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Modelling local patterns of child mortality risk: a Bayesian Spatio-temporal analysis.

Alejandro Lome-Hurtado1, Jacques Lartigue-Mendoza2, Juan C Trujillo3

  • 1Economics Department, Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Av. San Pablo 180, Col. Reynosa Tamaulipas, Alcaldía Azcapotzalco, C.P, 02200, CDMX, Mexico.

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Summary

Child mortality risk in Greater Mexico City was mapped, identifying high-risk areas in the east. The study highlights areas with increasing risk, crucial for targeted public health interventions to reduce child deaths.

Keywords:
Bayesian mappingChild mortality riskChildren’s healthMexicoSpace-time interactions

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Area of Science:

  • Public Health
  • Spatial Epidemiology
  • Demography

Background:

  • Child mortality remains a significant global public health challenge, particularly in Mexico.
  • There is a lack of spatio-temporal studies on child mortality within Mexico.
  • Child mortality rates in Mexico are concerning compared to North American standards.

Purpose of the Study:

  • To model the spatio-temporal evolution of child mortality risk at the municipality level in Greater Mexico City.
  • To identify and classify municipalities by child mortality risk (high, medium, low) over time.
  • To ascertain potential high-risk municipalities based on identified trends.

Main Methods:

  • Bayesian spatio-temporal analysis was employed to model geographical variations in child mortality risk.
  • The methodology accounts for space-time patterns in the data.
  • Analysis focused on municipality-level data within Greater Mexico City over a defined period.

Main Results:

  • High-risk municipalities for child mortality were predominantly located in eastern Greater Mexico City, with some in northern and western areas.
  • Increasing trends in child mortality risk were observed in certain municipalities.
  • Several municipalities with medium risk show potential to become high-risk areas due to observed trends.
  • An overall decreasing tendency in child mortality risk was noted across the 7-year study period.

Conclusions:

  • Identifying high-risk municipalities and their trends provides valuable data for policymakers.
  • The findings support the use of geographically targeted interventions to reduce child mortality.
  • Spatially-targeted public health policies are essential for mitigating child mortality rates.