Abstract
Understanding the spatial and temporal patterns of mortality rates in a highly heterogeneous metropolis, is a matter of public policy interest. In this context, there is no, to the best of our knowledge, previous studies that correlate both spatio-temporal and age-specific mortality rates in Mexico City. Spatio-temporal Kriging modeling was used over five age-specific mortality rates (from the years 2000 to 2016 in Mexico City), to gain both spatial (borough and neighborhood) and temporal (year and trimester) data level description. Mortality age-specific patterns have been modeled using multilevel modeling for longitudinal data. Posterior tests were carried out to compare mortality averages between geo-spatial locations. 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, the commercial, touristic and cultural core downtown of Mexico City. On the contrary, Tlalpan borough is the one with the lowest mortality rates in all the study groups. Post-productive mortality is the first age-specific cause of death, followed by infant, productive, pre-school and scholar groups. The combinations of spatio-temporal Kriging estimation and time-evolution linear mixed-effect models, allowed us to unveil relevant time and location trends that may be useful for public policy planning in Mexico City.
Highlights
Identifying trends in Metropolitan Mortality (MM) is a challenging problem nowadays
Depending on the age-specific mortality group, the initial guesses are different for the nugget, range, sill and spatio-temporal anisotropy
If we pick the borough with the highest mortality rate (Cuauhtemoc), we found that the model presented in Eqs (1)–(4) is not well-suited for this level description data
Summary
Identifying trends in Metropolitan Mortality (MM) is a challenging problem nowadays. Systematic approaches to discriminate the relevance of social, economic, demographic, educational, environmental or criminal factors in MM are matters of intense current research [1,2,3].MM can be differentiated from rural mortality since the causes and risk factors are substantially different [3]. Identifying trends in Metropolitan Mortality (MM) is a challenging problem nowadays. Systematic approaches to discriminate the relevance of social, economic, demographic, educational, environmental or criminal factors in MM are matters of intense current research [1,2,3]. MM can be differentiated from rural mortality since the causes and risk factors are substantially different [3]. To develop a useful model that may explain the evolution in time and space.
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