- Specific atypical response patterns (item-nonresponse, random errors, multivariate outliers, incompatible patterns) were significantly associated with increased incident dementia risk.
- Effect sizes ranged HR 1.06 to 1.31 across indicators; extreme responding was not associated with dementia.
- These survey-derived indicators provide a scalable, low-burden complement for early detection, with stronger associations under age 75 and higher education.
J Gerontol B Psychol Sci Soc Sci. 2026 Sep 18:gbag197. doi: 10.1093/geronb/gbag197. Online ahead of print.
ABSTRACT
OBJECTIVES: 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.
METHODS: 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.
RESULTS: 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.
DISCUSSION: 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 | DOI:10.1093/geronb/gbag197
Share Evidence Blueprint
Save to Google Notes
Search Google Scholar
Save as PDF
⭐ My Revision List
