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@ARTICLE{Wild:148717,
      author       = {B. Wild and D. Heider and D. Schellberg and F. Böhlen and
                      B. Schöttker$^*$ and D. C. Laetsch$^*$ and H.-H. König and
                      J. Slaets},
      title        = {{C}aring for the elderly: {A} person-centered segmentation
                      approach for exploring the association between health care
                      needs, mental health care use, and costs in {G}ermany.},
      journal      = {PLOS ONE},
      volume       = {14},
      number       = {12},
      issn         = {1932-6203},
      address      = {San Francisco, California, US},
      publisher    = {PLOS},
      reportid     = {DKFZ-2019-03243},
      pages        = {e0226510 -},
      year         = {2019},
      abstract     = {Person-centered care demands the evaluation of needs and
                      preferences of the patients. In this study, we conducted a
                      segmentation analysis of a large sample of older people
                      based on their bio-psycho-social-needs and functioning. The
                      aim of this study was to clarify differences in health care
                      use and costs of the elderly in Germany.Data was derived
                      from the 8-year follow-up of the ESTHER study-a German
                      epidemiological study of the elderly population. Trained
                      medical doctors visited n = 3124 participants aged 57 to 84
                      years in their home. Bio-psycho-social health care needs
                      were assessed using the INTERMED for the Elderly (IM-E)
                      interview. Further information was measured using
                      questionnaires or assessment scales (Barthel index, Patients
                      Health Questionnaire (PHQ) etc.). The segmentation analysis
                      applied a factor mixture model (FMM) that combined both a
                      confirmatory factor analysis and a latent class analysis.In
                      total, n = 3017 persons were included in the study. Results
                      of the latent class analysis indicated that a
                      five-cluster-model best fit the data. The largest cluster
                      $(48\%)$ can be described as healthy, one cluster $(13.9\%)$
                      shows minor physical complaints and higher social support,
                      while the third cluster $(24.3\%)$ includes persons with
                      only a few physical and psychological difficulties ('minor
                      physical and psychological complaints'). One of the profiles
                      $(10.5\%)$ showed high and complex bio-psycho-social health
                      care needs ('complex needs') while another profile $(2.5\%)$
                      can be labelled as 'frail'. Mean values of all psychosomatic
                      variables-including the variable health care costs-gradually
                      increased over the five clusters. Use of mental health care
                      was comparatively low in the more burdened clusters. In the
                      profiles 'minor physical and psychological complaints' and
                      'complex needs', only half of the persons suffering from a
                      mental disorder were treated by a mental health
                      professional; in the frail cluster, only a third of those
                      with a depression or anxiety disorder received mental health
                      care.The segmentation of the older people of this study
                      sample led to five different clusters that vary profoundly
                      regarding their bio-psycho-social needs. Results indicate
                      that elderly persons with complex bio-psycho-social needs do
                      not receive appropriate mental health care.},
      cin          = {C070},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31856192},
      doi          = {10.1371/journal.pone.0226510},
      url          = {https://inrepo02.dkfz.de/record/148717},
}