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BioMed Research International
Volume 2015, Article ID 841645, 9 pages
Research Article

Space-Time Analysis to Identify Areas at Risk of Mortality from Cardiovascular Disease

1Public Health and Environment Program, National School of Public Health (ENSP), FIOCRUZ, 21041-210 Rio de Janeiro, RJ, Brazil
2Faculty of Medical Sciences, University of the State of Mato Grosso (UNEMAT), 78200-000 Cáceres, MT, Brazil
3Department of Geography, Federal University of Mato Grosso (UFMT), 78060-900 Cuiabá, MT, Brazil

Received 23 March 2015; Accepted 31 August 2015

Academic Editor: Giacomo Frati

Copyright © 2015 Poliany C. O. Rodrigues et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study aimed at identifying areas that were at risk of mortality due to cardiovascular disease in residents aged 45 years or older of the cities of Cuiabá and Várzea Grande between 2009 and 2011. We conducted an ecological study of mortality rates related to cardiovascular disease. Mortality rates were calculated for each census tract by the Local Empirical Bayes estimator. High- and low-risk clusters were identified by retrospective space-time scans for each year using the Poisson probability model. We defined the year and month as the temporal analysis unit and the census tracts as the spatial analysis units adjusted by age and sex. The Mann-Whitney test was used to compare the socioeconomic and environmental variables by risk classification. High-risk clusters showed higher income ratios than low-risk clusters, as did temperature range and atmospheric particulate matter. Low-risk clusters showed higher humidity than high-risk clusters. The Eastern region of Várzea Grande and the central region of Cuiabá were identified as areas at risk of mortality due to cardiovascular disease in individuals aged 45 years or older. High mortality risk was associated with socioeconomic and environmental factors. More high-risk clusters were observed at the end of the dry season.