modeling determinants of neonatal mortality in Ethiopia

Bacha ewunetu Gemechu (bacha0194@gmail.com)
statistics, Arbaminch University
February, 2016
 

Abstract

Background: Although neonatal mortality estimates continue to decline in Ethiopia over the years, it is a matter of a great concern among stake holders as the decline is not enough to reduce NM.
Objective: The study aimed to investigate significant factors and appropriate model of neonatal mortality in Ethiopia and also to assess the effect region as a cluster.
Method: The data was obtained from the EDHS, 2011.The study sample (n = 2604) was based on infants (0–1 months old) during the survey period; extracted from the women data base. Two model families, generalized estimating equation and alternating logistic regression models from marginal model family, and generalized linear mixed model from cluster specific model family were used for the analysis. AIC and QIC were used for model selection.
Result: the result showed that among eligible children the proportion of NM was 14.78%. Alternating logistic regression model was best fits the data for population-averaged effects of the given factors on neonatal mortality than generalized estimating equation model and generalized linear mixed model with two random intercepts was the best model to evaluate within and between regional heterogeneity of neonatal mortality. From all the fitted model age of respondents (mothers), multiplicity of birth, birth interval, and birth order, age at first birth, residence, and birth size were found to be significant factors of neonatal mortality; whereas wealth, mothers educational level, sex of a child and place of delivery were non-significant factors.
Conclusion: More importantly, this study contributes to the understanding of the individual and collective effect of maternal, socio-economic and child related factors influencing neonatal mortality in Ethiopia.


Keywords: Neonatal mortality; Generalized Estimating Equation; Alternating logistic regression; Generalized Linear Mixed Model