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Article: 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

TitleSurvival 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
Authors
Issue Date2009
PublisherOxford University Press. The Journal's web site is located at http://eurpub.oxfordjournals.org/
Citation
European Journal Of Public Health, 2009, v. 19 n. 3, p. 308-312 How to Cite?
AbstractBackground: With the intention to aid planning for elderly focused public health and residential care needs in rapidly aging societies, a simple model using only age, gender and three Minimum Data Set (MDS) subscales (MDS-ADL Self-Performance Hierarchy, MDS-Cognitive Performance and the MDS-Changes in Health, End-stage disease and Symptoms and Signs scales) was used to estimate long-term survival of older people moving into nursing homes. Methods: A total of 1820 nursing home residents were assessed by the MDS 2.0 and their mortality status 5 years later was used to develop a survival prediction model. Result: In December 2006, 54.2 of subjects were dead. Older age at nursing home admission (HR 1.036 per 1-year increment, 95 CI 1.0281.045), men (HR 1.895, 95 CI 1.6512.175), higher impairment level according to the MDS-ADL (HR 1.135 per 1-unit increment, 95 CI 1.0991.173) and MDS-CPS (HR 1.077 per 1-unit increment, 95 CI 1.0331.123), and more frail on the MDS-CHESS (HR 1.150 per 1-unit increment, 95 CI 1.0421.268), were all independent predictors of shorter survival after nursing home admission in multivariate analysis. Survival function was derived from the fitted Cox regression model. Survival time of nursing home residents with different combinations of risk factors were estimated through the survival function. Conclusion: The MDS-ADL, MDS-CPS and MDS-CHESS scales, in addition to age and gender, provide prognostic information in terms of survival time after institutionalization. The model may be useful for health care and residential care planning in an ageing community.
Persistent Identifierhttp://hdl.handle.net/10722/178300
ISSN
2015 Impact Factor: 2.751
2015 SCImago Journal Rankings: 1.284
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLee, JSWen_US
dc.contributor.authorChau, PPHen_US
dc.contributor.authorHui, Een_US
dc.contributor.authorChan, Fen_US
dc.contributor.authorWoo, Jen_US
dc.date.accessioned2012-12-19T09:45:08Z-
dc.date.available2012-12-19T09:45:08Z-
dc.date.issued2009en_US
dc.identifier.citationEuropean Journal Of Public Health, 2009, v. 19 n. 3, p. 308-312en_US
dc.identifier.issn1101-1262en_US
dc.identifier.urihttp://hdl.handle.net/10722/178300-
dc.description.abstractBackground: With the intention to aid planning for elderly focused public health and residential care needs in rapidly aging societies, a simple model using only age, gender and three Minimum Data Set (MDS) subscales (MDS-ADL Self-Performance Hierarchy, MDS-Cognitive Performance and the MDS-Changes in Health, End-stage disease and Symptoms and Signs scales) was used to estimate long-term survival of older people moving into nursing homes. Methods: A total of 1820 nursing home residents were assessed by the MDS 2.0 and their mortality status 5 years later was used to develop a survival prediction model. Result: In December 2006, 54.2 of subjects were dead. Older age at nursing home admission (HR 1.036 per 1-year increment, 95 CI 1.0281.045), men (HR 1.895, 95 CI 1.6512.175), higher impairment level according to the MDS-ADL (HR 1.135 per 1-unit increment, 95 CI 1.0991.173) and MDS-CPS (HR 1.077 per 1-unit increment, 95 CI 1.0331.123), and more frail on the MDS-CHESS (HR 1.150 per 1-unit increment, 95 CI 1.0421.268), were all independent predictors of shorter survival after nursing home admission in multivariate analysis. Survival function was derived from the fitted Cox regression model. Survival time of nursing home residents with different combinations of risk factors were estimated through the survival function. Conclusion: The MDS-ADL, MDS-CPS and MDS-CHESS scales, in addition to age and gender, provide prognostic information in terms of survival time after institutionalization. The model may be useful for health care and residential care planning in an ageing community.en_US
dc.languageengen_US
dc.publisherOxford University Press. The Journal's web site is located at http://eurpub.oxfordjournals.org/en_US
dc.relation.ispartofEuropean Journal of Public Healthen_US
dc.rightsEuropean Journal of Public Health. Copyright © Oxford University Press.-
dc.subject.meshActivities Of Daily Livingen_US
dc.subject.meshAge Factorsen_US
dc.subject.meshAgeden_US
dc.subject.meshAged, 80 And Overen_US
dc.subject.meshCognitionen_US
dc.subject.meshCritical Illnessen_US
dc.subject.meshFemaleen_US
dc.subject.meshFrail Elderly - Statistics & Numerical Dataen_US
dc.subject.meshGeriatric Assessmenten_US
dc.subject.meshHealth Statusen_US
dc.subject.meshHomes For The Aged - Statistics & Numerical Dataen_US
dc.subject.meshHumansen_US
dc.subject.meshLong-Term Care - Methods - Statistics & Numerical Dataen_US
dc.subject.meshMaleen_US
dc.subject.meshModels, Biologicalen_US
dc.subject.meshNursing Homes - Statistics & Numerical Dataen_US
dc.subject.meshProportional Hazards Modelsen_US
dc.subject.meshRisk Factorsen_US
dc.subject.meshSex Factorsen_US
dc.subject.meshSurvival Analysisen_US
dc.titleSurvival 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 scalesen_US
dc.typeArticleen_US
dc.identifier.emailChau, PPH: phpchau@hku.hken_US
dc.identifier.authorityChau, PPH=rp00574en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1093/eurpub/ckp006en_US
dc.identifier.pmid19221020-
dc.identifier.scopuseid_2-s2.0-66249148988en_US
dc.identifier.hkuros158573-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-66249148988&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume19en_US
dc.identifier.issue3en_US
dc.identifier.spage308en_US
dc.identifier.epage312en_US
dc.identifier.isiWOS:000266346700018-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridLee, JSW=14028651000en_US
dc.identifier.scopusauthoridChau, PPH=7102266397en_US
dc.identifier.scopusauthoridHui, E=15123893300en_US
dc.identifier.scopusauthoridChan, F=14059603800en_US
dc.identifier.scopusauthoridWoo, J=36040369400en_US
dc.identifier.citeulike5657869-

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