1. Introduction

The global agenda for several decades now has been the emphasis on reducing under-five mortality and to some extent neonatal mortality ignoring the critical and crucial twenty four hours of life described as the most vulnerable period in human life [1-5]^[1]. The birth of a child is an event of celebration, but for several parents it is silent grief due to the loss of a precious life that has not been named, documented or properly celebrated. UNICEF estimates that of the 6.9 million under-five deaths in 2013, 16% or 1.05 million deaths occurred among newborn that did not live beyond their first twenty four hours of life; eighty percent of these first-day deaths occur in only two regions of the world: sub-Saharan Africa and South Asia; and that sub-Saharan Africa contributes around 397,000 first-day deaths ^[6]. Furthermore, four of the ten countries that shoulder 64% (673,200) of the global first-day mortality are in sub-Saharan Africa: Nigeria, DR Congo, Ethiopia and Tanzania, in that order; and among the top twenty countries with the highest first-day mortality rate only Pakistan and Afghanistan are outside of sub-Saharan Africa ^[6]. Worldwide, two factors are responsible for the largest portion of first-day mortality: complications arising from prematurity and birth complications; these two factors constituting 58% of first-day deaths ^[7]. However, in sub-Saharan Africa the factors are multitudes ranging from maternal undernutrition, early child marriage, shorter birth interval from low contraceptive use to increase birth intervals, poor skilled attendance at delivery and shortage of skilled health workforce to deliver essential health care [8-20]^[8].

In the Nigerian context the situation of child mortality continues to be public health challenge despite great effort to reduce child mortality under the global effort of Millennium Development Goals (MDGs). Efforts under the MDGs in reducing child mortality in Nigeria showed a negligible impact with an annual average reduction of 1.4%, which is not enough to achieve MDG 4 by the deadline ^[7]. In terms of first-day mortality, the situation is equally gloomy where Nigeria is ranked as the 11^th in terms of first-day mortality rate of 14 per 1000 live births and contributing about 90,000 first-day deaths or 9% of global first-day deaths, second only to India ^[7]. With huge coverage gaps in the essential maternal, newborn and child health packages with respect to ANC, skilled birth attendance and postnatal care the unacceptable high first-day mortality rate for Nigeria does not come as a surprise. Recent figures indicates that only about half (or 51%) of pregnant women had at least four or more ANC visits, only 38% of women were delivered by skilled professional health worker, 63% of births took place at home and nearly one-in-five deliveries happened with no one present ^[21]. There is scanty literature on first-day mortality until the Save the Children’s analysis of the situation with its first-ever publication of Birth Day Risk Index that compares first-day death rates for babies in 186 countries to identify the safest and most dangerous places to be born. In this publication, Nigeria’s newborns are safer than those countries that are either politically unstable or experiencing civil war or have just emerged from civil conflicts or political impasse: Guinea-Bissau, Mali, Somalia, Cote d’Ivoire, Chad, DR Congo, CAR, Sierra Leone, Angola and Burundi ^[7]. This publication has underscored the urgent need to investigate factors associated with first-day mortality in Nigeria with the overall aim of providing evidence for programming and policy development as we prepare for post-2015 agenda.

2. Methods

The data for this investigation comes from the most recent Nigeria DHS, the 2013 Nigeria DHS. The 2013 Nigeria DHS was a cross-sectional, descriptive design collecting information from eligible men and women. The 2013 Nigeria DHS is the fifth in the series of nation-wide organized data collection project supported by USAID and implemented by the National Population Commission with technical assistance from ICF Macro International ^[21]. The 2013 survey consists of nationally representative sample of 38,948 women aged 15-49 years and 17,359 men aged 15-59 years living in 38,904 households. The purpose of the survey is to provide policy makers and researchers with “updated estimates of basic demographic and health indicators” for planning, policy-making and programming. Therefore, it collected information on various demographic and health issues and indicators such fertility levels, trends and preferences, nuptiality, sexual activity, awareness and use of contraception, child nutrition and feeding practices including nutritional statuses, child morbidity and mortality, health seeking behaviors among mothers. The instruments used to collect the data for the 2013 NDHS consisted of six questionnaires; for the purpose of this research birth recode (NGBR6AFL) was utilized for the analysis

For the purpose of this study, the study population composed of all births within the 5-year period preceding the survey in 2013 (119, 148) and all the first-day deaths within the 5-year period prior the survey in 2013 (2215). In calculating first-day mortality, the DHS age-at-death data was analysed (variable b6). To avoid calendar bias in estimating first-day deaths (“day-0”), age-at-death 100 and 101 were merged to avoid this calendar bias.

The variables of interest in this study are broadly classified into dependent (or outcome) and independent (or explanatory) variable. The primary outcome variable is death within 24 hours of birth and the explanatory variables include those acting distally or the socio-economic factors such as mother’s, household wealth index, religion, rural/urban location, region of residence and household structure (sex of the household head); and those acting proximally such as maternal age and body mass index (BMI), parity, birth weight, sex of baby, and ANC-related factors such utilization and number of ANC visits, number of tetanus toxoid injections received, BP measurement, urine test, blood test, information about complication); delivery-related factors (place of delivery). Table 1 below gives a summary of these variables. An index of ANC adequacy was created to using six elements/components of ANC services. These elements are having blood pressure measurement, having at least two doses of tetanus toxoid vaccination, consuming iron tablets supplements, taking two doses of Fansidar tablets to prevent malaria and having done urine and blood tests. This index is dichotomous, that is either a pregnant woman received all of these elements or not receiving any at all. This index is constructed in this way to optimize the benefit of ANC since all the elements are important components of the ANC service; receiving one less means not having the optimum benefit and can lead to adverse pregnancy outcome.

The first step in the data analysis was recoding of some relevant variables into appropriate categories such as age of mother, region of residence, household headships (male versus female), educational attainment of the mother, wealth index, first-day death, birth weight, use ANC, blood pressure (BP) measurement, urine test, blood test, tetanus toxoid injections and index of ANC adequacy. Then descriptive statistics were generated in terms of first-day mortality across background characteristics and first-day mortality rates were subsequently generated from first-day deaths and live births.

Bivariate analyses were generated to establish any statistical association between the outcome variable and the explanatory variables as listed in Table 1. The bivariate analyses are the unadjusted regression models; they provided the crude hazard ratios. Multivariate analysis was also conducted using stepwise backward elimination method to identify those factors that were significantly associated with first-day mortality. The statistical model for investigating the factors influencing first-day mortality is the Cox proportional hazards regression model ^[22]. The Cox was chosen for this analysis since it represents the typical “time-to-event” data we are dealing with. First-day death is a form of failure data in which we are trying to estimate the risk or probability of first-day death from birth. Thus, Cox model provides the most appropriate analytical model for analyzing the effect of various factors on first-day death. In this model, it is proposed that the hazard or risk or probability for a subject j in the data experiencing the event is given by the semi-parametric relationship:

the h₀ component represents the survival or the hazard function while the β component stand for the multivariate component or the regressions coefficients to be estimated from the data and the x’s multiplied by β are the explanatory variables i=1, 2, 3, …….n; n denotes the number of the explanatory variables in the model. The h₀ represents that baseline hazard function when all the explanatory variables are zero. A model with one explanatory (independent) variable looks like this:

and if the individual (live birth in this case) is exposed to any of the factors under investigation, the model is of the form: (i.e. x=1)

However if the individual is not exposed, then the model takes the form of: (i.e. x=0)

In this model the risk or probability of first-day death was measured in terms hazard ratio; representing increased (or decreased) risk of first-day death. Hazard ratio of more than one indicates increased risk of first-day death while hazard ratio of less than one indicates reduced risk of first-day death; hazard ratio of one means the exposure/characteristic/factor has no effect on early neonatal death. The Cox model is implemented in Stata v13 ^[23] using the Stata’s stcox command that fits the Cox proportional hazard models. However, the data was first stset that is ‘telling’ the Stata to treat the data as a form of ‘time-to-event’ or survival analysis. Further, because the data for the analysis was collected using complex three-stage cluster sampling design, the analyses were conducted incorporating this sampling design and also applying the sampling weight (wgt) generated by dividing v005 by 1,000,000. The Stata survey command svy was utilized to ‘inform’ Stata about the nature of the data in terms sampling design and by so doing, Stata handles the data appropriately. The sampling weight was also stset, as usual to ‘tell’ Stata to handle this weight as survival data. The Cox model was finally fitted to assess the effect of each factor on first-day death. All output were generated incorporating the svy command to adjust for the complex sampling design.

Table 1. List of variables, variable definition and variable coding

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3. Results

Table 2. Live births, first day mortality and first day mortality rates, Nigeria DHS 2013

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Table 3. Unadjusted and adjusted hazard ratios (95% confidence interval [CI]) for variables associated with first day mortality

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The results are displayed in Table 2 and Table 3. Table 2 provides number of live births, first-day mortality and first-day mortality rates disaggregated by background characteristics. It also show that the average first day mortality rate is 19 per 1000 live births. Background characteristics with above average first-day mortality include being a young mother, overweight and obese, residing in Southern part of Nigeria, living in rural areas, having secondary school education or more, being a working mother, being either a Christian or traditionalist, being either of parity one or five, being a male newborn, pregnancy that was not wanted any more, facility delivery, Caesarean delivery, being a large baby or small sized-baby and the newborn being either the first or the fourth. These descriptive statistics gives an idea on the direction of the relationship between the background characteristics and risk of first-day mortality. Table 3 presents the crude and adjusted hazard ratio of first-day mortality and some selected background characteristics. All the background characteristics are significantly influencing first-day mortality except those newborns born to women in the rich wealth index, female newborns, those delivered via a Caesarean operation, large babies and babies that are either first or third born. Increased hazard of first-day mortality are associated with factors such as age above 25 years, rural residence (HR=1.17, 95%CI: 1.16-1.19), having at least primary education or more, having a mother that is working (HR=1.06, 95%CI: 1.05-1.08), being born into a polygynous family (HR=1.05, 95%CI: 1.03-1.06), either being an underweight mother (HR=1.09, 95%CI: 1.07-1.12) or overweight (HR=1.03, 95%CI: 1.02-1.05) or obese mother (HR=1.02, 95%CI: 1.00-1.04). An unexpected finding is that of increased hazard of first-day mortality among female compared to male newborns (HR=1.15, 95%CI: 1.13-1.17).

The last three columns of Table 3 presents the adjusted regression model where increasing age of the mother (35 years+) (aHR=1.12, 95%CI: 1.01-1.24), rural location (aHR=1.30, 95%CI: 1.22-1.40), living anywhere in Northern part of Nigeria (aHR=1.18, 95%CI: 1.10-1.26) and having formal education (secondary and more) (aHR=1.17, 95%CI: 1.07-1.29). Only three factors appear to significantly reduce the risk of first-day mortality: Islamic religion (aHR=0.90, 95%CI: 0.84-0.94), utilization of focused ANC (aHR=0.87, 95%CI: 080-0.93) and utilization of postnatal care within 24 hours of delivery (aHR=0.94, 95%CI: 0.89-0.99).

4. Discussion

The hours surrounding labour and delivery has been described as the most risky period for the survival of mankind and provision of skilled medical intervention during this period can significantly reduce threat to life of newborn and mankind in general. Analysis of trend of child mortality has indicated a new paradigm shift where more deaths are clustering around the time of time implying that interventions targeted around this time can substantially reduce the burden of child mortality ^[24].

We assessed factors potentially associated with first-day mortality using the most recent DHS data for Nigeria. Geographical zone (North versus South) of residence and rural residence appear in our model to significantly influence first-day mortality. Expectedly, newborn in the North have increased hazard of first-day mortality compared to their counterparts in the South. The North-South differential in first-day mortality could be a reflection of overall socio-economic disadvantage of the North compared to that in the South. Large proportion of women in the North have lower educational attainment than that of the South, implying that health knowledge, attitudes and practices regarding pregnancy and prenatal care, skilled delivery attendance and child care particularly postnatal care are significantly low compared to their counterparts in the South. On poverty index, more states in the North are in the poorest poverty index than in the South; more people in the North fall within the poverty line than in South. Geographically, more of the North is described as being rural than the South. This has the implication that access to quality health care is restricted in rural areas and since majority of people live in rural areas in the North they are obvious disenfranchised in access and utilization of medical care. Overall, the combinations of poverty, poor state of health facilities and poorly equipped and staffed health facilities, poor access and utilization of health services, low levels education among the women makes the North have the poor health outcomes compared to the South ^{[25, 26]}.

Our findings indicates that having at least four ANC visits during pregnancy reduces the risk of first-day mortality. Antenatal care started in Europe in early 19^th century and it was provided routinely to all pregnant women without recourse to numbers and elements of care until WHO conducted a trial about the numbers of visit and content of care in 2001 and recommended a new model of care called focused antenatal care ^[27]. This new method was rapidly adopted in many countries including Nigeria. Our findings confirms some of the previous results supporting the protective effect of ANC on child outcome; that having four ANC visits decreases the chance of first-day mortality. The study by Singh ^[28] pooling DHS data from Africa, Latin America/Caribbean and Asia revealed that having at least four ANC visits was associated with reduced odds of first-day mortality or a reduction of about 29%-35% few neonatal deaths. Antenatal care exerts this mortality-decreasing effect via four possible mechanisms: early detection of adverse condition that can risk the survival of the newborn; allows timely intervention in situation where adverse conditions have been detected; transmission of health knowledge to the pregnant women and re-enforcement of healthy behaviors and practices ^{[29, 30]}. Additional advantage is derived when women who attended ANC are more likely to deliver in presence of skilled personnel thereby increasing the survival chances of the mother-baby pair ^{[31, 32]}.

According to the 2013 Nigeria DHS, postnatal care (among the newborn) happens to be most poorly utilized newborn care in the continuum of maternal and child health care; only 14% of newborn received postnatal care within the first two days of delivery, 12% of postnatal care was from skilled professionals and that up to 36% of newborn something else was placed on the umbilical stump after cutting ^[21]. Postnatal care is a recommended strategy aimed at reducing both maternal and child health that contributes to achievement of MDGs 4 and 5. Our study indicates that utilizing postnatal care within the first two days reduces the hazard of first-day mortality. This is not surprising since provision of some proven interventions at around delivery and immediately after delivery has been shown to improve maternal and health and consequently reduce maternal and child mortality ^{[33, 34]}. However, our findings contradicts the findings of Singh (to some extent) when they reported that postnatal care utilization is within 24 hours is not associated with neonatal mortality in India ^[35].

Notable factors appear not to influence first-day mortality in contradiction to some previously reported literatures. Of particular importance is the sex of child and place of delivery. Studies consistently indicates the mortality advantage of female babies over that of male babies. These include those reported from Indonesia, Nepal and Nigeria ^{[36, 37, 38]}. Our results indicates that sex of the baby is not a significant predictor of first-day neonatal mortality both in bivariate and multivariate models. It is possible that whatever factor that is responsible for this survival advantage of females over males is yet to express itself within the first 24 hours of delivery that is as the newborns become older sex differentials become more pronounced for some unknown reasons. However, closely related our result is the study by Wu in which they reported survival advantage of male babies over female ones ^[39]. We are cautious in this comparison since these studies cited hear refer to either neonatal mortality ^{[36, 37, 38]} or early neonatal mortality ^[40]. Also, the role of place of delivery and neonatal mortality is inconclusive. Oti [43] have reported that place of delivery does not influence neonatal mortality while a meta-analysis indicates that place of delivery reduces risk of neonatal mortality by as much as 29% ^[41]. As earlier mentioned, cautioned needs to be exercised in making these comparison with previous reports as almost all of them referred to either neonatal or early neonatal mortality. This, in our opinion indicates paucity of research in this field.

5. Conclusion

This paper explored the factors associated with first-day mortality. Two factors significantly determine first-day mortality in Nigeria: advanced maternal age, residing in northern and rural Nigeria and advanced maternal education. Utilizing focused ANC and postnatal care reduced the hazard of first-day mortality.

Acknowledgements

The acknowledged the useful comments of Dr. Latifat Ibisomi of Witwatersrand School of Public Health, Johannesburg. Also we are grateful to MEASURE DHS for permission to use the data set in this study.

Competing Interest

Author declared no conflict of interest. Source of funding: this study was independently conducted by the author without financial support from any agency or organization both within and outside Nigeria.

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