Asociación de la mortalidad humana con factores atmosféricos a diferentes escalas geográficas

Human mortality data is often modeled using a demographic approach. This approach does not present an adequate fit model for the number of deaths with great variability. For this reason, additional social, economic and environmental information is required to complement demographic modeling. This work evaluated the association between human mortality data (segregated by age and sex) with meteorological and air pollutant covariates at two geographical levels: country and macroclimatic regions. The modeling was based on a generalized linear modeling framework and takes into account the common feature of over-dispersion in human mortality data by applying a negative binomial distribution. The proposed model improved the dynamic behavior of the Farrington-like model (basic demographic model) and took into account extreme meteorological events and natural air pollution.