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Six-month trajectories of self-reported depressive symptoms in long-term care

Published online by Cambridge University Press:  10 August 2015

Jane McCusker ,
Martin G. Cole ,
Philippe Voyer ,
Johanne Monette ,
Nathalie Champoux ,
Antonio Ciampi ,
Minh Vu ,
Eric Belzile  and
Chun Bai
Jane McCusker*
Affiliation:
St. Mary's Research Centre, St. Mary's Hospital Center, Montreal, Quebec, Canada Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
Martin G. Cole
Affiliation:
Department of Psychiatry, St Mary's Hospital and McGill University, Montreal, Quebec, Canada
Philippe Voyer
Affiliation:
Faculty of Nursing Sciences, Laval University, Quebec City, Quebec, Canada
Johanne Monette
Affiliation:
Division of Geriatric Medicine, Sir Mortimer B. Davis Jewish General Hospital, Montreal, Quebec, Canada Donald Berman Maimonides Geriatric Center, Montreal, Quebec, Canada
Nathalie Champoux
Affiliation:
Département de Médecine Familiale, Université de Montréal, Montreal, Quebec, Canada
Antonio Ciampi
Affiliation:
St. Mary's Research Centre, St. Mary's Hospital Center, Montreal, Quebec, Canada Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
Minh Vu
Affiliation:
Division of Geriatric Medicine, Centre Hospitalier de l’Université de Montréal, and Department of Medicine, Université de Montréal, Montreal, Quebec, Canada
Eric Belzile
Affiliation:
St. Mary's Research Centre, St. Mary's Hospital Center, Montreal, Quebec, Canada
Chun Bai
Affiliation:
St. Mary's Research Centre, St. Mary's Hospital Center, Montreal, Quebec, Canada
*
Correspondence should be addressed to: Dr. J. McCusker, St. Mary's Research Centre, 3830 Avenue Lacombe #4720, Montreal, Quebec H3 T 1M5, Canada. Phone: +514-345-3511-5062; Fax: +514-734-2652. Email: jane.mccusker@mcgill.ca.
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Abstract

Background:

Depression is a common problem in long-term care (LTC) settings. We sought to characterize depression symptom trajectories over six months among older residents, and to identify resident characteristics at baseline that predict symptom trajectory.

Methods:

This study was a secondary analysis of data from a six-month prospective, observational, and multi-site study. Severity of depressive symptoms was assessed with the 15-item Geriatric Depression Scale (GDS) at baseline and with up to six monthly follow-up assessments. Participants were 130 residents with a Mini-Mental State Examination score of 15 or more at baseline and of at least two of the six monthly follow-up assessments. Individual resident GDS trajectories were grouped using hierarchical clustering. The baseline predictors of a more severe trajectory were identified using the Proportional Odds Model.

Results:

Three clusters of depression symptom trajectory were found that described “lower,” “intermediate,” and “higher” levels of depressive symptoms over time (mean GDS scores for three clusters at baseline were 2.2, 4.9, and 9.0 respectively). The GDS scores in all groups were generally stable over time. Baseline predictors of a more severe trajectory were as follows: Initial GDS score of 7 or more, female sex, LTC residence for less than 12 months, and corrected visual impairment.

Conclusions:

The six-month course of depressive symptoms in LTC is generally stable. Most residents who experience a more severe symptom trajectory can be identified at baseline.

Keywords

aged caredepressionlongitudinal studieslong-term care
Type
Research Article
Information
International Psychogeriatrics , Volume 28 , Issue 1 , January 2016 , pp. 71 - 81
Copyright
Copyright © International Psychogeriatric Association 2015 

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References

Abraham, I. L. (1991). The Geriatric Depression Scale and Hopelessness Index: longitudinal psychometric data on frail nursing home residents. Perceptual and Motor Skills, 72, 875–880.CrossRefGoogle ScholarPubMed
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, AC–19, 716–723.CrossRefGoogle Scholar
Boorsma, M. et al. (2012). The incidence of depression and its risk factors in Dutch nursing homes and residential care homes. The American Journal of Geriatric Psychiatry, 20, 932–942.Google Scholar
Bravo, G., Dubois, M.-F., Hébert, R., De Wals, P. and Messier, L. (2002). A prospective evaluation of the Charlson Comorbidity Index for use in long-term care patients. Journal of the American Geriatrics Society, 50, 740–745.CrossRefGoogle ScholarPubMed
Bruce, M. L. (2001). Depression and disability in late life. Directions for future research. The American Journal of Geriatric Psychiatry, 9, 102–112.Google Scholar
Buntinx, F., Niclaes, L., Suetens, C., Jans, B., Mertens, R. and Van den Akker, M. (2002). Evaluation of Charlson's Comorbidity Index in elderly living in nursing homes. Journal of Clinical Epidemiology, 55, 1144–1147.Google Scholar
Calinski, T. and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3, 1–27.Google Scholar
Charlson, M. E., Pompei, P., Ales, K. L. and MacKenzie, R. C. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of Chronic Diseases, 40, 373–383.Google Scholar
Chi, F. W. and Weisner, C. M. (2008). Nine-year psychiatric trajectories and substance use outcomes: an application of the group-based modeling approach. Evaluation Review, 32, 39–58.Google Scholar
Ciampi, A., Campbell, H., Dyachenko, A., Rich, B., McCusker, J. and Cole, M. (2012). Model-based clustering of longitudinal data: application to modeling disease course and gene expression trajectories. Communications in Statistics-Simulation and Computation, 41, 992–1005.Google Scholar
Field, T. S. et al. (2001). Risk factors for adverse drug events among nursing home residents. Archives of Internal Medicine, 161, 1629–1634.Google Scholar
Folstein, M. F., Folstein, S. E. and McHugh, P. R. (1975). “Mini-Mental State”: a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.CrossRefGoogle ScholarPubMed
Galecki, A. and Burzykowski, T. (2013). Linear Mixed-Effects Models Using R: A Step-by-Step Approach. New York, NY: Springer.Google Scholar
Gerety, M. B. et al. (1994). Performance of case-finding tools for depression in the nursing home: influence of clinical and functional characteristics and selection of optimal threshold scores. Journal of the American Geriatrics Society, 42, 1103–1109.Google Scholar
Hawkins, D. M., Muller, M. W. and Ten Krooden, J. A. (1982). Cluster analysis. In Hawkins, D. M. (ed.), Topics in Applied Multivariate Analysis (pp. 303–356). Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Hoover, D. R. et al. (2010). Depression in the first year of stay for elderly long-term nursing home residents in the USA. International Psychogeriatrics, 22, 1161–1171.CrossRefGoogle ScholarPubMed
Inouye, S. K., VanDyck, C. H., Alessi, C. A., Balkin, S., Siegal, A. P. and Horwitz, R. J. (1990). Clarifying confusion: the Confusion Assessment Method. A new method for detection of delirium. Annals of Internal Medicine, 113, 941–948.Google Scholar
Jongenelis, K., Pot, A. M., Eisses, A. M. H., Beekman, A. T. F., Kluiter, H. and Ribbe, M. W. (2004). Prevalence and risk indicators of depression in elderly nursing home patients: the AGED study. Journal of Affective Disorders, 83, 135–142.CrossRefGoogle Scholar
Jongenelis, K. et al., (2005). Diagnostic accuracy of the original 30-item and shortened versions of the Geriatric Depression Scale in nursing home patients. International Journal of Geriatric Psychiatry, 20, 1067–1074.Google Scholar
Kaasalainen, S. and Crook, J. (2003). A comparison of pain-assessment tools for use with elderly long-term-care residents. The Canadian Journal of Nursing Research, 35, 58–71.Google Scholar
Kafonek, S., Ettinger, W. H., Roca, R., Kittner, S., Taylor, N. and German, P. S. (1989). Instruments for screening for depression and dementia in a long-term care facility. Journal of the American Geriatrics Society, 37, 29–34.Google Scholar
Kaufman, L. and Rousseeuw, P. J. (2005). Finding Groups in Data: An Introduction to Cluster Analysis. Hoboken, NJ: John Wiley.Google Scholar
Kerber, C., Dyck, M. J., Culp, K. R. and Buckwalter, K. (2005). Comparing the Geriatric Depression Scale, minimun data set, and primary care provider diagnosis for depression in rural nursing home residents. Journal of the American Psychiatric Nurses Association, 11, 269–275.Google Scholar
Kuchibhatla, M. N., Fillenbaum, G. G., Hybels, C. F. and Blazer, D. G. (2012). Trajectory classes of depressive symptoms in a community sample of older adults. Acta Psychiatrica Scandinavica, 125, 492–501.Google Scholar
Leisch, F. (2003). FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R. Vienna. WU Vienna University of Economics and Business Contract No. 86. Available at: http://epub.wu.ac.at/id/eprint/712; last accessed 22 July 2015.Google Scholar
Mahoney, F. I. and Barthel, D. W. (1965). Functional evaluation: the Barthel Index. Maryland State Medical Journal, 14, 61–65.Google Scholar
McCusker, J. et al. (2007). Twelve-month course of depressive symptoms in older medical inpatients. International Journal of Geriatric Psychiatry, 22, 411–417.Google Scholar
McCusker, J. et al. (2011). Prevalence and incidence of delirium in long-term care. International Journal of Geriatric Psychiatry, 26, 1152–1161.Google Scholar
McCusker, J. et al. (2014). Observer-rated depression in long-term care: frequency and risk factors. Archives of Gerontology and Geriatrics, 58, 332–338.Google Scholar
McGivney, S. A., Mulvihill, M. and Taylor, B. (1994). Validating the GDS depression screen in the nursing home. Journal of the American Geriatrics Society, 42, 490–492.CrossRefGoogle ScholarPubMed
McNutt, L. A., Wu, C., Xue, X. and Hafner, J. P. (2003). Estimating the relative risk in cohort studies and clinical trials of common outcomes. American Journal of Epidemiology, 157, 940–943.Google Scholar
Melzack, R. (1975). The McGill Pain Questionnaire: major properties and scoring methods. Pain, 1, 277–299.Google Scholar
Parmelee, P. A., Katz, I. R. and Lawton, P. M. (1992). Incidence of depression in long-term care settings. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 47, M189–M196.Google Scholar
Payne, J. L. et al. (2002). Incidence, prevalence, and outcomes of depression in residents of a long-term care facility with dementia. International Journal of Geriatric Psychiatry, 17, 247–253.Google Scholar
Pinheiro, J. C. and Bates, D. M. (2000). Mixed-Effects Models in S and S-PLUS. New York, NY: Springer-Verlag.Google Scholar
Rovner, B. W., Casten, R. J., Hegel, M. T., Leiby, B. E. and Tasman, W. S. (2007). Preventing depression in age-related macular degeneration. Archives of General Psychiatry, 64, 886–892.Google Scholar
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464.Google Scholar
Seitz, D., Purandare, N. and Conn, D. (2010). Prevalence of psychiatric disorders among older adults in long-term care homes: a systematic review. International Psychogeriatrics, 22, 1025–1039.Google Scholar
Shah, S., Vanclay, F. and Cooper, B. (1989). Improving the sensitivity of the Barthel Index for stroke rehabilitation. Journal of Clinical Epidemiology, 42, 703–709.Google Scholar
Singer, J. D. and Willett, J. B. (2003). Applied Longitudinal Data Analysis. New York, NY: Oxford University Press.Google Scholar
Smalbrugge, M., Jongenelis, L., Pot, A. M., Eefsting, J. A., Ribbe, M. W. and Beekman, A. T. F. (2006). Incidence and outcome of depressive symptoms in nursing home patients in the Netherlands. The American Journal of Geriatric Psychiatry, 14, 1069–1076.Google Scholar
Sylvestre, M. P., McCusker, J., Cole, M., Regeasse, A., Belzile, E. and Abrahamowicz, M. (2006). Classification of patterns of delirium severity scores over time in an elderly population. International Psychogeriatrics, 18, 667–680.Google Scholar
Tombaugh, T. N. and McIntyre, N. J. (1992). The Mini-Mental State Examination: a comprehensive review. Journal of the American Geriatrics Society, 40, 922–935.Google Scholar
von Gunten, A., Mosimann, U. P. and Antonietti, J.-P. (2013). A longitudinal study on delirium in nursing homes. American Journal of Geriatric Psychiatry, 21, 963–972.Google Scholar
Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244.CrossRefGoogle Scholar
Whitehead, J. (1993). Sample size calculations for ordered categorical data. Statistics in Medicine, 12, 2257–2271.Google Scholar
Williams, R. (2006). Generalized ordered logit/partial proportional odds models for ordinal dependent variables. The STATA Journal, 6, 58–52.Google Scholar
Yee, T. W. (2010). The VGAM package for categorical data analysis. Journal of Statistical Software, 32, 1–34.Google Scholar