Exportation and Validation of Latent Constructs for Dementia Case Finding in a Mexican American Population–based Cohort

BackgroundThe latent variable “δ” has been validated as a dementia phenotype. δ can be extracted from Spearman’s general intelligence factor “g” in any data set that contains measures of cognition and instrumental activities of daily living (IADL). We used δ composites (“d-scores”) to estimate the prevalence of dementia in the Hispanic Established Population for Epidemiological Studies in the Elderly (H-EPESE).Methodδ was constructed from Mini-Mental State Examination, a clock-drawing task (CLOX), and IADL. δ’s H-EPESE factor weights were validated in the well-characterized Texas Alzheimer’s Research and Care Consortium (TARCC). Optimal thresholds for the discrimination between “Alzheimer’s disease” (AD) versus normal controls (NCs) were determined by receiver operating characteristic curve. Those thresholds were used to estimate the prevalence of dementia in H-EPESE. Results: Each δ homolog fits its source’s data well. d-scores were strongly associated with Clinical Dementia Rating scale Sum of Boxes (r = .74–.85, all p < .001], and accurately distinguished AD cases from NCs, in both Mexican Americans (MAs) and non-Hispanic Whites (NHWs) [c = 0.94–0.96]. The TARCC MA threshold estimated the prevalence of dementia at 21.4% in H-EPESE. The NHW threshold estimated the prevalence of dementia at 21.0%.Conclusions: It is possible to export δ composites from populations to well-characterized cohorts for validation.