Abstract
BackgroundNeonatal mortality contributes a large proportion towards early childhood mortality in developing countries, with considerable geographical variation at small areas within countries.MethodsA geo-additive logistic regression model is proposed for quantifying small-scale geographical variation in neonatal mortality, and to estimate risk factors of neonatal mortality. Random effects are introduced to capture spatial correlation and heterogeneity. The spatial correlation can be modelled using the Markov random fields (MRF) when data is aggregated, while the two dimensional P-splines apply when exact locations are available, whereas the unstructured spatial effects are assigned an independent Gaussian prior. Socio-economic and bio-demographic factors which may affect the risk of neonatal mortality are simultaneously estimated as fixed effects and as nonlinear effects for continuous covariates. The smooth effects of continuous covariates are modelled by second-order random walk priors. Modelling and inference use the empirical Bayesian approach via penalized likelihood technique. The methodology is applied to analyse the likelihood of neonatal deaths, using data from the 2000 Malawi demographic and health survey. The spatial effects are quantified through MRF and two dimensional P-splines priors.ResultsFindings indicate that both fixed and spatial effects are associated with neonatal mortality.ConclusionsOur study, therefore, suggests that the challenge to reduce neonatal mortality goes beyond addressing individual factors, but also require to understanding unmeasured covariates for potential effective interventions.
Highlights
Despite declining trends in childhood mortality in many developing countries [1], neonatal mortality still remains a huge health concern worldwide [2,3,4]
Recent estimates from nationalwide household surveys show that considerable burden of neonatal mortality still remain in low to middle-income countries, the majority of which are in the sub-Saharan Africa [2,3]
Experts agree that in evaluating Millennium Development Goal (MDG) number 4, which emphasizes for the need to reduce underfive childhood and infant mortality [5], neonatal mortality is a key child survival indicators to monitor
Summary
Despite declining trends in childhood mortality in many developing countries [1], neonatal mortality still remains a huge health concern worldwide [2,3,4]. The underlying causes of neonatal mortality are multi-sectoral and inter-woven [6]. These operate at individual, family, community and regional levels and the effects can be direct or intermediary. The relationship between socioeconomic and bio-demographic factors and neonatal mortality are well established [1,7,8]. Neonatal mortality contributes a large proportion towards early childhood mortality in developing countries, with considerable geographical variation at small areas within countries
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