Alejandro Lome-Hurtado1, Jacques Lartigue-Mendoza2, Juan C Trujillo3. 1. Economics Department, Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Av. San Pablo 180, Col. Reynosa Tamaulipas, Alcaldía Azcapotzalco, C.P, 02200, CDMX, Mexico. 2. Universidad Anáhuac México, Economics and Business School, Avenida de las Torres 131, Colonia Olivar de los Padres, C.P, 01780, Ciudad de México, Mexico. jacques.lartigue@anahuac.mx. 3. Department of Environment and Geography, University of York, York, North Yorkshire, YO10 5NG, UK.
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. 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. 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. 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.
BACKGROUND: Globally, childmortality 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, childmortality 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 childmortality 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 the space-time patterns of data, the study performs a Bayesian spatio-temporal analysis. This methodology permits the modelling of the geographical variation of childmortality risk across municipalities, within the studied time span. 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 childmortality 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 childmortality risk illustrates an overall decreasing tendency throughout the 7-year studied period. CONCLUSIONS: The identification of high-risk municipalities and risk trends may provide a useful input for policymakers seeking to reduce the incidence of childmortality. The results provide evidence that supports the use of geographical targeting in policy interventions.
Authors: W J Alonso; R Acuña-Soto; R Giglio; J Nuckols; S Leyk; C Schuck-Paim; C Viboud; M A Miller; B J J McCormick Journal: Epidemiol Infect Date: 2011-04-14 Impact factor: 2.451
Authors: Anna E Bauze; Linda N Tran; Kim-Huong Nguyen; Sonja Firth; Eliana Jimenez-Soto; Laura Dwyer-Lindgren; Andrew Hodge; Alan D Lopez Journal: PLoS One Date: 2012-05-25 Impact factor: 3.240