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Telephone-based Minnesota Cognitive Acuity Screen predicts time to institutionalization and homecare

Published online by Cambridge University Press:  25 September 2017

Seth A. Margolis Open the ORCID record for Seth A. Margolis [Opens in a new window] ,
George D. Papandonatos ,
Geoffrey Tremont  and
Brian R. Ott
Seth A. Margolis*
Affiliation:
Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA Department of Neurology, Warren Alpert Medical School of Brown University, Providence, RI, USA Rhode Island Hospital, Providence, RI, USA
George D. Papandonatos
Affiliation:
Department of Biostatistics, Brown University, Providence, RI, USA
Geoffrey Tremont
Affiliation:
Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA Rhode Island Hospital, Providence, RI, USA
Brian R. Ott
Affiliation:
Department of Neurology, Warren Alpert Medical School of Brown University, Providence, RI, USA Rhode Island Hospital, Providence, RI, USA
*
Correspondence should be addressed to: Seth A. Margolis, Ph.D., Alzheimer's Disease & Memory Disorders Center, Rhode Island Hospital, 593 Eddy St., Providence, RI, 02903, USA. Phone: 401-444-7735; Fax: 401-444-6643. Email: seth.margolis@lifespan.org.
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Abstract

Background:

We assessed the ability of a telephone-administered cognitive screening test – Minnesota Cognitive Acuity Screen (MCAS) – to predict time to assisted living/nursing home placement (i.e. institutionalization) and homecare/institutionalization in healthy controls (HC), mild cognitive impairment (MCI), and Alzheimer's disease (AD).

Methods:

Participants (N = 146; HC = 37; MCI = 70; AD = 39) had baseline MCAS testing and were re-contacted over eight years for dates of starting homecare, institutionalization, and death. Occasionally, outcomes were obtained via medical records. Accounting for informative censoring due to death within a competing risks framework, Cox regression examined the associations of baseline MCAS performance with the start of (a) institutionalization and (b) homecare/institutionalization.

Results:

Hazard ratios (HR) captured the effect of a ten-point difference in baseline MCAS scores, corresponding to a change from the MCI/HC to AD/MCI boundaries. In unadjusted models, increased baseline cognitive impairment was associated with nearly two-fold increases in the hazard of institutionalization (HR = 1.81, 95% CI = 1.32, 2.48) and homecare/institutionalization (HR = 1.87, 95% CI = 1.44, 2.42). However, hazards were not proportional over time in models adjusting for sex. This was resolved when regressions were run for men and women separately. Both sexes showed significant increases in the hazard of institutionalization (Females: HR = 2.39, 95% CI = 1.53–3.74; Males: HR = 1.68, 95% CI = 1.02–2.76) and homecare/institutionalization (Females: HR = 2.31, 95% CI = 1.66, 3.21; Males: HR = 1.98, 95% CI = 1.32, 2.96) with increased impairment, although hazards were lower for males.

Conclusions:

Telephone-administered MCAS provides useful information about the risk of needing homecare assistance or institutionalization. It may be particularly useful when office/home visits are prohibitive but cognitive monitoring is indicated.

Keywords

screeningmild cognitive impairmentdementianursing homescommunity care
Type
Research Article
Information
International Psychogeriatrics , Volume 30 , Issue 3: Issue Theme: Psychosocial Interventions for Dementia , March 2018 , pp. 365 - 373
Copyright
Copyright © International Psychogeriatric Association 2017 

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References

Alzheimer's Association (2015). Changing the Trajectory of Alzheimer's Disease: How a Treatment by 2025 Saves Lives and Dollars. Available at: https://www.alz.org/documents_custom/trajectory.pdf; last accessed 20 November 2016.Google Scholar
Banaszak-Holl, J. et al. (2004). Predicting nursing home admission: estimates from a 7 year follow up of a nationally representative sample of older Americans. Alzheimer Disease and Associated Disorders, 18, 83–89.Google Scholar
Crooks, V. C., Clark, L., Petitti, D. B., Chui, H. and Chiu, V. (2005). Validation of multi- stage telephone-based identification of cognitive impairment and dementia. BMC Neurology, 5, 8. doi:10.1186/1471-2377-5-8.Google Scholar
Gallo, J. J. and Breitner, J. C. (1995). Alzheimer's disease in the NAS-NRC Registry of aging twin veterans, IV. Performance characteristics of a two-stage telephone screening procedure for Alzheimer's dementia. Psychological Medicine, 25, 1211–1219. doi:10.1017/S0033291700033183.Google Scholar
Gaugler, J. E., Kane, R. L., Kane, R. A. and Newcomber, R. (2003). Caregiving and institutionalization of cognitively impaired older people: utilizing dynamic predictors of change. Gerontologist, 43, 219–229. doi:10.1093/geront/43.2.219.CrossRefGoogle ScholarPubMed
Gnjidic, D. et al. (2012). Mild cognitive impairment predicts institutionalization among older men: a population-based cohort study. PLoS One, 7, e46061. doi:10.1371/journal.pone.0046061.Google Scholar
Gonyea, J. (1997). The emergence of the oldest old: challenges for public policy. In Hudson, R. (ed.), The Future of Age-Based Public Policy (pp. 77–91). Baltimore: Johns Hopkins University Press.Google Scholar
Herrmann, N. et al. (2006). The contribution of neuropsychiatric symptoms to the cost of dementia care. International Journal of Geriatric Psychiatry, 21, 972–976. doi:10.1002/gps.1594.CrossRefGoogle Scholar
Huss, A., Stuck, A. E., Rubenstein, L. Z., Egger, M. and Clough-Gorr, K. M. (2008). Multidimensional geriatric assessment: back to the future multidimensional preventive home visit programs for community-dwelling older adults: a systematic review and meta-analysis of randomized controlled trials. The Journal of Gerontology: Biological Sciences, 63A, 298–307. doi:10.1093/gerona/63.3.298.Google Scholar
Katz, S. J., Kabeto, M. and Langa, K. M. (2000). Gender disparities in the receipt of home care for elderly people with disability in the United States. Journal of the American Medical Association, 284, 3022–3027. doi:10.1001/jama.284.23.3022.Google Scholar
Knopman, D. S. et al. (2010). Validation of the telephone interview for cognitive status modified in subjects with normal cognition, mild cognitive impairment, or dementia. Neuroepidemiology, 34, 34–42. doi:10.1159/000255464.Google Scholar
Knopman, D. S., Knudson, D., Yoes, M. E. and Weiss, D. J. (2000). Development and standardization of a new telephonic cognitive screening test: the minnesota cognitive acuity screen (MCAS). Neuropsychiatry, Neuropsychology and Behavioural Neurology, 13, 286–296.Google Scholar
Lines, C., McCarroll, K., Lipton, R., Block, G. and Prevention of Alzheimer's in Society's Elderly Study Group. (2003). Telephone screening for amnestic mild cognitive impairment. Neurology, 60, 261–266. doi:10.1212/01.WNL.0000042481.34899.13.Google Scholar
Luppa, M. et al. (2012). Age-related predictors of institutionalization: results of the German study on ageing, cognition and dementia in primary care patients (AgeCoDe). Social Psychiatry and Psychiatric Epidemiology, 47, 263–270. doi:10.1007/s00127-010-0333-9.Google Scholar
Luppa, M., Luck, T., Weyerer, S., König, H. H., Brähler, E. and Riedel-Heller, S. G. (2010). Prediction of institutionalization in the elderly. A systematic review. Age and Ageing, 39, 31–38. doi:10.1093/ageing/afp202.CrossRefGoogle ScholarPubMed
Luppa, M., Luck, T., Wyerer, S., Konig, H. H. and Riedel-Heller, S. G. (2009). Gender differences in predictors of nursing home placement in the elderly: a systematic review. International Psychogeriatrics, 21, 1015–1025. doi:10.1017/S1041610209990238.Google Scholar
Martin-Khan, M., Wootton, R. and Gray, L. (2010). A systematic review of the reliability of screening for cognitive impairment in older adults by use of standardised assessment tools administered via the telephone. Journal of Telemedicine and Telecare, 16, 422–428. doi:10.1258/jtt.2010.100209.Google Scholar
McKhann, G. M. et al. (2011). The diagnosis of dementia due to Alzheimer's disease: recommendations from the national institute on aging and the Alzheimer's association workgroup. Alzheimers and Dementia, 7, 263–269. doi:10.1016/j.jalz.2011.03.005.Google Scholar
Mittelman, M. S., Haley, W. E., Clay, O. J. and Roth, D. L. (2006). Improving caregiver well-being delays nursing home placement of patients with Alzheimer's disease. Neurology, 67, 1592–1599. doi:10.1212/01.wnl.0000242727.81172.91:1526-632X.Google Scholar
Pendlebury, S. T., Welch, S. J., Cuthbertson, F. C., Mariz, J., Mehta, Z. and Rothwell, P. M. (2013). Telephone assessment of cognition after transient ischemic attack and stroke: modified telephone interview of cognitive status and telephone montreal cognitive assessment versus face-to-face montreal cognitive assessment and neuropsychological battery. Stroke, 44, 227–229. doi:10.1161/STROKEAHA.112.673384.Google Scholar
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G. and Kokmen, E. (1999). Mild Cognitive Impairment: clinical characterization and outcome. Archives of Neurology, 56, 303–308. doi:10.1001/archneur.56.3.303.Google Scholar
Quentin, W., Riedel-Heller, S. G., Luppa, M., Randolph, A. and Konig, H. H. (2010). Cost-of-illness studies of dementia: a systematic review focusing on stage dependency of costs. Acta Psychiatrica Scandinavia, 121, 243–259. doi:10.1111/j.1600-0447.2009.01461.x.Google Scholar
Scrucca, L., Santucci, A. and Aversa, F. (2010). Regression modeling of competing risk using R: an in depth guide for clinicians. Bone Marrow Transplant, 45, 1388–1395. doi:10.1038/bmt.2009.359.Google Scholar
Smith, M. M., Tremont, G. and Ott, B. R. (2009). A review of telephone-administered screening tests for dementia diagnosis. American Journal of Alzheimers Disease and Other Dementias, 24, 58–69. doi: 10.1177/1533317508327586.Google Scholar
Springate, B., Tremont, G. and Ott, B. R. (2012). Predicting functional impairments in cognitively impaired older adults using the Minnesota Cognitive Acuity Screen. Journal of Geriatric Psychiatry and Neurology, 25, 195–200. doi:10.1177/0891988712464820.Google Scholar
Strain, L. A., Blandford, A. A., Mitchell, L. A. and Hawranik, P. G. (2003). Cognitively impaired older adults: risk profiles for institutionalization. International Psychogeriatrics, 15, 351–366. doi:10.1017/S1041610203009608.Google Scholar
Tremont, G. et al. (2011). Use of the telephone-administered Minnesota Cognitive Acuity Screen to detect mild cognitive impairment. American Journal of Alzheimers Disease and Other Dementias, 26, 555–562. doi:10.1177/1533317511428151.Google Scholar
Tremont, G., Papandonatos, G. D. and Kelley, P. (2016). Prediction of cognitive and functional decline using the telephone-administered Minnesota Cognitive Acuity Screen. Journal of the American Geriatrics Society, 64, 608–613. doi:10.1111/jgs.13940.CrossRefGoogle ScholarPubMed
Wattmo, C., Paulsson, E., Minthon, L. and Londos, E. (2013). A longitudinal study of risk factors for community based home help services in Alzheimer's disease: the influence of cholinesterase inhibitor therapy. Clinical Interventions in Aging, 8, 329–339. doi:10.2147/CIA.S40087.Google Scholar
Yaari, R., Fleisher, A. S., Gamst, A. C., Bagwell, V. P. and Thal, L. J. (2006). Utility of the telephone interview for cognitive status for enrollment in clinical trials. Alzheimers and Dementia, 2, 104–109. doi:10.1016/j.jalz.2006.02.004.Google Scholar