Metropolitan age-specific mortality trends at borough and neighbourhood level: The case of Mexico City

Abstract

Background Understanding the spatial and temporal patterns of mortality rates in a highly inhomogeneous metropolis, is a matter of public policy interest. In this context, there is no previous study that correlates both spatio-temporal and age-specific mortality rates in Mexico City. Methods Spatio-temporal kriging modelling was used over six age-specific mortality rates (from the years 2000 to 2016 in Mexico City), to gain both spatial (borough and neighbourhood) and temporal (year and trimester) data level description. Resulting data were modelled using time-evolution mixed effect models to unblurred mortality age-specific patterns. Posterior tests were carried out to compare mortality averages between geospatial locations. Results Mortality correlation extends in all study groups for as long as 12 years and as far as 13.27 km. The highest mortality rate takes place in the Cuauhtémoc borough, as it is the commercial, touristic and cultural core downtown Mexico City. On the contrary, Tlalpan borough is the one with the lowest mortality rates in all the study groups. Interestingly, post-productive mortality is the first age-specific cause of death, followed by infant, productive, pre-school and scholar groups. Conclusion The combinations of spatio-temporal Kriging estimation and time-evolution mixed effect models, allowed us to unveil relevant time and location trends that may be useful for public policy planning in Mexico City.

Spatio-Temporal Representativeness of Air Quality Monitoring Stations in Mexico City: Implications for Public Health

“…Now, if we move forward toward spatio-temporal correlations, the unidimensional concepts for space ( h ) and time ( u ) need to introduce a covariance structure with its associated semivariogram γ( h, u ) form for the different implementations included in R package implementation as described in Pebesma ( 41 ), Gräler et al ( 42 ), and Baca-Lopez et al ( 43 ) for the following models: separable equation (5), productSum equation (6 and 7), metric equation (8), sumMetric equation (9), and simpleSumMetric equation (10).…”

“…The initial semivariogram parameter values were obtained from the empirical spatio-temporal pollutant measurements using gstat R package implementation as described in Pebesma ( 41 ), Gräler et al ( 42 ), and Baca-Lopez et al ( 43 ). Here, the initials values are computed as follows:…”

Random Fields in Physics, Biology and Data Science

“…As in the case of random field hydrodynamics (mentioned in the physics section), an empirical approach based on continuous field estimators was chosen. Continuous spatio-temporal correlation structures via so-called Kriging methods extending the ideas of discrete random fields are commonly used in environmental analysis and risk assessment [121,122].…”