Evidence map›Paper›PMID 36266620›Full record

ArticleBMC geriatrics2022

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

Finaba Berete, Stefaan Demarest, Rana Charafeddine, Karin De Ridder, Johan Vanoverloop, Herman Van Oyen, Olivier Bruyère, Johan Van der Heyden

Open access · goldAbstract read
In one paragraph

Article in BMC geriatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
6.7field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 26 citations in OpenAlex.

  1. The socioeconomic burden of pelvic floor disorders.Acta obstetricia et gynecologica Scandinavica · 2026
    Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Finaba BereteDepartment of Epidemiology and public health, Sciensano, Juliette Wytsmanstraat 14, 1050, Brussels, Belgium. finaba.berete@sciensano.be.
Stefaan DemarestDepartment of Epidemiology and public health, Sciensano, Juliette Wytsmanstraat 14, 1050, Brussels, Belgium.
Rana CharafeddineDepartment of Epidemiology and public health, Sciensano, Juliette Wytsmanstraat 14, 1050, Brussels, Belgium.
Karin De RidderDepartment of Epidemiology and public health, Sciensano, Juliette Wytsmanstraat 14, 1050, Brussels, Belgium.
Johan VanoverloopIntermutualistic Agency (IMA-AIM), Brussels, Belgium.
Herman Van OyenDepartment of Epidemiology and public health, Sciensano, Juliette Wytsmanstraat 14, 1050, Brussels, Belgium.
Olivier BruyèreWHO Collaborating Centre for Public Health aspects of musculoskeletal health and ageing, Division of Public Health, Epidemiology and Health Economics, University of Liège, Liège, Belgium.
Johan Van der HeydenDepartment of Epidemiology and public health, Sciensano, Juliette Wytsmanstraat 14, 1050, Brussels, Belgium.
Sciensano (Belgium) · BEGhent University · BEIntermutualist Agency · BEUniversity of Liège · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis 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.

methodsData 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.

resultsOver 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.

conclusionsOur 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.

Indexed as

Alzheimer DiseaseUrinary IncontinenceAgedBelgiumFemaleFollow-Up StudiesHumansMaleNursing HomesAdministrative dataCompeting risk analysisInstitutionalizationLinkageNursing home admissionOlder adultsPredictors

Identifiers

PMID36266620
PMCPMC9585772
OpenAlexW4306924919

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.