title: Patterns of chronic multimorbidity in the elderly population.
abstract: OBJECTIVE
  To describe patterns of comorbidity and multimorbidity in elderly people.
  
  
  METHODS
  A community-based survey.
  
  
  METHODS
  Data were gathered from the Kungsholmen Project, a urban, community-based prospective cohort in Sweden.
  
  
  METHODS
  Adults aged 77 and older living in the community and in institutions of the geographically defined Kungsholmen area of Stockholm (N=1,099).
  
  
  METHODS
  Diagnoses based on physicians' examinations and supported by hospital records, drug use, and blood samples. Patterns of comorbidity and multimorbidity were evaluated using four analytical approaches: prevalence figures, conditional count, logistic regression models, and cluster analysis.
  
  
  RESULTS
  Visual impairments and heart failure were the diseases with the highest comorbidity (mean 2.9 and 2.6 co-occurring conditions, respectively), whereas dementia had the lowest (mean 1.4 comorbidities). Heart failure occurred rarely without any comorbidity (0.4%). The observed prevalence of comorbid pairs of conditions exceeded the expected prevalence for several circulatory diseases and for dementia and depression. Logistic regression analyses detected similar comorbid pairs. The cluster analysis revealed five clusters. Two clusters included vascular conditions (circulatory and cardiopulmonary clusters), and another included mental diseases along with musculoskeletal disorders. The last two clusters included only one major disease each (diabetes mellitus and malignancy) together with their most common consequences (visual impairment and anemia, respectively).
  
  
  CONCLUSIONS
  In persons with multimorbidity, there exists co-occurrence of diseases beyond chance, which clinicians need to take into account in their daily practice. Some pathological mechanisms behind the identified clusters are well known; others need further clarification to identify possible preventative strategies.
authors.affiliation: Aging Research Center, Department of Neurobiology, Karolinska Institutet, Stockholm, Sweden. Alessandra.Marengoni@ki.se
authors.forename: Alessandra
authors.initials: A
authors.lastname: Marengoni
authors.forename: Debora
authors.initials: D
authors.lastname: Rizzuto
authors.forename: Hui-Xin
authors.initials: HX
authors.lastname: Wang
authors.forename: Bengt
authors.initials: B
authors.lastname: Winblad
authors.forename: Laura
authors.initials: L
authors.lastname: Fratiglioni
mesh_terms: D000368:Aged; D016000:Cluster Analysis; D015897:Comorbidity / Q000639:trends*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D001523:Mental Disorders / Q000453:epidemiology; D009140:Musculoskeletal Diseases / Q000453:epidemiology; D015995:Prevalence; D013548:Sweden / Q000453:epidemiology; D014652:Vascular Diseases / Q000453:epidemiology