Modelling Local Patterns of Child Mortality Risk. A Bayesian Spatio-Temporal Analysis. Alejandro Lome-Hurtado (
[email protected] ) Universidad Anahuac Mexico https://orcid.org/0000-0003-1241-4553 Jacques Lartigue Mendoza Universidad Anahuac Mexico Juan Carlos Trujillo University of York Research article Keywords: Children’s health, Bayesian mapping, child mortality risk, space-time interactions, Mexico. Posted Date: November 12th, 2019 DOI: https://doi.org/10.21203/rs.2.11961/v2 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Version of Record: A version of this preprint was published at BMC Public Health on January 6th, 2021. See the published version at https://doi.org/10.1186/s12889-020-10016-9. Page 1/17 Abstract Background : At the international scale the number of child deaths is still high; however, this gure is susceptible to be reduced implementing proper spatially-targeted health public policies. Due to its alarming rate in comparison to North American standards, child mortality is a particular health concern in Mexico. Despite this fact, there remains a dearth of studies that address the spatial-temporal identication of child mortality in Mexico. The aims of this study are i) to model the evolution of child mortality risk at the municipality level in Greater Mexico City, (ii) to identify municipalities with high, medium and low risk over time, and (iii) using municipality trends, to ascertain potential high-risk municipalities. Methods : In order to control for space-time patterns of data, the study performs a Bayesian spatio-temporal analysis. This methodology allows to model the geographical variation of child mortality risk across municipalities within the studied time span.