assessment and neuroimaging protocols. These studies are collaborations of Kaiser Permanente Northern California Division of Research and UC Davis and are prospective lifecourse studies of participants who are long-term enrollees in Kaiser Permanente and completed comprehensive health checkups in the 1960’s-80’s.

Work Groups: Work groups will explore: 1) different approaches to exposure by ethnoracial group interactions; 2) approaches to mediation or decomposition to examine risk and resilience pathways 3) longitudinal modeling of cognitive level and change over time, accounting for selection due to death or loss to follow-up; 4) analysis of intersectionality using multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA); and 5) analysis of bias in measurement using invariance analysis.