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BackgroundAlthough the definition of multimorbidity as “the simultaneous presence of two or more chronic diseases” is well established, its operationalization is not yet agreed. This study aims to provide a clinically driven comprehensive list of chronic conditions to be included when measuring multimorbidity.MethodsBased on a consensus definition of chronic disease, all four-digit level codes from the International Classification of Diseases, 10th revision (ICD-10) were classified as chronic or not by an international and multidisciplinary team. Chronic ICD-10 codes were subsequently grouped into broader categories according to clinical criteria. Last, we showed proof of concept by applying the classification to older adults from the Swedish National study of Aging and Care in Kungsholmen (SNAC-K) using also inpatient data from the Swedish National Patient Register.ResultsA disease or condition was considered to be chronic if it had a prolonged duration and either (a) left residual disability or worsening quality of life or (b) required a long period of care, treatment, or rehabilitation. After applying this definition in relation to populations of older adults, 918 chronic ICD-10 codes were identified and grouped into 60 chronic disease categories. In SNAC-K, 88.6% had ≥2 of these 60 disease categories, 73.2% had ≥3, and 55.8% had ≥4.ConclusionsThis operational measure of multimorbidity, which can be implemented using either or both clinical and administrative data, may facilitate its monitoring and international comparison. Once validated, it may enable the advancement and evolution of conceptual and theoretical aspects of multimorbidity that will eventually lead to better care.

ObjectivesThe primary objective of this study was to identify the existence of chronic disease multimorbidity patterns in the primary care population, describing their clinical components and analysing how these patterns change and evolve over time both in women and men. The secondary objective of this study was to generate evidence regarding the pathophysiological processes underlying multimorbidity and to understand the interactions and synergies among the various diseases.MethodsThis observational, retrospective, multicentre study utilised information from the electronic medical records of 19 primary care centres from 2008. To identify multimorbidity patterns, an exploratory factor analysis was carried out based on the tetra-choric correlations between the diagnostic information of 275,682 patients who were over 14 years of age. The analysis was stratified by age group and sex.ResultsMultimorbidity was found in all age groups, and its prevalence ranged from 13% in the 15 to 44 year age group to 67% in those 65 years of age or older. Goodness-of-fit indicators revealed sample values between 0.50 and 0.71. We identified five patterns of multimorbidity: cardio-metabolic, psychiatric-substance abuse, mechanical-obesity-thyroidal, psychogeriatric and depressive. Some of these patterns were found to evolve with age, and there were differences between men and women.ConclusionsNon-random associations between chronic diseases result in clinically consistent multimorbidity patterns affecting a significant proportion of the population. Underlying pathophysiological phenomena were observed upon which action can be taken both from a clinical, individual-level perspective and from a public health or population-level perspective.