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      Prevalence, Determinants and Patterns of Multimorbidity in Primary Care: A Systematic Review of Observational Studies

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          Multimorbidity Patterns in the Elderly: A New Approach of Disease Clustering Identifies Complex Interrelations between Chronic Conditions

          Objective Multimorbidity is a common problem in the elderly that is significantly associated with higher mortality, increased disability and functional decline. Information about interactions of chronic diseases can help to facilitate diagnosis, amend prevention and enhance the patients' quality of life. The aim of this study was to increase the knowledge of specific processes of multimorbidity in an unselected elderly population by identifying patterns of statistically significantly associated comorbidity. Methods Multimorbidity patterns were identified by exploratory tetrachoric factor analysis based on claims data of 63,104 males and 86,176 females in the age group 65+. Analyses were based on 46 diagnosis groups incorporating all ICD-10 diagnoses of chronic diseases with a prevalence ≥ 1%. Both genders were analyzed separately. Persons were assigned to multimorbidity patterns if they had at least three diagnosis groups with a factor loading of 0.25 on the corresponding pattern. Results Three multimorbidity patterns were found: 1) cardiovascular/metabolic disorders [prevalence female: 30%; male: 39%], 2) anxiety/depression/somatoform disorders and pain [34%; 22%], and 3) neuropsychiatric disorders [6%; 0.8%]. The sampling adequacy was meritorious (Kaiser-Meyer-Olkin measure: 0.85 and 0.84, respectively) and the factors explained a large part of the variance (cumulative percent: 78% and 75%, respectively). The patterns were largely age-dependent and overlapped in a sizeable part of the population. Altogether 50% of female and 48% of male persons were assigned to at least one of the three multimorbidity patterns. Conclusion This study shows that statistically significant co-occurrence of chronic diseases can be subsumed in three prevalent multimorbidity patterns if accounting for the fact that different multimorbidity patterns share some diagnosis groups, influence each other and overlap in a large part of the population. In recognizing the full complexity of multimorbidity we might improve our ability to predict needs and achieve possible benefits for elderly patients who suffer from multimorbidity.
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            Patterns of chronic multimorbidity in the elderly population.

            To describe patterns of comorbidity and multimorbidity in elderly people. A community-based survey. Data were gathered from the Kungsholmen Project, a urban, community-based prospective cohort in Sweden. Adults aged 77 and older living in the community and in institutions of the geographically defined Kungsholmen area of Stockholm (N=1,099). 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. 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). 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.
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              Defining chronic conditions for primary care with ICPC-2.

              With the increasing prevalence of chronic conditions, there is need for a standardized definition of chronicity for use in research, to evaluate the population prevalence and general practice management of chronic conditions. Our aims were to determine the characteristics required to define chronicity, apply them to a primary care classification and provide a defined codeset of chronic conditions. A literature review evaluated characteristics used to define chronic conditions. The final set of characteristics was applied to the International Classification of Primary Care-Version 2 (ICPC-2) through more specific terms available in ICPC-2 PLUS, an extended terminology classified to ICPC-2. A set of ICPC-2 rubrics was delineated as representing chronic conditions. Factors found to be relevant to a definition of chronic conditions for research were: duration; prognosis; pattern; and sequelae. Within ICPC-2, 129 rubrics were described as 'chronic', and another 20 rubrics had elements of chronicity. Duration was the criterion most frequently satisfied (98.4% of chronic rubrics), while 88.2% of rubrics met at least three of the four criteria. Monitoring the prevalence and management of chronic conditions is of increasing importance. This study provided evidence for multifaceted definitions of chronicity. While all characteristics examined could be used by those interested in chronicity, the list has been designed to identify chronic conditions managed in Australian general practice, and is therefore not a nomenclature of all chronic conditions. Subsequent analysis of chronic conditions using pre-existing data sets will provide a baseline measure of chronic condition prevalence and management in general practice.
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                10.1371/journal.pone.0102149
                http://creativecommons.org/licenses/by/4.0/

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