• David Monterde, Emili Vela, Montse Clèries, Luis García Eroles, and Pol Pérez Sust.
• Oficina de Estadística, Sistemas de Información, Instituto Catalán de la Salud, Departamento de Salud de la Generalidad de Cataluña, Barcelona, España. Electronic address: dmonterde@gencat.cat.
• Aten Primaria. 2019 Mar 1; 51 (3): 153-161.

ObjectiveTo compare the performance in terms of goodness of fit and explanatory power of 2morbidity groupers in primary care (PC): adjusted morbidity groups (AMG) and clinical risk groups (CRG).DesignCross-sectional study.LocationPC in the Catalan Institute for the Health (CIH), Catalonia, Spain.ParticipantsPopulation allocated in primary care centers of the CIH for the year 2014.Main MeasurementsThree indicators of interest are analyzed such as urgent hospitalization, number of visits and spending in pharmacy. A stratified analysis by centers is applied adjusting generalized lineal models from the variables age, sex and morbidity grouping to explain each one of the 3variables of interest. The statistical measures to analyze the performance of the different models applied are the Akaike index, the Bayes index and the pseudo-variability explained by deviance change.ResultsThe results show that in the area of the primary care the explanatory power of the AMGs is higher to that offered by the CRGs, especially for the case of the visits and the pharmacy.ConclusionsThe performance of GMAs in the area of the CIH PC is higher than that shown by the CRGs.Copyright © 2018 The Authors. Publicado por Elsevier España, S.L.U. All rights reserved.

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