Atypical survey response patterns as early indicators of dementia onset: A coordinated analysis across longitudinal aging cohorts.

Authors

Stefan Schneider, Pey-Jiuan Lee, Doerte U Junghaenel, Erik Meijer, Raymond Hernandez, Haomiao Jin, Arthur A Stone, Arie Kapteyn, Bart Orriens, Hongxin Gao, Elizabeth M Zelinski

Published in

The journals of gerontology. Series B, Psychological sciences and social sciences. Sep 18, 2026. Epub Sep 18, 2026.

Abstract

Atypical survey response patterns, such as inconsistent or implausible responses, have been linked to lower cognitive functioning in later life. This study examined whether these behaviors predict future dementia onset across diverse longitudinal studies of aging.
We conducted a coordinated analysis of data from eight longitudinal studies of aging (total N = 76,350). We derived five common types of atypical response pattern indicators from participants' questionnaire data. Associations with subsequent dementia risk were examined within each cohort using Cox proportional hazards models adjusted for demographic covariates and accounting for death as a competing risk. Results were synthesized using random-effects meta-analysis methods.
Across cohorts, significant associations with incident dementia were observed for item-nonresponse (overall hazard ratio [HR] per SD increase = 1.06), random response errors (HR = 1.25), multivariate outlier responses (HR = 1.31), and incompatible response patterns indexed by Guttman errors and person-fit statistics (HRs = 1.22-1.25). Extreme responding was not significantly associated with dementia risk. Secondary analyses suggested stronger associations among individuals younger than 75 years and those with higher levels of education.
Routine survey responses contain behavioral signals associated with emerging dementia risk. Because these indicators can be derived from existing survey data without additional testing burden, they may provide a scalable complement to traditional approaches for studying cognitive aging in large population-based cohorts.

PMID:
42760255
Bibliographic data and abstract were imported from PubMed on 19 Sep 2026.

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