The Adjusted Morbidity Groups (GMA) is a new morbidity measurement developed and adapted to the Spanish healthcare System. It enables the population to be classified into 6 morbidity groups, and in turn divided into 5 levels of complexity, along with one healthy population group. Consequently, the population is divided into 31 mutually exclusive categories. The results of the stratification in Catalonia are presented. GMA is a method for grouping morbidity that is comparable to others in the field, but has been developed with data from the Spanish health system. It can be used to stratify the population and to identify target populations. It has good explanatory and predictive results in the use of health resources indicators. The Spanish Ministry of Health is promoting the introduction of the GMA into the National Health System.

Aims Heart failure (HF) is one of the diseases with greater healthcare expenditure. However, little is known about the cost of HF at a population level. Hence, our aim was to study the population-level distribution and predictors of healthcare expenditure in patients with HF. Methods and results This was a population-based longitudinal study including all prevalent HF cases in Catalonia (Spain) on 31 December 2012 (n = 88 195). We evaluated 1-year healthcare resource use and expenditure using the Health Department (CatSalut) surveillance system that collects detailed information on healthcare usage for the entire population. Mean age was 77.4 (12) years; 55% were women. One-year mortality rate was 14%. All-cause emergency department visits and unplanned hospitalizations were required at least once in 53.4% and 30.8% of patients, respectively. During 2013, a total of €536.2 million were spent in the care of HF patients (7.1% of the total healthcare budget). The main source of expenditure was hospitalization (39% of the total) whereas outpatient care represented 20% of the total expenditure. In the general population, outpatient care and hospitalization were the main expenses. In multivariate analysis, younger age, higher presence of co-morbidities, and a recent HF or all-cause hospitalization were independently associated with higher healthcare expenditure. Conclusions In Catalonia, a large portion of the annual healthcare budget is devoted to HF patients. Unplanned hospitalization represents the main source of healthcare-related expenditure. The knowledge of how expenditure is distributed in a non-selected HF population might allow health providers to plan the distribution of resources in patients with HF.

Heart failure (HF) is frequent and its prevalence is increasing. We aimed to evaluate the epidemiologic features of HF patients, the 1-year follow-up outcomes and the independent predictors of those outcomes at a population level. Population-based longitudinal study including all prevalent HF cases in Catalonia (Spain) on December 31st, 2012. Patients were divided in 3 groups: patients without a previous HF hospitalization, patients with a remote (>1 year) HF hospitalization and patients with a recent (<1 year) HF admission. We analyzed 1year all-cause and HF hospitalizations, and all-cause mortality. Logistic regression was used to identify the independent predictors of each of those outcomes. A total of 88,195 patients were included. Mean age was 77 years, 55% were women. Comorbidities were frequent. Fourteen percent of patients had never been hospitalized, 71% had a remote HF hospitalization and 15% a recent hospitalization. At 1-year follow-up, all-cause and HF hospitalization were 53% and 8.8%, respectively. One-year all-cause mortality rate was 14%, and was higher in patients with a recent HF hospitalization (24%). The presence of diabetes mellitus, atrial fibrillation or chronic kidney disease was independently associated with all-cause and HF hospitalization and all-cause mortality. Hospital admissions and emergency department visits the previous year were also found to be independently associated with the three study outcomes. Outcomes are different depending on the HF population studied. Some comorbidity, an all-cause hospitalization or emergency department visit the previous year were associated with a worse outcome.

Background: Population-based studies on neuromyelitis optica spectrum disorders (NMOSD) are limited, and it is unclear whether the rates have changed with the implementation of the new 2015 criteria. Objectives: To estimate the incidence and prevalence of NMOSD in Catalonia (Spain), using both the 2006 and the 2015 criteria. Methods: In this clinic-based retrospective study, patients diagnosed with NMOSD between 2006 and 2015 were identified using multiple sources, including direct contact to all Catalan hospitals, identification of cases through the Catalan Health Surveillance System, and registry of antibodies to aquaporin-4 (AQP4-IgG) and myelin oligodendrocyte glycoprotein (MOG-IgG) in a reference laboratory. The incidence rate was calculated for the period 1 January 2006–1 January 2016 and prevalence for the date 1 January 2016. Results: We identified 74 patients (by the 2015 criteria). Most patients were Caucasian (81%), and female (76%) with a median age at disease onset of 42 years (range, 10–76 years). In total, 54 (73%) patients were positive for AQP4-IgG, 11 (15%) double-seronegative, and 9 (12%) MOG-IgG-positive. Rates of incidence and prevalence (0.63/1,000,000 person-years and 0.89/100,000, respectively) were 1.5-fold higher than those reported by the 2006 criteria. Lowest rates were seen in children and elder people and highest in women and middle-aged people (40–59 years). The female predominance was lost in incident AQP4-IgG-seronegative children and AQP4-IgG-positive elder people. MOG-IgG and double-seronegativity contributed similarly but did not influence the long-term outcome. Conclusion: The new criteria increase the estimates, but NMOSD remains as a rare disease. The differences in age- and sex-specific estimates highlight the importance of the serologic classification.

Background: Multimorbidity is highly relevant for both service commissioning and clinical decision-making. Optimization of variables assessing multimorbidity in order to enhance chronic care management is an unmet need. To this end, we have explored the contribution of multimorbidity to predict use of healthcare resources at community level by comparing the predictive power of four different multimorbidity measures. Methods: A population health study including all citizens ≥18 years (n = 6,102,595) living in Catalonia (ES) on 31 December 2014 was done using registry data. Primary care service utilization during 2015 was evaluated through four outcome variables: A) Frequent attendants, B) Home care users, C) Social worker users, and, D) Polypharmacy. Prediction of the four outcome variables (A to D) was carried out with and without multimorbidity assessment. We compared the contributions to model fitting of the following multimorbidity measures: i) Charlson index; ii) Number of chronic diseases; iii) Clinical Risk Groups (CRG); and iv) Adjusted Morbidity Groups (GMA). Results: The discrimination of the models (AUC) increased by including multimorbidity as covariate into the models, namely: A) Frequent attendants (0.771 vs 0.853), B) Home care users (0.862 vs 0.890), C) Social worker users (0.809 vs 0.872), and, D) Polypharmacy (0.835 vs 0.912). GMA showed the highest predictive power for all outcomes except for polypharmacy where it was slightly below than CRG. Conclusions: We confirmed that multimorbidity assessment enhanced prediction of use of healthcare resources at community level. The Catalan population-based risk assessment tool based on GMA presented the best combination of predictive power and applicability.

Objectives Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme (http://www.act-programme.eu). The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario. Settings The five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL). Participants Responsible teams for regional data management in the five ACT regions. Primary and secondary outcome measures We characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction. Results There was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment. Conclusions The results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation.