@article{ref1,
title="Modelling local patterns of child mortality risk: a Bayesian Spatio-temporal analysis",
journal="BMC public health",
year="2021",
author="Lartigue-Mendoza, Jacques and Lome-Hurtado, Alejandro and Trujillo, Juan C.",
volume="21",
number="1",
pages="e29-e29",
abstract="BACKGROUND: Globally, child mortality rate has remained high over the years, but the figure can be reduced through proper implementation of spatially-targeted public  health policies. Due to its alarming rate in comparison to North American standards,  child mortality is particularly a health concern in Mexico. Despite this fact, there  remains a dearth of studies that address its spatio-temporal identification in the  country. 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. <br><br>METHODS: In  order to control for the space-time patterns of data, the study performs a Bayesian  spatio-temporal analysis. This methodology permits the modelling of the geographical  variation of child mortality risk across municipalities, within the studied time  span. <br><br>RESULTS: The analysis shows that most of the high-risk municipalities were in  the east, along with a few in the north and west areas of Greater Mexico City. In  some of them, it is possible to distinguish an increasing trend in child mortality  risk. The outcomes highlight municipalities currently presenting a medium risk but  liable to become high risk, given their trend, after the studied period. Finally,  the likelihood of child mortality risk illustrates an overall decreasing tendency  throughout the 7-year studied period. <br><br>CONCLUSIONS: The identification of high-risk  municipalities and risk trends may provide a useful input for policymakers seeking  to reduce the incidence of child mortality. The results provide evidence that  supports the use of geographical targeting in policy interventions.<p /> <p>Language: en</p>",
language="en",
issn="1471-2458",
doi="10.1186/s12889-020-10016-9",
url="http://dx.doi.org/10.1186/s12889-020-10016-9"
}