Using spatial analysis to identify areas vulnerable to infant mortality

Area of Science:

• Public Health
• Spatial Epidemiology
• Biostatistics

Background:

• Infant mortality remains a critical public health concern globally.
• Understanding its spatial distribution is crucial for targeted interventions.
• Previous studies have explored geographical variations in infant mortality.

Purpose of the Study:

• To analyze the spatial distribution of infant mortality rates.
• To identify geographical clusters with a high risk of infant death.
• To evaluate the effectiveness of the Thiessen (Voronoi) polygon method in spatial analysis.

Main Methods:

• The Thiessen (Voronoi) polygon method was employed for spatial analysis of infant mortality rates at the municipal level.
• Data from the 2006-2008 triennium were used to calculate average infant mortality rates.
• Spatial autocorrelation was assessed using Moran's index and the G-statistic.

Main Results:

• Infant mortality rates exhibited significant spatial heterogeneity, not being constant across the study area.
• Both Moran's index (0.34, P < 0.01) and G-statistic (0.03, P < 0.01) confirmed significant spatial autocorrelation.
• The Thiessen polygon method successfully identified clusters of elevated infant mortality risk.

Conclusions:

• The Thiessen (Voronoi) polygon method is a robust tool for the spatial analysis of infant mortality.
• This method accurately predicts clusters with a high risk of infant mortality.
• Findings support the use of spatial analysis for targeted public health strategies to reduce infant mortality.