ABSTRACT
The study identified and modelled Birth weight of New Born Children in Enugu State using data extracted from the antenatal care unit of the University of Nigeria Teaching Hospital Ituku/Ozala, Enugu. The data covered the period from January 1st 2013 to December 31st, 2017. The objectives of the study were to identify socio-economic and demographic determinant of birth weight of children born at UNTH, Enugu. It also applied Multinomial Logistic Regression Model to establish the association between the socio-economic and demographic characteristics of the mother and child’s sex. The results showed that only employment status and parity had significance effect on birth weight at 0.019 and 0.030 level respectively.
TABLE OF CONTENTS
Title
Page i
Declaration ii
Certification iii
Dedication iv
Acknowledgements v
Table
of Contents vi
List
of Tables viii
Abstract ix
CHAPTER
1: INTRODUCTION
1.1
Background of the Study 1
1.2
Statement of the Problem 4
1.3
Objectives of the Study 4
1.4 significance of the Study 5
1.5 Scope of the Study 5
CHAPTER 2: REVIEW OF RELATED LITERATURE
2.1 Incidenceof Low Birth Weight (LBW) 6
2.2 Socio-economic and Demographic Risk
Factors on
Birth weight 7
2.3 Empirical Review 9
CHAPTER
3: MATERIALS AND METHODS
3.1
Sources of Data 12
3.2 Study Population 12
3.3
Variable Specification 12
3.4 Method of Analysis 13
3.4.1 Descriptive
statistics 13
3.4.2 Multinomial logistic regression analysis 13
3.4.3 Estimating response probability of low birth
weight categories 15
3.5 Multinomial
Logistic Regression Model Assumptions 19
3.6 Multicollinearity
Test 19
3.7 Statistical Test of Individual Predictors 21
3.7.1 Likelihood ratio test 21
3.7.2 Estimating
the wald test statistic 22
3.8 Estimating
the Pseudo R2 Test Statistic 23
3.9 Chi-Square goodness of fit test 24
CHAPTER
4:RESULTSAND
DISCUSSION
4.1 Descriptive Result 26
4.2` Multinomial
Logics Regression Results 30
4.2.1 Result
of classification model 30
4.3 Multi-Collinearity
Diagnostic Test 31
4.3.1 Examination of the Pearson correlation 31
4.3.2 Multicollinearity
test using variance inflation factor (VIF) 32
4.4 Multinomial Logistics Regression Model
Results 33
4.5 Predicting the Probability of a Birth
Belonging to any
of the Birth Weight
Categories 40
4.6: Fitting
A Statistical Model of Birth Weight Data among
New Born Children at UNTH:
2013-2017 41
CHPTER 5: SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary
of Findings 42
5.1 Conclusion 42
5.3 Recommendations 42
REFERENCES 43
LIST OF TABLES
3.1:
Dependent and Independent Variables and their Categories 13
4.1: Percentage
Distribution of New Born by Birth Weight and
selected
background characteristics, UNTH, Enugu. 2013-2017 27
4.2: Percentage Distribution of Births by
Birth Weight According to Sex and
Selected Characteristics 29
4.3: Classification
table 30
4.4:
Pearson Correlation for the Explanatory Variables 31
4.5:
Multi-collinearity of test of independent variables 32
4.6: Coefficient of
Multinomial Regression: Normal Birth Weight
versus Low Birth Weight 34
4.7: Model fitting information for normal birth weight versus
Low Birth Weight 35
4.8: Pseudo R-Square for Normal
Weight versus Low Birth Weight 35
4.9: Coefficient of multinomial regression: High Birth Weight versus
Low Birth Weight 37
4.10: Model fitting information High Birth Weight versus
Low Birth Weight 38
4.11: Pseudo R-Square of High Birth Weight versus Low Birth Weight 38
4.12: Logit Coefficient of Multinomial Logistics
Regression of a
Birth Falling into 1 of 3 Birth Weight
Categories Verses
Low Birth Weight on Selected Predictors
among New Borns in
UNTH, Enugu, 2013 – 2017 39
4.13: Estimated Probability of a New Born
Belonging to any of
the Birth Weight Categories Among New Borns at
UNTH, Enugu 2013 41
CHAPTER
1
INTRODUCTION
1.1 BACKGROUND OF THE STUDY
One of the salient slogans of the World Health
Organization (WHO) is “Children's health is tomorrow's wealth.” The concern for
children’s health and survival finds expression in the continuous monitoring by
WHO of low birth weight (LBW) worldwide as a public health indicator (WHO,
2006). The World Health Organization has defined low birth weight as weight of
a baby taking immediately after birth less than 2, 500 grams (2.5 kilograms or
5.5 pounds) (WHO, 1992). Based on epidemiological findings, infants weighing
less than 2,500 g are around 20 times more probable to die than bigger infants.
Low birth weight is still a major issue for
worldwide public health, and it has a variety of short- and long-term effects.
Over 20 million babies a year, or 15% to 20% of all births worldwide are
thought to be low birth weight. The goal of Global Nutrition Target is to
reduce by 30% the number of infants born weighing less than 2500g by the 2025.
This would translate into 3% relative reduction per year between 2012 and 2025
and a reduction from approximately 20 million to about 14 million infants with
low weight at birth.
Preterm birth weight is the most common direct
cause of neonatal mortality (DWCD/MHRD/NNP, 2013). Every year, 1.1 million babies die from
complication of preterm birth. Recent studies have indicated that low birth
weight increases the risk for non-communicable diseases including diabetes and
cardiovascular diseases later in life. Low birth weight is a prominent
predictor of prenatal morbidity and mortality (Kumar N et al, 2007). There is
considerable variation in the prevalence of low-birth-weight across regions and
within countries; however, the great majority of births with low birth weight
occur in low and middle-income countries and especially in the most vulnerable
population (Sharma MK
et al, 2009). Regional estimates of low birth weight includes 28%
in south Asia, 13% in sub-Sahara Africa and 9% in Latin America (United N, 2003). It is worth
nothing that these rates are high in spite of the fact that the data on low
birth weight remain limited or unreliable, as many deliveries transpire in homes
or small health clinic and are not reported.
The Millennium Development Goal (MDG) for
reducing child mortality also benefits greatly from the lowering of low birth weight.
Activities aimed at achieving the SDGs must make sure that children have a healthy
start in life by ensuring that pregnant women are healthy, well-nourished, and
experience pregnancy and childbirth safely. Low birth weight is consequently an
imperative indicator for checking progress towards these internationally agreed
goals meanwhile in 2018, the shows that low birth weight reduced to 7percent (WHO,
2018).
WHO and UNICEF published the first global,
regional and country estimates of low birth weight rates in 1992. At that time,
the rate of low birth weight in industrialized nations hovered at 7%, whereas
it ranged from 5% to 33%, with an average of 17%, in less developed nations. UNICEF
and WHO stepped up their efforts to calculate local and worldwide rates around
the year 2000.The process of tracking progress toward the reduction of low
birth weight targets at the international level increased awareness of the
data's limitations, particularly the comparatively low number of newborns who
were weighed at delivery. In response, UNICEF suggested utilizing data from
household surveys that had been adjusted to account for underreporting of low
birth weight (Umeoraet al.,2011). A
plethora of fresh information was also offered by the historic household survey
activity that took place during the end-of-decade evaluation of progress toward
the World Summit for Children targets. Low birth weight incidence in Nigeria is
estimated by the 2008 Nigerian Demographic and Health Survey to be 14%, but
there are significant differences among social strata and geographic regions
(NPC and ORC Macro, 2009).
Poor nutritional status during pregnancy has
been associated with poor brain development and intelligence which may lead to
irreversible damage to the infant brain and central nervous system (Kayoed et al., 2014).
A unifying framework in research
findings is the large maternal and socioeconomic disparities in the birth
weight of infants; in line with this, many authors have highlighted the
importance of considering social and class factors in addition to biological
ones to explain LBW. Many of the known
determinants of a baby's birth weight are not within a woman's immediate
control. Clearly, birth weight and lifestyle risk factors have a complicated
relationship that is influenced by psychosocial, socioeconomic, and biological
factors; it is also clear that birth weight outcomes are socially stratified.
Some of the major determinants of birth weight in developing countries include
maternal nutritional status at conception, gestational weight gain in
accordance with dietary intake. In this study,
socioeconomic determinant of low birth weight of new borns was analyzed using Multinomial
Logistic Regression; this was done by employing1653data collected form UNTH Ituku/Ozala,
Enugu, between 2013 to 2017. Multinomial logistic regression models were used
for estimations where the dependent variable had more than two categories that
are discrete, have nominal characteristics, and were not ordered; the dependent
variable of which exhibit multinomial distribution, while there are constraints
over independent variables. (Hosmer and Lemeshow, 2000).
1.2 STATEMENT OF THE PROBLEM
Low birth weight contributes to high infant
mortality rates. Children who survive in this condition have a higher incidence
of diseases, retardation in cognitive development. There is also evidence that
small size births are associated with a predisposition to higher rates of
diabetes, cardiac diseases and other future chronic health problems (Sabine et
al, 2004). In Nigeria, though health situation has improved substantially over
the years, the incidence of low birth weight (LBW) is still high about 15
percent (Onyiriuka, 2010). Nearly all studies addressing factors linked to poor
delivery outcomes have been based on hospital statistics (Were, 1998). In
developing nations where the bulk of births do not take place in medical
facilities, this is a significant limitation (UNICEF, 2004). While it is
important to acknowledge a fair documentation of scholarly literature on low
birth weight like the ones carried out by (Vakrilova et al., 2002), none of these studies are on the socio-economic
determinants of LBW in Nigeria. Hence this study intends to fill the existing
gap by investigating the socio-economic determinants of Low Birth Weight in
University of Nigeria Teaching Hospital Ituku/Ozala, Enugu State.
1.3 OBJECTIVE OF THE STUDY
The general objective of this study is to
investigate the determinants of Low Birth Weight among birth that occurred at
the University of Nigeria Teaching Hospital Ituku/Ozala, Enugu State. The
specific objectives include:
(i) To identify
socio-economic determinants of birth weight of the children born in University
of Nigeria Teaching Hospital, Enugu.
(ii) To apply Multinomial
Logistic Regression Model to establish the association between birth weight of
the child and age of the mother, child’s sex, employment status, educational level,
place of residence, Parity/ Gravidity, Mother’s body mass index ad Gestational
period.
1.4 SIGNIFICANCE
OF THE STUDY
Birth
weight is a strong predictor of infant growth and survival. Infants born with
low birth weights begin life immediately disadvantaged and have extremely poor
survival chances. In most developing countries it was estimated that every ten
seconds an infant die from a disease or infection that can be attributed to low
birth weight (Grupo,2002).Birth weight is, therefore, and essential indicator
for checking progress toward these internationally agreed-goals.
It
is hoped that the result of this study will help to inform health authorities
about the factors influencing low birth weight in order to introduce program to
reduce the predominance of low birth weight in Enugu State.
1.5 SCOPE
OF THE STUDY
The
study focused on modeling and identification determinant of birth weight of new
borns in Enugu State using University of Nigeria Teaching Hospital (UNTH) as
case study. The data covered the period from January 1st 2013 to
December 31st 2017. The modeling and identification of determinants
of birth weight were done using Multinomial Logistic Regression.
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