THE ANALYSIS OF PREMATURITY EFFECT AS ONE AMONG THE CAUSES OF NEONATAL MORTALITY IN KINONDONI DISTRICT

THE ANALYSIS OF PREMATURITY EFFECT AS ONE AMONG THE CAUSES OF NEONATAL MORTALITY IN KINONDONI DISTRICT, 2017
A Case Study of Mwananyamala Hospital

BY

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

Arnold Amos Mwijage

A Research Project Report submitted to the Department of Statistics in partial fulfilment for the Award of the Bachelor Degree of Official Statistics of Eastern Africa Statistical Training Centre
July 2018
?
CERTIFICATION
The undersigned below certifies that the research work prepared by Arnold Amos Mwijage, entitled: The analysis of prematurity effect as one among the causes of neonatal mortality in Kinondoni district, 2017 and submitted in partial fulfillment of the requirements for the Bachelor Degree in Official statistics complies with the regulations of the EASTC and meets the accepted standards with respect to originality and quality.

_____________________
Mr. Leguma Lathima Bakari
(Supervisor)

______________________
Date

DECLARATION AND COPYRIGHT
I, Arnold Amos Mwijage, declare hereby that this research project is a true reflection of my own original work, and that this work, or part thereof has not been submitted for a degree in any other institution of higher education.

Signature__________________________
Date______________________________

This dissertation is copyright material protected under the Berne Convention, the Copyright Act 1999 and other international and national enactment in that behalf, on intellectual property. No part of this desertification may be produced, stored in any retrieval system, or transmitted in any form or by any means without prior written permission of both author and EASTC.

ACKNOWLEDGEMENTS
I wish to express my thanks and appreciation to the following; First, my thanks and praise to God for the strength and health to complete this study. Then special thanks to Mr. Leguma Lathima Bakari, my supervisor at Eastern Africa Statistical Training Centre (EASTC) for his guidance, encouragement, and everything he taught me, Eastern Africa Statistical Training Centre (EASTC), for permission to conduct the study, My family and my friends, for encouraging and supporting me, Mr. Chrisker Masaki as the research coordinator and Dr Siamarie Lyaro for everything she taught me.
To all, my sincere thanks and love and I wish you strength in your endeavours – may people be as caring and helpful to you as you have been to me.
?
LIST OF ABBREVIATIONS
WHO World Health Organization
UNICEF United Nations Children’s Fund
SPSS Statistical package for social science
MNH Muhimbili National Hospital
UNICEF United Nations Children’s Fund

?
ABSTRACT
The current decline in under-five mortality shows an increase in share of neonatal deaths because of prematurity, birth asphyxia and natural sepsis. In order to address neonatal mortality and possibly identify areas of prevention and intervention, we studied causes of neonatal mortality; prematurity, birth asphyxia and natural sepsis and tested for significance difference between them in a neonatal ward at Mwananyamala Hospital, Kinondoni, Dar Es Salaam. Statistical analysis was performed in SPSS 21.0 by conducting descriptive statistics, analysis of anova, frequency contribution and correlation statistics. Leading single causes of death were birth asphyxia (54.2%), prematurity (32.3%) and natural sepsis /infections (13.5%) of 40.3% of all deaths due to prematurity, asphyxia and natural sepsis/infections. Overall neonatal mortality rate was 2.09 (2017). Birth asphyxia was the most frequent cause of neonatal death. Birth asphyxia, prematurity and natural sepsis/infections are accounted for one third of all neonatal deaths at Mwananyamala hospital in 2017. Strategies directed towards making obstetric and newborn care timely available with proper antenatal, maternal and newborn care support with regular training on resuscitation skills would improve child survival.
?
Contents
DECLARATION Error! Bookmark not defined.
ACKNOWLEDGEMENTS i
LIST OF ABBREVIATIONS v
ABSTRACT vi
CHAPTER ONE 1
INTRODUCTION 1
Background 1
1.2 Statement of the Research Problem. 3
1.3 Research objectives 3
1.3.1 Main objective: 3
1.3.2 Specific Objectives 4
1.3.3 Research questions. 4
1.4 Significance of the Study 4
CHAPTER TWO 5
LITERATURE REVIEW 5
2.1 Introduction 5
2.2 Causes of preterm births 6
2.2.1 Alcohol use 7
2.2.2 Illicit drug use 7
2.2.3 Poor nutrition 8
2.2.4 Infections. 8
2.2.5 Douching before and during pregnancy 9
2.2.6 Stress 9
2.2.7 Tobacco use 10
2.2.8 Increased use of reproductive technology. 11
2.2.9 Early pregnancies. 11
2.3 Managing Risk Factors through Behavior Change 11
2.3.1 Antibiotics 12
2.3.2 Prenatal Care 12
2.4 Neonatal mortality 13
2.5 Research Gap 13
CHAPTER THREE 14
METHODOLOGY 14
3.1 Introduction 14
3.2 Study Population 14
3.3 Study Area 14
3.4 Variables 14
3.5 Dependent Variable 14
3.6 Independent Variable. 15
3.7 Source of Data 15
3.8 Data Analysis 15
3.10 Descriptive statistics 16
3.11 Correlation analysis 16
3.12 ANOVA 16
CHAPTER FOUR 17
Data analysis and interpretation 17
4.1 Introduction 17
4.2 Descriptive statistics 17
4.3 Analysis of Variance 18
4.3.1 Analysis of Variance 18
4.3.2 Multiple comparison 19
4.4 Contribution of prematurity, birth asphyxia and natural sepsis to neonatal mortality. 22
4.5 Relationship between prematurity to neonatal mortality. 22
4.6 Neonatal mortality rate in Kinondoni district. 24
4.7 Findings and Discussion 24
Chapter FIVE 26
Conclusion, limitations and recommendations 26
5.1 Introduction 26
5.2. Conclusion 26
5.3 Limitations 27
5.4 Recommendations 27
References 28
Appendices 32

LIST OF TABLES
Table 4.2.1: Descriptive statistics 17
Table 4.3.1: ANOVA 18
Table 4.3.2 Multiple comparison 20
Table 4.4.1 Frequency Table 22
Table 4.5.1 Correlation statistics 23

CHAPTER ONE
INTRODUCTION
The introduction comprises of the background, statement of the research problem, research objectives and significance of the study.
1.1 Background
Neonatal mortality is defined as death before 28 days. Premature birth (also known as preterm birth) is defined medically as childbirth occurring earlier than 37 completed weeks of gestation. Most pregnancies last about 40 weeks (Quinn, et al., 2016). Preterm deliveries are a major cause of neonatal deaths. The presumed causes of neonatal deaths globally have remained unchanged over the past decade and include infections (30%), prematurity (30%), and asphyxia (25%) (PEDIATRICS et al. 2012). Great uncertainty surrounds these estimates, and in addition, cases are likely misclassified as stillbirths.
Worldwide, prematurity and its complications constitute 12percent of global under five mortality and 30percent of neonatal mortality (Sajjad ur & Walid, 2012). 2.6 million children died in the first month of life in 2016 approximately 7,000 newborn deaths every day most of which occurred in the first week, with about 1 million dying on the first day and close to 1 million dying within the next six days (Unicef, 2017). The first 28 days of life (the neonatal period) are the most vulnerable time for a child’s survival. Children face the highest risk of dying in their first month of life, at a global rate of 19 deaths per 1,000 live births (Wardlaw, You, Hug, Amouzou, ; ; Newby, 2014). Globally, the main causes of neonatal deaths were preterm birth (premature births) complications (35 per cent) from 1990 to 29 as maximum (Blencowe, et al., Born Too Soon: The global epidemiology of 15 million preterm births, 2013).
In Africa, the neonatal mortality rate has been decreasing from the year 1990 to 2015. A further 1.16 million babies die in their first month of life up to half on the first day (Digital Campus Moodle, 2014). In 2006 in developing countries, the risk of death in the neonatal period was six times greater than in developed countries; in the least developed countries it was over eight times higher. With 41 neonatal deaths per 1000 live births, the risk of neonatal death was highest in Africa; the sub-Saharan regions of Eastern, Western and Central Africa had between 42 and 49 neonatal deaths per 1000 live births (WHO,2006).
Tanzania made good progress on the target for Millennium Development Go (World Health Organization, 2006)al 4. Under-five deaths per 1,000 live births declined steadily from 166 in 1990 to 112 in 2005 and 67 in 2015. Infant mortality decreased from 68 to 43 per 1,000 live births between 2005 and 2015 (Unicef, 2015). Mortality rate; neonatal (per 1;000 live births) in Tanzania was reported at 21.7 in 2016, according to the World Bank collection of development indicators, compiled from officially recognized sources.
In Kinondoni district, according to the neonatal ward at MNH report, for July 2002 to June 2003, the number neonates admitted was 7,988; with 1,809 (22.6%) being premature. The one among the main causes of death was prematurity (36.7%) apart from asphyxia (30.72%) of the neonates conducted at MNH (Nkuba, 2007).
1.2 Statement of the Research Problem.
Neonatal mortality remains as one among the serious challenge in Tanzania. Progress is tracked through neonatal mortality rates (NMR), yet robust national data on these outcomes is difficult and expensive to ascertain, and mask wide variation (Armstrong, 2015). The government’s implementation of high-impact health programmed such as routine immunization, Vitamin A supplementation, prevention of mother-to-child transmission of HIV, and improved management of common childhood illnesses, has saved the lives of thousands of children across the country (UNICEF, 2015). However, challenges persist, and preventable and treatable neonatal conditions continue to claim the lives of Tanzanian children every day (UNICEF, 2015). To most of the researches that have been done referring to the UNICEF Tanzania, they stick on other causes of neonatal deaths rather than preterm births. This shows that there are struggles on implementation of high-impact health programmed such as routine immunization, Vitamin A supplementation, prevention of mother-to-child transmission of HIV and improving management of common childhood illnesses forgetting the impacts of prematurity and its possible causes so as they can be dealt with to avoid/decrease the rate of neonatal deaths due to prematurity. There are no recent researches done in Kinondoni district concerning prematurity rate and neonatal mortality rate.
1.3 Research objectives
1.3.1 Main objective:
To analyze the effect of prematurity on neonatal mortality.
1.3.2 Specific Objectives
I. To compare the significance effect of prematurity rate to the neonatal mortality rate and other causes.
II. To determine the association of prematurity rate and neonatal mortality in Kinondoni district.
1.3.3 Research questions.
I. What is the significance effect of prematurity to the neonatal mortality in Kinondoni district?
II. What is the association of prematurity and neonatal mortality in Kinondoni district?
1.4 Significance of the Study
This study will add knowledge in the awareness of the causes of premature births and its impacts to the preemies or preemies. The study will provide the best possible solutions on how to decrease the neonatal mortality rate. Also, it will be among the reference source for further studies on prematurity and neonatal mortality.

CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This chapter contains the possible causes according to the researches done about prematurity and neonatal mortality, managing risks for the prematurity and neonatal mortality in Kinondoni district.
According to the workshop on The Role of Environmental Hazards in Premature Birth in 2001, the normal length of pregnancy is 40 weeks as calculated from first day of the woman’s last normal menstrual cycle. A baby born prior to week 37 of gestation is considered premature (World Health Organization, 2018).
Preterm births are classified into two categories: (1) indicated preterm birth of which involves those deliveries initiated by the clinician for the benefit of either the fetus or the mother, and (2) spontaneous preterm birth of which involves those that follow either spontaneous preterm labor or spontaneous rupture of the membranes (Mattison D. , 2003).
Indicated preterm births are usually occurring because the mother is severely ill with a life-threatening condition or the fetus shows signs of deterioration and risk of fetal death (Institute of Medicine, Board on Health Sciences Policy, Roundtable on Environmental Health Sciences, Research, and Medicine, 2003).
Spontaneous preterm births are often divided into those that occur early and those that occur later (Mattison D. , 2003). Early preterm births, occurring at less than 30 week’s gestation, generally are associated with an intrauterine infection or placental hemorrhage (Kemp, 2014). Later preterm babies, which occur between 35 and 36 weeks gestation, are for the most part not associated with infection, placental hemorrhage, or a specific etiologic factor (Mattison, 2003). Instead, these preterm births appear to happen when the normal mechanisms responsible for term labor take place earlier than usual (Institute of Medicine, Board on Health Sciences Policy, Roundtable on Environmental Health Sciences, Research, and Medicine, 2003). Women who have a preterm birth at 35 to 36 weeks gestation often have an increased number of risk factors such as they are underweight, they smoke, and/or they have various psychosocial characteristics,
but often no specific precipitating cause is identified for the spontaneous labor or rupture of membranes (Institute of Medicine, Board on Health Sciences Policy, Roundtable on Environmental Health Sciences, Research, and Medicine, 2003).
2.2 Causes of preterm births
There are different causes of preterm births. The causes comprise the behavioral, psychological, medical and environmental (Behrman & Butler, 2007). They increase the risk of prematurity occurrence. There are such as tobacco use, alcohol use, illicit drug use, poor nutrition, infections, douching before and during pregnancy, stress, low socio-economic factor, ignorance (unawareness of pregnant women on prematurity and its causes), and increased use of reproductive technology (Rice, 2016).
2.2.1 Alcohol use
High levels of alcohol use during pregnancy have obvious adverse effects on fetal development (Behrman R E, 2007). For example, women who have more than one drink per day, on average, have an increased risk of preterm birth (Behrman R E, 2007). Heavy alcohol use during pregnancy can lead to a baby being born early since the alcohol gets contained in blood of which the fetus is fed from within. So, the blood crosses to the fetus via the placenta, putting the fetus at a risk of prematurity and fetal syndrome. If the mother continues to drink throughout her pregnancy, this constant exposure to alcohol’s effects will cause cumulative effects (Hankin, 2002).
2.2.2 Illicit drug use
Marijuana and cocaine are the drugs that have been most commonly studied for their potential effects on preterm births (Behrman ; Butler, 2007). There is little indication that marijuana use influences preterm birth (Behrman ; Butler, 2007). Women who use cocaine during pregnancy often have other strongly associated lifestyle factors that could well constitute the underlying cause of preterm birth, such as infection or poor nutrition
(Behrman ; butler, ?2007). According to John Hopkins health library, A mother taking illegal drugs during pregnancy increases her risk for anemia, blood and heart infections, skin infections, hepatitis, and other infectious diseases. She also is at greater risk for sexually transmitted diseases. Almost every drug pass from the mother’s bloodstream through the placenta to the fetus (Sachdeva, Patel, & Patel, 2009). Illicit substances that cause drug dependence and addiction in the mother cause the fetus to be addicted too (Keegan, Parva, Finnfegan, Gerson, & Belden, 2010). Women found using marijuana/cocaine are at a higher risk of preterm births.
2.2.3 Poor nutrition
Nutrition is the science that interprets the interaction of nutrients and other substances in food in relation to maintenance, growth, reproduction, health and disease of an organism (Raju, 2017). Poor nutrition can result to other factors such as pre-pregnancy. Pre-pregnancy weight is not a behavior but is associated with patterns of diet and nutrition (Abu-Saad, 2010). Evidence suggests that a low Prepregnancy weight is associated with an increased risk of preterm birth (Behrman & Butler, 2007). Also, obese women are at a markedly decreased risk of spontaneous preterm birth (Hendler et al., 2005). Pregnant women who receive inadequate nutrition experience greater maternal morbidity and have a higher risk of poor pregnancy outcomes such as premature birth (Abu-Saad, 2010). Maternal malnutrition can adversely affect the division and replication of cells in the embryo at this stage, impairing its development (Tzanetakou, Mikhailidis, & & Perrea, 2011). Impaired embryo development in turn adversely affects the development of the fetus in the later stages of pregnancy, thus there is a high risk of premature birth (Abu-Saad, 2010) .
2.2.4 Infections.
Throughout life, we all meet many viruses and bacteria. As part of our defense mechanism, the body makes antibodies to help fight infection (Klimpel, 1996). Women may sometimes likely to face infections during their pregnancies such as hepatitis B, chickenpox and cytomegalovirus (CMV) (Carlson, Norwitz, & Stiller, 2010). Cytomegalovirus (CMV) is the most common infection that can be transmitted to a fetus. Primary maternal CMV infection carries a 30% to 40% risk of vertical transmission to the fetus (Carlson, Norwitz, & Stiller, 2010). This can cause prematurity since most of infections can be transmitted to the foetus, thus, causing difficulties in growth and finally prematurity (Waldorf & & McAdams, 2013).
2.2.5 Douching before and during pregnancy
Many indirect lines of evidence suggest that the practice of vaginal douching might increase the risk of preterm birth (Behrman & Butler, 2007). Douching alters the vaginal microflora and may well facilitate the passage of vaginal pathogens to the upper reproductive tract, which contributes to inflammation and, possibly, to preterm birth (Amabebe & & Anumba, 2018). Few empirical evaluations of this hypothesis have been conducted thus far. Women rarely douche during pregnancy, so the analyses have focused on douching during the period before pregnancy begins (Bruce et al.,2002). Douching influences pathways linked to preterm delivery, continued evaluations of the effects of this behavior are warranted (Fiscella et al., 2002).
2.2.6 Stress
Stress is defined as demands that tax or exceed the adaptive capacity of an organism and that result in psychological and biological changes (Cohen and Syme, 1985). The definition includes the environmental demand and responses to them at levels like cognitive, affective, immune, endocrine, cardiovascular, and so on (Behrman & Butler, 2007). Stress can also occur from the life events. Life events such as divorce, a death in the family, illness, injury, catastrophes or the loss of a job can lead to stress and finally increase the risk of preterm birth to the pregnant (Witt, Litzelman, Cheng, Wakeel, & & Barker, 2014). For instance, according to assessment of the impact of the time of gestation at the time of the World Trade Center terrorist attack on September 11, 2001, 300 non smoking women in New York City who were pregnant in the first trimester at the time of the attack delivered infants of significantly shorter gestations (Lederman, et al., 2004).
2.2.7 Tobacco use
Cigarette smoking is recognized to be among the most prevalent, preventable causes of adverse pregnancy outcomes (Mohammed, et al., 2012). Smoking is strongly related to placental abruption, reduced birth weight, and infant mortality (Mohammed, et al., 2012). The relationship of cigarette smoking to preterm birth is not entirely consistent (Behrman & Butler, 2007). The influence of smoking on pregnancy outcome is dependent on whether it occurs in the later part of pregnancy, and no increased risk has been detected for former smokers who quit before the onset of pregnancy or early in pregnancy (Shane & Ira, 2008). The association between smoking and preterm birth is modest association (Smith, et al., 2015). Some of the findings show there is no relationship between smoking and preterm birth, yet some of the findings show there is a strong association between smoking and preterm births (Burgueta, et al., 2004).
Smoking is associated with dose-dependent increases in risks of preterm birth due to preterm premature rupture of membranes and late pregnancy bleedings (Kyrklund-Blomberg, Granath, & Cnattingius, 2005). Smoking during pregnancy has been associated with a host of complications, including, premature rupture of the membranes, placenta previa, placental abruption, and preterm birth.
2.2.8 Increased use of reproductive technology.
Assisted reproductive technology (ART) is the technology used to achieve pregnancy in procedures such as fertility medication, in vitro fertilization and surrogacy (Kamel, 2013). It is reproductive technology used primarily for infertility treatments and is also known as fertility treatment (Kamel, 2013).
Infertility treatment can sometimes cause the imbalance of the hormones and increase the risk of prematurity (Vannuccini, et al., 2016).
2.2.9 Early pregnancies.
Prematurity is also caused by early pregnancies due to that most of the adolescents have not grown well in their reproductive organs and are likely to attend the clinics while it’s late (Nkuba, 2007). Among the total neonates admitted to MNH (7,988 neonates), 36.7% of neonates died of prematurity (Nkuba,2007).
2.3 Managing Risk Factors through Behavior Change
Worldwide an estimated 11.1% of all livebirths in 2010 were born preterm (14.9 million babies born before 37 weeks of gestation), with preterm birth rates increasing in most countries with reliable trend data. Direct complications of preterm birth account for one million deaths each year, and preterm birth is a risk factor in over 50% of all neonatal death (Blencowe, et al., 2013). Many survivors face a lifetime of disability, including learning disabilities and visual and hearing problems (Menon, 2012).
Globally, prematurity is the leading cause of death in children under the age of 5 years. And in almost all countries with reliable data, preterm birth rates are increasing (Blencowe, et al.., June 2012).
2.3.1 Antibiotics
Infections, such as bacterial vaginosis, syphilis, gonorrhea, and periodontal disease, have all been associated with increased rates of preterm birth and may account for the preterm births of unknown etiology (Institute of Medicine, Board on Health Sciences Policy, Roundtable on Environmental Health Sciences, Research, and Medicine, 2003). The mounting evidence of infection as a risk factor suggested that the use of antibiotics may be important for the prevention of preterm birth. Bacterial vaginosis is an independent risk factor associated with preterm birth, which is complicated by the fact that many women are asymptomatic (Lamont, 2015).
2.3.2 Prenatal Care
Prenatal care, also known as antenatal care, is a type of preventive healthcare. Its goal is to provide regular check-ups that allow doctors or midwives to treat and prevent potential health problems throughout the course of the pregnancy and to promote healthy lifestyles that benefit both mother and child (World Health Organization). It involves the provision of social support, home visiting, and nutritional counseling, has been hypothesized to be inversely related to the incidence of preterm birth (Mattison D. , 2003). For example, nutritional Interventions Epidemiological studies have found a relationship between women who gain very little weight during pregnancy and the incidence of preterm birth (Abu-Saad, 2010).
2.4 Neonatal mortality
A neonatal death is defined as a death during the first 28 days of life (0-27 days). It is recognized as the most vulnerable time in an infant’s life (Pathirana, et al., 2016). The first estimates of causes of neonatal deaths were published in 2005, for the year 2000 and in almost all countries with reliable data, preterm birth rates are increasing (WHO,2015).
2.5 Research Gap
The main research gap exists because many of the findings in different done researches do not consider much prematurity as a large cause of the neonatal mortality. There are several studies conducted on prematurity, but they don’t show clearly how prematurity rate contributes to neonatal mortality. There is no recent study on the prematurity effecting to neonatal mortality. So, this study will show recent rates of prematurity and neonatal mortality generally in Kinondoni district.

CHAPTER THREE
METHODOLOGY
3.1 Introduction
This chapter describes the method of analysis that means study population, study area, source of data ethical considerations, data analysis, benefit to the respondent, model of specification, multiple regression and descriptive statistics.
3.2 Study Population
The total children born at Mwananyamala Hospital in Kinondoni district in 2017 will be the study population.
3.3 Study Area
The study area will be the Kinondoni district, Dar es Salaam Tanzania.
3.4 Variables
This study will use one dependent variable and three independent variables. The dependent variable is neonatal mortality and independent variables will be prematurity, asphyxia and infections.
3.5 Dependent Variable
The dependent variable of this research will be the neonatal mortality. This variable depends on the prematurity. So, this means the prematurity rate affects the neonatal mortality. It will be used to check the trend of the neonatal mortality in recent years due to prematurity.
3.6 Independent Variable.
The independent variable will be used to determine the rate of prematurity occurrences and compare the significance effect of prematurity to the neonatal mortality and other causes (prematurity, asphyxia and natural sepsis).
3.7 Source of Data
This study will use secondary data sourced from the Mwananyamala hosptal, Dar Es Salaam region. It’s because it is a district hospital. Kinondoni Municipality has one government hospital and eleven private hospitals; two government health centres, one parastatal and six private health centres; 24 government dispensaries, five voluntary agency dispensaries, five parastatals and 126 private dispensaries (Nkuba, 2007). Data will be collected from Mwnanyamala hospital because it is a district referral hospital, so it receives the related cases from other hospitals, health centres and dispensaries within the district.
3.8 Data Analysis
Data will be analyzed using the SPSS. Descriptive statistics will be used for the analysis of the neonatal mortality rate and prematurity rate of different recent years in Kinondoni district (Nkuba, 2007). Also, I will compare the significance effect of the prematurity to the neonatal mortality and other causes, whereby I will run the multiple regression to compare/see the relationship between prematurity and the other causes to neonatal mortality.
3.10 Descriptive statistics
Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures.
Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data.
From the objective to determine the rate of neonatal mortality in Kinondoni district, I will use the descriptive statistics either by tables or graphs.
3.11 Correlation analysis
Correlation analysis is a method of statistical evaluation used to study the strength of a relationship between two, numerically measured, continuous variables. This analysis is useful when a researcher wants to establish if there are possible connections between variables. I will use the correlation analysis to determine the association and its strength of prematurity rate to neonatal mortality rate in Kinondoni district.
3.12 ANOVA
One Way anova compares the means of two or more independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. One-way anova is a parametric test. I will use the one-way anova to compare the frequency in terms of proportions, to check the proportions of prematurity, asphyxia and natural sepsis to neonatal mortality in Kinondoni district. The independent variables are prematurity, asphyxia and natural sepsis
CHAPTER FOUR
Data analysis and interpretation
4.1 Introduction
This chapter presents the data analysis and interpretation.
4.2 Descriptive statistics
The descriptives table (see below) provides some very useful descriptive statistics, including the mean and standard deviation for the dependent variable (neonatal mortality) for the cause of neonatal deaths (premature, birth asphyxia and natural sepsis), as well as when they are combined (Total).
Table 4.2.1: Descriptive statistics
N Mean Std. Deviation
premature 31 23.04 10.033
birth asphyxia 52 24.45 10.589
natural sepsis 13 22.62 7.124
Total 96 23.46 9.769

In this Descriptive Statistics box, the mean for the premature is 23.04. The mean for the birth asphyxia is 24.45and the mean for the natural sepsis is 22.62. The number of neonates died due to the causes analyzed in each condition (N) is 96. The number of neonates died throughout the year 31 were due to prematurity, 52 due to birth asphyxia and 13 due to natural sepsis.
4.3 Analysis of Variance
This section covers the testing the difference between cause of deaths (premature, birth asphyxia and natural sepsis) means.
4.3.1 Analysis of Variance
Table 4.3.1: ANOVA
Sum of Squares Df Mean Square f Sig.
Between Groups
Within Groups
Total 48.948
9106.677
9155.625 2
93
95 24.474
97.921 .250
.779

The first column provides us with the between, within, and total sums of squares. In this case we have SSbetween = 48.948, SSwithin = 9106.677, and SStotal =9155.625.
The second column presents the degrees of freedom between causes of neonatal deaths. In this case the df between is 2 (# of groups -1), because we have 3 causes of deaths of neonates. Degrees of freedom within is 93 (N- # of groups), because we have 96 subjects and 3 causes of neonatal deaths and the df total is 95 (N-1), because we have 96 neonates dead due to the three causes (prematurity, birth asphyxia and natural sepsis).
The third column presents the Mean Square (MS) between and within, respectively. In this case MSbetween = 24.474 and MSwithin = 97.921.
The fourth and fifth columns present the final F statistic and its associated level of significance, respectively. In this case we have an F obtained of 35.613 and the level of significance 0.779.
Therefore Sig. value is 0.779 is larger than 0.05. Because of this, we conclude that there is no statistically significant difference in causes of neonatal mortality between prematurity, birth asphyxia and natural sepsis.
4.3.2 Multiple comparison
Multiple comparison tests are a test of the statistical significance of differences between group means calculated after (“post”) having done an analysis of variance (ANOVA) that shows an overall difference.
The one used below is called the Least Significant Differences test (LSD). It compares all possible pairs of levels of the independent variable and tests each for significance in a way that controls what’s referred to as the experiment-wide error rate.
Table 4.3.2 Multiple comparison
Dependent variable: neonatal mortality
LSD
(I) cause of death (J) cause of death Mean Difference
(I-J) Std. Error Sig.

premature birth asphyxia -1.413 2.245 .531
natural sepsis .423 3.068 .891
birth asphyxia premature 1.413 2.245 .531
natural sepsis 1.836 3.270 .576
natural sepsis premature -.423 3.068 .891
birth asphyxia -1.836 3.270 .576

Within each major row the remaining two variables comprise minor rows of their own. For the first row, major row 1 (premature) contain the minor rows birth asphyxia and natural sepsis, respectively. Each of these minor rows represents an LSD t-test where the major row variable is the first mean in the equation and the minor row variable is the second variable. In all major rows: minor rows, I assessed whether the LSD t obtained to check if the means are significant.
The first column of data, labeled Mean Difference (I-J), the numerator of the LSD t-test. For premature: birth asphyxia, the mean difference is -1.413, for premature: natural sepsis, the mean difference is 0.423, for birth asphyxia: premature, the mean difference is 1.413, for birth asphyxia: natural sepsis, the mean difference is 1.836, for nature sepsis: premature, the mean difference is -0.423 and for natural sepsis: birth asphyxia, the mean difference is -1.836. The second and the third column of data respectively, labeled Stand. Error and sig, presents the denominator of the LSD t-test and level of significance. For the premature-birth asphyxia comparison the Standard Error is 2.245 and level of significance of 0.531. In this case the difference between the premature and birth asphyxia, mean is not significant. For the premature-natural sepsis comparison the Standard Error is 3.068 and level of significance of 0.891. In this case the difference between the premature and natural sepsis, mean is not significant. For the birth asphyxia-premature comparison the Standard Error is 2.245 and level of significance of 0.531. In this case the difference between the birth asphyxia and premature, mean is not significant. For the birth asphyxia-premature comparison the Standard Error is 2.245 and level of significance of 0.531. In this case the difference between the birth asphyxia and premature, mean is not significant. For the birth asphyxia-natural sepsis comparison the Standard Error is 3.270 and level of significance of 0.576. In this case the difference between the birth asphyxia and natural sepsis, mean is not significant. For natural sepsis-premature comparison the Standard Error is 3.068 and level of significance of 0.891. In this case the difference between the natural sepsis and premature, mean is not significant. For natural sepsis-birth asphyxia comparison the Standard Error is 3.270 and level of significance of 0.576. In this case the difference between the natural sepsis and premature, mean is not significant.
4.4 Contribution of prematurity, birth asphyxia and natural sepsis to neonatal mortality.
This section shows by how much percentage where the three factors prematurity, birth asphyxia and natural sepsis to the neonatal mortality.
Table 4.4.1 Frequency Table
cause of death
Frequency Percent Valid Percent Cumulative Percent
Valid premature 31 32.3 32.3 32.3
birth asphyxia 52 54.2 54.2 86.5
natural sepsis 13 13.5 13.5 100.0
Total 96 100.0 100.0

From the table above, 32.2% of the neonatal mortality where contributed by the prematurity, 54.2% contributed by birth asphyxia and 13.5% contributed by natural sepsis
4.5 Relationship between prematurity to neonatal mortality.
Correlations measure how variables are related. The values of the correlation coefficient range from -1 to 1. The sign of the correlation coefficient indicates the direction of the relationship (positive or negative). The absolute value of the correlation coefficient indicates the strength, with larger absolute values indicating stronger relationships. The correlation coefficients on the main diagonal are always 1, because each variable has a perfect positive linear relationship with itself.
Table 4.5.1 Correlation statistics
Correlations
prematurity neonatal mortality
prematurity Pearson Correlation 1 .069
Sig. (2-tailed) .504
N 96 96
neonatal mortality Pearson Correlation .069 1
Sig. (2-tailed) .504
N 96 96

From the table above, the correlation coefficient for prematurity and neonatal mortality is 0.069. The number of respondents (neonates) is 488. There is a very weak downhill (negative) linear relationship. SPSS does not give p-values to more than three decimal places.
The statistical hypothesis test for this p-value is:
H0: There is no significant relationship between premature and neonatal mortality.
Ha: There is a statistically significant relationship between premature and neonatal mortality.
The p value is 0.504, therefore p ; 0.05, then the relationship is not statistically significant.
4.6 Neonatal mortality rate in Kinondoni district.
The neonatal mortality rate is defined as the number of deaths at 0 to 27 days after live birth in a given year per 1000 live births in the same year (Mohangoo, et al., 2013). Therefore, it is the number of resident newborns in a specified geographic area (country, state, county, etc.) dying at less than 28 days of age divided by the number of resident live births for the same geographic area (for a specified time period, usually a calendar year) and multiplied by 1,000 (Mohangoo, et al., 2013).
Calculation:
(Number of resident neonatal deaths/Number of resident live births) x 1,000.
For the case of Mwananyamala hospital:
238= neonatal deaths in 2017 among neonates born alive.
11,389 = live births of neonates in 2017.
(238/11389) x 1,000 = 2.09 neonatal deaths per 1,000 live births in 2017 among live births
Therefore, the neonatal mortality rate is 2.09.
4.7 Findings and Discussion
Our study found birth asphyxia is the leading cause of death. We checked on the contribution of each cause to neonatal mortality via frequencies distribution table. We found birth asphyxia contributed 54.2%, while prematurity contributed 32.3% and neonatal mortality contributed 13.5%. In contrast, the global pattern and studies from university and tertiary care hospitals found prematurity to be the leading cause of death (Mmbaga, Lie, ; Olomi R, 2012) while our study found prematurity is the second cause of neonatal deaths. Thus, asphyxia and prematurity are the major causes to neonatal mortality. In contrast to the study of Mbawala et al., (2014), Asphyxia and infections are among the major causes of neonatal mortality globally, premature babies are even more vulnerable with very poor outcome when affected by these complications.

CHAPTER FIVE
Conclusion, limitations and recommendations
5.1 Introduction
The purpose of the study was to analyze the effect of prematurity on neonatal mortality in Kinondoni district.
This chapter discusses the conclusions and limitations of the study and makes
recommendations for strategies to improve the services to pregnant women and neonates.
5.2. Conclusion
The general objective of this research was to analyze the effect of prematurity on neonatal mortality. To achieve the general objective, the following were specifically aimed; to compare the significance effect of prematurity rate to the neonatal mortality rate and other causes and to determine the association of prematurity rate and neonatal mortality in Kinondoni district.
Firstly, to compare the significance effect of prematurity rate to the neonatal mortality rate and other causes. The study found that birth asphyxia is the leading cause of death with contribution of 54.2%, while prematurity contributed 32.3% and neonatal mortality contributed 13.5%. Birth asphyxia, prematurity and natural sepsis/infections are accounted for one third of all neonatal deaths at Mwananyamala hospital in 2017.
Secondly, to determine the association of prematurity rate and neonatal mortality in Kinondoni district. The study found that the relationship between prematurity and neonatal mortality was very weak but insignificant.
Lastly, the study found that, the neonatal mortality in Kinondoni district is 2.09.
5.3 Limitations
This study encountered some challenges during data collection. The challenges were reluctance from the respondents delayed by claiming that they were busy. This made the researcher wait for more than planned time. Respondents were reluctant to give more information hoping that they would be exposing the institutions’ inside information.
5.4 Recommendations
Management of premature babies requires high specialized equipment, highly trained personnel and financial support.
Basic training on newborn resuscitation skills and proper newborn resuscitation immediate after birth has proved to reduce mortality among babies born with birth asphyxia up to 40%.

i

References
Abu-Saad, K. (2010, april 1). Maternal Nutrition and Birth Outcomes. Epidemiologic Reviews, pp. 5-25.
Amabebe, E., & & Anumba, D. (2018). The Vaginal Microenvironment: The Physiologic Role of Lactobacilli. Frontiers in Medicine .
Behrman, R. E., & Butler, A. S. (2007). Preterm Birth: Causes, Consequences, and Prevention. Washington (DC): National Academies Press.
Blencowe, H., Cousens, S., Oestergaard, M., Chou, D., Moller, A. B., Narwal, R., . . . & Lawn, E. (June 2012). National, regional and worldwide estimates of preterm birth. The Lancet.
Blencowe, H., Cousens, S., Oestergaard, M., Chou, D., Moller, A., Narwal, R., . . . . (2013). Born Too Soon: The global epidemiology of 15 million preterm births. Reproductive Health.
Burgueta, A., Kaminskib, M., Abraham-Lerata, L., Schaalc, J.-P., Cambonied, G., Fressone, J., . . . & Roze, J.-C. (2004). The complex relationship between smoking in pregnancy and very preterm delivery. Results of the Epipage study. International Journal of Obstetrics and Gynaecology, 258-265.
Butler, R., Behrman, E., & & Adrienne, S. (n.d.). Preterm Birth: Causes, Consequences, and Prevention.
Carlson, A., Norwitz, E., & & Stiller, R. (2010). Cytomegalovirus Infection in Pregnancy: Should All Women Be Screened? Obstetrics & Gynecology, 172–179.
Cohen, S., & & Syme, L. (1985). Issues in the Study and Application of Social Support. Social Support and Health, pp. 3-22.
Davey, A., Watson, L., Rayner, A., & & Rowlands, S. (2011). Risk scoring systems for predicting preterm birth with the. Australia: John Wiley & Sons, Ltd.
Digital Campus Moodle. ( 2014, July 11). Retrieved from https://moodle.digital-campus.org/mod/page/view.php?id=17274
Fiscella, K., Franks, P., Doescher, M. P., & & Saver, B. G. (2002, January). Disparities In Health Care By Race, Ethnicity, And Language Among The Insured. Findings from a national sample.
Hankin, J. (2002). Fetal Alcohol Syndrome Prevention Research. Michigan.
Hendler, I., Goldenberg, L., Mercer, M., Iams, D., Meis, J., Moawad, H., . . . & Sorokin, Y. (2005, March). The Preterm Prediction Study. Association Between Maternal Body Mass Index And Spontaneous And Indcated Preterm Birth.
Igwilo, Sabina, N., Ibeagha, C., Nwankwo, j., Clementina, U., Ajawuihe, &., & Peace, C. (2016). Reducing Malnutrition In Pregnancy Through Awareness And Utilization Of Moringa Oleifera – A Nutrition Supplement In Awka South Local Government Area Of Anambra State. Nigeria: International Journal of Development Research.
Institute of Medicine, Board on Health Sciences Policy, Roundtable on Environmental Health Sciences, Research, and Medicine. (2003). The Role of Environmental Hazards in Premature Birth. National Academies Press.
Kamel, M. R. (2013). Assisted Reproductive Technology after the Birth of Louise Brown. Journal of Reproduction & Infertility, 96-109.
Keegan, J., Parva, M., Finnegan, M., Gerson, A., & & Belden, M. (2010). Addiction in Pregnancy. Journal of Addictive Diseases, 175-191.
Kemp, M. W. (2014, December 1). Frontiers In Immunology. Preterm Birth, Intrauterine Infection, and Fetal Inflammation.
Klimpel, G. (1996). Medical Microbiology. Texas.
Kyrklund-Blomberg, N., Granath, F., & & Cnattingius, S. (2005). Maternal smoking and causes of very preterm birth. Acta Obstet Gynecol Scand, 572-577.
Lamont, R. (2015). Advances in the Prevention of Infection-Related Preterm Birth. Front Immunol, 566.
Lawn, J., Mongi, P., & & Cousens, S. (n.d.). Africa’s newborns–.
Lederman, A. S., Rauh, V., Weiss, L., Stein, J., Hoepner, L., Becker, M., ; ; Perera, F. (2004). The Effects of the World Trade Center Event on Birth Outcomes among Term Deliveries at Three Lower Manhattan Hospitals. Environmental Health Disparities, 1772-1778.
Mattison, D. (2003). The Role of Environmental Hazards in Premature Birth. Washington (DC): National Academies Press (US).
Mattison, D., Wilson, S., Coussens, C., ; ; Gilbert, D. (n.d.). The Role of Environmental Hazards in Premature Birth:. THE NATIONAL ACADEMIES PRESS.
Mbawala, G., Fredrick, F., Kamugisha, E., Konje, E., ; ;Hokororo, A. (2014). Factors Associated With Mortality Among Premature Babies. East African Journal of Public Health.
Menon, R. (2012). Preterm birth: a global burden on maternal and child health. Pathogens and Global Health, 139-140.
Mmbaga, T. B., Lie, T. R., ; Olomi R, M. M. (2012). Cause-specific neonatal mortality in a neonatal care unit in Northern Tanzania: A registry based cohort study. BMC Pediatrics.
Mohammed, H., Refaat, Khaled, Fischer-Hammadeh, Constanze, Hammadeh, ; Eid, M. (2012). The impact of smoking on infertility, pregnancy outcomes and fetal development. Teratology Studies.
Mohangoo, A. D., Blondel, B., Gissler, M., Velebil, P., Macfarlane, A., Zeitlin, J., ; Committee, ;. E.-P. (2013). International Comparisons of Fetal and Neonatal Mortality Rates in High-Income Countries: Should Exclusion Thresholds Be Based on Birth Weight or Gestational Age?
Nkuba, M. P. (2007). Factors Contributing To High Adolescent Pregnancy Rate. South Africa.
Pathirana, J., Muñoz, F., Abbing-Karahagopian, V., Bhat, N., Harris, T., Kapoor, A., . . . ; Sona, O. (2016). Neonatal death: Case definition ; guidelines for data collection, analysis, and presentation of immunization safety data. Vaccine, 6027–6037.
Quinn, J.-A., Munoz, F., Gonik, B., Frau, L., Cutland, C., Mallett-Moore, T., . . . Huang, W.-T. (2016). Preterm birth: Case definition ; guidelines for data collection, analysis, and presentation of immunisation safety data. Vaccine, 6047–6056.
Raju, K. V. (2017). Medical nutrition in mental health and disorders. Indian Journal of Psychiatry, 143-148.
Rice, S. C. (2016, november 7). Premature Infant.
Sachdeva, P., Patel, G. B., ; ; Patel, K. B. (2009). Drug Use in Pregnancy; a Point to Ponder! Indian Journal of Pharmaceutical Sciences, 1-7.
Sajjad ur, R., ; Walid, E. A. (2012). Neonatal Mortality: Incidence, Correlates and Improvement, Strategies, Perinatal Mortality, . (O. Ezechi, Ed.) InTech. Retrieved from http://www.intechopen.com/books/perinatal-mortality/neonatal-mortality
Shaila, S., Mohammed, S., Hassan, J., ; ; Sithara, S. (2017). Effects of Stress on Preterm Birth – A Case Study in a Tertiary Care Center. Journal Of Medical Science And Clinical Research.
Shane, R., ; ; Ira, B. (2008). Effects of maternal tobacco-smoke exposure on fetal growth and neonatal size. Expert Review of Obstetrics ; Gynecology, 719-730.
Smith, K. L., Draper, S. E., Evans, A., Field, D., Johnson, S., Manktelow, B., . . . ; Boyle, E. (2015). Associations between late and moderately preterm birth and smoking, alcohol, drug use and diet: a population-based case–cohort study. BMJ Journals.
Tzanetakou, I., Mikhailidis, D., ; ; Perrea, D. (2011). Nutrition During Pregnancy and the Effect of Carbohydrates on the Offspring’s Metabolic Profile: In Search of the “Perfect Maternal Diet”. The Open Cardiovasc Medicine, 103-109.
Unicef. (2017). Levels & Trends in Child Mortality.
UNICEF TANZANIA. (2015). Marternal & child health – The situation.
Vannuccini, S., Clifton, V., Fraser, I., Taylor, H., Critchley, H., Giudice, L., & & Petraglia, F. (2016). Infertility and reproductive disorders: impact of hormonal and inflammatory mechanisms on pregnancy outcome. Human Reproduction Update, 104-115.
Waldorf, A. K., & & McAdams, R. (2013). Influence of Infection During Pregnancy on Fetal Development. Reproduction, 151-162.
Wardlaw, T., You, D., Hug, L., Amouzou, A., & & Newby, H. (2014). UNICEF Report: enormous progress in child survival but greater focus on newborns urgently needed. Reproduction Health.
Witt, W., Litzelman, K., Cheng, E., Wakeel, F., & & Barker, E. (2014). Measuring Stress Before and During Pregnancy: A Review of Population-Based Studies of Obstetric Outcomes. Matern Child Health .
World Health Organization. (2006). Neonatal and perinatal mortality : country, regional and global estimates. Switzerland.
World Health Organization. (2006). Neonatal and perinatal mortality: country, regional and global estimates.
World Health Organization. (2018, February 19). Preterm birth.

Appendices
SPSS OUTPUT
ONEWAY nmotalty BY diagnosis
/MISSING ANALYSIS
/POSTHOC=LSD ALPHA(0.05).
Oneway

DataSet1 E:\ANOD.sav
ANOVA
neonatal mortalty
Sum of Squares df Mean Square F Sig.
Between Groups 48.948 2 24.474 .250 .779
Within Groups 9106.677 93 97.921
Total 9155.625 95

Post Hoc Tests
Multiple Comparisons
Dependent Variable: neonatal mortalty
LSD
(I) cause of death (J) cause of death Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval
Lower Bound Upper Bound
premature birth asphyxia -1.413 2.245 .531 -5.87 3.05
natural sepsis .423 3.068 .891 -5.67 6.52
birth asphyxia premature 1.413 2.245 .531 -3.05 5.87
natural sepsis 1.836 3.270 .576 -4.66 8.33
natural sepsis premature -.423 3.068 .891 -6.52 5.67
birth asphyxia -1.836 3.270 .576 -8.33 4.66

FREQUENCIES VARIABLES=nmotalty diagnosis
/ORDER=ANALYSIS.
Frequencies
cause of death
Frequency Percent Valid Percent Cumulative Percent
Valid premature 31 32.3 32.3 32.3
birth asphyxia 52 54.2 54.2 86.5
natural sepsis 13 13.5 13.5 100.0
Total 96 100.0 100.0

Statistics
neonatal mortalty cause of death
N Valid 96 96
Missing 0 0
Frequency Table
neonatal mortalty
Frequency Percent Valid Percent Cumulative Percent
Valid 8 3 3.1 3.1 3.1
12 4 4.2 4.2 7.3
13 8 8.3 8.3 15.6
14 6 6.3 6.3 21.9
16 7 7.3 7.3 29.2
18 14 14.6 14.6 43.8
22 9 9.4 9.4 53.1
24 6 6.3 6.3 59.4
26 21 21.9 21.9 81.3
41 18 18.8 18.8 100.0
Total 96 100.0 100.0

Correlations
premature cause of death
neonatal mortalty Pearson Correlation 1 .069
Sig. (2-tailed) .504
N 96 96
premature Pearson Correlation .069 1
Sig. (2-tailed) .504
N 96 96