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Spatial analysis for the epidemiological study of cardiovascular diseases: A systematic literature search

Carlos Mena, Cesar Sepúlveda, Eduardo Fuentes, Yony Ormazábal, Iván Palomo
  • Carlos Mena
    Geomatics Centre, Faculty of Forestry Sciences, University of Talca, Chile | cmena@utalca.cl
  • Cesar Sepúlveda
    Thrombosis Research Center, Department of Clinical Biochemistry and Immunohematology, Faculty of Health Sciences, Interdisciplinary Excellence Research Program on Healthy Aging (PIEI-ES), University of Talca, Chile
  • Eduardo Fuentes
    Thrombosis Research Center, Department of Clinical Biochemistry and Immunohematology, Faculty of Health Sciences, Interdisciplinary Excellence Research Program on Healthy Aging (PIEI-ES); Multidisciplinary Scientific Nucleus, University of Talca, Chile
  • Yony Ormazábal
    Geomatics Centre, Faculty of Forestry Sciences, University of Talca, Chile
  • Iván Palomo
    Thrombosis Research Center, Department of Clinical Biochemistry and Immunohematology, Faculty of Health Sciences, Interdisciplinary Excellence Research Program on Healthy Aging (PIEI-ES), University of Talca, Chile

Abstract

Cardiovascular diseases (CVDs) are the primary cause of death and disability in de world, and the detection of populations at risk as well as localization of vulnerable areas is essential for adequate epidemiological management. Techniques developed for spatial analysis, among them geographical information systems and spatial statistics, such as cluster detection and spatial correlation, are useful for the study of the distribution of the CVDs. These techniques, enabling recognition of events at different geographical levels of study (e.g., rural, deprived neighbourhoods, etc.), make it possible to relate CVDs to factors present in the immediate environment. The systemic literature presented here shows that this group of diseases is clustered with regard to incidence, mortality and hospitalization as well as obesity, smoking, increased glycated haemoglobin levels, hypertension physical activity and age. In addition, acquired variables such as income, residency (rural or urban) and education, contribute to CVD clustering. Both local cluster detection and spatial regression techniques give statistical weight to the findings providing valuable information that can influence response mechanisms in the health services by indicating locations in need of intervention and assignment of available resources.

Keywords

Spatial analysis; Cluster; Cardiovascular diseases.

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Submitted: 2017-05-22 14:43:11
Published: 2018-05-07 17:15:37
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Copyright (c) 2018 Eduardo Fuentes, Carlos Mena, Cesar Sepulveda, Yony Ormazabal, Ivan Palomo

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