Objective: Detecting dementia in people who are illiterate or have a low level of education is complicated because many cognitive screening tests are not suitable for these persons. Caregiver or informant-based judgment of cognitive status may aid diagnosis. Our goal was to investigate the diagnostic accuracy of the Informant Questionnaire for Cognitive Decline in the Elderly (IQCODE) in a population of elderly non-Western migrants with a high illiteracy rate. Second, we wanted to investigate the diagnostic accuracy of IQCODE and Rowland Universal Dementia Screening (RUDAS) combined. Method: 109 geriatric outpatients and 20 community controls were included. Geriatricians provided a research diagnosis of intact cognition (n = 27), mild cognitive impairment (MCI; n = 33) or dementia (n = 49). Diagnostic accuracy was calculated for the clinical sample (n = 109). ROC curves for prediction of group status for IQCODE, RUDAS and the combination of both were created. Results: Predictive validity was high for both IQCODE and RUDAS and was highest for the combination (Area Under the Curve.91). Sensitivity, specificity, Youden index, predictive value, and likelihood ratio for IQCODE and RUDAS are reported. Conclusions: In this study in non-Western elderly migrants, half of whom were illiterate, the IQCODE proved to be a valid instrument for dementia detection, and adding the RUDAS increased accuracy. Combining performance-based and informant-based data is recommended to enhance diagnostic precision.

Additional Metadata
Keywords cognitive screening, cross-cultural comparison, Dementia, illiteracy, informant based measures
Persistent URL dx.doi.org/10.1080/13854046.2020.1711967, hdl.handle.net/1765/124089
Journal Clinical Neuropsychologist
Citation
Goudsmit, M. (Miriam), van Campen, J.P.C.M, Franzen, S. (Sanne), van den Berg, E. (Esther), Schilt, T, & Schmand, B. (2020). Dementia detection with a combination of informant-based and performance-based measures in low-educated and illiterate elderly migrants. Clinical Neuropsychologist. doi:10.1080/13854046.2020.1711967