http://orcid.org/0000-0001-5289-9378
Figures
Abstract
Neonatal (NMR), infant (IMR) and under-five (U5M) mortality rates remain high in Nigeria. Evidence-based knowledge of trends and drivers of child mortality will aid proper interventions needed to combat the menace. Therefore, this study assessed the trends and drivers of NMR, IMR, and U5M over a decade in Nigeria. A nationally representative data from three consecutive Nigeria Demographic and Household Surveys (NDHS) was used. A total of 66,158 live births within the five years preceding the 2003 (6029), 2008 (28647) and 2013 (31482) NDHS were included in the analyses. NMR was computed using proportions while IMR and U5 were computed using life table techniques embedded in Stata version 12. Probit regression model and its associated marginal effects were used to identify the predisposing factors to NMR, IMR, and U5M. The NMR, IMR, and U5M per 1000 live births in 2003, 2008 and 2013 were 52, 41, 39; 100, 75, 69; and 201, 157, 128 respectively. The NMR, IMR, and U5M were consistently lower among children whose mothers were younger, living in rural areas and from richer households. Generally, the probability of neonate death in 2003, 2008 and 2013 were 0.049, 0.039 and 0.038 respectively, the probability of infant death was 0.093, 0.071 and 0.064 while the probability of under-five death was 0.140, 0.112 and 0.092 for the respective survey years. While adjusting for other variables, the likelihood of infant and under-five deaths was significantly reduced across the survey years. Maternal age, mothers’ education, place of residence, child’s sex, birth interval, weight at birth, skill of birth attendant, delivery by caesarean operation or not significantly influenced NMR, IMR, and U5M. The NMR, IMR, and U5M in Nigeria reduced over the studied period. Multi-sectoral interventions targeted towards the identified drivers should be instituted to improve child survival.
Citation: Morakinyo OM, Fagbamigbe AF (2017) Neonatal, infant and under-five mortalities in Nigeria: An examination of trends and drivers (2003-2013). PLoS ONE 12(8): e0182990. https://doi.org/10.1371/journal.pone.0182990
Editor: Umberto Simeoni, Centre Hospitalier Universitaire Vaudois, FRANCE
Received: March 13, 2017; Accepted: July 27, 2017; Published: August 9, 2017
Copyright: © 2017 Morakinyo, Fagbamigbe. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data are available to download free of charge by registered users from the DHS Program at https://dhsprogram.com/data/. Following new user registration (https://dhsprogram.com/data/new-user-registration.cfm), all DHS survey datasets underlying this study are available to request and download individually, without restriction, using the 'Download Manager' tool (https://dhsprogram.com/data/dataset_admin/download-manager.cfm). Full dataset access instructions are available at the following link: https://dhsprogram.com/data/Using-DataSets-for-Analysis.cfm.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Child mortality has been at the hub of health discourse since time immemorial. Governments, health professionals, and policymakers have reserved an exclusive interest in reducing the prevalence of childhood deaths globally [1]. This interest has not only extended into the international scene, it has led to the development of sound interventions to reducing child mortality among children under the age of five between 1990 and 2015, and between 2015 and 2030 as tagged in the United Nation’s Millennium Development Goals (MDGs) [1], and Sustainable Development Goals (SDGs) respectively [2].
As recommended by different United Nations (UN) organisations, most countries use the reduction in under-five and maternal mortalities as bases for key development [3]. It’s also pertinent for countries to estimate their neonatal and infant mortalities so as develop appropriate intervention programs to reduce preventable child deaths. Neonatal mortality remains a significant public health problem worldwide. In the year 2015, 2.7 million deaths occurred in the first 28 days of life; representing a significant reduction to 19 deaths per 1,000 live births from the previously 36 deaths per 1000 live births in 1990 [4]. Though there was a decline in neonatal rates in some sub-Sahara African countries, such as Ghana and Uganda [5], the Nigerian Neonatal Mortality Rate (NMR) reduced by 20.4%, from 49 deaths per 1000 live births in 1990 to 39 in 2011 [6], 37 in 2013 [7] and to 34 deaths per 1000 live births in 2015 [8]. Globally, Nigeria ranks second to India with the highest number of neonatal deaths [6].
Similarly, Under-Five Mortality Rates (U5M) remain high in Nigeria despite recent interventions. A U5M of 128 deaths per 1000 live births as evidenced in the year 2013 Nigeria Demographic Health Survey suggests that about one in every eight children in Nigeria dies before their fifth birthday—approximately 21 times the average rate in developed countries [9]. Spanning a period of over two decades (1990 to 2015) in Nigeria, Infant Mortality Rates (IMR) have reduced by 57% (from 126 to 69 deaths per 1000 births); U5M fell by approximately 49% (from 213 to 109 deaths) [8].
Child deaths commonly result from several risk factors and preventable diseases. Acute Respiratory Infections (ARIs), diarrhoea, malaria and chronic malnutrition contribute largely to disease morbidity and mortality among children [10]. Deaths in the first 28 days of life has been linked to the endogenous (genetically-induced malfunctions, premature births) status of a child, quality of antenatal care, whether assistance was given during delivery and post-partum care [11]. However, deaths in the succeeding 11 months are often tied to the wealth status of the households, environmental factors, health behaviour and nutritional practices [11]. Several other factors have been linked with infant and child deaths. Such factors among others include maternal education, early marriage, place of residence, regional variations, short birth intervals, fertility behaviour, breastfeeding practices, use of health services by mother and or child, child’s sex, ethnicity and religion [12–16].
Notwithstanding the considerable gains achieved in reducing childhood deaths globally, the recorded progress was insufficient in meeting the MDG 4 target. The recommended SDG target for child mortality signifies a renewed commitment to the world’s children. Achieving this renewed commitment is dependent on monitoring the drivers of preventable deaths among children. The outcome of this study would serve as a bedrock for planning suitable interventions for halting preventable deaths of newborns and under-fives. Therefore, this study presents a population-based study on the trends and drivers of neonatal, infant and under-five mortalities over a decade in Nigeria.
Methods
Ethical consideration
The Institutional Review Board (IRB) of the National Institute of Medical Research, Nigeria approved the study protocol, survey instrument, and materials prior to the commencement of the surveys. Details of the ethical approvals have been reported earlier [17]. Informed consent was obtained from all parents and guardians who participated in the surveys.
Study setting
Nigeria consists of 6 geopolitical regions; North-East, North-West, North-Central, South-East, South-South, and South-West which are sub-divided into 36 administrative states and the Federal Capital Territory (FCT). The population in each of the geopolitical regions and states are relatively homogeneous and share similar socio-cultural characteristics. Also, health-related characteristics like access to health care, environment, housing system etc. are similar within the regions and states.
Sources of data and sampling
We pooled data from three consecutive nationally representative Nigeria Demographic and Household Surveys (NDHS) in 2003, 2008 and 2013. The survey uses three-stage sampling technique to select the respondents. Firstly, Local Government Areas (LGAs) are selected, then the Enumeration Areas (EA), which are the Primary Sampling Units (PSU) and referred to as clusters and lastly the selection of households within the selected EAs. Primary information about households, sexual and reproductive health and history were collected from women aged 15–49 years within the selected households. Usually, the survey collects birth history of all women interviewed. More specifically, the survey collects information on all births to a woman. We, therefore, used the “child recode data” which contains all follow-up information on all children born to the interviewed women within five years preceding the survey.
Data analysis
Among the 7620, 33385 and 38948 women who participated in 2003, 2008, and 2013 surveys respectively, there were 6029, 28647 and 31482 children born within five years preceding each of the surveys. All analysis in this study were therefore based on the survivorship of the 66158 children within first five years of their life.
Variables
There are three outcome variables in this study, they are neonatal deaths, infant deaths, and under-five children (U5) deaths. According to the NDHS, neonatal deaths, infant deaths, and under-five children (U5) deaths are deaths within the first 28 days, one year and five years respectively [18].
Based on past literature, the independent variables included in this study are:
- Sociodemographic characteristics of mothers: maternal age (15–19, 20–24; 25–34; 35+), marital status (never married, currently married or living with sexual partner, formerly married), mother education (none, primary, secondary, higher), household wealth (poorest, middle, richest), religion (Islam, Christian/Catholic, others), media use (not at all, at least once a week, less than once a week), Residence (urban, rural), zone (North Central; North East; North West; South East; South-South; South West).
- Characteristics of child: sex (male, female), birth order (1, 2–3, 4–6, 7+), birth interval (1st birth, <24 months, 24-47months, 48+ months), weight at birth (average or higher, small, very small), delivery mode (normal, caesarean).
- Perceived benefit/need of health care: adequacy of antenatal care (ANC) use, based on the World Health Organisation (WHO) recommended four visits (none, inadequate, adequate), the skill of birth attendant (none, skilled, unskilled), tetanus injection received (yes, no).
- Environmental characteristics: drinking water sources (unimproved, improved), toilet type (unimproved, improved), cooking fuel (unclean/biomass, clean fuel).
The groupings of the environmental characteristics were in tandem with those adopted in the 2013 NDHS [18] and the 2010 WHO and UNICEF document on progress on sanitation and drinking water [18]. The “source of drinking water” was grouped into either improved or not. Improved sources include piped into dwelling/yard/plot, public tap/standpipe, tube-well or borehole, protected well and spring, rain water, and bottle water. The improved toilet types are “flush/pour flush to piped sewer system”, “flush/pour flush to septic tank”, “flush/pour flush to pit latrine”, “ventilated improved pit (VIP) latrine”, “pit latrine with slab or composting toilet” while any other types of toilet facilities were categorised as non-improved.
Statistical analysis
Descriptive statistics were used to show the distribution of the under-five children by the studied characteristics in Table 1. We then computed the NMR using proportions while IMR and U5M were computed using life table techniques embedded in Stata version 12 as presented in Table 2. Bivariate analyses were carried out to determine the significant association between each of the outcome variables and the independent variables using Pearson Chi-square (x2) test of association and also presented in Table 2. Probit regression model was used to identify the predisposing factors to neonatal death, infant mortality and under-five mortality.
In probit regression model, attempt is made to model the (conditional) probability of a "successful" outcome, that is,
(1)
It is expressed as a derivative of Eq (1) as shown in Eq (2)
(2)
where Φ(·) is the cumulative distribution function of the standard normal distribution. That is, conditional on the explanatory variables, the probability that the outcome variable, Yi = 1, is a certain function of a linear combination of the explanatory variables. A positive regression coefficient indicates that an increase in the predictor leads to an increase in the predicted probability while a negative coefficient is an indication that t an increase in the predictor would reduce the predicted probability.
We provided the marginal effects of the explanatory variables. The marginal effects estimated using the “delta method” involves the use of calculus to show how much the (conditional) probability of the outcome variable changes when there is a change in the value of an explanatory variable, holding all other explanatory constant at their values. It is worth noting that unlike the linear regression case where the estimated regression coefficients are the marginal effects, there is a need for the additional level of computation to estimate the marginal effects haven computed the probit regression.
In the case of a discrete explanatory variable, the change in the probability is
(3)
Sampling weights were applied, statistical significance was determined at 5% and Stata 12 used for all analysis while multicollinear variables were removed in the final model. There are four distinct columns in the Tables 3, 4, 5 and 6. The first column is the marginal effects computed from the coefficients of the probit model. It shows changes in a particular category with respect to the reference category. The second column is the standard error of the estimate in the 1st column while the 3rd column is the associated p-value. However, the 4th column presented the estimated increase/reduction per 1000 live births with respect to the reference category.
Results
Over a third, (36.6%) of the mothers of the children interviewed were aged 30–39 years while 28.3% were aged 25–29 years. The majority (95.7%) were either currently married or living with sexual partners and nearly half, 46.2% are from households in the poorest wealth quintile. Regarding education, 48.3% had no formal education, 67.9% resided in a rural area while the sex ratio of the children was about 1 to 1 (Table 1).
Multivariate analysis
Each of the three indicators (NMR, IMR, and U5M) were analysed separately for each of the years as reported in Table 2. The NMR, IMR, and U5M seemed to have reduced in subsequent survey years. For instance, NMR fell from 52 per 1000 live births in 2003 through 41 in 2008 to 39 in 2013 (Table 2 and Fig 1). Similarly, the overall IMR (100, 75 and 69) and U5M (201, 157 and 128) reduced consistently in year 2003, 2008 and 2013 respectively. The NMR, IMR, and U5M were consistently lower among children whose mothers were younger than among those with older parents and also among the currently or formerly marrieds than the single mothers.
The mortality rates considered were also higher in rural areas than the urban areas and also higher among children from poorer households as shown in Table 2. On sex of child, NMR (61, 50 and 46), IMR (110, 87 and 84) and U5M (199, 170 and 134) were higher among males than the females at NMR (42, 33 and 36), IMR (97, 72 and 65) and U5M (206, 156 and 114) in 2003, 2008 and 2013 respectively. Similar patterns were noticed along the birth intervals and weights at the birth of the children as children with shorter birth intervals experienced higher mortalities and higher among children with smaller birth weights. Also, mortalities were significantly higher among children who had water from unimproved sources and used unimproved toilet types.
Neonatal deaths
Generally, the probability of neonate deaths in 2003, 2008 and 2013 were 0.049, 0.039 and 0.038 respectively. Table 3 shows the outcomes of probit regression model used to identify significant determinants of neonatal deaths for each of the survey years. For instance, the probability of neonate deaths was 0.022 lower among children whose mothers were aged 20–24 years than those aged 15–19 years in 2003. Considering mother’ education, having higher education reduced the probability of neonate deaths by 0.016 in 2013. On the area of residence, living in an urban area reduced the chances of neonate deaths by 0.017 in 2003, 0.007 in 2008 and 0.011 in 2013. Similar trends were noticeable along the sources of drinking water and type of toilets used. Childs sex, Mothers’ age, education, wealth status, place and zone of residence, birth order and birth interval, adequacy of ANC visits and type of toilet facility were significant predictors of neonatal mortality in all the survey years. Marital status, religion, receiving tetanus injection after birth, skill of birth attendant were significant to neonate deaths in 2003 but was not significant in 2008 and 2013. However, delivery mode, media use was significant in 2008 and 2013 only, while the type of cooking fuel and source of drinking water was only significant in 2008.
Infant death
The probability of infant death in 2003, 2008 and 2013 were 0.093, 0.071 and 0.064 respectively. In Table 4, we present the outcomes of probit regression model used to identify significant determinants of infant deaths for the respective survey years. The probability of infant death was 0.025, 0.019 and 0.030 lower among children whose mothers were aged 20–24 years than those aged 15–19 years in 2003, 2008 and 2013 respectively. Households in the richest wealth quintiles recorded a reduction in the number of infant deaths by 0.065, 0.023 and 0.028 in 2003, 2008 and 2013 respectively compared with children from poorest households. Considering mother’ education, compared with mothers with no education, having higher education reduced the probability of infant deaths by 0.045, 0.039 and 0.032 in 2003, 2008 and 2013 respectively. Similar trends were noticeable along the birth intervals, weight at birth, sources of drinking water and type of toilets used. Mothers’ education, wealth status, place and zone of residence, media use, birth order and birth interval, weight at birth, receiving tetanus injection after birth, adequacy of ANC visits, source of drinking water, type of cooking fuel and type of toilet facility were significant predictors of infant mortality in all the survey years. However, mothers’ age, marital status, child’s sex, delivery mode, were significant in 2008 and 2013 only.
Under-five deaths
Overall, the probability of under-five death in 2003, 2008 and 2009 were 0.140, 0.112 and 0.092 respectively. In Table 5, the outcomes of probit regression model used to identify significant determinants of under-five deaths for the respective survey years are presented. The probability of under-five death was 0.013, 0.000 and 0.026 lower among children whose mothers were aged 20–24 years than those aged 15–19 years in 2003, 2008 and 2013 respectively. Been from households in richest in richest wealth quintiles reduced the chances of under-five death by 0.092, 0.050 and 0.057 in 2003, 2008 and 2013 respectively compared with children from poorest households. Considering mother’ education, compared with mothers with no education, having higher education reduced the probability of under-five deaths by 0.091, 0.085 and 0.068 in 2003, 2008 and 2013 respectively. Similar trends were noticeable in religion, sex of the child, birth intervals, weight at birth, sources of drinking water and type of toilets used. Mothers’ education, wealth status, place and zone of residence, religion, media use, birth order and birth interval, weight at birth, receiving tetanus injection after birth, adequacy of ANC visits, skill of birth attendants, type of cooking fuel and type of toilet facility were significant predictors of under-five mortality in all the survey years. Whereas, mothers’ age, marital status, child’s sex, the source of drinking water, were significant in 2008 and 2013 only.
The adjusted factors influencing neonatal, infant and under-five mortalities are presented in Table 6. In the presence of other variables, the probability of neonate death did not reduce significantly across the survey years, although the probability of infant and under-five deaths reduced significantly in the same period. While controlling for other variables, maternal age, mothers’ education, place of residence, religion, child’s sex, birth interval, weight at birth, skill of birth attendant, delivery by caesarean operation or not significantly influenced neonatal mortality. Considering infant mortality, the significantly adjusted determinants include maternal age, marital status, mothers’ education, place of residence, religion, child’s sex, birth interval, weight at birth, the skill of birth attendant, delivery by caesarean operation or not. Also, maternal age, marital status, mothers’ education, place of residence, the zone of residence, child’s sex, birth interval, weight at birth, delivery by caesarean operation or not were the significant risk factors of under-five mortality.
Discussion
In this study, we determined the trends and drivers of neonatal, infant and under-five mortalities in Nigeria using data from three consecutive NDHS in 2003, 2008 and 2013. We found that there was a consistent reduction in NMR, IMR, and U5M for each of the surveys years. These reductions might be a reflection of the gains of the efforts put in place by member states of the United Nations in achieving the millennium development goals (MDGs) between 2000 and 2015.
In Nigeria, the successes recorded may be as a result of the various interventions of government in ensuring wider immunization coverage for all vaccine preventable diseases. In the year 2002, Nigeria endorsed the United Nations Special Session (UNGASS) goals on children of achieving by the year 2010 full immunization of children under one year of age at 90% coverage nationally with at least 80% coverage in every district or equivalent administrative unit [19]. A national policy on sustainable development with the sole aim of reducing infant and under-five mortality rates was formulated between 2003 and 2006 [17,20]. The WHO in the year 2006 and 2007 also endorsed the introduction of Haemophilus Influenza b (Hib) and Pneumococcal conjugate vaccines respectively into all national immunisation programs [21].
Moreover, the pentavalent vaccines that protect against Hib, hepatitis b, diphtheria, tetanus, and whooping cough were introduced by the Nigerian government to the immunization schedule in 2011 [21]. It was postulated that with the introduction of the pentavalent vaccine, about 400,000 cases of Hib would be prevented and about 27,000 lives saved annually [22]. Studies have shown that the use of vaccines to prevent the occurrence of diseases such measles, diphtheria, pertussis, Hib, and pneumococcus, has the potential to largely reduce disease incidence in children [23,24].
Though, there was a consistent reduction in NMR, IMR, and U5M for each of the surveys years, their prevalence was still high. The decline recorded between 2003 and 2013 for NMR and IMR was slower than that for U5M. Previous studies corroborate our position that increases in child survival is more pronounced within the first five years of life compared to the proportions of childhood deaths between birth and 28 completed days [15,25]. It could be explained that interventions from government and other relevant agencies targeted towards the post-neonatal age may account for lower U5M [26].
Our findings showed that maternal characteristics play a significant role in child mortality. The NMR, IMR, and U5M were consistently higher among children whose mothers were younger than among those with older parents across the survey years. Naivety in child nurturing, physical immaturity and pregnancy complications are plausible factors responsible for this trend among younger mothers [27,28]. The relationship between mother’s education and childhood mortality across the three age ranges as pointed in our study is well documented in previous studies [16,29,30]. Children of less-educated mothers have a higher probability of dying at all ages [31,32]. The basis for this consistent findings was that educated mothers may likely have improved income, better health education and make healthier choices for their health and that of their children [29,30].
Also, findings from this study show that the mortality rates considered were higher in rural areas than the urban areas, and also higher among children from poorer households. A similar trend has been reported in Bangladesh [26], Burkina Faso [26], and Rwanda [26]. Rural-urban differentials have previously been elucidated in relation to environmental factors (unavailability of improved water sources and inadequate basic sanitation facilities) and limited access to healthcare, social and economic services [30,33,34]. Household wealth is a significant predictor of U5M [29,35]. Poverty is a driver of unclean fuel use in many low-and-middle-income countries [36]. Half of all deaths linked to acute respiratory infections in the year 2014 resulted from children exposure to fumes from unclean fuels [37].
Our study revealed that NMR, IMR and U5 mortalities were highest among women whose prenatal care was provided by an unskilled attendant. Evidence abounds in the literature that antenatal care for pregnant women provided by skilled professionals is essential for ensuring optimal health outcomes for mother and child [7,15,38]. Furthermore, we found that the risk of dying among children across the three age groups surveyed in our study was lower among females than males. Previous studies have revealed that new-born boys have a lesser biological advantage in survival over new-born girls [39]. Biological [27,40,41] and genetic [39] factors may be the likely explanation for increased risk of male deaths. In addition, delay in the maturity of the lung in the first week of life among male children promote the higher occurrence of respiratory infections among them than females [42].
A wider interval between succeeding births and birth order present lower mortality risks in our study. This is in line with other studies [15,43] that show a significant association between short birth intervals and poor child and maternal health outcomes. Quick sequence of births may erode the reproductive and nutritional status of the mother thus, leading to maternal depletion disorder and other associated health infections [42]. Birth order had been noted in the literature as an essential determinant of childhood death [34,44,45]. A similar study have also shown that lower ranked birth order presents a lower risk of deaths among under-five children [42].
In addition, our study revealed that women who used biomass/unclean fuel, water from unimproved sources and unimproved toilet facilities had experienced higher neonatal, infant and under-five deaths than those using clean fuel, improved water sources and improved toilet facilities respectively. Previous studies have pointed out that children <5 years of age living in houses using solid fuels have a higher probability of dying than those in houses using cleaner fuels [44–46]. Wealthy households are more likely to use water from improved sources [42]. Diarrhea disease in children has been reported to result from sourcing water from unimproved sources and poor sanitation practices [47].
Conclusion
Findings from our study showed a reduction in the proportions of NMR, IMR, and U5 mortalities in Nigeria, and that many of the drivers of NMR, IMR, and U5 mortalities had varying degrees and trends of change between 2003 and 2013. Maternal age, mothers’ education, place of residence, child’s sex, birth interval, weight at birth, the skill of birth attendant, delivery by caesarean operation or not, use of unclean fuel, unimproved water and unimproved toilet significantly influenced neonatal, infant and under-five mortalities. Though these factors have earlier been pointed out in literature, not much progress has been recorded in the reduction of the burden of childhood deaths. Multi-sectoral interventions and comprehensive health policies targeted towards the drivers of childhood mortality revealed in our study should be instituted to improve child survival.
Acknowledgments
We acknowledged the support of the National Population Commission, Abuja, Nigeria and other partners involved in the NDHS. We express our appreciation to the National Population Commission and ICF Macro Calverton, Maryland, USA for releasing the data for this study. The authors also acknowledge the technical support received from the Consortium for Advanced Research Training in Africa (CARTA). Both authors are CARTA fellows and they received training in research conception and manuscript writing.
References
- 1.
Fox J. Public Health, Poor Relief and Improving Urban Child Mortality Outcomes in the Decade Prior to the New Deal. Rostock, Germany; 2011. Report No.: 2011–005.
- 2.
United Nations. Sustainable Development Goals (SDG). Washington, DC; 2015.
- 3.
Lander T. Neonatal and Perinatal Mortality: Country, Regional, and Global Estimates. Fourth. Lander T, editor. Geneva WHO Press. Geneva, Switzerland: World Health Organization; 2006.
- 4.
UNICEF/WHO/World Bank/UN. Levels and trends in child mortality. UNICEF. New York, USA; 2015.
- 5. Lawn JE, Blencowe H, Oza S, You D, Lee ACC, Waiswa P, et al. Every newborn: Progress, priorities, and potential beyond survival. Lancet. 2014;384:189–205. pmid:24853593
- 6.
UNICEF/WHO/Word Bank/UN. Levels and Trends in Child Mortality. New York, USA; 2012.
- 7. Oestergaard M, Inoue M, Yoshida S, Mahanani W, Gore F, Cousens S, et al. Neonatal Mortality Levels for 193 Countries in 2009 with Trends since 1990: A Systematic Analysis of Progress, Projections, and Priorities. PLoS Med. 2011;8:e1001080. pmid:21918640
- 8.
UNICEF. Diarrhoeal disease, Current status and progress. New York, USA; 2015.
- 9.
UNICEF/WHO/World Bank/UN. Levels and Trends in Child Mortality. USA; 2013.
- 10. Adeyele IT, Ofoegbu DI. Infant and Child Mortality in Nigeria : An Impact Analysis. 2015;3:122–32.
- 11. Adebayo SB, Fahrmeir L, Klasen S. Analyzing infant mortality with geoadditive categorical regression models: A case study for Nigeria. Econ. Hum. Biol. 2004;21:229–44.
- 12. Adebowale AS, Yusuf BO, Fagbamigbe AF. Survival probability and predictors for woman experience childhood death in Nigeria: “analysis of north—south differentials”. BMC Public Health. 2012;12:430. pmid:22691616
- 13. Antai D. Regional inequalities in under-5 mortality in Nigeria: a population-based analysis of individual- and community-level determinants. Popul. Health Metr. 2011;9:6. pmid:21388522
- 14. Adedini SA, Odimegwu C, Imasiku ENS, Ononokpono DN, Ibisomi L. Regional Variations in Infant and Child Mortality in Nigeria: a Multilevel Analysis. J. Biosoc. Sci. J. Biosoc. Sci. J. Biosoc. Sci. J. Biosoc. Sci. 2015;47:165–87. pmid:24411023
- 15. Akinyemi JO, Bamgboye EA, Ayeni O. Trends in neonatal mortality in Nigeria and effects of bio-demographic and maternal characteristics. BMC Pediatr. 2015;15:36. pmid:25886566
- 16. Fagbemigbe AF, Alabi O. Differentials and Correlates of Infant Mortality in Nigeria: A Comparative Survival Analysis Between North-East and South-West. Int. J. Trop. Dis. Heal. 2014;4:869–86.
- 17.
(FMOH) FM of H. National HIV/AIDS and Reproductive Health and Serological Survey, 2012 (NARHS Plus). Abuja, Nigeria; 2013.
- 18.
National Population Commission (Nigeria) and ICF International. Nigeria Demographic and Health Survey 2013. Abuja, Nigeria; 2014.
- 19. Ophori EA, Tula MY, Azih A V, Okojie R, Ikpo PE. Current trends of immunization in Nigeria: prospect and challenges. Trop. Med. Health. 2014;42:67–75. pmid:25237283
- 20.
National Population Commission (Nigeria). Population and the Quality of Life in Nigeria. Abuja, Nigeria; 2003.
- 21. Liu L, Johnson HL, Cousens S, Perin J, Scott S, Lawn JE, et al. Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. Lancet [Internet]. Elsevier Ltd; 2012;379:2151–61. Available from: http://dx.doi.org/10.1016/S0140-6736(12)60560-1
- 22.
World Health Organization (WHO). Nigeria Launches Penta Vaccine [Internet]. 2017 [cited 2017 Mar 1]. http://www.afro.who.int/en/nigeria/press-materials/item/4735-nigeria-launches-penta-vaccine.html
- 23. Lagos R, Horwitz I, Toro J, San Martin O, Abrego P, Bustamante C, et al. Large scale, postlicensure, selective vaccination of Chilean infants with PRP-T conjugate vaccine: practicality and effectiveness in preventing invasive Haemophilus influenzae type b infections. Pediatr. Infect. Dis. J. 1996;15:216–22. pmid:8852909
- 24. Heath PT. Haemophilus influenzae type b conjugate vaccines: a review of efficacy data. Pediatr Infect Dis. 1998;17:S117–22.
- 25.
Hong R, Ayad M, Rutstein S, Ren RL. Childhood mortality in Rwanda: levels, trends, and differentials. DHS Furth. Anal. Rep. Calverton, USA; 2009.
- 26. Kazembe L, Clarke A, Kandala N-B. Childhood mortality in sub-Saharan Africa: cross-sectional insight into small-scale geographical inequalities from Census data. BMJ Open. 2012;2:1–12.
- 27. Ezeh OK, Agho KE, Dibley MJ, Hall JJ, Page AN. Risk factors for postneonatal, infant, child and under-5 mortality in Nigeria: a pooled cross-sectional analysis. BMJ Open [Internet]. 2015;5:e006779. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25818271%5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4386230
- 28. Kamal SMM. What is the association between maternal age and neonatal mortality? An analysis of the 2007 Bangladesh Demographic and Health Survey. Asia. Pac. J. Public Health. 2015;27:NP1106–17. pmid:22186392
- 29. Izugbara C. Whose child is dying? Household characteristics and under-5 mortality in Nigeria. SAJCH South African J. Child Heal. 2014;8:16–22.
- 30. Dejene T, Girma E. Social determinants of under-five mortality in Ethiopia: Event history analysis using evidence from Ethiopian Demographic and Health Survey (EDHS). Health (Irvine. Calif). 2013;5:879–84.
- 31. Gakidou E, Cowling K, Lozano R, Murray CJ. Increased educational attainment and its effect on child mortality in 175 countries between 1970 and 2009: A systematic analysis. Lancet. Elsevier Ltd; 2010;376:959–74.
- 32. Akinyemi J, Adebowale A, Bamgboye E, Ayeni O. Child survival dynamics in Nigeria: Is the 2006 child health policy target met? Niger. J. Heal. Sci. 2015;15:18.
- 33. Günther I, Harttgen K. Deadly Cities? Spatial Inequalities in Mortality in sub-Saharan Africa. Popul. Dev. Rev. 2012;38:469–86.
- 34. Kayode GA, Adekanmbi VT, Uthman OA. Risk factors and a predictive model for under-five mortality in Nigeria: evidence from Nigeria demographic and health survey. BMC Pregnancy Childbirth. 2012;12:10. pmid:22373182
- 35. Ezeh OK, Agho KE, Dibley MJ, Hall JJ, Page AN. Risk factors for postneonatal, infant, child and under-5 mortality in Nigeria: a pooled cross-sectional analysis. BMJ Open. 2015;5:e006779. pmid:25818271
- 36. Ezeh OK, Agho KE, Dibley MJ, Hall J, Page AN. Determinants of neonatal mortality in Nigeria: evidence from the 2008 demographic and health survey. BMC Public Health. 2014;14:521. pmid:24886517
- 37.
WHO. Burden of disease from Household Air Pollution for 2012 [Internet]. 2012 [cited 2016 Nov 10]. http://www.who.int/phe/health_topics/outdoorair/databases/HAP_BoD_results_March2014.pdf.
- 38. Kassar SB, Melo AMC, Coutinho SB, Lima MC, Lira PIC. Determinants of neonatal death with emphasis on health care during pregnancy, childbirth and reproductive history. J. Pediatr. (Rio. J). Sociedade Brasileira de Pediatria; 2013;89:269–77.
- 39. Mekonnen Y, Tensou B, Telake DS, Degefie T, Bekele A. Neonatal mortality in Ethiopia: trends and determinants. BMC Public Health. 2013;13.
- 40. Alonso V, Fuster V, Luna F. Causes of Neonatal Mortality in Spain (1975–98): Influence of Sex, Rural—Urban Residence and Age At Death. J. Biosoc. Sci. 2006;38:537–51. pmid:16762089
- 41. Bhuiya A, Streatfield K. Mothers’ Education and Survival of Female Children in a Rural Area of Bangladesh. Popul. Stud. (NY). 1991;45:253–64.
- 42. Morakinyo OM, Adebowale SA, Oloruntoba EO. Wealth status and sex differential of household head: implication for source of drinking water in Nigeria. Arch. Public Health. 2015;73:58. pmid:26609363
- 43. Gupta M Das. Death Clustering, Mothers’ Education and the Determinants of Child Mortality in Rural Punjab, India. Popul. Stud. (NY). 1990;44:1–1.
- 44. Bassani DG, Jha P, Dhingra N, Kumar R. Child mortality from solid-fuel use in India: a nationally-representative case-control study. BMC Public Health. 2010;10:491. pmid:20716354
- 45. Pandey S, Lin Y. Adjusted effects of domestic violence, tobacco use, and indoor air pollution from use of solid fuel on child mortality. Matern. Child Health J. 2013;17:1499–507. pmid:23065299
- 46. Gajate-Garrido G. The Impact of Indoor Air Pollution on the Incidence of Life Threatening Respiratory Illnesses: Evidence from Young Children in Peru. J. Dev. Stud. 2012;49:500–15.
- 47.
WHO/UNICEF. WHO/UNICEF Joint statement: Home visits for the newborn child: a strategy to improve survival. WHO Libr. Geneva, Switzerland; 2009.