TY - JOUR
T1 - Evaluating the Beijing Version of Montreal Cognitive Assessment for Identification of Cognitive Impairment in Monolingual Chinese American Older Adults
AU - Hong, Yue
AU - Zeng, Xiaoyi
AU - Zhu, Carolyn W.
AU - Neugroschl, Judith
AU - Aloysi, Amy
AU - Sano, Mary
AU - Li, Clara
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by NIH National Institute on Aging grants (P30AG066514, R03AG061439, 5P30AG028741-11). The NACC database is funded by NIA/NIH Grant U01 AG016976.
Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by NIH National Institute on Aging grants [R03AG061439, P50AG05138, P30AG028741]. The NACC database is funded by NIA/NIH Grant [U01 AG016976]. NACC data are contributed by the NIA-funded ADRCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG005131 (PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD).
Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by NIH National Institute on Aging grants [R03AG061439, P50AG05138, P30AG028741]. The NACC database is funded by NIA/NIH Grant [U01 AG016976]. NACC data are contributed by the NIA-funded ADRCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG005131 (PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD). The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by NIH National Institute on Aging grants (P30AG066514, R03AG061439, 5P30AG028741-11). The NACC database is funded by NIA/NIH Grant U01 AG016976.
Publisher Copyright:
© The Author(s) 2021.
PY - 2022/7
Y1 - 2022/7
N2 - Objective: This study aims to evaluate the performance of a Chinese version of the Montreal Cognitive Assessment (MoCA) as a screener to detect mild cognitive impairment (MCI) and dementia from normal cognition in the monolingual Chinese-speaking immigrant population. Method: A cohort of 176 Chinese-speaking older adults from the National Alzheimer’s Coordinating Center Uniform Data Set is used for analysis. We explore the impact of demographic variables on MoCA performance and calculate the optimal cutoffs for the detection of MCI and dementia from normal cognition with appropriate demographic adjustment. Results: MoCA performance is predicted by age and education independent of clinical diagnoses, but not by sex, years of living in the U.S., or primary Chinese dialect spoken (i.e., Mandarin vs. Cantonese). With adjustment and stratification for education and age, we identify optimal cutoff scores to detect MCI and dementia, respectively, in this population. These optimal cutoff scores are different from the established scores for non-Chinese-speaking populations residing in the U.S. Conclusions: Our findings suggest that the Chinese version of MoCA is a valid screener to detect cognitive decline in older Chinese-speaking immigrants in the U.S. They also highlight the need for population-based cutoff scores with appropriate considerations for demographic variables.
AB - Objective: This study aims to evaluate the performance of a Chinese version of the Montreal Cognitive Assessment (MoCA) as a screener to detect mild cognitive impairment (MCI) and dementia from normal cognition in the monolingual Chinese-speaking immigrant population. Method: A cohort of 176 Chinese-speaking older adults from the National Alzheimer’s Coordinating Center Uniform Data Set is used for analysis. We explore the impact of demographic variables on MoCA performance and calculate the optimal cutoffs for the detection of MCI and dementia from normal cognition with appropriate demographic adjustment. Results: MoCA performance is predicted by age and education independent of clinical diagnoses, but not by sex, years of living in the U.S., or primary Chinese dialect spoken (i.e., Mandarin vs. Cantonese). With adjustment and stratification for education and age, we identify optimal cutoff scores to detect MCI and dementia, respectively, in this population. These optimal cutoff scores are different from the established scores for non-Chinese-speaking populations residing in the U.S. Conclusions: Our findings suggest that the Chinese version of MoCA is a valid screener to detect cognitive decline in older Chinese-speaking immigrants in the U.S. They also highlight the need for population-based cutoff scores with appropriate considerations for demographic variables.
KW - Montreal Cognitive Assessment
KW - aged
KW - dementia
KW - emigrants and immigrants
KW - mild cognitive impairment
KW - sensitivity and specificity
UR - http://www.scopus.com/inward/record.url?scp=85112297313&partnerID=8YFLogxK
U2 - 10.1177/08919887211036182
DO - 10.1177/08919887211036182
M3 - Article
AN - SCOPUS:85112297313
SN - 0891-9887
VL - 35
SP - 586
EP - 593
JO - Journal of Geriatric Psychiatry and Neurology
JF - Journal of Geriatric Psychiatry and Neurology
IS - 4
ER -