IDENTIFICATION AND MODELING OF BIRTH WEIGHT OF NEW BORNS (CASE STUDY OF UNIVERSITY OF NIGERIA TEACHING HOSPITAL)

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Product Code: 00009906

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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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