Nigeria’s efforts aimed at reducing avoidable child deaths have been met with gradual and sustained progress. Despite the decline in childhood mortality in Nigeria in the last two decades, its prevalence still remain high in comparison to the global standard of mortality for children under the age of five which stands at 25 deaths per 1000 live births. Knowledge of the chances of Nigeria achieving this goal for childhood mortality will aid proper interventions needed to reduce the occurrence. Therefore, this paper employed the Auto-Regressive Integrated Moving Average (ARIMA) model for time series analysis to make forecast of under-five mortality in Nigeria up to 2030 using data obtained from the United Nation’s Inter Agency Group for Childhood Mortality Estimate (UN-IGME). The ARIMA (2, 1, 1) model predicted a reduction of up to 37.3% by 2030 at 95% confidence interval. Results from the study also showed that a reduction of over 300% in under-five mortality is required for Nigeria to be able to achieve the SDG goal for under-five mortality.

This paper presents a statistical analysis of the levels, trends and determinants of infant mortality in Nigeria using the logistic regression model. Infant mortality data for each of the five years preceding the 2003, 2008, 2013 and 2018 Nigeria Demographic Health Survey (NDHS) was retrieved and used for the analysis. Findings from the study revealed that infant mortality rates decline have stagnated in the five year period prior to the 2018 survey with an Annual Rate of Reduction (ARR) of 0% relative to an initial ARR of 5.7% between 2003 and 2008. The ARR of 2.039% over the 15 year period spanning 2003 to 2018 suggests that the rate of infant mortality reduction is slow. This study also showed that maternal characteristics such as age and educational levels as well as cultural practises like use of clean water and toilet facilities were statistically significant determinants of infant mortality in Nigeria with P-values < 0.05 across each of the survey years.

The so-called Kumaraswamy distribution is a special probability distribution developed to model doubled bounded random processes for which the mode do not necessarily have to be within the bounds. In this article, a generalization of the Kumaraswamy distribution called the T-Kumaraswamy family is defined using the T-R {Y} family of distributions framework. The resulting T-Kumaraswamy family is obtained using the quantile functions of some standardized distributions. Some general mathematical properties of the new family are studied. Five new generalized Kumaraswamy distributions are proposed using the T-Kumaraswamy method. Real data sets are further used to test the applicability of the new family.

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

In this paper, we propose Complementary Kumaraswamy Weibull Power Series (CKWPS) Distributions. The method is obtained by compounding the Kumaraswamy-G distribution and Power Series distribution on a latent complementary distance problem base. The mathematical properties of the proposed class of distribution are studied. The method of Maximum Likelihood Estimation is used for obtaining the estimates of the model parameters. A member of the family is investigated in detail. Finally an application of the proposed class is illustrated using a real data set.