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Subpoblaciones con perfiles epidemiológicos y de riesgo singulares en La Habana, Cuba: diabetes, hipertensión y tabaquismo Translated title: Subpopulations with particular epidemiologic profiles and risks in Havana, Cuba: diabetes, hypertension, and tobacco-related illnesses

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      Abstract

      OBJETIVO: Identificar y caracterizar grupos poblacionales con perfiles de mayor riesgo y morbilidad para la diabetes de tipo II, la hipertensión y el tabaquismo. MÉTODOS: Se realizó un estudio transversal en 840 familias asistidas en 12 consultorios del médico y la enfermera de la familia en La Habana, Cuba. Se aplicó un análisis de conglomerados basado en modelos (model-based cluster analysis) para identificar subpoblaciones con perfiles de morbilidad y riesgo singulares. Las subpoblaciones se compararon con respecto a indicadores clave. RESULTADOS: Se identificó y caracterizó una subpoblación con alta carga de morbilidad y un perfil particular de riesgo. La variable determinante en su definición es la percepción subjetiva de la situación económica. Las familias de este grupo tienen mayor densidad por vivienda de diabetes, hipertensión y tabaquismo y se distribuyen entre todos los consultorios. En promedio son familias más pequeñas, y tienen entre dos y tres años menos de escolaridad, una percepción peor de su situación económica y una edad promedio mayor. CONCLUSIONES: Se identificaron subpoblaciones con niveles más altos de concentración de la carga de morbilidad y con perfiles de riesgo propios. Estas subpoblaciones exhiben ciertos rasgos consistentes con las tendencias en la arquitectura social de las familias cubanas, que se han ido consolidando en los dos últimos decenios.

      Translated abstract

      OBJECTIVE: Identify and describe population groups with greater risk and morbidity profiles for type II diabetes, hypertension, and tobacco-related illnesses. METHODS: A cross-sectional study was carried out of 840 families under the care of 12 family doctors and nurses in Havana, Cuba. A model-based cluster analysis was used to identify subpopulations with specific morbidity and risk profiles. Key indicators of the subpopulations were compared. RESULTS: A subpopulation with a high disease burden and a particular risk profile was identified and described. The determining factor in defining this group is the subjective perception of their economic situation. The families in this group have the highest household density of diabetes, hypertension, and tobacco-related illnesses and are distributed among all the facilities visited. On average, the subpopulation consisted of smaller families and have between two and three years less schooling, a more negative perception of their economic situation, and an older average age, when compared to the study group as a whole. CONCLUSIONS: Subpopulations were identified with higher proportions of the disease burden and with particular risk profiles. These subpopulations exhibit certain features consistent with trends in the social structure of Cuban families that have been evolving over the past two decades.

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      Most cited references 47

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      Model-Based Gaussian and Non-Gaussian Clustering

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        How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis

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          MCLUST: Software for Model-Based Cluster Analysis

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            Author and article information

            Affiliations
            [1 ] Centro de Investigaciones y Referencia de Aterosclerosis de La Habana Cuba
            [2 ] Universidad Médica de La Habana Cuba
            Contributors
            Role: ND
            Role: ND
            Role: ND
            Journal
            rpsp
            Revista Panamericana de Salud Pública
            Rev Panam Salud Publica
            Organización Panamericana de la Salud (Washington )
            1020-4989
            July 2012
            : 32
            : 1
            : 9-14
            S1020-49892012000700002 10.1590/s1020-49892012000700002

            http://creativecommons.org/licenses/by/4.0/

            Product
            Product Information: SciELO Public Health
            Categories
            Health Policy & Services

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