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      Age and gender differences in the prevalence and patterns of multimorbidity in the older population

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          Abstract

          Background

          The coexistence of several chronic diseases in one same individual, known as multimorbidity, is an important challenge facing health care systems in developed countries. Recent studies have revealed the existence of multimorbidity patterns clustering systematically associated distinct clinical entities. We sought to describe age and gender differences in the prevalence and patterns of multimorbidity in men and women over 65 years.

          Methods

          Observational retrospective multicentre study based on diagnostic information gathered from electronic medical records of 19 primary care centres in Aragon and Catalonia. Multimorbidity patterns were identified through exploratory factor analysis. We performed a descriptive analysis of previously obtained patterns (i.e. cardiometabolic (CM), mechanical (MEC) and psychogeriatric (PG)) and the diseases included in the patterns stratifying by sex and age group.

          Results

          67.5% of the aged population suffered two or more chronic diseases. 32.2% of men and 45.3% of women were assigned to at least one specific pattern of multimorbidity, and 4.6% of men and 8% of women presented more than one pattern simultaneously. Among women over 65 years the most frequent pattern was the MEC pattern (33.3%), whereas among men it was the CM pattern (21.2%). While the prevalence of the CM and MEC patterns decreased with age, the PG pattern showed a higher prevalence in the older age groups.

          Conclusions

          Significant gender differences were observed in the prevalence of multimorbidity patterns, women showing a higher prevalence of the MEC and PG patterns, as well as a higher degree of pattern overlapping, probably due to a higher life expectancy and/or worse health. Future studies on multimorbidity patterns should take into account these differences and, therefore, the study of multimorbidity and its impact should be stratified by age and sex.

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          Most cited references16

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          Causes and consequences of comorbidity: a review.

          A literature search was carried out to identify and summarize the existing information on causes and consequences of comorbidity of chronic somatic diseases. A selection of 82 articles met our inclusion criteria. Very little work has been done on the causes of comorbidity. On the other hand, much work has been done on consequences of comorbidity, although comorbidity is seldom the main subject of study. We found comorbidity in general to be associated with mortality, quality of life, and health care. The consequences of specific disease combinations, however, depended on many factors. We recommend more etiological studies on shared risk factors, especially for those comorbidities that occur at a higher rate than expected. New insights in this field can lead to better prevention strategies. Health care workers need to take comorbid diseases into account in monitoring and treating patients. Future studies on consequences of comorbidity should investigate specific disease combinations.
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            Guiding principles for the care of older adults with multimorbidity: an approach for clinicians: American Geriatrics Society Expert Panel on the Care of Older Adults with Multimorbidity.

            (2012)
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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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                Author and article information

                Contributors
                Journal
                BMC Geriatr
                BMC Geriatr
                BMC Geriatrics
                BioMed Central
                1471-2318
                2014
                17 June 2014
                : 14
                : 75
                Affiliations
                [1 ]Department of Health Wellbeing and Family, Government of Aragón, Zaragoza, Spain
                [2 ]Department of Microbiology, Preventive Medicine and Public Health, University of Zaragoza, Zaragoza, Spain
                [3 ]EpiChron Research Group on Chronic Diseases, Aragón Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Via Universitas 36, 50017 Zaragoza, Spain
                [4 ]Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Carlos III Health Institute, Zaragoza, Spain
                [5 ]Teaching Unit of Preventive Medicine and Public Health, Aragón Health Sciences Institute (IACS), IIS Aragón, Zaragoza, Spain
                [6 ]Zaragoza Sector III Primary Care Directorate, Aragon Health Service (SALUD), Zaragoza, Spain
                [7 ]Planning Management, Badalona Serveis Assistencials S.A., Badalona, Spain
                [8 ]Socio-Sanitary Assessment Unit, Miguel Servet University Hospital, Zaragoza, Spain
                Article
                1471-2318-14-75
                10.1186/1471-2318-14-75
                4070347
                24934411
                4312d982-fd2e-4fd9-b058-0ee7cca4bf89
                Copyright © 2014 Abad-Díez et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 23 September 2013
                : 12 June 2014
                Categories
                Research Article

                Geriatric medicine
                multimorbidity,comorbidity,chronic disease,primary health care,prevalence,frail elderly

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