OBJECTIVE: To examine the relationship between secular trends in energy supply and body mass index (BMI) among several countries. DESIGN: Aggregate level analyses of annually reported country food data against anthropometric data collected in independent cross-sectional samples from 34 populations in 21 countries from the early 1980s to the mid-1990s. SUBJECTS: Population randomly selected participants aged 35-64 y. MEASUREMENTS: BMI data were obtained from the WHO MONICA Project. Food energy supply data were derived from the Food Balance Sheet of the Food and Agriculture Organization of the United Nations. RESULTS: Mean BMI as well as the prevalence of overweight (BMI Z25 kg/m 2 ) increased in virtually all Western European countries, Australia, the USA, and China. Decreasing trends in BMI were seen in Central and Eastern European countries. Increasing trends in total energy supply per capita were found in most high-income countries and China while decreasing trends existed in Eastern European countries. Between country differences in temporal trends of total energy supply per capita explained 41% of the variation of trends in mean BMI; the effect was similar upon the prevalence of overweight and obesity. Trends in percent of energy supply from total fat per capita had a slight effect on the trends in mean BMI ( þ 7% increment in R 2 ) when the total energy supply per capita was adjusted for, while energy supply from total sweeteners per capita had no additional effect. CONCLUSION: Increasing energy supply is closely associated with the increase of overweight and obesity in western countries. This emphasizes the importance of dietary issues when coping with the obesity epidemic.

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.

ObjectivesPopulation-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.SettingsThe five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL).ParticipantsResponsible teams for regional data management in the five ACT regions.Primary and secondary outcome measuresWe 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.ResultsThere 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.ConclusionsThe 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.

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.