Cristina Colls1, Montse Mias1, Anna García-Altés2. 1. Agència de Qualitat i Avaluació Sanitàries de Catalunya, Departament de Salut de Catalunya, Barcelona, España. 2. Agència de Qualitat i Avaluació Sanitàries de Catalunya, Departament de Salut de Catalunya, Barcelona, España; CIBER de Epidemiología y Salud Pública (CIBERESP), España; Institut d'Investigació Biomèdica (IIB Sant Pau), Barcelona, España. Electronic address: agarciaaltes@gencat.cat.
Abstract
OBJECTIVE: To build a deprivation index for the assignation of the budgets of the primary healthcare teams in Catalonia (Spain) valid for both urban and rural environments and updatable with greater frequency than indices built from census variables. METHOD: Starting from a review of the most common deprivation indices, variables were selected from sources that allow frequent updating and are representative at the territorial level of primary care. The correlations were calculated between the chosen variables and variables of need for healthcare and morbidity. principal components analysis was applied. Finally, the correlations of the index built with the MEDEA index and with variables of use of healthcare resources and morbidity was calculated stratifying by geographical dispersion. RESULTS: The variables of income, occupation and education are the ones with the highest correlation with the need for healthcare and morbidity. The composed socioeconomic index (CSI) ranges from -.01 to 5.68, with an average value of 2.60 and a standard deviation of .91. The correlation between the CSI and the MEDEA index is .89. The CSI correlates with use for healthcare in both urban and rural environments, although in rural environments the association is lower. CONCLUSIONS: The CSI was built with data that allow frequent updating and was integrated in the model for allocating resources to primary healthcare starting in 2017.
OBJECTIVE: To build a deprivation index for the assignation of the budgets of the primary healthcare teams in Catalonia (Spain) valid for both urban and rural environments and updatable with greater frequency than indices built from census variables. METHOD: Starting from a review of the most common deprivation indices, variables were selected from sources that allow frequent updating and are representative at the territorial level of primary care. The correlations were calculated between the chosen variables and variables of need for healthcare and morbidity. principal components analysis was applied. Finally, the correlations of the index built with the MEDEA index and with variables of use of healthcare resources and morbidity was calculated stratifying by geographical dispersion. RESULTS: The variables of income, occupation and education are the ones with the highest correlation with the need for healthcare and morbidity. The composed socioeconomic index (CSI) ranges from -.01 to 5.68, with an average value of 2.60 and a standard deviation of .91. The correlation between the CSI and the MEDEA index is .89. The CSI correlates with use for healthcare in both urban and rural environments, although in rural environments the association is lower. CONCLUSIONS: The CSI was built with data that allow frequent updating and was integrated in the model for allocating resources to primary healthcare starting in 2017.
Keywords:
Administrative data; Análisis de componentes principales; Datos administrativos; Deprivation index; Desigualdades en salud; Financiación de la atención primaria; Health inequalities; Primary care financing; Principal components analysis; Índice de privación
Authors: Quim Zaldo-Aubanell; Ferran Campillo I López; Albert Bach; Isabel Serra; Joan Olivet-Vila; Marc Saez; David Pino; Roser Maneja Journal: Int J Environ Res Public Health Date: 2021-04-04 Impact factor: 3.390
Authors: Rosa Maria Vivanco-Hidalgo; Israel Molina; Elisenda Martinez; Ramón Roman-Viñas; Adrián Sánchez-Montalvá; Joan Fibla; Caridad Pontes; César Velasco Muñoz Journal: Euro Surveill Date: 2021-03