SSOAR Logo
    • Deutsch
    • English
  • Deutsch 
    • Deutsch
    • English
  • Einloggen
SSOAR ▼
  • Home
  • Über SSOAR
  • Leitlinien
  • Veröffentlichen auf SSOAR
  • Kooperieren mit SSOAR
    • Kooperationsmodelle
    • Ablieferungswege und Formate
    • Projekte
  • Kooperationspartner
    • Informationen zu Kooperationspartnern
  • Informationen
    • Möglichkeiten für den Grünen Weg
    • Vergabe von Nutzungslizenzen
    • Informationsmaterial zum Download
  • Betriebskonzept
Browsen und suchen Dokument hinzufügen OAI-PMH-Schnittstelle
JavaScript is disabled for your browser. Some features of this site may not work without it.

Download PDF
Volltext herunterladen

(615.5 KB)

Zitationshinweis

Bitte beziehen Sie sich beim Zitieren dieses Dokumentes immer auf folgenden Persistent Identifier (PID):
https://nbn-resolving.org/urn:nbn:de:0168-ssoar-98832-1

Export für Ihre Literaturverwaltung

Bibtex-Export
Endnote-Export

Statistiken anzeigen
Weiterempfehlen
  • Share via E-Mail E-Mail
  • Share via Facebook Facebook
  • Share via Bluesky Bluesky
  • Share via Reddit reddit
  • Share via Linkedin LinkedIn
  • Share via XING XING

Multimorbidity clusters and their contribution to well-being among the oldest old: Results based on a nationally representative sample in Germany

[Zeitschriftenartikel]

Hajek, André
Gyasi, Razak M.
Kostev, Karel
Soysal, Pinar
Veronese, Nicola
Smith, Lee
Jacob, Louis
Oh, Hans
Pengpid, Supa
Peltzer, Karl
König, Hans-Helmut

Abstract

Our aim was to identify multimorbidity clusters and, in particular, to examine their contribution to well-being outcomes among the oldest old in Germany. Methods: Data were taken from the large nationally representative D80+ study including community-dwelling and institutionalized individuals aged 8... mehr

Our aim was to identify multimorbidity clusters and, in particular, to examine their contribution to well-being outcomes among the oldest old in Germany. Methods: Data were taken from the large nationally representative D80+ study including community-dwelling and institutionalized individuals aged 80 years and over residing in Germany (n=8,773). The mean age was 85.6 years (SD: 4.1). Based on 21 chronic conditions, latent class analysis was carried out to explore multimorbidity (≥2 chronic conditions) clusters. Widely used tools were applied to quantify well-being outcomes. Results Approximately nine out of ten people aged 80 and over living in Germany were multimorbid. Four multimorbidity clusters were identified: relatively healthy class (30.2%), musculoskeletal class (44.8%), mental illness class (8.6%), and high morbidity class (16.4%). Being part of the mental disorders cluster was consistently linked to reduced well-being (in terms of low life satisfaction, high loneliness and lower odds of meaning in life), followed by membership in the high morbidity cluster. Conclusions Four multimorbidity clusters were detected among the oldest old in Germany. Particularly belonging to the mental disorders cluster is consistently associated with low well-being, followed by belonging to the high morbidity cluster. This stresses the need for efforts to target such vulnerable groups, pending future longitudinal research.... weniger

Thesaurusschlagwörter
Hochbetagter; chronische Krankheit; Lebenszufriedenheit; Einsamkeit; psychische Gesundheit; psychische Krankheit; Morbidität; Bundesrepublik Deutschland

Klassifikation
Gerontologie, Alterssoziologie

Freie Schlagwörter
D80+; Mulitimorbidität; Hochaltrige; Clusters; Latent class analysis

Sprache Dokument
Englisch

Publikationsjahr
2025

Zeitschriftentitel
Archives of Gerontology and Geriatrics (2025) 130

DOI
https://doi.org/10.1016/j.archger.2024.105726

ISSN
1872-6976

Status
Veröffentlichungsversion; begutachtet (peer reviewed)

Lizenz
Creative Commons - Namensnennung 4.0


GESIS LogoDFG LogoOpen Access Logo
Home  |  Impressum  |  Betriebskonzept  |  Datenschutzerklärung
© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.
 

 


GESIS LogoDFG LogoOpen Access Logo
Home  |  Impressum  |  Betriebskonzept  |  Datenschutzerklärung
© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.