An implementation of Hurdle models for spatial count data. Study case: civil war as a risk factor for the development of childhood leukemia in Colombia - Montilla Velásquez, María del Pilar; Bohorquez Castañeda, Martha Patricia; Rentería Ramos, Rafael (Frontiers in Applied Mathematics and Statistics, 2023)
We propose a novel, efficient and powerful methodology to deal with overdispersion, excess zeros, heterogeneity and spatial correlation. It is based on the combination of Hurdle models and Spatial filtering Moran eigenvectors. Hurdle models are the best option to manage the presence of overdispersion and excess of zeros, separating the model into two parts: the first one models the probability of the zero value, and the second one models the probability of the non-zero values. Finally, gathering the spatial information in new covariates through spatial filtering Moran vector method, involves spatial correlation and spatial heterogeneity to improve the model fitting and explain spatial effects of variables that were not possible to measure. Thus, our proposal adapts usual regression models for count data so that it is possible to deal with phenomena where the usual theoretical assumptions, such as constant variance, independence and unique distribution are not fulfilled. In addition, this research shows how a prolonged armed-conflict can impact the health of children. The data includes children exposed to armed-conflict in Colombia, a country enduring a non-international armed-conflict lasting over 60 years. The findings indicate that children exposed to high levels of violence, as measured by the armed-conflict index, demonstrate a significant association with the incidence and mortality rate of LAP in children. This fact is illustrated here using one of the most catastrophic conditions in childhood, as is Pediatric Acute Leukemia (LAP). The association between armed conflict and LAP has its conceptual basis in the epidemiology literature, given that, the incidence and mortality rates of neoplastic diseases increase with exposure to toxic and chronic stress during gestation and childhood. Our methodology provides a valuable framework for complex data analysis and contributes to understanding the health implications in conflict-affected regions.