Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Prevalence and determinants of childhood mortality in Nigeria

BMC Public HealthBMC series – open, inclusive and trusted201717:485

https://doi.org/10.1186/s12889-017-4420-7

Received: 16 October 2016

Accepted: 12 May 2017

Published: 22 May 2017

Abstract

Background

Childhood mortality has remained a major challenge to public health amongst families in Nigeria and other developing countries. The menace of incessant childhood mortality has been a major concern and this calls for studies to generate new scientific evidence to determine its prevalence and explore predisposing factors associated with it in Nigeria.

Method

Data was obtained from Nigeria DHS, 2013. The study outcome variable was the total number of children lost by male partners and female partners respectively who were married. The difference between the numbers of child births and the number of living children was used to determine the number of children lost. Study variables were obtained for 8658 couples captured in the data set. Descriptive statistics were computed to examine the presence of over-dispersion and zero occurrences. Data were analysed using STATA Software version 12.0. Zero-inflated negative binomial (ZINB) regression analysis was carried out to determine the factors associated with childhood mortality. Results of ZINB were reported in terms of IRR and 95% confidence interval (CI).

Results

The age (mean ± std.) of male and female participants were 36.88 ± 7.37 and 28.59 ± 7.30 respectively. The data showed that 30.8% women reported loss of children and 37.3% men reported the same problem. The study revealed age (years), region, residence, education, wealth index, age at first birth and religion of father and mother as factors associated with childhood mortality. In terms of education, secondary and tertiary educated fathers exhibited 3.8% and 12.1% lower risk of childhood mortality respectively than non-educated fathers. The results showed that the risk of childhood mortality are 26.7%, 39.7 and 45.9% lower among the mothers having primary, secondary and tertiary education respectively than those with no formal education. The mothers living in rural areas experienced 28.3% increase in childhood mortality than those in urban areas, while the fathers in rural areas experienced 33.5% increase in childhood mortality than the urban areas. The risk of childhood mortality was significantly lower in middle, richer and richest (11.1%, 37.5 and 49%) economic quintiles respectively when compared to the risk of childhood mortality with female spouse who are poorest. Similar results were obtained for the fathers, with reduction in the incidence-rate ratio of 3.3%, 20.2 and 28.7% for middle, richer and richest economic quintiles respectively, compared to the poorest status. Furthermore, region and religion were found to be significant factors associated with childhood mortality in Nigeria.

Conclusion

The findings suggested that age, region, residence, education, wealth index, age at first birth and religion of fathers and mothers are key determinants associated with childhood mortality. The correlation between childhood mortality and fathers’ and mothers’ ages were found to increase the incidence of the outcome for every unit increase in age. The converse was however, true for age at first birth which was also statistically significant. The implication of this study is that policy makers and stakeholders in health care should provide for improved living standards to achieve good life expectancy meeting SDG3.

Keywords

Zero-inflated negative binomial Maternal health Infant mortality Neonatal mortality Child mortality Global health Nigeria

Background

Childhood mortality is a fundamental measurement of a country’s level of socio-economic and demographic development, and quality of life, especially of families. The Oxford English Dictionary defines a child as “a young human being below the legal age of maturity”. In Nigeria, the legal age of maturity is 18 years [1].

Reports from Nigeria, sub-Saharan Africa and the world at large have revealed that mortality experiences ranging from neonatal mortality, infant and child mortality to maternal mortality are still high [24]. Nigeria still has high prevalence of mortalities reflected in infants and children amongst others [57].

In Nigeria, the childhood mortality rate stands at 128 per 1000 live births, with large disparities in her different regions [8]. Report from the Nigeria Demographic and Health Survey, 2013 showed that childhood mortality rates range widely across geopolitical zones [9]. Regarding child mortality as a persistent public health challenge in Nigeria and other developing countries, researchers have made immense efforts to identify factors responsible for this menace [1012].

Studies have shown that mortality rates and risk factors varied by bio-demographic and socio-economic characteristics [1316]. The factors associated with childhood mortality from studies done in Brazil and America were reported as maternal obesity, maternal malnutrition, maternal short stature and maternal age less than 25 years or greater than 35 years [17, 18]. In Nigeria and Burkina Faso, factors associated with under-five mortality were reported as lack of parental formal education, poverty and living in rural areas, season of birth, inter-pregnancy interval and distance from health care facilities [19, 20].

Tackling the death of children, whether during perinatal, early or late neonatal, childhood or adolescent age is posing a difficult task in Nigeria. Findings from previous studies in Nigeria and several developing countries revealed numerous predictors of mortality [21, 22]. These findings triggered intervention initiatives which aimed to identify the factors responsible for the high mortality rates and the most appropriate techniques for tackling them. Nigeria is yet to meet the Sustainable Development Goals (SDGs) targets, regardless of national and international implementation projects on the reduction of mortality.

A key factor such as inadequate health care services remains a frontline problem in Nigeria. SDGs identify the minimum requirements to improve the general wellbeing of a population [2325]. One of the stated goals is the Good Health and Well-being (SDG3). Ensuring healthy lives and promoting the well-being of the general population at all ages is essential to sustainable development.

Progress has been made on increasing access to basic needs to enhance life expectancy, but more effort is needed to fully eradicate a wide range of barriers and address a plethora of persistent and emerging health issues. Statistics show that most sub-Saharan countries in Africa, including Nigeria, have a lot to do in achieving the SDG3. More research is therefore needed to inform the formulation of policies and implementation of programs for appropriate health intervention. There is need for data on the lifetime experience of childhood mortality. Thus, this study examined the factors associated with childhood mortality in Nigeria using NDHS, 2013 dataset.

Methods

Data source

Data was obtained from Nigeria DHS, 2013. The survey was done across the entire population. The country was divided into the six geopolitical zones which in turn are made up of the 36 states and the federal capital territory. From the 2006 Population census implementation in Nigeria, each region was subdivided into Enumeration Areas. The sample frame was drawn from a list of the enumeration areas. The sampling method for the 2013 Nigeria DHS was a three-stage stratified random sample. In the first stage, each State was stratified into rural and urban areas, and this brought a list of localities. In the second stage, one enumeration area was selected at random from a selected list of localities and the resulting list of households gave the list for the selection of households at the last stage. In the third stage, forty-five households were chosen in every rural and urban cluster through systematic sampling using the sample frame.

Data extraction

Our study focused on the total number of children lost by male partners and female partners respectively who were married. The difference between the numbers of child births and the number of living children was used to determine the number of children lost. Study variables were obtained for 8658 couples captured in the data set.

Ethical clearance

The demographic and health survey program has its own standards for protecting the privacy of participants. Prior to each interview, the interviewer is made to read the consent statement and inform the participant of voluntary participation and that he/she has the freewill to terminate the interview at any stage of the process. Furthermore, the ICF International certifies that the survey complies with the United States Department of Health and Human Services rules for the protection of participants and ensures that the survey follows the laws and regulations of the nation. However, approval for this study was not needed since the data is secondary.

Data analysis

Data analysis was conducted using STATA Software version 12.0. Zero-inflated negative binomial regression (ZINB) analysis was carried out to determine the risk factors of childhood mortality. Descriptive statistics were computed to examine the presence of over-dispersion and zero occurrences. Results of ZINB were reported in terms of IRR and 95% confidence interval (CI). p-value of <0.05 was considered statistically significant. Zero-inflated negative binomial regression is for modeling count variables with excess zeros and it is used for over-dispersed count outcome variables. Furthermore, theory suggests that a separate process from the count values generates the excess zeros and that the excess zeros can be modeled independently.

Results

Descriptive statistics

The age (mean ± std.) of male and female participants were 36.88 ± 7.37 and 28.59 ± 7.30 respectively, showing that male partners had higher mean age than their female counterparts. The data showed that 2667 (30.8%) women reported loss of children compared to 3231 (37.3%) men. The test of proportionality showed statistical significant difference between men and women who had lost children (z = −9.04; 95% CI: -0.079, −0.051; p < 0.001).

Table 1 showed the frequency distribution of the study variables. About one-third of female partners were in their late 20’s and only about one-tenth of them were above 39 years old. But the age interval reported by male partners showed that the men were predominantly above 30 years old. Couples from South East were least captured in the study while North West had the highest representation of about one-third. Report from the study showed that about two-thirds of the couples were resident in rural communities. In addition, more women than men were reported as having no formal educational qualifications. Also, two times the male partners (14.6%) had higher educational qualifications compared to the female partners (7.5%); this clearly shows that male partners were more educated than their female counterparts. Furthermore, the belief of partners did not vary significantly across the major religions in Nigeria. The report also showed that a uniform spread (about one-fifth) was obtained across the wealth index level of couples. Current unemployed male partners were minimal (4.2%) when compared to the female partners (32.6%). More so, about 95.8% of male partners were currently employed while only about two-thirds of female partners were currently employed. This shows that more economic strength rests on the male partners than the female partners.
Table 1

Study variables of participants

Variable

Frequency

Percentage

Age group of female partners

 15–19

872

10.1

 20–24

1674

19.3

 25–29

2305

26.6

 30–34

1703

19.7

 35–39

1318

15.2

 40–44

608

7.0

 45–49

178

2.1

 Total

8658

100.0

Age group of male partners

 15–19

20

0.2

 20–24

323

3.7

 25–29

1113

12.9

 30–34

1636

18.9

 35–39

1945

22.5

 40–44

1785

20.6

 45–49

1836

21.2

 Total

8658

100.0

Region of couples

 North Central

1412

16.3

 North East

1719

19.9

 North West

2851

32.9

 South East

515

5.9

 South South

1043

12.0

 South West

1118

12.9

 Total

8658

100.0

Residence of couples

 Urban

2821

32.6

 Rural

5837

67.4

 Total

8658

100.0

Education of female partners

 No formal education

3942

45.5

 Primary

1767

20.4

 Secondary

2301

26.6

 Higher

648

7.5

 Total

8658

100.0

Education of male partners

 No formal education

2757

31.8

 Primary

1947

22.5

 Secondary

2688

31.0

 Higher

1266

14.6

 Total

8658

100.0

Religion of female partners

 Christianity

3421

39.8

 Islam

5099

59.3

 Traditional

80

0.9

 Total

8600

100.0

Religion of male partners

 Christianity

3366

39.1

 Islam

5132

59.6

 Traditional

108

1.3

 Total

8606

100.0

Wealth Index of couples

 Poorest

1963

22.7

 Poorer

1891

21.8

 Middle

1576

18.2

 Richer

1613

18.6

 Richest

1615

18.7

 Total

8658

100.0

Current working status of female partners

 Employed

5807

67.4

 Not employed

2803

32.6

 Total

8610

100.0

Current working status of male partners

 Employed

8276

95.8

 Not employed

367

4.2

 Total

8643

100.0

Assessing the effect of predictor variables for female partners in childhood mortality using the ZINB model

Table 2 showed that age (years) of female partners was statistically significant with childhood mortality. For every unit increase in age, there was 8.1% increase in the incidence of childhood mortality (IRR = 1.081; 95%CI = 1.073–1.089; p < 0.001). For the geographical zones/regions in the ZINB model, the risk of mortality among children increased by 21.5% in North East (IRR = 1.215; 95% CI: 1.012–1.460; p < 0.001), 38.3% in North West (IRR = 1.383; 95% CI: 1.161–1.647; p < 0.001), 76% in South East (IRR = 1.760; 95% CI: 1.405–2.205; p < 0.001), 34.1% in South South (IRR = 1.341; 95% CI: 1.086–1.657; p < 0.001), and 36.4% in South West (IRR = 1.364; 95% CI: 1.117–1.667; p < 0.001) compared to the risk of mortality among children in the North Central. For location of residence, there was a 28.3% increase in the risk of mortality among children of female partners in rural locations (IRR = 1.283; 95% CI: 1.130–1.458; p < 0.001) compared to the risk of mortality among children of female partners in urban locations.
Table 2

Parameter estimates in the Zero-inflated negative binomial regression model for childhood mortality among female partners

Variable

IRR

Std.err

95% CI

P

Age (years)

1.081

0.004

1.073–1.089

<0.001***

Region

 North Central (ref)

1

   

 North East

1.215

0.114

1.012–1.460

0.037***

 North West

1.383

0.123

1.161–1.647

<0.001***

 South East

1.760

0.203

1.405–2.205

<0.001***

 South South

1.341

0.145

1.086–1.657

0.007***

 South West

1.364

0.140

1.117–1.667

0.002***

Residence

 Urban (ref)

1

   

 Rural

1.283

0.083

1.130–1.458

<0.001***

Education

 No Education (ref)

1

   

 Primary

0.733

0.046

0.648–0.830

<0.001***

 Secondary

0.603

0.048

0.515–0.705

<0.001***

 Tertiary

0.541

0.074

0.413–0.707

<0.001***

Wealth Index

 Poorest (ref)

1

   

 Poorer

0.973

0.053

0.875–1.083

0.619

 Middle

0.889

0.071

0.760–1.040

0.141

 Richer

0.625

0.054

0.527–0.742

<0.001***

 Richest

0.510

0.059

0.406–0.641

<0.001***

Age at 1st birth (years)

0.926

0.006

0.914–0.938

<0.001***

Religion

 Christianity (ref)

1

   

 Islam

1.184

0.108

0.990–1.416

0.064

 Traditionalist

1.432

0.142

1.179–1.740

<0.001***

Employment

 No (ref)

1

   

 Yes

1.096

0.054

0.996–1.207

0.061

***Significant at p < 0.05; IRR = Incidence-rate ratios. Vuog test of ZINB vs standardized negative binomial z = 4.97; p < 0.001. McFadden Pseudo R2 = 0.322

For the educational level categories of female partners in the ZINB model, the risk of mortality among children of female partners reduced by 26.7% in Primary (IRR = 0.733; 95% CI: 0.648–0.830; p < 0.001), 39.7% in Secondary (IRR = 0.602; 95% CI: 0.515–0.705; p < 0.001), and 45.9% in Tertiary (IRR = 0.541; 95% CI: 0.413–0.707; p < 0.001) compared to female partners with no formal education. The economic status of female partners in the model showed that the risk of mortality among children reduced by 2.7% in Poorer (IRR = 0.973; 95% CI: 0.875–1.083; p = 0.619), 11.1% in Middle (IRR = 0.889; 95% CI: 0.760–1.040; p = 0.141), 37.5% in Richer (IRR = 0.625; 95% CI: 0.527–0.742; p < 0.001) and 49% in Richest (IRR = 0.510; 95% CI: 0.406–0.641; p < 0.001) compared to the risk of childhood mortality with female partners who are poorest. For every unit increase in the age (years) of female partners at 1st birth, there was 7.4% reduction in the incidence of childhood mortality (IRR = 0.926; 95%CI = 0.914–0.938; p < 0.001). Religion of female partners impacted mortality among children; childhood mortality increased by 18.4% in Islam (IRR = 1.184; 95% CI: 0.990–1.416; p < 0.064) and 43.2% in Traditionalist (IRR = 1.432; 95% CI: 1.179–1.740; p < 0.001) compared to Christianity. Children of employed women had 9.6% increase in the risk of childhood mortality (IRR = 1.096; 95% CI: 0.996–1.207; p = 0.061) compared to unemployed female partners. The proportion of the variance of the outcome variable that is explained by the factors was (McFadden) Pseudo R2 = 0.322, which showed that the fitness of the model was satisfactory.

Assessing the effect of predictor variables for male partners in childhood mortality using the ZINB model

From Table 3, for every unit increase in age, there was 8.6% increase in childhood mortality (IRR = 1.086; 95%CI = 1.078–1.093; p < 0.001). For the geographical region of male partners in the ZINB model, the risk of mortality among children increased by 12.5% in North East (IRR = 1.125; 95% CI: 0.958–1.322; p = 0.152), 35.8% in North West (IRR = 1.358; 95% CI: 1.156–1.597; p < 0.001) and 24% in South South (IRR = 1.240; 95% CI: 0.993–1.548; p = 0.057) when compared to North Central. However, there was a 13% reduction in South East (IRR = 0.870; 95% CI: 0.651–1.160; p = 0.342) and a 24.9% reduction in South West (IRR = 0.751; 95% CI: 0.585–0.964; p = 0.024) when compared to North Central. For location of residence of male partners, there was a 33.5% increase in the risk of mortality among children in rural locations (IRR = 1.335; 95% CI: 1.174–1.518; p < 0.001) compared to the risk of mortality among children in urban locations. For the educational level of male partners in the ZINB model, the risk of mortality among children reduced by 3.8% in Secondary (IRR = 0.962; 95% CI: 0.855–1.083; p = 0.520) and 12.1% in Tertiary (IRR = 0.879; 95% CI: 0.747–1.034; p = 0.119) levels compared to no formal education.
Table 3

Parameter estimates in the Zero-inflated negative binomial regression model for childhood mortality among male partners

Variable

IRR

Std.err

95% CI

P

Age (years)

1.086

0.004

1.078–1.093

<0.001***

Region

 North Central (ref)

1

   

 North East

1.125

0.092

0.958–1.322

0.152

 North West

1.358

0.112

1.156–1.597

<0.001***

 South East

0.870

0.128

0.651–1.160

0.342

 South South

1.240

0.140

0.993–1.548

0.057

 South West

0.751

0.096

0.585–0.964

0.024***

Residence

 Urban (ref)

1

   

 Rural

1.335

0.087

1.174–1.518

<0.001***

Education

 No Education (ref)

1

   

 Primary

1.074

0.051

0.979–1.179

0.131

 Secondary

0.962

0.058

0.855–1.083

0.520

 Tertiary

0.879

0.073

0.747–1.034

0.119

Wealth Index

 Poorest (ref)

1

   

 Poorer

1.016

0.048

0.926–1.114

0.738

 Middle

0.967

0.062

0.853–1.096

0.601

 Richer

0.798

0.073

0.667–0.954

0.013***

 Richest

0.713

0.090

0.558–0.913

0.007***

Age at 1st birth (years)

0.973

0.003

0.968–0.979

<0.001***

Religion

 Christianity (ref)

1

   

 Islam

1.059

0.115

0.856–1.311

0.596

 Traditionalist

1.531

0.169

1.232–1.901

<0.001***

Employment

 No (ref)

1

   

 Yes

0.997

0.072

0.865–1.150

0.972

***Significant at p < 0.05; IRR = Incidence-rate ratios. Vuog test of ZINB vs standardized negative binomial z = 7.17; p < 0.001. McFadden Pseudo R2 = 0.309

On the contrary, there was a 7.4% increase in the risk of mortality among children of male partners in Primary (IRR = 1.074; 95% CI: 0.979–1.179; p = 0.131) compared to male partners with no formal education. For the economic status category, the model showed that the risk of mortality among children of male partners was reduced by 3.3% in Middle (IRR = 0.967; 95% CI: .0853–1.096; p = 0.601), 20.2% in Richer (IRR = 0.798; 95% CI: 0.667–0.954; p = 0.013) and 28.7% in Richest (IRR = 0.713; 95% CI: 0.558–0.913; p = 0.007) compared to the poorest. But result showed a 1.6% increase in Poorer (IRR = 1.016; 95% CI: 0.926–1.114; p = 0.738) compared to the Poorest which was not statistically significant. For every unit increase in the age (years) at 1st birth, there was 2.7% reduction in childhood mortality (IRR = 0.973; 95% CI = 0.968–0.979; p < 0.001). Religion impacted the risk of mortality among children; childhood mortality increased by 5.9% in Islam (IRR = 1.059; 95% CI: 0.856–1.311; p = 0.596) and 53.1% in Traditionalist (IRR = 1.531; 95% CI: 1.232–1.901; p < 0.001) compared to Christianity. Children of employed male partners had 0.3% reduction in mortality rates (IRR = 0.997; 95% CI: 0.865–1.150; p = 972) compared to unemployed male partners. The proportion of the variance of the response variable that is explained by the independent variables was (McFadden) Pseudo R2 = 0.309, which showed that the fitness of the model was satisfactory.

Discussion

This study has shown the prevalence and factors associated with childhood mortality in Nigeria. The study reported that for every unit increase in the ages of male and female partners, the risk of childhood mortality increases and this is similar to the findings of Adetoro & Amoo [26]. From the results, couples who had their first child at an earlier age were more susceptible to the occurrence of childhood mortality. These findings show consistency with the results obtained by Nazrul, Kamal & Korban [27]. Findings from the study reveal that couples with formal education experienced lower childhood mortality than those without formal education. For example, fathers with secondary and tertiary education had 3.8% and 12.1% reduction in childhood mortality respectively than non-educated fathers. It has been consistently claimed that mother’s education is a prominent factor in explaining risk of childhood mortality.

The results showed that the risk of childhood mortality are 26.7%, 39.7% and 45.9% lower among the mothers having primary, secondary and tertiary education respectively than those who have no formal education. Studies assert that mothers’ education helps in teaching quality health practices and improving health behaviour such as feeding habits and child care. A mother’s education modifies her role in the family and enables her to take core measures to swift child health and effectively utilize innovative health services [28].

The incidence-rate ratio for the place of residence reveals that the chance of childhood mortality is lower in the urban area than in rural area. The mothers living in rural areas experienced 28.3% increase in childhood mortality than the urban areas, while the fathers living in rural areas experienced 33.5% increase than the urban areas [29, 30]. It is perceived that urban areas are connected not only with quality health care services, but also with good education and employment opportunities for mothers, implying a lower experience in childhood death. Childhood mortality was significantly lower in middle, richer and richest (11.1%, 37.5% and 49%) economic status respectively when compared to the risk of childhood mortality with female spouse who are poorest. Similar results were obtained for the fathers with reduction in the incidence-rate ratio (3.3%, 20.2% and 28.7%) for middle, richer and richest economic status respectively compared to the poorest status [30, 31]. Furthermore, region and religion were found to be significant factors in the risk of childhood mortality in Nigeria and this is consistent with the study of Antai [32, 33].

Strengths and limitations

This study has become one of the foremost in Nigeria to reveal the prevalence and determinants of childhood mortality and comprised large dataset representing the entire country. In addition, the non-response rate was very low, less than 10%. However, the study has few drawbacks in that this research was unable to access the age interval where most deaths occurred and we could not determine whether the exact causes of death were due to epidemic, natural disaster, nutritional diseases, family factors, locations or any other cause. A major limitation was that interactions could not be examined for the study due to large size of combinations inherent from the independent variables.

Conclusion

Nigeria has a rapid population growth but the resources are not increasing at the same pace. As a result, the major portion of the population is faced with low chances of survival. The poverty inherent in the rapid population growth has led to lack of formal education, child labour and sometimes other serious health problems that can increase mortality rate. This study applied the nationally representative data from the Nigeria Demographic and Health Survey, 2013 to explore factors associated with childhood mortality in Nigeria. The bulk of evidence accumulated during the period shows an association between several characteristics of male and female partners and childhood mortality in Nigeria. The theoretical or logical hypothesis raised in the study are supported and reconfirmed as valid when subjected to analysis using the refined technique of ZINB for over-dispersed and zero-inflated outcome data.

The findings suggest that age, region, residence, education, wealth index, age at first birth and religion of fathers and mothers are prominent factors associated with childhood mortality. The association between childhood mortality and fathers’ and mothers’ ages was found to increase the incidence of the outcome for every unit increase in age. The converse was however true for age at first birth which was also statistically significant. Childhood mortality was found to be higher for rural dwellers.

Strategies to reduce the risk of childhood mortality in the country should involve more investments on parents’ empowerment programs in terms of education and economic opportunities, which could reduce poor health outcomes of their children. The implication of this study is that policy makers and stakeholders in health care will be exceedingly optimistic about the ability of health campaigns to solely encourage utilization of appropriate living standards to improve life expectancy.

Declarations

Acknowledgments

The authors thank the DHS Program for their support and for free access to the original data.

Funding

The authors have no support or funding to report.

Availability of data and materials

Data for this study were sourced from 2013 Nigeria Demographic and Health Survey (NDHS), implemented by the National Population Commission (NPC) and available here: http://dhsprogram.com/publications/publication-fr293-dhs-final-reports.cfm

Authors’ contributions

SY, ME and GT participated in the conception and design of the study and coordinated the study. SY, ME, GT, GB, VS and BK were involved in data cleaning and analysis, results interpretation, drafting and revision of the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Ethics approval for this study was not required since the data is secondary and is available in the public domain.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
School of International Development and Global Studies, University of Ottawa
(2)
Women’s Health and Action Research Centre
(3)
Hospitals Management Board
(4)
Faculty of Health Sciences, University of Ottawa
(5)
Department of Social Medicine and Health Management, Tongji Medical College
(6)
Faculty of Health Sciences, University of Ottawa

References

  1. Sexual Offences Bill [Internet]. National Assembly of the Federal Republic of Nigeria; 2013. Available: www.nassnig.org/document/download/1347
  2. Adeboye MA, Ojuawo A, Ernest SK, Fadeyi A, Salisu OT. Mortality pattern within twenty-four hours of emergency paediatric admission in a resource-poor nation health facility. West Afr J Med. 2010;29(4):249–52.PubMedGoogle Scholar
  3. Yaya S, Bishwajit G, Shah V. Wealth, education and urban–rural inequality and maternal healthcare service usage in Malawi. BMJ Global Health. 2016;1(2):e000085.View ArticleGoogle Scholar
  4. Okonofua F, Yaya S, Owolabi T, Ekholuenetale M, Kadio B. Unlocking the benefits of emergency obstetric Care in Africa. Afr J Reprod Health. 2016;20(1):10.Google Scholar
  5. dekanmbi VT, Kayode GA, Uthman OA. Individual and contextual factors associated with childhood stunting in Nigeria: a multilevel analysis. Matern Child Nutr. 2013;9(2):244-59. doi:10.1111/j.1740-8709.2011.00361.x. Epub 2011 Oct 18.
  6. Aremu O, Lawoko S, Dalal K. Neighbourhood socioeconomic disadvantage, individual wealth status and patterns of delivery of care utilization in Nigeria: a multilevel discrete choice analysis. Int J Womens Health. 2011;3:167–74.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Ojaegbu OO. Perceived challenges of using maternal health care services in Nigeria. Art Soc Sci J. 2013;ASSJ-65:1–7.Google Scholar
  8. Nigeria Demographic and Health Survey, 2013.Google Scholar
  9. National Population Commission, and ICF Macro. Nigeria Demographic and Health Survey 2008 Report Abuja, Nigeria. 2009.Google Scholar
  10. Becher H, Muller O, Jahn A, Gbangou A, Kynast-Wolf G, Kouyate B. Risk factors of infant and child mortality in rural Burkina Faso. Bull World Health Organ. 2004;82:265–73.PubMedPubMed CentralGoogle Scholar
  11. Buor D. Mothers’ education and childhood mortality in Ghana. Health Policy. 2003;64:297–309. doi:10.1016/S0168-8510(02)00178-1.View ArticlePubMedGoogle Scholar
  12. Adetunji JA. Infant mortality and mother’s education in Ondo State, Nigeria. Soc Sci Med. 1995;40:253–63. doi:10.1016/0277-9536(94)E0067-3.View ArticlePubMedGoogle Scholar
  13. Antai D. Regional inequalities in under-5 mortality in Nigeria: a population-based analysis of individual- and community-level determinants. Population Health Metrics. 2011;9(6):1–27.Google Scholar
  14. Antai D, Wedre S, Bellocco R, Moradi T. Migration and child health inequities in Nigeria: a multilevel analysis of contextual- and individual-level factors. Trop Med Int Health. 2010;15(12):1464–74.View ArticlePubMedGoogle Scholar
  15. Anyamele OD. Urban and rural differences across countries in child mortality in sub-Saharan Africa. J. Health Care Poor Underserved. 2009;20(4 suppl):90–8.View ArticlePubMedGoogle Scholar
  16. Argeseanu S. Risks, Amenities, and Child Mortality in Rural South Africa. Afr Popul Stud. 2004;19(1):13–33.Google Scholar
  17. Felisbino-Mendes MS, Moreira AD, Velasquez-Melendez G. Association between maternal nutritional extremes and offspring mortality: a population-based cross-sectional study, Brazil, demographic health survey 2006. Midwifery. 2015;31(9):897–903.View ArticlePubMedGoogle Scholar
  18. Myrskylä M, Fenelon A. Maternal age and offspring adult health: evidence from the health and retirement study. Demography. 2012;49:1231.View ArticlePubMedGoogle Scholar
  19. Ezeh OK, et al. Risk factors for postneonatal, infant, child and under-5 mortality in Nigeria: a pooled cross-sectional analysis. BMJ Open. 2015;5:e006779.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Heiko B, et al. Risk factors of infant and child mortality in rural Burkina Faso. Bull. World Health Organ. 2004;82:265–73.Google Scholar
  21. Fayeun, Olufunke, & Omololu, Obafemi. Ethnicity and Child Survival in Nigeria. Afr. Popul. Stud., 25 Supplement 2011: (1), 92–112.Google Scholar
  22. Omariba DWR, Boyle MH. Family structure and child mortality in sub-Saharan Africa: cross-National Effects of Polygyny. J Marriage Fam. 2007;69:528–43.View ArticleGoogle Scholar
  23. Ghose B, Yaya S, Tang S. Anemia status in relation to body mass index among women of childbearing age in Bangladesh. Asia Pac J Public Health. 2016;28(7):611–9.View ArticlePubMedGoogle Scholar
  24. Glass RI, Guttmacher AE, Black RE. Ending preventable child death in a generation. JAMA. 2012;308:141–2.View ArticlePubMedGoogle Scholar
  25. Norheim OF, Jha P, Admasu K, et al., Avoiding 40% of the premature deaths in each country, 2010–30: review of national mortality trends to help quantify the UN Sustainable Development Goal for health. Lancet, 2014: published online Sept 18.Google Scholar
  26. Adetoro GW, Amoo EO. A statistical analysis of child mortality: evidence from Nigeria. J Demogr Soc Stat. 2014;1:110–20.Google Scholar
  27. Nazrul IM, Kamal H, Korban A. Factors influencing infant and child mortality: a case study of Rajshahi District, Bangladesh. J Hum Ecol. 2009;26(1):31–9.Google Scholar
  28. Buor D. Mothers' education and childhood mortality in Ghana. Health Policy. 2003;64(3):297–309.View ArticlePubMedGoogle Scholar
  29. Ruiz-López MJ, Espeso G, Evenson DP, Roldan ER, Gomendio M. Paternal levels of DNA damage in spermatozoa and maternal parity influence offspring mortality in an endangered ungulate. Proc R Soc B. 2010;277(1693):2541–6.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Argeseanu S. Risks, Amenities, and Child Mortality in Rural South Africa. Afr Popul Stud, 2004: 19(1).Google Scholar
  31. El AI, Abed Y, Majdi A. Determinants and risk factors of neonatal mortality in the Gaza strip, occupied Palestinian territory: a case-control study. Lancet. 2012;1:S25–6.Google Scholar
  32. Kim D, Saada A. The social determinants of infant mortality and birth outcomes in western developed nations: a cross-country systematic review. Int J Environ Res Public Health. 2013;10:2296–335. doi: 10.3390/ijerph10062296.
  33. Özaltin E, Hill K, Subramanian SV. Association of maternal stature with offspring mortality, underweight, and stunting in low- to middle-income countries. J. Am. Med. Assoc. 2010;303(15):1507–16.View ArticleGoogle Scholar

Copyright

© The Author(s). 2017