MODELING OF INFANT MORTALITY IN NIGERIA

CHAPTER 1

INTRODUCTION

1.1   BACKGROUND TO THE STUDY

Death remains an unpredictable event which means that it can happen at any time.  Death is a phenomenon that is common to every mankind regardless of tribe, nationality, status or any other factors. The human society, having acknowledged this universal truth, has been continuously trying to postpone and manage death since the dawn of civilization. The developed nations of the world to some extent according to Islam-Uddin et al.,(2007) have been very successful in their efforts towards reducing overall mortality in general and infant and child mortality in particular but this is not the case in developing countries(Islam-Uddin, et al.,2007).

Infant mortality rates are important indicators of societal and national development as they have been described as key markers of health equity and access. Ssewanyana and Younger (2008) observed that reducing infant mortality is one of the sustainable development goals and in fact it is the third sustainable development goal (SDG3) which states that infant and child mortality rates are to be reduced by two-thirds between 2015-2030. Kayode et al., (2012) described infant mortality rate in sub-Saharan Africa as high and that of Southern Asia as moderate. The high rate of infant mortality in sub- Saharan Africa has made it to generate much research attention of health practitioners, scholars, the academia and other relevant stakeholders in the health sector. 

Infant mortality rate is undoubtedly a global population and health indicator of policies, programmes and research significance. It is one of the widely acknowledged demographic barometer for assessing a population's overall health status, quality of living condition, level of social and economic development and efficiency of a country's health system (Mac Dorman and Mathews, 2009; Syamala, 2004). For instance, reducing infant mortality rate is central to the achievement of Sustainable Development Goals (SDGs)- specifically, the SDG 3 which focused on reducing mortality rate among children under five. Hence, there has been a renewed commitment to complementing and sustaining the MDG achievements during the post-2015 period. Moreover, IMR is a vital component in the measurement of the Human Development Index (HDI) (Mustafa and Odimegwu, 2008) which is a composite global indicator for assessing and comparing countries' level of achievement in three critical components of human development comprising measures of a long and healthy life, knowledge and a decent living standard.

The loss of a child can be regarded as the loss of innocent, most vulnerable, dependent and defenseless individual. The loss of a child can be likened to a loss of hopes, dreams and loss of future. This is because children are the future of a family. Therefore, the death of a child is probably the most traumatic and devastating experience for a couple and a nation, as this amounts to the death of their future. This fact makes the issue of infant mortality very crucial. Infant mortality is one of the most sensitive health indicators of the people. In fact it is one of the major measure of child health and overall development of a nation. Infant mortality also helps in examining the living standard, social and economic status of a country.

Reducing the prevalence of infant mortality is one of the targets of Sustainable Development Goals (SDGs) of United Nations (UN) of which Nigeria is signatory to. Hence, more efforts have been made to ensure that this goal is actualized. Despite successes recorded so far, the prevalence of infant mortality especially in developing country like Nigeria is still very high as more than 16,000 infants die each day in the world (WHO, 2015).There are several factors that could be responsible for the high incidence of infant mortality. Researchers have made considerable efforts to indentify the determinants factors driving the phenomenon. Antai (2010) indentified some socio-economic and bio-demographic factors as major determinants of infant mortality. These factors and classified them into two broad categories which are endogenous and exogenous. Exogenous factors of infant mortality are factors that have to do with the environment in which an infant is exposed and some of which include exposure to infectious diseases, parasitic and respiratory diseases. Such causes normally increase the risk of death in the post neonatal period and they are easier to control. The endogenous causes of infant mortality on the other hand are the biological factors which include congenital malformation and circumstances of the delivery. These endogenous causes come to manifestation in the neonatal period and oftentimes are difficult to control.

Jinadu et al., (1999) posited that several of diseases causing child mortality have connections with hygiene condition and unclean environment; these include feeding bottles, utensils, inadequate disposal of house hold refuse, poor water storage, to mention a few. Other researchers like Osonwa, et al., (2012) and Caldwell (2009) have also indentified some other factors that can be associated with infant mortality. These include maternal age at birth, sex of the child, type of marriage, maternal education and wealth index. Caldwell, (2009) posited that children from poorer or rural households are more vulnerable than their counterparts from richer or urban households. Also, United Nations Children’s Fund (2010) posited that a child born to a financially deprived and less educated family is at risk of death within the first month of life. The reasons for these are obvious since the mother may be poorly nourished during pregnancy, had little or no antenatal care and likely to deliver in ill-equipped health facility.

Moreover, a lot of models have been applied to study the determinant of infant mortality and one of the models that have gained popularity in this regards is the Cox Regression Model developed by Cox in 1972. The Cox regression has been widely used in survival analysis. There are various forms of Cox regression but the major difference is in the distribution that the timing function is assumed to follow (Wegbom et al., 2016). Researchers like Wegbom et al., (2016) examined the determinants of child mortality in Nigeria. The study made use of Weibull distribution because it has the ability to model hazard function that are monotonically decreasing or increasing.

Weibull distribution is one of the most widely used distribution in modeling infant mortality where the timing function is assumed to follow Weibull distribution (Wegbom et al., 2016).  This distribution has become the distribution of choice because of its suitability for hazard function that is either monotonically decreasing or increasing (Wegbom et al., 2016). This is true because mortality in human population is usually high in the first year of life, then it declines in other ages of childhood and throughout most of the teenage years, then  increasing slowly in adult ages to old ages. Although, the Weibull distribution proposed by Weibull (1939) has been established to be suitable in modeling mortality (Wegbom et al., 2016), but over the years there have been an improvement on the Weibull distribution which has led to the development of other forms of this distribution.

There have been other forms of robust Weibull distribution that have been proposed using Beta generator and the method of transmutation (Eugene et al., 2002; Shaw and Buckley, 2007).Prominent among these improved Weibull distribution is the Beta-Weibull distribution by Famoye et al., (2005), exponentiated Weibull by Gupta et al., (1998). These new classes of Weibull distributions have been shown to give better representation and flexibility than Weibull distribution. Therefore, the distribution that the timing function is assumed to follow is very important in Cox regression model. Despite this development, these recent forms of Weibull distribution have not been applied in determining the determinant of mortality in Nigeria. Therefore, this study intends to use the recent forms of Weibull distribution as the timing function other than the convectional Weibull distribution.

1.2        STATEMENT OF PROBLEM

Infant mortality has been an international social problem for a very long time. In a developing country like Nigeria, the government has achieved remarkable decrease in infant mortality through the implementation of health policies which improves infant health care and hence increasing their survival rate. Yet, the prevalence is still high. Meeting the Sustainable Development Goal (SDG) 3 of reducing infant mortality to as low as 12 per 1000 live births by 2030 remains a mirage as several studies reveal shocking pictures of infant death.

Many researchers have identified that infant mortality varies by demographic and socioeconomic factors (Antai 2010), A lot of models have been applied to study the determinants of mortality and one of the models that have gained popularity in this regard is the Cox proportional hazard  model developed by Cox in 1972. The Cox regression has been widely used in survival analysis (Amusa and Gatta, 2016, Murithiand Murithi, 2015, Dahiru 2015, Maxwell et al., 2017, Nasejjeet.al., 2015).There are various forms of Cox regression but the major difference is based on the distribution that timing function is assumed to follow. Researchers like (Wegbom, et al., 2016) examined the multivariate analysis of child mortality in rural Nigeria, the study made use of Weibull distribution because it has the ability to model Hazard functions that are monotonically decreasing or increasing.

 Therefore, this study intends to use standard Cox proportional hazard models, Weibull distribution and Exponentiated-Weibull distribution as the survival function to determine infant mortality in Nigeria.

1.3       AIMS AND OBJECTIVES OF STUDY

The aim of this study is to identify the determinants of infant mortality in Nigeria using Cox proportional hazard model, Weibull Cox proportional hazard model and Exponentiated Weibull proportional hazard model. The specific objectives of the study are:

1.                   To identify the determinants of infant mortality in Nigeria

2.                  To investigate the effect of unobserved factors on infant mortality by fitting frailty model using the best fitted model. 

3.                  To compare the results obtained in (1) using the three statistical tools applied in the analysis.

1.4      SIGNIFICANCE OF STUDY

This study would be beneficial to the government in the area of policy formulation that would help reduce the risk of infant death in Nigeria. It is expected that when the rate of infant mortality is reduced, it would be beneficial to parents because the rate of child loss will be reduced significantly.  This study would be beneficial to other researchers as it will help contribute to the existing literature on modeling the determinants of infant mortality in Nigeria.

1.5       SCOPE OF STUDY

The scope of this study is limited to identifying the determinants of infant mortality in Nigeria. The data used in the study are derived from the 2013 Nigeria Demographics and Health Survey data set.

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