Hostname: page-component-848d4c4894-wzw2p Total loading time: 0 Render date: 2024-05-18T10:13:52.844Z Has data issue: false hasContentIssue false
  • English
  • Français

Differential Item Functioning of the Everyday Cognition (ECog) Scales in Relation to Racial/Ethnic Groups

Published online by Cambridge University Press:  24 January 2020

Teresa Filshtein Open the ORCID record for Teresa Filshtein [Opens in a new window] ,
Michelle Chan ,
Dan Mungas ,
Rachel Whitmer ,
Evan Fletcher ,
Charles DeCarli  and
Sarah Farias
Teresa Filshtein*
Affiliation:
Department of Epidemiology and Biostatistics, University of California, San Francisco, CA94158, USA
Michelle Chan
Affiliation:
Department of Neurology, University of California, Davis, CA95817, USA
Dan Mungas
Affiliation:
Department of Neurology, University of California, Davis, CA95817, USA
Rachel Whitmer
Affiliation:
Department of Public Health Sciences, University of California, Davis, CA95616, USA
Evan Fletcher
Affiliation:
Department of Neurology, University of California, Davis, CA95817, USA
Charles DeCarli
Affiliation:
Department of Neurology, University of California, Davis, CA95817, USA
Sarah Farias
Affiliation:
Department of Neurology, University of California, Davis, CA95817, USA
*
Correspondence and reprint requests to: Teresa Filshtein, Department of Epidemiology and Biostatistics, University of California, UCSF Mission Bay, Mission Hall, 550 16th Street, San Francisco, CA 94158, USA. Phone: +1 860 803 4577. E-mail: tjfilshtein@ucdavis.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective:

The Everyday Cognition (ECog) scales measure cognitively based across domains of everyday abilities that are affected early in the course of neurodegenerative disorders such as Alzheimer’s disease. However, the degree to which the ECog may be differentially influenced by ethnic/racial background is unknown. This study evaluates measurement invariance of the ECog across non-Hispanic White (NHW), Black, and Hispanic individuals.

Methods:

Participants included 1177 NHW, 243 Black, and 216 Hispanic older adults from the UC Davis Alzheimer’s Disease Center Cohort who had an ECog. Differential item functioning (DIF) for each ECog domain was evaluated separately for Black and Hispanic participants compared to NHW participants. An iterative multiple group confirmatory factor analysis approach for ordinal scores was used to identify items whose measurement properties differed across groups and to adjust scores for DIF. Adjusted scores were then evaluated to test whether they were more strongly associated with cognitive function (concurrent and longitudinal change in cognition) and brain volumes (measured by brain imaging).

Results:

Varying levels, patterns, and impacts of DIF were found across domains and groups. However, the impact of DIF was relatively small, and DIF effects on scores generally were less than one-half standard error of measurement. There were no meaningful differences in associations with cognition and brain injury between DIF adjusted and unadjusted scores.

Conclusions:

Varying patterns of DIF were observed across the Black and Hispanic participants across select ECog domains. Overall, DIF effects were relatively small and did not change the relationship between the ECog and other indicators of disease.

Keywords

Differential item functioningInstrumental activities of daily livingFunctional abilitiesNeuroimagingExecutive functionEpisodic memoryDementia
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 26 , Issue 5 , May 2020 , pp. 515 - 526
Copyright
Copyright © INS. Published by Cambridge University Press, 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

These authors contributed equally.

References

REFERENCES

Administration for Community Living (2019). Minority Aging. Retrieved from https://acl.gov/aging-and-disability-in-america/data-and-research/minority-aging.Google Scholar
Aljabar, P., Heckemann, R., Hammers, A., Hajnal, J., & Rueckert, D. (2009). Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. Neuroimage, 46(3), 726–738.CrossRefGoogle ScholarPubMed
Brewster, P., Melrose, R., Marquine, M., Johnson, J., Napoles, A., MacKay-Brandt, A., & Mungas, D. (2014). Life experience and demographic influences on cognitive function in older adults. Neuropsychology, 28(6), 846–858.CrossRefGoogle ScholarPubMed
Farias, S., Lau, K., Harvey, D., Denny, K., Barba, C., & Mefford, A.N. (2017). Early functional limitations in cognitively normal older adults predict diagnostic conversion to mild cognitive impairment. Journal of the American Geriatrics Society, 65(6), 1152–1158.CrossRefGoogle ScholarPubMed
Farias, S., Mungas, D., Reed, B., Cahn-Weiner, D., Jagust, W., Baynes, K., & Decarli, C. (2008). The measurement of everyday cognition (ECog): Scale development and psychometric properties. Neuropsychology, 22(4), 531–544.CrossRefGoogle ScholarPubMed
Farias, S., Mungas, D., Reed, B., Harvey, D., & Decarli, C. (2009). Progression of mild cognitive impairment to dementia in clinic vs community-based cohort. Archives of Neurology, 66(9), 1157–1151.CrossRefGoogle Scholar
Farias, S., Mungas, D., Reed, B., Harvey, D., Cahn-Weiner, D., & Decarli, C. (2006). MCI is associated with deficits in everyday functioning. Alzheimer’s Disease and Associated Disorders, 20(4), 217–223.CrossRefGoogle ScholarPubMed
Farias, S., Quitania, L., Harvey, D., Simon, C., Reed, B., Carmichael, O., & Mungas, D. (2013). Everyday cognition in older adults: Associations with neuropsychological performance and structural brain imaging. Journal of the International Neuropsychological Society, 19(4), 430–431.CrossRefGoogle ScholarPubMed
Farias, S.T., Park, L.Q., Harvey, D.J., Simon, C., Reed, B.R., Carmichael, O., & Mungas, D. (2013). Everyday cognition in older adults: Associations with neuropsychological performance and structural brain imaging. Journal of the International Neuropsychological Society, 19(4):430–441. doi:10.1017/S1355617712001609CrossRefGoogle ScholarPubMed
Fleishman, J.A., Spector, W.D., & Altman, B.M. (2002). Impact of differential item functioning on age and gender differences in functional disability. The Journals of Gerontology: Series B, 57(5), S275–S284.CrossRefGoogle ScholarPubMed
Fletcher, E. (2014). Using prior information to enhance sensitivity of longitudinal brain change computation. In Chen, C.H. (Ed.), Frontiers of medical imaging (pp. 63–81). Singapore: World Scientific.CrossRefGoogle Scholar
Fletcher, E., Gavett, B., Harvey, D., Farias, S., Olichney, J., Beckett, L., & Mungas, D. (2018). Brain volume change and cognitive trajectories in aging. Neuropsychology, 32(4), 436–449.CrossRefGoogle Scholar
Fletcher, E., Knaack, A., Singh, B., Lloyd, E., Wu, E., Carmichael, O., & DeCarli, C. (2013). Combining boundary-based methods with tensor-based morphometry in the measurement of longitudinal brain change. IEEE Transactions on Medical Imaging, 32(2), 223–236.CrossRefGoogle ScholarPubMed
Fletcher, E., Singh, B., Harvey, D., Carmichael, O., & DeCarli, C. (2012). Adaptive image segmentation for robust measurement of longitudinal brain tissue change. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 5319–5322). IEEE.CrossRefGoogle Scholar
Gavett, B., Fletcher, E., Harvey, D., Farias, S., Olichney, J., Beckett, L., DeCarli, C., & Mungas, D. (2018). Ethnoracial differences in brain structure change and cognitive change. Neuropsychology, 32(5), 529–540.CrossRefGoogle ScholarPubMed
Gregorich, S. (2006). Do self-report instruments allow meaningful comparisons across diverse population groups? Testing measurement invariance using the confirmatory analysis framework. Medical Care, 44(11), 78–94.CrossRefGoogle ScholarPubMed
Hallquist, M. & Wiley, J. (2018). MplusAutomation: An R package for facilitating large-scale latent variable analyses in Mplus. Structural Equation Modeling, 25(4), 621–368. doi:10.1080/10705511.2017.1402334CrossRefGoogle Scholar
Hinton, L., Carter, K., Reed, B., Beckett, L., Lara, E., DeCarli, C., & Mungas, D. (2010). Recruitment of a community-based cohort for research on diversity and risk of dementia. Alzheimer Disease and Associated Disorders, 24(3), 234–241.Google ScholarPubMed
Jones, R. (2006). Technical Appendix for Jones RN. Identification of measurement differences between English and Spanish language versions of the Mini-Mental State Examination: Detecting differential item functioning using MIMIC modeling. Medical Care, 44 (11 Suppl 3), S124–S133.CrossRefGoogle Scholar
Jorm, A. (1994). A short form of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE): Development and cross-validation. Psychological Medicine, 24(1), 145–153.CrossRefGoogle Scholar
Klein, A., Ghosh, S., Bao, F., Giard, J., Hame, Y, Stavsky, E., & Keshavan, A. (2017). Mindboggling morphometry of human brains. PLoS Computational Biology, 13(2), e1005350.CrossRefGoogle ScholarPubMed
Kochunov, P., Lancaster, J., Thompson, P., Woods, R., Mazziotta, J., Hardies, J., & Fox, P. (2001). Regional spatial normalization: Toward an optimal target. Journal of Computer Assisted Tomography, 25(5), 805–816.CrossRefGoogle ScholarPubMed
Lau, K., Parikh, M., Harvey, D., Huang, C., & Farias, S. (2015). Early cognitively based functional limitations predict loss of independence in instrumental activities of daily living in older adults. Journal of the International Neuropsychological Society, 21(9), 688–698.CrossRefGoogle ScholarPubMed
Lee, D.Y., Fletcher, E., Martinez, O., Zozulya, N., Kim, J., Tran, J., & DeCarli, C. (2010). Vascular and degenerative processes differentially affect regional interhemispheric connections in normal aging, mild cognitive impairment, and Alzheimer disease. Stroke, 41(8), 1791–1797.CrossRefGoogle ScholarPubMed
Mayeda, E., Glymour, M., Quesenberry, C., & Whitmer, R. (2016). Inequalities in dementia incidence between six racial and ethnic groups over 14 years. Alzheimer’s Dementia, 12(3), 216–224.CrossRefGoogle ScholarPubMed
Melrose, R., Brewster, P., Marquine, M., MacKay-Brandt, A., Reed, B., Farias, S., & Mungas, D. (2015). Early life development in a multiethnic sample and the relation to late life cognition. Journals of Gerontology Series B, Psychological Sciences and Social Sciences, 70(4), 519–531.CrossRefGoogle Scholar
Morris, J., Weintraub, S., Chui, H., Cummings, J., Decarli, C., Ferris, S., & Kukull, W. (2006). The Uniform Data Set (UDS): Clinical and cognitive variables and descriptive data from Alzheimer Disease Centers. Alzheimer Disease and Associated Disorders, 20(4), 210–216.CrossRefGoogle ScholarPubMed
Mungas, D., Reed, B., Crane, P., Haan, M., & Gonzalez, H. (2004). Spanish and English Neuropsychological Assessment Scales (SENAS): Further development and psychometric characteristics. Psychological Assessment, 16(4), 347–359.CrossRefGoogle ScholarPubMed
Mungas, D., Reed, B., Haan, M., & Gonzalez, H. (2005) Spanish and English Neuropsychological Assessment Scales: Relationship to demographics, language, cognition, and independent function. Neuropsychology, 19(4), 466–475.CrossRefGoogle ScholarPubMed
Mungas, D., Reed, B., Farias, S., & DeCarli, C. (2005). Criterion-referenced validity of a neuropsychological test battery: Equivalent performance in elderly Hispanic and non-Hispanic Whites. Journal of the International Neuropsychological Society, 11(5), 620–630.CrossRefGoogle ScholarPubMed
Mungas, D., Reed, B., Marshall, S., & Gonzalez, H. (2000). Development of psychometrically matched English and Spanish neuropsychological tests for older persons. Neuropsychology, 14(2), 466–475.CrossRefGoogle ScholarPubMed
Muthén, L. & Muthén, B. (1998–2017). Mplus user’s guide (8th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
R Core Team (2017). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/.Google Scholar
Royall, D., Lauterbach, E., Kaufer, D., Malloy, P., Coburn, K., & Black, K. (2007). The cognitive correlates of functional status: A review from the Committee on Research of the American Neuropsychiatric Association. The Journal of Neuropsychiatry and Clinical Neurosciences, 19(3), 249–265.CrossRefGoogle Scholar
Rueckert, D., Aljabar, P., Heckemann, R., Hajnal, J., & Hammers, A. (2006). Diffeomorphic registration using B-splines. In International Conference on Medical Image Computing and Computer-Assisted Intervention: MICCAI, Vol. 4191, (pp. 702–709). Berlin, Heidelberg, Germany: Springer.Google Scholar
Satorra, A. & Bentler, P. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507–514.CrossRefGoogle Scholar
Tang, M., Cross, P., Andrews, H., Jacobs, D., Small, S., Bell, K., & Mayeux, R. (2001). Incidence of AD in African–Americans, Caribbean Hispanics, and whites in northern Manhattan. Neurology, 56(1), 49–56.CrossRefGoogle ScholarPubMed
Teresi, J.A. (2006). Different approaches to differential item functioning in health applications: Advantages, disadvantages and some neglected topics. Medical Care, 44(11), S152–S170.CrossRefGoogle ScholarPubMed
Teri, L. (1997). Behavior and caregiver burden: Behavioral problems in patients with Alzheimer’s disease and its association with caregiver distress. Alzheimer Disease and Associated Disorders, 11(4), 35–38.Google ScholarPubMed
U.S. Census Bureau (2018). Quick Facts: United States. Retrieved from https://www.census.gov/quickfacts/fact/table/US/PST045218.Google Scholar
Supplementary material: File

Filshtein et al. supplementary material

Filshtein et al. supplementary material 1

Download Filshtein et al. supplementary material(File)
File 13.6 KB
Supplementary material: PDF

Filshtein et al. supplementary material

Filshtein et al. supplementary material 2

Download Filshtein et al. supplementary material(PDF)
PDF 448.4 KB