A comparison of dementia-free life expectancy estimates across competing algorithmic classifications: new knowledge and considerations from the health and retirement study.

OBJECTIVES

To create new dementia-free life expectancy estimates based on three competing algorithmic classifications of dementia and compare these estimates with dementia-free life expectancy estimates based on the well-established Langa-Weir (LW) dementia classification to ascertain the consistency of competing algorithmic methods on the overall burden of quality of life measured by the number of years lived dementia-free and the proportion of late-life years dementia-free to total life expectancy.

METHODS

Data from the Health and Retirement Study (1998-2016) are used to estimate Sullivan-based life tables of dementia-free life expectancies by race/ethnicity, nativity, and gender for adults aged 70 and older. Dementia ascertainment was operationalized using the Langa-Weir (LW), Expert, Hurd, and LASSO classifications. We test for both within-group differences (i.e., nativity for Latinos) and gender differences to comprehensively document the overall burden of dementia among at-risk populations.

RESULTS

Expert, LASSO, and Hurd competing algorithm estimates were generally equal to the LW estimates for dementia prevalence after age 70, with a few exceptions. White men and women, and Black women exhibit better cognitive functioning in the LW model, whereas the results for other race/ethnic and nativity groups are more consistent across models.

DISCUSSION: Small differences in population-level estimates across algorithmic classification are observed; however, these small differences may be important for understanding disparities across groups. Therefore, more work is needed (1) to understand for whom and which models are most accurately identifying dementia and (2) to place these models relative to gold-standard adjudicated dementia assessments.