RESUMEN
Geographical Information Systems [GIS] have been used extensively for the development of epidemiological maps of malaria but not in the Coffee Triangle region of Colombia, endemic for P. vivax, P. falciparum and P. malariae. Surveillance case data [2007-2011] were used to estimate annual incidence rates per Plasmodium spp. [cases/100,000 pop] to develop the first malaria maps in the 53 municipalities of this region [departments Caldas, Quindio, Risaralda]. The GIS software used was Kosmo Desktop 3.0RC1[R]. Thirty thematic maps were developed according to the municipalities, years, parasite etiology, and uncomplicated and complicated cases. A total of 6582 cases were reported [6478 uncomplicated and 104 complicated, 77.8% Risaralda], for a cumulated rate of 269.46 cases/100,000 pop. Among uncomplicated cases, 5722 corresponded to P. vivax [234.25 cases/100,000 pop], 475 to P. falciparum [19.45 cases/100,000 pop], 8 to P. malariae [0.33 cases/100,000 pop] and 273 mixed [P. falciparum/P. vivax] [11.18 cases/100,000 pop]. The highest rate reported was in the more undeveloped and rural municipality of Risaralda [Pueblo Rico, 57.7 cases/1000 pop, 2009]. The burden of disease was concentrated in one department [>75% of the region]. The use of GIS-based epidemiological maps helps to guide decision-making for the prevention and control of this public health problem that still represents a significant issue in the region and the country, particularly in children
RESUMEN
Dengue continues to be the most important viral vector-borne disease in the world, particularly in Asia and Latin America, and is significantly affected by climate variability. The influence of climate in an endemic region of Colombia, from 2010 to 2011, was assessed. Epidemiological surveillance data [weekly cases] were collected, and incidence rates were calculated. Poisson regression models were used to assess the influence of the macroclimatic variable ONI [Oscillation Nino Index] and the microclimatic variable pluviometry [mm of rain for Risaralda] on the dengue incidence rate, adjusting by year and week. During the study period, 13,650 cases were reported. In 2010, the rates ranged from 8.6 cases/100,000 pop. up to a peak of 75.3 cases/100,000 pop. for a cumulative rate of 456.2 cases/100,000 pop. in that week. The climate variability in 2010 was higher [ONI 1.6, El Nino to -1.5, La Nina] than in 2011 [ONI -1.4, La Nina to -0.2, Neutral]. The mean pluviometry was 248.45 mm [min 135.9-max 432.84]. During El Nino, cases were significantly higher [mean 433.81] than during the climate neutral period [142.48] and during the La Nina [52.80] phases [ANOVA F = 66.59; p < 0.001]. Regression models showed that the ONI [coefficient 0.329; 95%CI 0.209-0.450] and pluviometry [coefficient 0.003; 95%CI 0.002-0.004] were highly significant independent variables associated with dengue incidence rate, after adjusting by year and week [p < 0.001, pseudo r[2] = 0.6913]. El Nino significantly affected the incidence of dengue in Risaralda. This association with climate change and variability should be considered in the elements influencing disease epidemiology. In addition, predictive models should be developed further with more available data from disease surveillance