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This is the final version. Available on open access from Nature Research via the DOI in this record ; This study aimed to analyse the trajectories and mortality of multimorbidity patterns in patients aged 65 to 99 years in Catalonia (Spain). Five year (2012–2016) data of 916,619 participants from a primary care, population-based electronic health record database (Information System for Research in Primary Care, SIDIAP) were included in this retrospective cohort study. Individual longitudinal trajectories were modelled with a Hidden Markov Model across multimorbidity patterns. We computed the mortality hazard using Cox regression models to estimate survival in multimorbidity patterns. Ten multimorbidity patterns were originally identified and two more states (death and drop-outs) were subsequently added. At baseline, the most frequent cluster was the Non-Specific Pattern (42%), and the least frequent the Multisystem Pattern (1.6%). Most participants stayed in the same cluster over the 5 year follow-up period, from 92.1% in the Nervous, Musculoskeletal pattern to 59.2% in the Cardio-Circulatory and Renal pattern. The highest mortality rates were observed for patterns that included cardio-circulatory diseases: Cardio-Circulatory and Renal (37.1%); Nervous, Digestive and Circulatory (31.8%); and Cardio-Circulatory, Mental, Respiratory and Genitourinary (28.8%). This study demonstrates the feasibility of characterizing multimorbidity patterns along time. Multimorbidity trajectories were generally stable, although changes in specific multimorbidity patterns were observed. The Hidden Markov Model is useful for modelling transitions across multimorbidity patterns and mortality risk. Our findings suggest that health interventions targeting specific multimorbidity patterns may reduce mortality in patients with multimorbidity. ; Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain) ; European Regional Development Fund ; Department of Health of the Catalan Government ; Catalan Government

BACKGROUND: Health surveys (HS) are a well-established methodology for measuring the health status of a population. The relative merit of using information based on HS versus electronic health records (EHR) to measure multimorbidity has not been established. Our study had two objectives: 1) to measure and compare the prevalence and distribution of multimorbidity in HS and EHR data, and 2) to test specific hypotheses about potential differences between HS and EHR reporting of diseases with a symptoms-based diagnosis and those requiring diagnostic testing. METHODS: Cross-sectional study using data from a periodic HS conducted by the Catalan government and from EHR covering 80% of the Catalan population aged 15 years and older. We determined the prevalence of 27 selected health conditions in both data sources, calculated the prevalence and distribution of multimorbidity (defined as the presence of ≥2 of the selected conditions), and determined multimorbidity patterns. We tested two hypotheses: a) health conditions requiring diagnostic tests for their diagnosis and management would be more prevalent in the EHR; and b) symptoms-based health problems would be more prevalent in the HS data. RESULTS: We analysed 15,926 HS interviews and 1,597,258 EHRs. The profile of the EHR sample was 52% women, average age 47 years (standard deviation: 18.8), and 68% having at least one of the selected health conditions, the 3 most prevalent being hypertension (20%), depression or anxiety (16%) and mental disorders (15%). Multimorbidity was higher in HS than in EHR data (60% vs. 43%, respectively, for ages 15-75+, P <0.001, and 91% vs. 83% in participants aged ≥65 years, P <0.001). The most prevalent multimorbidity cluster was cardiovascular. Circulation disorders (other than varicose veins), chronic allergies, neck pain, haemorrhoids, migraine or frequent headaches and chronic constipation were more prevalent in the HS. Most symptomatic conditions (71%) had a higher prevalence in the HS, while less than a third of conditions ...