Patterns of Chronic Multimorbidity in the Elderly Population

• Published in: Journal of the American Geriatrics Society (JAGS) 2009-02, Vol.57 (2), p.225-230
• Main Authors: Marengoni, Alessandra, Rizzuto, Debora, Wang, Hui-Xin, Winblad, Bengt, Fratiglioni, Laura
• Format: Article
• Language: English
• Subjects: Aged; Biological and medical sciences; chronic diseases; Chronic illnesses; Cluster Analysis; comorbidity; Comorbidity - trends; Female; General aspects; Humans; Logistic Models; Male; Medical sciences; Medicin och hälsovetenskap; Mental Disorders - epidemiology; Miscellaneous; Morbidity; multimorbidity; Musculoskeletal Diseases - epidemiology; old age; Older people; Prevalence; Public health. Hygiene; Public health. Hygiene-occupational medicine; Sweden - epidemiology; Vascular Diseases - epidemiology
• ISSN: 0002-8614; 1532-5415; 1532-5415
• DOI: 10.1111/j.1532-5415.2008.02109.x

OBJECTIVES: To describe patterns of comorbidity and multimorbidity in elderly people. DESIGN: A community‐based survey. SETTING: Data were gathered from the Kungsholmen Project, a urban, community‐based prospective cohort in Sweden. PARTICIPANTS: Adults aged 77 and older living in the community and in institutions of the geographically defined Kungsholmen area of Stockholm (N=1,099). MEASUREMENTS: 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). CONCLUSION: 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.