Neonatal Mortality and Maternal/Child Health Care in Nigeria: An Impact Analysis

Abstract

Reducing the rate of mortality in neonates to as low as 12 per 1,000 live births is one of the clearly spelt out aims of the third tenet of the Sustainable Development Goals (SDG) because of its importance to the dynamics of population. While there have been various studies focused majorly on the causes, rates and determinants of neonatal mortality in Nigeria, studies on the impact of maternal/child care characteristics on neonatal mortalityand the potential implication of failing to attain the SDG target for neonatal mortality have seemingly been neglected. In this study, we undertake an analysis of the impact of maternal / child care characteristics on neonatal mortality using the logistic regression model. Results from the study showed that antenatal care (P-value = 0.000, odds ratio = 0.546 for women who visited the hospital during pregnancy on more than 5 occasions), post natal care (P-value = 0.004, odds ratio = 0.402 for women who received early neonatal care from skilled medical personnel), place ofdelivery (P-value = 0.000, odds ratio = 0.592 for babies that were delivered in a government hospital) and skill of birth attendant (P-value = 0.000, odds ratio = 0.706 for babies who were delivered by trained doctors/nurses/midwives) had significant impact on neonatal mortality at the 95% confidence level implying that improved maternal health care: before, during and immediately after delivery as well as the quality of care to motherand child are both important and necessary to the reduction of neonatal mortality in Nigeria. To achieve the sustainable development target for neonatal mortality, it is therefore recommended that stake holders in the public health sector improve the quality of existing health care facilities and access to quality services.
 Keywords: Neonatal mortality, logistic regression, maternal care, child health care, Nigeria

Levels, Trends and Determinants of Infant Mortality in Nigeria: An Analysis using the Logistic Regression Model

“…On a global level, the world has continued to press in the right direction and many countries in Europe and America have made steady progress over the years with respect to childhood mortality reduction [4]. Despite the global record of improved childhood survival and reduced average mortality rate as seen by the rise in the Annual Rate of Reduction (ARR) from 1.9% in the year 2000 to 3.5% in 2015 [12], [13], [8], childhood mortality at all levels are still unacceptably high and significantly distributed unevenly among the different regions, continents and countries with countries such as India, Bangladesh and Nigeria to mention a few in Asia and Africa still recording ridiculously high and unacceptable rates of neonatal, post neonatal, infant, under-five and childhood death [6], [2], [9]. The different stages of childhood mortality continue to respond differently to the various prevention and intervention programmes of the United Nations with the rate of reduction in under-five mortality being the most significant of all the reported reductions in childhood mortality (National Bureau of Statistics [15], [19]).…”