Background Persons with certain neurological conditions have higher mortality rates than the population without neurological conditions, but the risk factors for increased mortality within diagnostic groups are less well understood. The interRAI CHESS scale has been shown to be a strong predictor of mortality in the overall population of persons receiving health care in community and institutional settings. This study examines the performance of CHESS as a predictor of mortality among persons with 11 different neurological conditions. Methods Survival analyses were done with interRAI assessments linked to mortality data among persons in home care (n = 359,940), complex continuing care hospitals/units (n = 88,721), and nursing homes (n = 185,309) in seven Canadian provinces/territories. Results CHESS was a significant predictor of mortality in all 3 care settings for the 11 neurological diagnostic groups considered after adjusting for age and sex. The distribution of CHESS scores varied between diagnostic groups and within diagnostic groups in different care settings. Conclusions CHESS is a valid predictor of mortality in neurological populations in community and institutional care. It may prove useful for several clinical, administrative, policy-development, evaluation and research purposes. Because it is routinely gathered as part of normal clinical practice in jurisdictions (like Canada) that have implemented interRAI assessment instruments, CHESS can be derived without additional need for data collection.
References
[1]
World Health Organization (2007) Global burden of neurological disorders – estimates and projections. Neurological Disorders: Public Health Challenges, Geneva: World Health Organization. pp. 27–39.
[2]
Pritchard C, Mayers A, Baldwin D (2013) Changing patterns of neurological mortality in the 10 major developed countries – 1979–2010. Public Health 127: 357–368. doi: 10.1016/j.puhe.2012.12.018
[3]
Czira ME, Baune BT, Roesler A, Pfadenhauer K, Trenkwalder C, et al. (2014) Association between neurological disorders, functioning and mortality in the elderly. Acta Neurol Scand 129: 1–9. doi: 10.1111/ane.12220
[4]
Kingwell E, van der Kop M, Zhao Y, Shirani A, Zhu F, et al. (2012) Relative mortality and survival in multiple sclerosis: Findings from British Columbia, Canada. J Neurol Neurosurg Psychiatry 83: 61–66. doi: 10.1136/jnnp-2011-300616
[5]
Kaufmann P, Levy G, Thompson JLP, DelBene ML, Battista V, et al. (2005) The ALSFRSr predicts survival time in an ALS clinic population. Neurology 64: 38–43. doi: 10.1212/01.wnl.0000148648.38313.64
[6]
Scotton WJ, Scott KM, Moore DH, Almedom L, Wijesekera LH, et al. (2012) Prognostic categories for amyotrophic lateral sclerosis. Amyotroph Lateral Scler 13: 502–508. doi: 10.3109/17482968.2012.679281
[7]
Forsaa EB, Larsen JP, Wentzel-Larsen T, Alves G (2010) What predicts mortality in Parkinson's Disease? A prospective population-based long-term study. Neurology 75: 1270–1276. doi: 10.1212/wnl.0b013e3181f61311
[8]
Steyerberg EW, Mushkudiani N, Perel P, Butcher I, Lu J (2008) Predicting outcome after traumatic brain injury: Development and international validation of prognostic scores based on admission characteristics, PLoS Med. 5: 1251–1261. doi: 10.1371/journal.pmed.0050165
[9]
Hirdes JP, Frijters DH, Teare GF (2003) The MDS-CHESS scale: a new measure to predict mortality in institutionalized older people. J Am Geriatr Soc 51: 96–100. doi: 10.1034/j.1601-5215.2002.51017.x
[10]
Mor V, Intrator O, Unruh MA, Cai S (2011) Temporal and geographic variation in the validity and internal consistency of the nursing home Resident Assessment Minimum Data Set 2.0. BMC Health Serv Res 11: 78. doi: 10.1186/1472-6963-11-78
[11]
Hjaltadóttir I, Hallberg IR, Ekwall AK, Nyberg P (2011) Predicting mortality of residents at admission to nursing home: a longitudinal cohort study. BMC Health Serv Res 11: 86. doi: 10.1186/1472-6963-11-86
[12]
Lee JS, Chau PP, Hui E, Chan F, Woo J (2009) Survival prediction in nursing home residents using the Minimum Data Set subscales: ADL Self-Performance Hierarchy, Cognitive Performance and the Changes in Health, End-stage disease and Symptoms and Signs scales. Eur J Public Health19: 308–312. doi: 10.1093/eurpub/ckp006
[13]
Armstrong JJ, Stolee P, Hirdes JP, Poss JW (2010) Examining three frailty conceptualizations in their ability to predict negative outcomes for home-care clients. Age Ageing 39: 755–758. doi: 10.1093/ageing/afq121
[14]
Hirdes JP, Freeman S, Smith TF, Stolee P (2012) Predictors of caregiver distress among palliative home care clients in Ontario: evidence based on the interRAI Palliative Care. Palliat Support Care 10: 155–163. doi: 10.1017/s1478951511000824
[15]
Lutomski JE, Baars MAE, Buurman BM, den Elzen WPJ, Jansen APD, et al. (2013) Validation of a Frailty Index from the Older Persons and Informal Caregivers Survey Minimum Data Set. J Am Geriatr Soc 61: 1625–1627. doi: 10.1111/jgs.12430
[16]
Rockwood K, Mitnitski A (2011) Frailty defined by deficit accumulation and geriatric medicine defined by frailty. Clin Geriatr Med 27: 17–26. doi: 10.1016/j.cger.2010.08.008
[17]
Mitnitski AB, Mogilner AJ, MacKnight C, Rockwood K (2002) The mortality rate as a function of accumulated deficits in a frailty index. Mech Ageing Dev 123: 1457–1460. doi: 10.1016/s0047-6374(02)00082-9
[18]
Hirdes JP (2006) Addressing the health needs of frail elderly people: Ontario's experience with an integrated health information system. Age Ageing 35: 329–331. doi: 10.1093/ageing/afl036
[19]
Carpenter GI, Hirdes JP (2013) Using interRAI assessment systems to measure and maintain quality of long-term care in OECD/European Commission A Good Life in Old Age? Monitoring and Improving Quality in Long-term Care, OECD Health Policy Studies, OECD Publishing 93–139.
[20]
Tjam EY, Heckman GA, Smith S, Arai B, Hirdes J, et al. (2012) Predicting heart failure mortality in frail seniors: Comparing the NYHA functional classification with the Resident Assessment Instrument (RAI) 2.0. Int J Cardiol 155: 75–80. doi: 10.1016/j.ijcard.2011.01.031
[21]
Bernabei R, Gray L, Hirdes J, Pei X, Henrard J-C, et al.. (2009). International Gerontology in Hazzard's Geriatric Medicine and Gerontology 6th Edition, Halter JB, Ouslander JG, Tinetti ME, Studenski S, High KP, Asthana S (Eds), New York: McGraw Medical 69–96.
[22]
Caesar-Chavannes CR, MacDonald S (2013) National Population Health Study of Neurological Conditions in Canada. Chronic Dis Inj Can33: 188–191.
[23]
Hirdes JP, Ljunggren G, Morris JN, Frijters DH, Finne Soveri H, et al. (2008) Reliability of the interRAI suite of assessment instruments: a 12-country study of an integrated health information system. BMC Health Serv Res 8: 277. doi: 10.1186/1472-6963-8-277
[24]
Gray LC, Berg K, Fries BE, Henrard JC, Hirdes JP, et al. (2009) Sharing clinical information across care settings: the birth of an integrated assessment system. BMC Health Serv Res 9: 71. doi: 10.1186/1472-6963-9-71
[25]
Poss JW, Jutan NM, Hirdes JP, Fries BE, Morris JN, et al. (2008) A review of evidence on the reliability and validity of Minimum Data Set data. Healthc Manage Forum 21: 33–39. doi: 10.1016/s0840-4704(10)60127-5
[26]
Landi F, Tua E, Onder G, Carrara B, Sgadari A, et al. (2000) Rinaldi C, Gambassi G, Lattanzio F, Bernabei R; SILVERNET-HC Study Group of Bergamo. Minimum data set for home care: a valid instrument to assess frail older people living in the community. Med Care 38: 1184–1190. doi: 10.1097/00005650-200012000-00005
[27]
Hirdes JP, Poss JW, Caldarelli H, Fries BE, Morris JN, et al. (2013) An evaluation of data quality in Canada's Continuing Care Reporting System (CCRS): secondary analyses of Ontario data submitted between 1996 and 2011. BMC Med Inform Decis Mak 13: 27. doi: 10.1186/1472-6947-13-27
[28]
Foebel AD, Hirdes JP, Heckman GA, Kergoat MJ, Patten S, et al. (2013) Diagnostic data for neurological conditions in interRAI assessments in home care, nursing home and mental health care settings: a validity study. BMC Health Serv Res 13: 457. doi: 10.1186/1472-6963-13-457
[29]
Gambassi G, Landi F, Peng L, Brostrup-Jensen C, Calore K, et al. (1998) Validity of diagnostic and drug data in standardized nursing home resident assessments: potential for geriatric pharmacoepidemiology. SAGE Study Group. Systematic Assessment of Geriatric drug use via Epidemiology. Med Care 36: 167–179. doi: 10.1097/00005650-199802000-00006
[30]
Sabia S, Dumurgier J, Tavernier B, Head J, Tzourio C, et al. (2014) Change in fast walking speed preceding death: Results from a prospective longitudinal cohort study. J Gerontol A Biol Sci Med Sci 69: 354–356. doi: 10.1093/gerona/glt114