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

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          Abstract

          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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          Most cited references23

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          Prevalence of Dementia in the United States: The Aging, Demographics, and Memory Study

          Aim: To estimate the prevalence of Alzheimer’s disease (AD) and other dementias in the USA using a nationally representative sample. Methods: The Aging, Demographics, and Memory Study sample was composed of 856 individuals aged 71 years and older from the nationally representative Health and Retirement Study (HRS) who were evaluated for dementia using a comprehensive in-home assessment. An expert consensus panel used this information to assign a diagnosis of normal cognition, cognitive impairment but not demented, or dementia (and dementia subtype). Using sampling weights derived from the HRS, we estimated the national prevalence of dementia, AD and vascular dementia by age and gender. Results: The prevalence of dementia among individuals aged 71 and older was 13.9%, comprising about 3.4 million individuals in the USA in 2002. The corresponding values for AD were 9.7% and 2.4 million individuals. Dementia prevalence increased with age, from 5.0% of those aged 71–79 years to 37.4% of those aged 90 and older. Conclusions: Dementia prevalence estimates from this first nationally representative population-based study of dementia in the USA to include subjects from all regions of the country can provide essential information for effective planning for the impending healthcare needs of the large and increasing number of individuals at risk for dementia as our population ages.
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            Frequency of depression after stroke: a systematic review of observational studies.

            Although depression is an important sequelae of stroke, there is uncertainty regarding its frequency and outcome. We undertook a systematic review of all published nonexperimental studies (to June 2004) with prospective consecutive patient recruitment and quantification of depressive symptoms/illness after stroke. Data were available from 51 studies (reported in 96 publications) conducted between 1977 and 2002. Although frequencies varied considerably across studies, the pooled estimate was 33% (95% confidence interval, 29% to 36%) of all stroke survivors experiencing depression. Differences in case mix and method of mood assessment could explain some of the variation in estimates across studies. The data also suggest that depression resolves spontaneously within several months of onset in the majority of stroke survivors, with few receiving any specific antidepressant therapy or active management. Depression is common among stroke patients, with the risks of occurrence being similar for the early, medium, and late stages of stroke recovery. There is a pressing need for further research to improve clinical practice in this area of stroke care.
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              Probability of stroke: a risk profile from the Framingham Study.

              A health risk appraisal function has been developed for the prediction of stroke using the Framingham Study cohort. The stroke risk factors included in the profile are age, systolic blood pressure, the use of antihypertensive therapy, diabetes mellitus, cigarette smoking, prior cardiovascular disease (coronary heart disease, cardiac failure, or intermittent claudication), atrial fibrillation, and left ventricular hypertrophy by electrocardiogram. Based on 472 stroke events occurring during 10 years' follow-up from biennial examinations 9 and 14, stroke probabilities were computed using the Cox proportional hazards model for each sex based on a point system. On the basis of the risk factors in the profile, which can be readily determined on routine physical examination in a physician's office, stroke risk can be estimated. An individual's risk can be related to the average risk of stroke for persons of the same age and sex. The information that one's risk of stroke is several times higher than average may provide the impetus for risk factor modification. It may also help to identify persons at substantially increased stroke risk resulting from borderline levels of multiple risk factors such as those with mild or borderline hypertension and facilitate multifactorial risk factor modification.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2010
                29 December 2010
                02 January 2011
                : 5
                : 12
                : e15941
                Affiliations
                [1 ]Department of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
                [2 ]Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
                [3 ]Division of Health Economics, Health Policy and Health Services Research, Centre for Social Policy Research, University of Bremen, Bremen, Germany
                Yale University School of Medicine, United States of America
                Author notes

                Conceived and designed the experiments: HvdB HK IS HH KW. Analyzed the data: IS GS. Contributed reagents/materials/analysis tools: DK TK GS. Wrote the paper: IS EvL.

                Article
                PONE-D-10-00975
                10.1371/journal.pone.0015941
                3012106
                21209965
                30830c94-3f17-48a6-a116-1da9bc177e03
                Schäfer et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 19 August 2010
                : 30 November 2010
                Page count
                Pages: 10
                Categories
                Research Article
                Biology
                Population Biology
                Epidemiology
                Disease Informatics
                Medicine
                Clinical Research Design
                Epidemiology
                Diagnostic Medicine
                Epidemiology
                Clinical Epidemiology
                Disease Informatics
                Epidemiological Methods
                Epidemiology of Aging
                Social Epidemiology
                Non-Clinical Medicine
                Health Care Policy
                Disease Registries
                Public Health

                Uncategorized
                Uncategorized

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