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      Complexity in disease management: A linked data analysis of multimorbidity in Aboriginal and non-Aboriginal patients hospitalised with atherothrombotic disease in Western Australia

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          Abstract

          Background

          Hospitalisation for atherothrombotic disease (ATD) is expected to rise in coming decades. However, increasingly, associated comorbidities impose challenges in managing patients and deciding appropriate secondary prevention. We investigated the prevalence and pattern of multimorbidity (presence of two or more chronic conditions) in Aboriginal and non-Aboriginal Western Australian residents with ATDs.

          Methods and findings

          We used population-based de-identified linked administrative health data from 1 January 2000 to 30 June 2014 to identify a cohort of patients aged 25–59 years admitted to Western Australian hospitals with a discharge diagnosis of ATD. The prevalence of common chronic diseases in these patients was estimated and the patterns of comorbidities and multimorbidities empirically explored using two different approaches: identification of the most commonly occurring pairs and triplets of comorbid diseases, and through latent class analysis (LCA). Half of the cohort had multimorbidity, although this was much higher in Aboriginal people (Aboriginal: 79.2% vs. non-Aboriginal: 39.3%). Only a quarter were without any documented comorbidities. Hypertension, diabetes, alcohol abuse disorders and acid peptic diseases were the leading comorbidities in the major comorbid combinations across both Aboriginal and non-Aboriginal cohorts. The LCA identified four and six distinct clinically meaningful classes of multimorbidity for Aboriginal and non-Aboriginal patients, respectively. Out of the six groups in non-Aboriginal patients, four were similar to the groups identified in Aboriginal patients. The largest proportion of patients (33% in Aboriginal and 66% in non-Aboriginal) was assigned to the “minimally diseased” (or relatively healthy) group, with most patients having less than two conditions. Other groups showed variability in degree and pattern of multimorbidity.

          Conclusion

          Multimorbidity is common in ATD patients and the comorbidities tend to interact and cluster together. Physicians need to consider these in their clinical practice. Different treatment and secondary prevention strategies are likely to be useful for management in these cluster groups.

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          Most cited references39

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          Population-based linkage of health records in Western Australia: development of a health services research linked database

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

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: ResourcesRole: SoftwareRole: SupervisionRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Formal analysisRole: SoftwareRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: ResourcesRole: SoftwareRole: SupervisionRole: VisualizationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                14 August 2018
                2018
                : 13
                : 8
                : e0201496
                Affiliations
                [1 ] Western Australian Centre for Rural Health, The University of Western Australia, Geraldton, Western Australia, Australia
                [2 ] School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
                University of Oxford, UNITED KINGDOM
                Author notes

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

                Author information
                http://orcid.org/0000-0002-6012-3702
                http://orcid.org/0000-0003-0327-7155
                Article
                PONE-D-18-13811
                10.1371/journal.pone.0201496
                6091927
                30106971
                4195d0ec-648b-4d0d-9f55-c326c3a72122
                © 2018 Hussain 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
                : 6 May 2018
                : 16 July 2018
                Page count
                Figures: 3, Tables: 5, Pages: 18
                Funding
                The Western Australian Centre for Rural Health receives funding from the Australian Department of Health. This work was partly supported by the National Health and Medical Research Council of Australia (Grant Number 1031057). JMK is supported by a Heart Foundation of Australia Future Leader Fellowship.
                Categories
                Research Article
                Medicine and Health Sciences
                Vascular Medicine
                Blood Pressure
                Hypertension
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Pharmacology
                Pharmacokinetics
                Drug Metabolism
                Medicine and Health Sciences
                Health Care
                Health Statistics
                Morbidity
                Medicine and Health Sciences
                Nephrology
                Chronic Kidney Disease
                Medicine and Health Sciences
                Health Care
                Socioeconomic Aspects of Health
                Medicine and Health Sciences
                Public and Occupational Health
                Socioeconomic Aspects of Health
                Medicine and Health Sciences
                Cardiovascular Medicine
                Cardiovascular Diseases
                Medicine and Health Sciences
                Cardiology
                Heart Failure
                Custom metadata
                The data underlying this study belong to Western Australia linked data which are not publicly available due to patient confidentiality issues; however, access to the data is granted to researchers who have obtained approval from the relevant Data Custodians to ensure the data requested is appropriate for the purpose of the research and have obtained approval from the relevant Human Research Ethics Committee/s (HRECs) to conduct their proposed research. The details of the access to linked data are available at http://www.datalinkage-wa.org/access-and-application/access-linked-data. Application for data access can be made to DataServices@ 123456health.wa.gov.au . The authors did not have special access privileges.

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                Uncategorized

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