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

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          Abstract

          Introduction

          Multimorbidity is a major concern in primary care. Nevertheless, evidence of prevalence and patterns of multimorbidity, and their determinants, are scarce. The aim of this study is to systematically review studies of the prevalence, patterns and determinants of multimorbidity in primary care.

          Methods

          Systematic review of literature published between 1961 and 2013 and indexed in Ovid (CINAHL, PsychINFO, Medline and Embase) and Web of Knowledge. Studies were selected according to eligibility criteria of addressing prevalence, determinants, and patterns of multimorbidity and using a pretested proforma in primary care. The quality and risk of bias were assessed using STROBE criteria. Two researchers assessed the eligibility of studies for inclusion (Kappa  = 0.86).

          Results

          We identified 39 eligible publications describing studies that included a total of 70,057,611 patients in 12 countries. The number of health conditions analysed per study ranged from 5 to 335, with multimorbidity prevalence ranging from 12.9% to 95.1%. All studies observed a significant positive association between multimorbidity and age (odds ratio [OR], 1.26 to 227.46), and lower socioeconomic status (OR, 1.20 to 1.91). Positive associations with female gender and mental disorders were also observed. The most frequent patterns of multimorbidity included osteoarthritis together with cardiovascular and/or metabolic conditions.

          Conclusions

          Well-established determinants of multimorbidity include age, lower socioeconomic status and gender. The most prevalent conditions shape the patterns of multimorbidity. However, the limitations of the current evidence base means that further and better designed studies are needed to inform policy, research and clinical practice, with the goal of improving health-related quality of life for patients with multimorbidity. Standardization of the definition and assessment of multimorbidity is essential in order to better understand this phenomenon, and is a necessary immediate step.

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          Most cited references33

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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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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                21 July 2014
                : 9
                : 7
                : e102149
                Affiliations
                [1 ]Central Research Unit, Institut Universitari d'Investigació en Atenció Primària Jordi Gol, Barcelona, Catalunya, Spain
                [2 ]Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain
                [3 ]Centre of Academic Primary Care, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
                [4 ]Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
                [5 ]Institute of General Practice and Family Medicine, Jena University Hospital, Friedrich-Schiller-University, School of Medicine, Jena, Germany
                [6 ]Discipline of General Practice, National University of Ireland, Galway, Ireland
                [7 ]Institute of General Practice, Johann Wolfgang Goethe University, Frankfurt/Main, Frankfurt, Germany
                [8 ]Health Services & Policy Research Group, School of Medicine, University of Exeter, Exeter, United Kingdom
                INRCA, Italy
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JMV CV QFB. Performed the experiments: JMV CV QFB GFM CS JB MF LG CM. Analyzed the data: QFB CV GFM JMV. Contributed reagents/materials/analysis tools: JMV CV QFB GFM CS JB MF LG CM. Wrote the paper: QFB CV GFM JVM. Critically revised and approved final version of the manuscript: JMV CV QFB GFM CS JB MF LG CM.

                Article
                PONE-D-14-06227
                10.1371/journal.pone.0102149
                4105594
                25048354
                dbd2edc7-3cb4-4fb8-8498-ad6ade39ec4e
                Copyright @ 2014

                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
                : 14 February 2014
                : 16 June 2014
                Page count
                Pages: 9
                Funding
                Jose M. Valderas was supported by a Clinician Scientist Award from the National Institute for Health Research, Department of Health, United Kingdom. Improving the management of long term conditions with the clinical use of patient reported outcome measures in Primary Care. Period: 01/3/2011-01/3/2016. This work has been also co-funded by by the Ministry of Science and Innovation through the Instituto Carlos III (ISCiii) as part the Primary Care Prevention and Health Promotion Research Network (rediAPP), by ISCiii-RETICS (RD12/0005) and by a grant for research projects ISCiii (PI12/00427). QFB is supported by a scholarship awarded by Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Epidemiology
                Cardiovascular Disease Epidemiology
                Clinical Epidemiology
                Epidemiology of Aging
                Lifecourse Epidemiology
                Social Epidemiology
                Health Care
                Health Care Policy
                Health Care Quality
                Health Services Research
                Health Statistics
                Long-Term Care
                Primary Care
                Public and Occupational Health
                Preventive Medicine

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                Uncategorized

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