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Objective: To estimate the areas of greatest density of road traffic accidents with fatalities at 24 hours per km(2)/year in Spain from 2008 to 2011, using a geographic information system., Methods: Accidents were geocodified using the road and kilometer points where they occurred. The average nearest neighbor was calculated to detect possible clusters and to obtain the bandwidth for kernel density estimation., Results: A total of 4775 accidents were analyzed, of which 73.3% occurred on conventional roads. The estimated average distance between accidents was 1,242 meters, and the average expected distance was 10,738 meters. The nearest neighbor index was 0.11, indicating that there were aggregations of accidents in space. A map showing the kernel density was obtained with a resolution of 1 km(2), which identified the areas of highest density., Conclusions: This methodology allowed a better approximation to locating accident risks by taking into account kilometer points. The map shows areas where there was a greater density of accidents. This could be an advantage in decision-making by the relevant authorities., (Copyright © 2014 SESPAS. Published by Elsevier Espana. All rights reserved.)

Objective: An excess of mortality was detected in Spain in February and March 2012 by the Spanish daily mortality surveillance system and the «European monitoring of excess mortality for public health action» program. The objective of this article was to determine whether this excess could be attributed to influenza in this period., Methods: Excess mortality from all causes from 2006 to 2012 were studied using time series in the Spanish daily mortality surveillance system, and Poisson regression in the European mortality surveillance system, as well as the FluMOMO model, which estimates the mortality attributable to influenza. Excess mortality due to influenza and pneumonia attributable to influenza were studied by a modification of the Serfling model. To detect the periods of excess, we compared observed and expected mortality., Results: In February and March 2012, both the Spanish daily mortality surveillance system and the European mortality surveillance system detected a mortality excess of 8,110 and 10,872 deaths (mortality ratio (MR): 1.22 (95% CI:1.21-1.23) and 1.32 (95% CI: 1.29-1.31), respectively). In the 2011-12 season, the FluMOMO model identified the maximum percentage (97%) of deaths attributable to influenza in people older than 64 years with respect to the mortality total associated with influenza (13,822 deaths). The rate of excess mortality due to influenza and pneumonia and respiratory causes in people older than 64 years, obtained by the Serfling model, also reached a peak in the 2011-2012 season: 18.07 and 77.20, deaths per 100,000 inhabitants, respectively., Conclusion: A significant increase in mortality in elderly people in Spain was detected by the Spanish daily mortality surveillance system and by the European mortality surveillance system in the winter of 2012, coinciding with a late influenza season, with a predominance of the A(H3N2) virus, and a cold wave in Spain. This study suggests that influenza could have been one of the main factors contributing to the mortality excess observed in the winter of 2012 in Spain., (Copyright © 2014 SESPAS. Published by Elsevier Espana. All rights reserved.)

Objectives: To analyze the spatial pattern of legionellosis in Spain for men and women during the period 2003-2007 and to identify spatial clustering of risk., Methods: We identified the spatial pattern of the distribution of legionellosis rates based on calculation of rates by municipality through the direct method. Smoothing of these rates was performed by the Empirical Bayes method for studying the spatial pattern of disease for both sexes. We used Morańs index to analyze spatial autocorrelation rates globally. To calculate local rates, the Local Moran's Index [known as local indicators of spatial association (LISA)], was used to analyze the clusters of municipalities with the highest risk., Results: After smoothing the risk, the highest rates (over 50 per 100,000 inhabitants) were grouped in the eastern Mediterranean coastal areas and the north of the mainland, as well as in the Mediterranean islands. Moran's index smoothed rates were 0.15 for men and 0.23 for women. The spatial clusters of statistically significant higher rates calculated by the LISA index were distributed in the north and east for both sexes., Conclusions: These methods of spatial analysis allow patterns of disease distribution to be identified. All the methods used yielded similar results. These techniques are a complementary tool for epidemiological surveillance of infectious diseases., (Copyright © 2010 SESPAS. Published by Elsevier Espana. All rights reserved.)