ASSESSING DEMENTIA CLASSIFICATION IN THE NATIONAL CORE INDICATORS AGING AND DISABILITY SURVEY

Abstract

Abstract Improving the quality of long-term care services for older adults is a national priority. The National Core Indicators Aging and Disability (NCI-AD) collects data from multiple states to evaluate long-term care recipients’ service satisfaction and needs. Many analysts are interested in exploring the service environments of people living with dementia. However, dementia measurement and reporting varies in NCI-AD between states. Dementia status may be obtained from administrative records or self-reported during the survey. We explored the measurement of dementia in NCI-AD and the implications of relying on administrative or self-reported dementia status. We analyzed NCI-AD data from 2015-2018 representing 24,569 respondents age 65+, of which 5,502 (22.4%) were identified as having dementia. 42.9% of respondents had dementia status determined administratively and 57.1% had it determined during the survey. To assess dementia accuracy by data source, we fit separate LASSO models for both the administrative and survey subpopulations predicting dementia status using demographic and functional predictors. We then used each model to predict dementia status in the subpopulation with discordant dementia data source. Using the administrative model to predict survey reported status resulted in a higher sensitivity than using the survey model to predict administrative status (44.6% vs 32.2%). The diminished predictive accuracy of the survey model suggests administrative records may capture cases of dementia diagnosis that would be missed by self-report. These findings highlight analytical caveats for researchers interested study long-term care quality for people with dementia using NCI-AD, and emphasize the importance of moving towards more standardized dementia reporting.