Socioeconomic Status, Race/Ethnicity, and Unexpected Variation in Dementia Classification in Longitudinal Survey Data.
Academic Article
Overview
abstract
OBJECTIVES: As dementia affects a growing number of older adults, it is important to understand its detection and progression. We identified patterns in dementia classification over time using a longitudinal, nationally representative sample of older adults. We examined the relationship between socioeconomic status and race/ethnicity, and patterns in dementia classification. METHODS: Data for 7,218 Medicare beneficiaries from the 2011-2017 National Health and Aging Trends Study (NHATS) were classified into five categories: consistently no dementia, consistently cognitive impairment, "typical" dementia progression, "expected" variation, and "unexpected" variation. Multivariable multinomial logistic regression assessed relative risk of dementia classification by sociodemographic and health factors. RESULTS: Among NHATS respondents, 59.5% consistently were recorded as having no dementia, 7% consistently cognitively impaired, 13% as having typical progression, 15% as having expected variation, and 5.5% as having unexpected variation. In multivariable models, compared with consistent dementia classification, less education, Medicare-Medicaid-dual enrollment, and identifying as non-Hispanic Black were associated with increased likelihood of unexpected variation (e.g., non-Hispanic Black adjusted risk ratio: 2.12, 95% CI: 1.61-2.78, p < .0001). DISCUSSION: A significant minority of individuals have unexpected patterns of dementia classification over time, particularly individuals with low socioeconomic status and identifying as non-Hispanic Black. Dementia classification uncertainty may make it challenging to activate resources (e.g., health care, caregiving) for effective disease management, underscoring the need to support persons from at-risk groups and to carefully evaluate cognitive assessment tools to ensure they are equally reliable across groups to avoid magnifying disparities.
