CHAPTER ONE
1.0 INTRODUCTION
Infant mortality is defined as death during the first year of life. In child-level analysis, the variable is necessarily dichotomous (usually 0 for survival and 1 for dying in the given age range) so that probity or logistic regression appears appropriate. A problem with this approach is that children not fully exposed to the risk of death have to be dropped from the sample. More recent studies use a hazards model, for which the full sample can be used as the estimation takes account of the censoring for those children not fully exposed. If the mother rather than the child is the unit of analysis, then a “mortality rate” for the mother can be calculated, which may be treated as a continuous variable and OLS used for estimation. However, child-specific analysis is to be preferred since it allows for the inclusion of child-specific factors.
1.1 BACKGROUND OF THE STUDY
Mortality (Death) according to World Health Organization (W.H.O) is the absence of all traces of life at any time after birth. Mortality is thus the risk of dying in a given year, measured by death rate. However, among the deaths of people, the one that often generates concern is that of childhood mortality. This is due to the fact that this group of population are prone to health risk. According to WHO, Child mortality is a fundamental measurement of a country’s level of socio-economic development as well as the quality of life especially of the mothers. One of the important targets in the Millennium Development Goals (MDG) is to reduce child mortality rate by two-thirds between 1990 and 2030 (UNICEF, 2006) and infant mortality rate was one of the indicators of reaching the target. Some measures of childhood mortality used by demographers in health surveys include: neonatal mortality, which is the probability of dying within the first month of life; infant mortality, which is the probability of dying before the first birthday; and lastly, post natal mortality, which is the difference between infant and neonatal mortality. Infant mortality is however the focus of this study.
In the entire universe, many infants die due to one cause or the other. If these causes of death are not noticed, recognized and given proper attention, we may not know the exact causes of various infant deaths per hour. The best way to do this and make recommendation is to apply statistical techniques to extract the information as it relates to the population or sample of interest. In every aspect of our society today, collection and analysis of data is widely appreciated. Statistics now holds a central position in almost every field like industry, commerce, trade, physics, chemistry, economics, mathematics, biology, botany, psychology, astronomy etc. So, application of statistics is very wide. Relevant predictions, inferences, decisions and meaningful conclusions are drawn as a result of data analysis with appropriate statistical methods or techniques. Thus, statistical techniques are been used extensively to predict the significant factors responsible for infant mortality across the globe. Regression and correlation analyses statistical techniques that have gained wide application in mortality studies based on certain factors/variables that predict mortality.
Studies involving statistical techniques have revealed that socio-economic factors such as immunizations, exclusive breastfeeding, and the adoption and usage of insecticide-treated nets are strong predictors of child mortality especially in the developing countries. Included among these proximate determinants are the risk of morbidity and mortality, education of mother, sanitation facilities, access to safe drinking water as well as maternal and child health care services (Uddin, Hossain & Ullah, 2008). However, despite these known factors, infant mortality rate in Nigeria is abysmally far above the prevalent rate in other countries of the world.
Statistics revealed that up to 20 per cent of child deaths in sub-Saharan Africa occur in Nigeria. Also, the NBS report indicated that child mortality in Nigeria increased from 138 per 1,000 live births in 2007 to 158 per 1,000 live births in 2011 (National Bureau of Statistics (NBS), 2011; World Bank, 2013). However, within the infant group, there are specific periods of increased vulnerability. For instance, 60 percent of child mortality can be attributed to deaths that occur during the first year of life (infant), of which the first 24 hours of life is the most vulnerable period, followed by the first week and then the first month (Kyei, 2011). Hence, with statistical techniques, infant mortality data can be analysed to get useful information which can be helpful in knowing the actual predictors of infant mortality.
1.2 STATEMENT OF THE PROBLEM
According to UNICEF 2006 report, every year, nearly 10 million children die globally. About 4 million new-borns (40% of under-five deaths) die in the first four weeks of life. Infant mortality in Nigeria is estimated at 191 per 1000 live births. Ogunjimi, Ibe, and Ikorok (2012) stated that almost one million children die in Nigeria more than any other country in Africa, largely from preventable diseases. The greatest number of infant deaths in Nigeria occurs in northern states of Borno, Yobe, and Zamfara states where between 257 and 270 children die for every 1000 live births. As can be seen, most of the studies are on a broad perspective of child mortality and mostly carried out in Nigeria as a whole. This study therefore attempt to consider a case of Delta State in isolation and bring to fore the various predictors of infant mortality in the state as evidenced by data from Delta Central Hospital, Asaba.
1.3 RESEARCH QUESTIONS
This study will provide answers to the following questions:
- Is there a high rate in infant mortality in Delta State from 2001 to 2015?
- What is the trend in the infant mortality rate between 2001 and 2015 in Delta State?
- Is there any difference in the age specific death rate in infant mortality in Delta State from 2001 to 2015?
- What are the predictors of infant mortality in Delta State from 2001 to 2015?
1.4 Aims and Objectives
The aim of this study is to examine the predictors of infant mortality in Delta State using statistical techniques by employing data from the Nigeria Demographic Health Survey (NDHS) dataset. The objective of this study is to:
- i. Determine if there is a high rate in infant mortality in Delta State from 2001 to 2015
- ii. Examine the trend in the infant mortality rate between 2001 and 2015 in Delta State
- iii. Determine if there is any difference in the age specific death rate in infant mortality in Delta State from 2001 to 2015
- iv. Determine the predictors of infant mortality in Delta State from 2001 to 2015
1.5 SIGNIFICANCE OF THE STUDY
Researchers have for a long time been interested in the study of infant mortality, which can be classified as one of the components of population fluctuation. Infant mortality is an important indicator of a country’s overall health condition. This is due to the fact that these statistics indicates the effectiveness of the population and health programs and policies of any nation, as well as contributes to population projections of a country or a group of people. Infant mortality statistics also help identify specific populations that are prone to health risk.
1.6 DEFINITION OF TERMS
Some of the terms used in this research work were defined with a view to enhance full understanding of the study;
Statistics: A branch of mathematics that is used to arrange, classify, analyze and present data for the purpose of decision making.
Dependent Variable: The variable that is being predicted or estimated.
Independent Variable: The variable that provides the basis for estimation. It is the predictor variable.
Residual: This is the difference between the actual and the predicted value of the dependent variable.
Homoscedasticity: Is a situation where the residuals are same for all estimated values of the dependent variable.
Autocorrelation: This occurs if successive observations of the dependent variable correlated.
Regression Equation: An equation that defines the relationship between dependent variable and one or more independent variable
Least Square Principle: An equation determined by minimizing the sum of the squares of the vertical distances between the actual y values and the predicted values of y.
Standard Error: A measure of the scatter, or dispersion of the observed values around the line of regression.
Coefficient of Determination: The proportion of the total variation in the dependent variable y that is explained or accounted for by the variation in the independent variable x.
Infant: they are children less than one year old
Mortality: This refer to the number of deaths within a particular society and within a particular period of time.
Immunization: This involves giving the infants drugs to prevent them from diseases.
