Disentangling Multiple Sclerosis and depression: an adjusted depression screening score for patient-centered care
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Screening for depression can be challenging in Multiple Sclerosis (MS) patients due to the overlap of depressive symptoms with other symptoms, such as fatigue, cognitive impairment and functional impairment, for MS patients. The aim of this study was to understand these overlapping symptoms and subsequently develop an adjusted depression screening tool for better clinical assessment of depressive symptoms in MS patients. We evaluated 3,507 MS patients with a self-reported depression screening (PHQ-9) score using a multiple indicator multiple cause modeling approach. Our models showed significant differential item functioning effects denoting significant overlap of depressive symptoms with all MS symptoms under study and good model fit. The magnitude of the overlap was especially large for fatigue. Adjusted depression screening scales were formed based on factor scores and loadings that will allow clinicians to understand the depressive symptoms separate from other symptoms for MS patients for improved patient care.
KeywordsMultiple Sclerosis Fatigue Depression Structural equation modeling Factor analysis Multiple indicator multiple cause model
Financial support for this study was provided by a Grant from NIH/NCRR CTSA KL2TR000440 and by a Grant from Novartis. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report. We appreciate the contributions from Drs. Randall Cebul, Thomas Love, Irene Katzan, Neal Dawson, Center for Health Care Research and Policy, Drs. Richard Rudick, and Francois Bethoux, Mellen Center, and Dr. Martha Sajatovic, Departments of Psychiatry and Neurology at Case Western Reserve University School of Medicine.
Conflict of interest
Douglas Gunzler, Adam Perzynski, Nathan Morris, Steven Lewis and Deborah Miller declare that they have no conflict of interest. Robert Bermel has received research grants from Novartis.
Human and Animal Rights and Informed Consent
All procedures followed were in accordance with ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients for being included in the study.
- Aikens, J. E., Reinecke, M. A., Pliskin, N. H., Fischer, J. S., Wiebe, J. S., McCracken, L. M., et al. (1999). Assessing depressive symptoms in multiple sclerosis: Is it necessary to omit items from the original Beck Depression Inventory? Journal of Behavioral Medicine, 22, 127–142.CrossRefPubMedGoogle Scholar
- Alemayehu, D., Cappelleri, J. C., & Murphy, M. F. (2012). Conceptual and analytical considerations toward the use of patient-reported outcomes in personalized medicine. American Health & Drug Benefits, 5, 310–317.Google Scholar
- Blacker, D. (2009). Psychiatric rating scales. In B. J. Sadock, V. A. Sadock, & P. Ruiz (Eds.), Kaplan and Sadock’s comprehensive textbook of psychiatry (9th ed.). Philadelphia, PA: Lippincott Williams & Wilkins.Google Scholar
- Bollen, K. A., & Long, J. S. (1993). Testing structural equation models. Newbury Park, CA: Sage Publications.Google Scholar
- Brown, T. (2006). Confirmatory factor analysis for applied research (methodology in the social science). New York, NY: The Guilford Press.Google Scholar
- Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models. Newbury Park, CA: Sage Publications.Google Scholar
- Casella, G., & Berger, R. L. (2002). Statistical inference (2nd ed.). Pacific Grove, CA: Duxbury Press.Google Scholar
- Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10, 1–9.Google Scholar
- Gilbody, S., Richards, D., Brealey, S., & Hewitt, C. (2007). Screening for depression in medical settings with the Patient Health Questionnaire (PHQ): A diagnostic meta-analysis. Journal of General Internal Medicine, 11, 596–602.Google Scholar
- Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford.Google Scholar
- Knowledge Program developed at Cleveland Clinic’s Neurological Institute. (2008–2013). Retrieved from, http://my.clevelandclinic.org/neurological_institute/about/default.aspx
- Krupp, L. B. (2004). Fatigue in multiple sclerosis. New York, NY: Demos Medical Publishing.Google Scholar
- Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling A Multidisciplinary Journal, 11, 320–341.CrossRefGoogle Scholar
- Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland Clinic, Neurological Institute. (2013). Retrieved from, http://my.clevelandclinic.org/neurological_institute/mellen-center-multiple-sclerosis/default.aspx
- Multiple Sclerosis Association of America. (2014). Retrieved from, http://www.mymsaa.org/about-ms/faq/
- Muthén, L. K., & Muthén, B. O. (2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
- R Development Core Team. (2008). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
- Raykov, T., & Marcoulides, G. (2011). Introduction to psychometric theory. New York, NY: Taylor and Francis Group.Google Scholar
- SAS Institute Inc. (2008). SAS/STAT ® 9.2 user’s guide. Cary, NC: SAS Institute Inc.Google Scholar
- Sjonnesen, K., Berzins, S., Fiest, K. M., Bulloch, A. G., Metz, L. M., Thombs, B. D., et al. (2012). Evaluation of the 9-item Patient Health Questionnaire (PHQ-9) as an assessment instrument for symptoms of depression in patients with multiple sclerosis. Postgraduate Medicine, 124, 69–77.CrossRefPubMedGoogle Scholar