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Background Despite the growing utilization of data-driven methods to investigate multimorbidity patterns, there is currently no consensus or guidance on the conditions to include when identifying patterns. This scoping review aims to systematically examine the nature of conditions included in existing studies using data-driven techniques. Methods A comprehensive search of three electronic databases (MEDLINE, Web of Science and Scopus) was conducted to identify relevant publications from inception to 28 February 2022 using predefined search terms and inclusion/exclusion criteria. The reference lists and citations of relevant papers were also searched. Results Among 7326 search results, 5444 relevant articles were identified. After screening against the eligibility criteria, 60 articles were included in the review. Half of the reviewed studies reported selection criteria for conditions, with prevalence in the population of interest being the most common criterion (40%). Most studies included at least one neurological [59 (98.3%)], musculoskeletal [58 (96.7%)], respiratory [57 (95.0%)] or mental health [56 (93.3%)] condition. In contrast, only a small proportion of studies included skin [17 (28.3%)], infections [14 (23.3%)] or autoimmune conditions [10 (16.7%)]. Nine conditions (hypertension, diabetes, cancer, arthritis, COPD, asthma, depression, stroke and osteoporosis) were included by more than half of the studies. Conclusions This review highlights the considerable heterogeneity among the conditions included in analyses of multimorbidity patterns. Researchers should provide a clear rationale for the selection of conditions to facilitate comparisons across studies and ensure reproducibility, as well as consider selecting a diverse range of conditions to capture the complexity of multimorbidity. [ABSTRACT FROM AUTHOR]

Background: Multimorbidity research has focused on the prevalence and consequences of multimorbidity in older populations. Less is known about the accumulation of chronic conditions earlier in the life course. Methods: We identified patterns of longitudinal multimorbidity accumulation using 30 years of data from in-person exams, annual follow-ups, and adjudicated end-points among 4,945 participants of the Coronary Artery Risk Development in Young Adults (CARDIA) study. Chronic conditions included arthritis, asthma, atrial fibrillation, cancer, end stage renal disease, chronic obstructive pulmonary disease, coronary heart disease, diabetes, heart failure, hyperlipidemia, hypertension, and stroke. Trajectory patterns were identified using latent class growth curve models. Results: Mean age (SD) at baseline (1985-6) was 24.9 (3.6), 55% were female, and 51% were Black. The median follow-up was 30 years (interquartile range 25-30). We identified six trajectory classes characterized by when conditions began to accumulate and the rapidity of accumulation: (1) early-fifties, slow, (2) mid-forties, fast, (3) mid-thirties, fast, (4) late-twenties, slow, (5) mid-twenties, slow, and (6) mid-twenties, fast. Compared with participants in the early-fifties, slow trajectory class, participants in mid-twenties, fast were more likely to be female, Black, and currently smoking and had a higher baseline mean waist circumference (83.6 vs. 75.6 cm) and BMI (27.0 vs. 23.4 kg/m2) and lower baseline physical activity (414.1 vs. 442.4 exercise units). Conclusions: A life course approach that recognizes the heterogeneity in patterns of accumulation of chronic conditions from early adulthood into middle age could be helpful for identifying high risk subgroups and developing approaches to delay multimorbidity progression. [ABSTRACT FROM AUTHOR]