Analyzing infant mortality with geoadditive categorical regression models: a case study for Nigeria

Area of Science:

• Demography
• Public Health
• Spatial Epidemiology

Background:

• Infant mortality remains a critical public health concern in Nigeria.
• Understanding the spatiotemporal patterns and risk factors is crucial for targeted interventions.
• Previous analyses may not have sufficiently differentiated early versus later infant mortality periods.

Purpose of the Study:

• To analyze infant mortality patterns in Nigeria at the state level.
• To investigate the non-linear effects of maternal age on infant mortality.
• To compare determinants of death in the first month versus the remaining 11 months of the first year of life.

Main Methods:

• Utilized data from the 1999 Nigeria Demographic and Health Survey (NDHS).
• Employed Bayesian inference with Markov chain Monte Carlo (MCMC) techniques.
• Analyzed children born within 12 months preceding the survey to minimize selection bias.

Main Results:

• Identified significant spatial variations in infant mortality across Nigerian states.
• Demonstrated that risk factors for infant death differ between the neonatal period (0-1 month) and post-neonatal period (1-12 months).
• Observed non-linear relationships between mother's age at birth and infant mortality risk.

Conclusions:

• Spatial patterns and determinants of infant mortality are not uniform across Nigeria.
• Differentiating between early and later infant mortality periods reveals distinct risk profiles.
• The findings underscore the need for geographically tailored and age-specific infant mortality reduction strategies.