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      Prevalence and patterns of multimorbidity in Amazon Region of Brazil and associated determinants: a cross-sectional study

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          Abstract

          Objectives

          To estimate the prevalence of multimorbidity and to identify factors associated with it in the adult population from the metropolitan region of Manaus.

          Design

          Cross-sectional population-based study.

          Setting

          Interviews conducted between May and August of 2015 in eight cities that compose the metropolitan region of Manaus, Amazonas, Brazil.

          Participants

          4001 adults aged ≥18 years.

          Primary outcome measures

          Multimorbidity, measured by the occurrence of ≥2 and ≥3 chronic diseases, was the primary outcome. The associated factors were investigated by calculating the prevalence ratio (PR) obtained by Poisson regression, with robust adjustment of the variance in a hierarchical model. A factor analysis was conducted to investigate multimorbidity clusters.

          Results

          Half of the interviewees were women. The presence of a chronic disease was reported by 57.2% (95% CI 56.6% to 59.7%) of the interviewees, and the mean morbidity was 1.2 (1.1–1.2); 29.0% (95% CI 27.6% to 30.5%) reported ≥2 morbidities and 15.2% (95% CI 14.1% to 16.4%) reported ≥3 chronic conditions. Back pain was reported by one-third of the interviewees. Multimorbidity was highest in women, PR=1.66 (95% CI 1.50 to 1.83); the elderly, PR=5.68 (95% CI 4.51 to 7.15) and individuals with worse health perception, PR=3.70 (95% CI 2.73 to 5.00). Associated factors also included undergoing medical consultations, hospitalisation in the last year, suffering from dengue in the last year and seeking the same healthcare service. Factor analysis revealed a pattern of multimorbidity in women. The factor loading the most strength of association in women was heart disease. In men, an association was identified in two groups, and lung disease was the disease with the highest factorial loading.

          Conclusion

          Multimorbidity was frequent in the metropolitan region of Manaus. It occurred most often in women, in the elderly and in those with worse health perception.

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          Most cited references34

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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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            Socioeconomic status and multimorbidity: a systematic review and meta-analysis

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              Examining different measures of multimorbidity, using a large prospective cross-sectional study in Australian general practice

              Objectives Prevalence estimates of multimorbidity vary widely due to inconsistent definitions and measurement methods. This study examines the independent effects on prevalence estimates of how ‘disease entity’ is defined—as a single chronic condition or chapters/domains in the International Classification of Primary Care (V.2; ICPC-2), International Classification of Disease (10th revision; ICD-10) or the Cumulative Illness Rating Scale (CIRS), the number of disease entities required for multimorbidity, and the number of chronic conditions studied. Design National prospective cross-sectional study. Setting Australian general practice. Participants 8707 random consenting deidentified patient encounters with 290 randomly selected general practitioners. Main outcome measures Prevalence estimates of multimorbidity using different definitions. Results Data classified to ICPC-2 chapters, ICD-10 chapters or CIRS domains produce similar multimorbidity prevalence estimates. When multimorbidity was defined as two or more (2+) disease entities: counting individual chronic conditions and groups of chronic conditions produced similar estimates; the 12 most prevalent chronic conditions identified about 80% of those identified using all chronic conditions. When multimorbidity was defined as 3+ disease entities: counting individual chronic conditions produced significantly higher estimates than counting groups of chronic conditions; the 12 most prevalent chronic conditions identified only two-thirds of patients identified using all chronic conditions. Conclusions Multimorbidity defined as 2+ disease entities can be measured using different definitions of disease entity with as few as 12 prevalent chronic conditions, but lacks specificity to be useful, especially in older people. Multimorbidity, defined as 3+, requires more measurement conformity and inclusion of all chronic conditions, but provides greater specificity than the 2+ definition. The proposed concept of “complex multimorbidity”, the co-occurrence of three or more chronic conditions affecting three or more different body systems within one person without defining an index chronic condition, may be useful in identifying high-need individuals.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2018
                3 November 2018
                : 8
                : 11
                : e023398
                Affiliations
                [1 ] departmentPost-Graduate Program Health Sciences , University of Brasilia , Brasilia, Brazil
                [2 ] Getulio Vargas University Hospital, Federal University of Amazonas , Manaus, Brazil
                [3 ] departmentFaculty Medicine , Federal University of Amazonas , Manaus, Brazil
                [4 ] departmentPost-Graduate Program of Pharmaceutical Sciences , University of Sorocaba , Sorocaba, Brazil
                [5 ] departmentFaculty of Pharmaceutical Sciences , State University of Campinas , Campinas, Brazil
                [6 ] departmentDepartment of Nursing in Public Health , Federal University of Pelotas , Pelotas, Brazil
                [7 ] departmentFaculty of Medicine , University of Brasilia , Brasilia, Brazil
                Author notes
                [Correspondence to ] Dr Maria Elizete A Araujo; elizetemanaus@ 123456gmail.com
                Author information
                http://orcid.org/0000-0001-7708-4036
                http://orcid.org/0000-0002-7186-9075
                http://orcid.org/0000-0003-2072-4834
                Article
                bmjopen-2018-023398
                10.1136/bmjopen-2018-023398
                6231594
                30391918
                ed264f9b-9d65-40ce-b8d5-9f067fb7c88d
                © Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 04 April 2018
                : 21 August 2018
                : 26 September 2018
                Funding
                Funded by: Conselho Nacional para o Desenvolvimento Cientifico e Tecnologico;
                Categories
                Health Services Research
                Research
                1506
                1704
                Custom metadata
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                Medicine
                prevalence,multimorbidity,cross-sectional studies,population surveys,brazil
                Medicine
                prevalence, multimorbidity, cross-sectional studies, population surveys, brazil

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