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Background: Pediatric acute leukemia is a frequent chronic disease. A violent environment could cause chronic stress during gestation and activate oncogenes. Colombia is a country characterized by very high levels of poverty, and by being struck by a non-international armed conflict for more than 60 years. Aim: To determine the impact of perinatal exposure to collective violence and poverty, on the incidence and mortality rates due to leukemia for Colombian children born during the years 2002 to 2013. Methods: We apply spatial Poisson-Hurdle models to model the incidence and mortality rates for leukemia using as predictors: the Colombian index of armed conflict (IICA), Unmet Basic Needs (UBN), percentage of health coverage, percentage of people living in rural areas, and eigenvectors obtained based on Moran eigenvector spatial filtering. Results: There were 11,149,695 children born in 1122 municipalities of Colombia from 2002 to 2013. There were a total of 4781 cases of childhood leukemia from 2008 to 2016. The risk of suffering from leukemia or dying from this disease is higher for children exposed to armed conflict and is lower for children in extreme poverty. All variables show significant spatial autocorrelation and significant cross-correlation with the LR and LMR. Conclusion: The Colombian armed conflict index and poverty variables were significantly statistically associated with the incidence and mortality rate of Leukemia in children. Keywords: Leukemia, Colombian armed conflict index, Poverty, Spatial correlation, Moran index, Poisson-Hurdle models

This paper provides new tools for analyzing spatio-temporal event networks. We build time series of directed event networks for a set of spatial distances, and based on scan-statistics, the spatial distance that generates the strongest change of event network connections is chosen. In addition, we propose an empirical random network event generator to detect significant motifs throughout time. This generator preserves the spatial configuration but randomizes the order of the occurrence of events. To prevent the large number of links from masking the count of motifs, we propose using standardized counts of motifs at each time slot. Our methodology is able to detect interaction radius in space, build time series of networks, and describe changes in its topology over time, by means of identification of different types of motifs that allows for the understanding of the spatio-temporal dynamics of the phenomena. We illustrate our methodology by analyzing thefts occurred in Medellín (Colombia) between the years 2003 and 2015.