The 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. This 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. Multimorbidity 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. Non-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.

Although there is some experience in the study of mortality inequalities in Spanish cities, there are large urban centers that have not yet been investigated using the census tract as the unit of territorial analysis. The coordinated project was designed to fill this gap, with the participation of 10 groups of researchers in Andalusia, Aragon, Catalonia, Galicia, Madrid, Valencia, and the Basque Country. The MEDEA project has four distinguishing features: a) the census tract is used as the basic geographical area; b) statistical methods that include the geographical structure of the region under study are employed for risk estimation; c) data are drawn from three complementary data sources (information on air pollution, information on industrial pollution, and the records of mortality registrars), and d) a coordinated, large-scale analysis, favored by the implantation of coordinated research networks, is carried out. The main objective of the present study was to explain the methods for smoothing mortality indicators in the context of the MEDEA project. This study focusses on the methodology and the results of the Besag, York and Mollié model (BYM) in disease mapping. In the MEDEA project, standardized mortality ratios (SMR), corresponding to 17 large groups of causes of death and 28 specific causes, were smoothed by means of the BYM model; however, in the present study this methodology was applied to mortality due to cancer of the trachea, bronchi and lung in men and women in the city of Barcelona from 1996 to 2003. As a result of smoothing, a different geographical pattern for SMR in both genders was observed. In men, a SMR higher than unity was found in highly deprived areas. In contrast, in women, this pattern was observed in more affluent areas.

OBJECTIVES: To assess the impact of using data from the National Death Index (NDI) of Spain to estimate breast cancer survival rates among residents of Girona and Zaragoza diagnosed in 1995-1999. METHODS: This was an observational, longitudinal epidemiologic study, using two population- based cancer registries. Data collected were of female residents of Girona or Zaragoza who had been diagnosed with breast cancer in 1995-1999. Observed and relative 5-year survival rates were estimated, first using the information available from the Girona and Zaragoza cancer registries, and then with the inclusion of NDI data. The 5-year relative survival rate and corresponding 95% Confidence Intervals were estimated using the Hakulinen method. The Kaplan-Maier method and Log Rank test were used to compare survival curves. RESULTS: No statistically significant difference in survival curves was observed in Girona for the data obtained before and after cross-matching with the NDI. However, there was a significant difference in Zaragoza. A comparison of the relative survival rates of each of the two registries before NDI cross-matching showed differences of 3.9% (5-year) and 16.1% (10-year) between the two, whereas after the cross-match, the difference was only 0.5% (5-year) and 1.2% (10-year). CONCLUSIONS: In Spain it is imperative that there be systematic use of NDI data to supplement cancer registries, so that comparisons of relative survival rates between registries can be improved.