Literature DB >> 36266620

Predictors of nursing home admission in the older population in Belgium: a longitudinal follow-up of health interview survey participants.

Finaba Berete1,2, Stefaan Demarest3, Rana Charafeddine3, Karin De Ridder3, Johan Vanoverloop4, Herman Van Oyen3,5, Olivier Bruyère6, Johan Van der Heyden3.   

Abstract

BACKGROUND: This study examines predictors of nursing home admission (NHA) in Belgium in order to contribute to a better planning of the future demand for nursing home (NH) services and health care resources.
METHODS: Data derived from the Belgian 2013 health interview survey were linked at individual level with health insurance data (2012 tot 2018). Only community dwelling participants, aged ≥65 years at the time of the survey were included in this study (n = 1930). Participants were followed until NHA, death or end of study period, i.e., December 31, 2018. The risk of NHA was calculated using a competing risk analysis.
RESULTS: Over the follow-up period (median 5.29 years), 226 individuals were admitted to a NH and 268 died without admission to a NH. The overall cumulative risk of NHA was 1.4, 5.7 and 13.1% at respectively 1 year, 3 years and end of follow-up period. After multivariable adjustment, higher age, low educational attainment, living alone and use of home care services were significantly associated with a higher risk of NHA. A number of need factors (e.g., history of falls, suffering from urinary incontinence, depression or Alzheimer's disease) were also significantly associated with a higher risk of NHA. On the contrary, being female, having multimorbidity and increased contacts with health care providers were significantly associated with a decreased risk of NHA. Perceived health and limitations were both significant determinants of NHA, but perceived health was an effect modifier on limitations and vice versa.
CONCLUSIONS: Our findings pinpoint important predictors of NHA in older adults, and offer possibilities of prevention to avoid or delay NHA for this population. Practical implications include prevention of falls, management of urinary incontinence at home and appropriate and timely management of limitations, depression and Alzheimer's disease. Focus should also be on people living alone to provide more timely contacts with health care providers. Further investigation of predictors of NHA should include contextual factors such as the availability of nursing-home beds, hospital beds, physicians and waiting lists for NHA.
© 2022. The Author(s).

Entities:  

Keywords:  Administrative data; Competing risk analysis; Institutionalization; Linkage; Nursing home admission; Older adults; Predictors

Mesh:

Year:  2022        PMID: 36266620      PMCID: PMC9585772          DOI: 10.1186/s12877-022-03496-4

Source DB:  PubMed          Journal:  BMC Geriatr        ISSN: 1471-2318            Impact factor:   4.070


  40 in total

Review 1.  Prediction of institutionalization in the elderly. A systematic review.

Authors:  Melanie Luppa; Tobias Luck; Siegfried Weyerer; Hans-Helmut König; Elmar Brähler; Steffi G Riedel-Heller
Journal:  Age Ageing       Date:  2009-11-23       Impact factor: 10.668

2.  Competing risk of death: an important consideration in studies of older adults.

Authors:  Sarah D Berry; Long Ngo; Elizabeth J Samelson; Douglas P Kiel
Journal:  J Am Geriatr Soc       Date:  2010-03-22       Impact factor: 5.562

3.  Long-Term Nursing Home Entry: A Prognostic Model for Older Adults with a Family or Unpaid Caregiver.

Authors:  Jennifer L Wolff; John Mulcahy; David L Roth; Irena S Cenzer; Judith D Kasper; Jin Huang; Kenneth E Covinsky
Journal:  J Am Geriatr Soc       Date:  2018-08-09       Impact factor: 5.562

4.  Characteristics predicting nursing home admission in the program of all-inclusive care for elderly people.

Authors:  Susan M Friedman; Donald M Steinwachs; Paul J Rathouz; Lynda C Burton; Dana B Mukamel
Journal:  Gerontologist       Date:  2005-04

5.  Medically recognized urinary incontinence and risks of hospitalization, nursing home admission and mortality.

Authors:  D H Thom; M N Haan; S K Van Den Eeden
Journal:  Age Ageing       Date:  1997-09       Impact factor: 10.668

6.  Predictors of nursing home admission of individuals without a dementia diagnosis before admission - results from the Leipzig Longitudinal Study of the Aged (LEILA 75+).

Authors:  Melanie Luppa; Tobias Luck; Herbert Matschinger; Hans-Helmut König; Steffi G Riedel-Heller
Journal:  BMC Health Serv Res       Date:  2010-06-29       Impact factor: 2.655

7.  Urinary incontinence in elderly nursing home patients.

Authors:  J G Ouslander; R L Kane; I B Abrass
Journal:  JAMA       Date:  1982-09-10       Impact factor: 56.272

8.  PSHREG: a SAS macro for proportional and nonproportional subdistribution hazards regression.

Authors:  Maria Kohl; Max Plischke; Karen Leffondré; Georg Heinze
Journal:  Comput Methods Programs Biomed       Date:  2014-12-03       Impact factor: 5.428

9.  Development and validation of classifiers and variable subsets for predicting nursing home admission.

Authors:  Mikko Nuutinen; Riikka-Leena Leskelä; Ella Suojalehto; Anniina Tirronen; Vesa Komssi
Journal:  BMC Med Inform Decis Mak       Date:  2017-04-13       Impact factor: 2.796

Review 10.  Predicting nursing home admission in the U.S: a meta-analysis.

Authors:  Joseph E Gaugler; Sue Duval; Keith A Anderson; Robert L Kane
Journal:  BMC Geriatr       Date:  2007-06-19       Impact factor: 3.921

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