We performed an exploratory factor analysis of the 19 morbidities to determine multimorbidity patterns. We identified the tendencies of diseases to co-occur by selecting sets of variables with potentially common underlying causal factors. Factor analysis was used with a tetrachoric correlation matrix because conditions were expressed as binary variables . The extraction of the multimorbidity patterns involved the principal factor method, and the number of factors to extract was determined by the scree-test , with minimal eigenvalue of 1.0 (Kaiser criterion). A condition with loading factor > 0.25 had greater importance in a pattern, which indicates a stronger association . The Kaiser-Meyer-Olkin (KMO) method was used to estimate the adequacy of the data for our model on factor analysis. This parameter takes values between 0 and 1, which, with a greater goodness of fit, are close to 1. Cumulative variance of the sample was determined to describe the variance of the diagnostic data explained by the patterns. An oblique rotation (Oblimin) was applied to correlate factors with one another to obtain a better interpretation of the analysis factor. The results of this analysis could be interpreted as multimorbidity patterns (i.e., diseases that are non-randomly associated with each other).