TIME SERIES ANALYSIS OF UNDER-FIVE MORTALITY

IN MULAGO HOSPITAL (1990-2010)

BY

OKUDA BONIFACE

09/U/3224/PS

209004160

A DISSERTATION SUBMITTED TO THE SCHOOL OF STATISTICS AND

APLLIED ECONOMICS IN PARTIAL FULFILLMENTOF THE

REQUIREMENTS FOR THE AWARD OF BACHELOR OF STATISTICS

AT MAKERERE UNIVERSITY.

JUNE 2012

DECLARATION

I Okuda Boniface affirm that this proposal is entirely my original work and has not been

presented for any award of a degree in any institution of higher learning unless otherwise cited.

………………………………… ………………………………..,.

Signature Date

This proposal has been submitted with my approval as a University Supervisor.

………………………………………… ……………………………………….

Mr. Odur Benard Date

Lecturer

SSAE, Makerere University Kampala

ii

DEDICATION

This work is dedicated to my father Mr. Ogira Simon Peter, my mother Mrs. Akongo Sidonia,

brothers Ochen Benjamin and Ogira Gabriel, my sisters Akello Brenda and Achieng Mercy and

my friends for the support.

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ACKNOWLEDGEMENT

Special thanks to the almighty God for the special help and guidance. I am deeply indebted to

some individuals whose contributions made it possible to reach a successful completion of this

dissertation.

My utmost gratitude goes to my supervisor, Mr. Odur Bernard for his tireless effort in reading

and providing relevant comments and corrections that have enabled me produce this research

project.

Finaly special thanks goes to my father, mother and friends for all invaluable contributions both

financially and morally especially during this time.

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TABLEOFCONTENTS

DECLARATION ............................................................................................................................ i

DEDICATION ............................................................................................................................... ii

ACKNOWLEDGEMENT ........................................................................................................... iii

LIST OF TABLES ....................................................................................................................... vii

LIST OF FIGURES ................................................................................................................... viii

ACCRONYMS ............................................................................................................................. ix

DEFINITIONS AND CONCEPTS .............................................................................................. x

ABSTRACT ................................................................................................................................. xii

CHAPTER ONE: BACKGROUND ............................................................................................ 1

1.1 Introduction ............................................................................................................................... 1

1.2 Previous trends of child mortality ............................................................................................. 2

1.3 Problem statement ..................................................................................................................... 3

1.4 Objectives ................................................................................................................................. 4

1.5 Hypotheses ................................................................................................................................ 4

1.6 Significance of the study ........................................................................................................... 5

1.7 Scope of the Study .................................................................................................................... 5

1.8 Limitation of the study .............................................................................................................. 5

CHAPTER TWO: LITERATURE REVIEWS .......................................................................... 6

2.1 Introduction ............................................................................................................................... 6

2.2 demographic factors .................................................................................................................. 6

2.2.1 Sex of the child ...................................................................................................................... 6

2.2.2 Season .................................................................................................................................... 7

2.3 Infectious diseases and under-five mortality ............................................................................ 7

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2.3.1 Malaria and under-five mortality ........................................................................................... 7

2.3.2 Tuberculosis and under-five mortality ................................................................................... 8

2.3.3 Tetanus and under-five mortality ........................................................................................... 9

2.3.4 Measles and under-five mortality ........................................................................................ 10

2.3.5 Pneumonia and under-five mortality.................................................................................... 10

2.3.6 HIV/ AIDS and under-five mortality ....................................................................................11

2.4 Forecasting model ................................................................................................................... 12

CHAPTER THREE: METHODOLOGY ................................................................................. 14

3.1 Introduction ............................................................................................................................. 14

3.2 Sources and nature of data to be used ..................................................................................... 14

3.3 Techniques of data collection .................................................................................................. 14

3.4 Analysis software .................................................................................................................... 14

3.5 Data processing and analysis .................................................................................................. 14

3.5.1 Time series analysis ............................................................................................................. 14

3.5.2 Data exploration techniques ................................................................................................. 15

3.5.3 Autoregressive Integrated Moving Average (ARIMA) ........................................................ 19

3.6 Ethical considerations ............................................................................................................. 20

CHAPTER FOUR: DATA PRESENTATION AND ANALYSIS OF RESULTS ................... 21

4.1 Introduction ............................................................................................................................. 21

4.2. Graphical presentation of findings ......................................................................................... 21

4.3 Testing for stationarity in the mortality series ........................................................................ 27

4.4 Estimation of the model .......................................................................................................... 29

4.5 Diagnostic test ......................................................................................................................... 29

4.6: Forecasts of under-five mortality (2011Q1-2015Q4) ............................................................ 31

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4.7 Test of hypotheses ................................................................................................................... 31

4.7.1 Testing for death differentials by gender ............................................................................. 31

4.7.2 Testing for death differentials by year ................................................................................. 32

4.7.3 Testing for death differentials by disease ............................................................................. 33

4.7.4 Trend analysis of mortality series ........................................................................................ 33

4.7.5 Trend analysis of mortality series by gender ....................................................................... 34

4.7.6 Test for seasonality............................................................................................................... 34

4.8 Discussion ............................................................................................................................... 35

4.8.1 Child sex and under-five mortality ...................................................................................... 35

4.8.2 Seasonality and Under-five mortality .................................................................................. 36

4.8.3 Trend in under-five mortality ............................................................................................... 36

4.8.4 Infectious Diseases and Under-five mortality ...................................................................... 37

CHAPTER FIVE: SUMMARY OF FINDINGS, CONCLUSIONS AND

RECOMMENDATIONS ........................................................................... 38

5.1 Introduction ............................................................................................................................. 38

5.2 Summary of the findings ......................................................................................................... 38

5.3 Conclusions ............................................................................................................................. 39

5.4 Recommendations ................................................................................................................... 39

5.5 Areas for further studies .......................................................................................................... 40

REFFERENCES ......................................................................................................................... 41

APPENDICES ............................................................................................................................. 44

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LIST OF TABLES

Table 4. 1: Unit Root Test ............................................................................................................. 27

Table 4. 2: Correlogram of Mortality Series ................................................................................. 28

Table 4. 3: Autoregressive Moving Average Model (9, 0, 0) ........................................................ 29

Table 4. 4: Model Description ...................................................................................................... 31

Table 4. 5: Forecasted Mortality Values ........................................................................................ 31

Table 4. 6: Death Differential by Gender ...................................................................................... 32

Table 4. 7: Death Differential by Year (Period) ............................................................................ 32

Table 4. 8: Death Differential by Disease ..................................................................................... 33

Table 4. 9: Runs Test on Mortality Series ..................................................................................... 33

Table 4. 10: Runs Test on Mortality Series by Gender ................................................................. 34

Table 4. 11: Kruskal-Wallis Test for Seasonality .......................................................................... 35

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LIST OF FIGURES

Figure 4. 1: Trend in Under-Five Mortality by Gender from 1990-2010 ..................................... 21

Figure 4. 2: Percentage Distribution of Under-Five Mortality by Disease (1990-2010) .............. 22

Figure 4. 3: Percentage Distribution of Under-Five Mortality for Each Month 1990-2010 ........ 23

Figure 4. 4: General Trend in Mortality Series ............................................................................. 24

Figure 4. 5: Variations in Mortality Series 2005-2010 by Quarters .............................................. 25

Figure 4. 6: Causes of Under-Five Mortality for the Period 1990-2010 ...................................... 26

Figure 4. 7: Bartlett‟s Test for White Noise for Under-five mortality .......................................... 30

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ACCRONYMS

UDHS Uganda Demographic Health Survey

WHO World Health Organisation

UNICEF United Nations International Children‟s Emergency Fund

UN United Nations

CHERG Child Health Epidemiology Reference Group

MDGs Millenium Development Goals

PEAP Poverty Eradication Action Plan

HIV Human Immune Virus

AIDS Acquired Immune Deficiency Syndrome

NGOs Non Government Organisations

MOH Ministry Of Health

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DEFINITIONS AND CONCEPTS

Adequate compilation and measurement of vital events requires that the concepts used be given

formal definitions even though the meaning of these concepts may appear as obvious to most

people.

Hospital: This is a residential establishment which provides short and long term medical care

consisting of observational and rehabilitative service to persons suffering from diseases or

suspected to be suffering from an injury.

Health: The World Health Organisation (WHO) defined health in 1948 as a „state of complete

physical, mental and social wellbeing not merely the absence of disease or infirmity‟.

Live birth: This is the complete expulsion from the womb of its mother, the product of

conception irrespective of the duration of the pregnancy, after which it shows evidence of life

such as breathing, crying, etc.

Premature baby: Babies born before 37 completed weeks of pregnancy are called premature.

Injury: This is usually defined as physical harm to a person‟s body.

Disease: This is any disturbance or anomaly in the normal functioning of the body that probably

has a specific cause and identifiable symptoms.

Types of diseases

Diseases are classified according to the following, though a great deal of overlapping may be

found in the different classes:

1. Infectious diseases. These are communicable and capable of infecting a large number of

persons within relatively short time intervals. This kind of disease has the following

different causes;

a. Parasitic

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b. Bacterial

c. Viral

d. Fungal

2. Environmental diseases. in epidemiology, environmental disease is disease caused by

environmental factors that are not transmitted genetically or by infection. It can be

classified as follows;

a. Nutritional

b. Diseases due to unfavorable environmental factors

3. Other diseases

a. Diseases connected with eggs and fry

b. Tumors, genetic disorders

Mortality: This is the risk of dying in a given year, measured by the death rate which is the

number of deaths occurring per 100,000 people in a population.

Neonatal mortality: the probability of dying within the first month of life

Infant mortality: the probability of dying between birth and the first birthday

Post neonatal mortality: the arithmetic difference between infant and neonatal mortality

Child mortality: the probability of dying between exact age one and the fifth birth

Under-five mortality: the probability of dying between birth and the fifth birthday.

Cause specific mortality: mortality classified by cause.

Death: This is the permanent disappearance of all evidence of life after a life birth has occurred.

xii

ABSTRACT

The purpose of the current study was to carry out a time series analysis of under-five mortality in

Mulago hospital for the period of 1990-2010 with specific objectives of; establishing whether

there is trend in the mortality series over the time period, investigating the occurrence of

seasonality in the mortality series, to analyse mortality differences in terms of sex, cause and

period and lastly to make predictions of under-five mortality for the period of 2011-2015.

Secondary data obtained from the records department of Mulago hospital was used for this study.

Descriptive statistics showed that malaria accounted for most of the deaths (19.41%) followed by

Pneumonia and Diarrhoea with 12.68% and 10.80% respectively. Genital infection and oral

disease accounted for the least number of deaths recorded with 0.68% and 0.77% respectively.

Augmented Dickey-Fuller Test also revealed that the mortality series was stationary for the

recorded period of 1990-2010. Under-five mortality was also found to vary by gender, period and

sex, where the male deaths were higher than the female deaths. Run‟s test also revealed that the

mortality series did not exhibit any trend over the period of study. Whereas the mortality series of

the male did not exhibit trend, that of the female exhibited trend over the period of study.

Seasonality was also found to exist in the mortality series where most of the deaths were

recorded in the month of June, February, December, July and August and the least in January and

October. There was also a general reduction in mortality causes where causes due to measles and

tetanus had the least deaths in 2010.

The study therefore recommended political awareness, commitment and leadership that are

needed to ensure that child health receives the attention and resources needed to accelerate

progress towards MDG4, consistent use of treated mosquito nets for malaria prevention and

enhancing workers‟ skills through workshops. This would increase survival rates of children who

visit health units.

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CHAPTER ONE

BACKGROUND

1.1 Introduction

Infant and child mortality levels in Sub-Saharan Africa are the highest in the world. In the

median African country, more than 15 of 100 children die before their fifth birthday (Jameson et

al., 2006). This compares to less than 25 out of 1,000 in the richer parts of the world. Not only

are under-five mortality levels very high; in addition, progress in reducing child mortality is very

slow. Hence, Sub-Saharan Africa as a whole is seriously off track in terms of reaching MDG4.

In 2010, the world average under-five mortality was 57 (5.7%), down from 88 (8.8%) in 1990

and in 2006, the average in developing countries was 79 (down from 103 in 1990), whereas the

average in industrialized countries was 6 (down from 10 in 1990) (UNICEF press release, 2011).

A child in Sierra Leone, which has the world's highest child mortality rate 262 in 2007 (UNICEF

press release September, 12, 2010) is about 87 times more likely to die than one born in Sweden

with a rate of 3 (UNICEF Sweden statistics, 2010).

According to the World Health Organization, 2008 questions and answer archives, the main

causes of child death are pneumonia, diarrhea, malaria, measles, and HIV. Malnutrition is

estimated to contribute to more than one third of all child deaths in that 1 child dies every 5

seconds as a result of hunger ,700 every hour, 16 000 each day, 6 million each year (2002-2008

estimates Jacques Diouf). One in eight children in Sub-Saharan Africa dies before their fifth

birthday (UNICEF 2010). The biggest improvement between 1990 and 2006 was in Latin

America and the Caribbean, which cut their child mortality rates by 50% (UNICEF state of the

world‟s children report, 2008).

Child mortality was an important indicator of the successful implementation of the Poverty

Eradication Action Plan (PEAP) in Uganda, and for good reasons, the level of child mortality is a

consequence of a broad range of Government intervention areas in terms of access to education,

safe water, basic health care and provision of security and stability. Other determinants of child

mortality include household incomes, HIV/AIDS, gender disparities, cultural practices and

nutrition, all of which can be influenced by Government. Child mortality is therefore an

2

important health issue, but it must be stressed from the beginning that the health sector is not the

only sector responsible for the child mortality outcome.

Statistics from the Uganda Demographic and Health Survey (UDHS, 2006) reveal declining

trends in the levels of infant, under-five and maternal mortality. Between 2000 and 2005 infant

mortality decreased from 98 to 76 deaths per 1,000 births. This means that one in every 13

newborn Ugandan die within the first year of life. During the same period, under-five mortality

increased from 162 to 137 deaths per 1,000 births.

According to the world population data sheet of population reference bureau Washington (2009),

the average infant mortality rate was 46 deaths per 1000 live births in the world, 6 deaths per

1000 in the more developed world, 50 deaths per 1000 in the developing world and 76 deaths per

1000 in Uganda.

The World Bank policy study 2010 indicates that the highest rates of child mortality continue to

be in the Sub-Saharan Africa, where 1 child in 8 dies before age five that is nearly 20 times the

average of 1 in 167 for developed regions. Southern Asia has the second highest rates, with about

1 child in 14 children dying before age five.

1.2 Previous trends of child mortality

The global under-five mortality rate has declined by a third, from 89 deaths per 1,000 live births

in 1990 to 60 in 2009 (World Bank policy statement report, 2010). This report also highlights

that all regions except Asia and Oceania have seen reductions of at least 50 percent.

At regional levels, in 2009, the highest rates of under-five mortality continue to be in Sub-

Saharan Africa, where 1 child in 8 died before age of five (129 deaths per 1,000 live births) that

is nearly double the average in developing regions (66 deaths per 1,000 live births) and nearly 20

times the average in developed regions (6 deaths per 1,000 live births). For sub-Saharan Africa

as a whole there has been a decline in U5MR concentrated largely in the period between 1965

and 1990, during which the median U5MR dropped from 232 t o 170 per 1000. Since 1990, the

trend seems to have stalled. The pattern of this overall trend also characterizes each region,

3

though at different levels and speeds. The countries of the West region had the highest U5MR in

1960, with a median value around 290 per 1000 live births. This level fell Below 200 per 1000

by 1985, a level similar to that of the Middle region, which had a median around 260 per 1000 in

1960. The East region median oscillated around 200 per 1,000 prior to 1975 before declining to

170 per 1000 in 1990. The Southern Region had the lowest median U5MR in 1960 (around 200

per 1000) and experienced the sharpest decline to about 60 per 1000 by 1990. Declines appear to

have stalled in all regions in the 1990s. The West and Southern regions thus experienced the

fastest declines from 1960 t o 1990, with the countries of t he Middle and East regions showing

the slowest improvement.

In Uganda, Child mortality fell significantly between 1948 and 1970 as a result of political

stability, high economic growth, and increased access to health care and scientific progress

which, amongst others, increased access to vaccines against immunizable diseases. Uganda‟s

health sector was considered to be one of the best in Africa during this period (Hutchinson,

2001). The period from the early 1970s and mid-1980s was characterized by political turmoil and

conflict, severely limited access to health services, and a consequent stagnation in infant

mortality was observed. The recovery period of 1986-1995 with high economic growth, political

stability and poverty reduction under the NRM Government, produced a reduction in child

mortality (MFPED, 2002).

1.3 Problem statement

7.6 million Children under age five died in 2010, representing an under-five mortality rate of

57/1000 live births (WHO, 2011). Unlike in the developed countries where death rarely occurs

among infants and children, in developing countries like Uganda, it is estimated that on average

50% of the deaths occur to children aged 15 and below (UN, 2008).

According to various studies carried out, a small number of diseases and conditions are the

biggest killers of young children today. Pneumonia, measles, diarrhea, malaria, HIV and AIDS

and complications during pregnancy and after birth to mention but a few cause more than 90% of

deaths in children under five (WHO, 2010). Children who are malnourished are at far greater risk

of dying from these causes because they have low immunity.

4

The increasing focus on the reduction of child mortality arising from the Millennium Declaration

and from the Millennium Development Goal (MDG) 4 of “reducing by two-thirds, between 1990

and 2015, the under-five mortality rate”, has generated renewed interest in the development of

more accurate assessments of the number of deaths in children aged less than 5 years by cause.

Moreover, the monitoring of the coverage of interventions to control these deaths has become

crucial if MDG 4 is to be achieved; thus a more accurate establishment of the causes of deaths in

children aged less than 5 years becomes crucial.

Although various studies have been conducted about under-five mortality in the country, not

much has been done in Mulago concerning the documentation of trends, seasonality and

mortality by sex and cause of death hence the research would like to find out the behavior of

mortality rates over time and the specific causes of these deaths.

1.4 Objectives

The chief purpose of this study is to carry out a time series analysis of under-five mortality in

Mulago Hospital for the period 1990-2010.

Other objectives may include the following;

1. To establish if there is trend in under five mortality from 1990-2010

2. To investigate whether there is seasonality in the recorded figures from 1990-2010

3. To analyze death differentials by sex, year & diagnosis

4. To make predictions for under five mortality

5. To assess Cause reductions of under-five mortality overtime

1.5 Hypotheses

there is no trend in child mortality

there is no seasonality in child deaths

more male children die than female children

death differentials by sex, year & diagnosis is the same

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1.6 Significance of the study

This study is an important addition to the mortality research already done by scholars in

Uganda

The study will also be helpful to facilitate the improvement of the understanding of the

specific causes of death in infants on the basis of which proper policy measures for

prevention of diseases and reducing mortality can be developed.

The analysis of child mortality data will present the demographic status of the population

as well as its potential growth, which will be of great importance to policy makers and

planners.

1.7 Scope of the Study

Under-five mortality data from the records department of Mulago hospital for the period 1990-

2010 will be used for the study. The data set will consider children less than 5 years of age.

The variables that will be used include gender, period of occurrence and the cause of death.

1.8 Limitation of the study

There was a problem of extracting huge amount of data from the record files since Mulago

hospital does not have a Hospital information management system. This took a lot of time for the

researcher.

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CHAPTER TWO

LITERATURE REVIEWS

2.1 Introduction

In Uganda, according to UNICEF (2009), the causes of childhood morbidity and mortality like

elsewhere in Sub-Saharan Africa were malaria, diarrhoea, measles and acute respiratory

infections. In most recent years Acquired Immune Deficiency Syndrome (AIDS) has also joined

in as a major risk to women and children.

Despite droughts, natural disasters and famine, mortality appears to have fallen in all parts of

Africa though the rates of decline have shown substantial variation from one region to another.

The percentage of children dying before celebrating their fifth birth day almost halved in Ghana

over 30 years in the late 1930s and 1960s (from 37%-20%); in Congo over 20 years between the

1940s and the 1960s(from 29%-15%) and in Kenya over the 25 years between late 1940s and

early 1970s from 26-15% (UNICEF statistics-Ghana, 2010).

According to several studies conducted, age, sex and infectious disease have been found to be

major factors affecting mortality. But also season of the year play a role in determining mortality

levels (Kenneth Hill, 1988) hence mortality factors can be broken down into demographic factors

and infectious disease factors.

2.2 demographic factors

2.2.1 Sex of the child

In the reviewed micro-econometric studies, child characteristics typically show the expected

influence on mortality. Boys are often found to be significantly more likely to die than girls and

the same holds for first born children ( Lavy et al, 2000; Ssewanyana and Younger, 2007).

In terms of maternal proximate determinants, the studies in general confirm the important

influence in particular of mother‟s age and birth intervals (for example Mturi and Curtis, 1995;

Brockerhoff and Derose, 2000; Lavy et al, 2002; Lalou and Le Grand, 2000).

7

Overall, for the world as a whole, under-five mortality rates are the same for boys and girls.

However, the rate varies by income group and region. In general, under-five mortality is higher

for boys than it is for girls among low income countries and upper middle and high income

countries. The pattern seems reversed for lower middle income countries. Similarly, under-five

mortality is higher among boys for most regions of the world except the South East Asia region

where it is reversed, and there is little difference among boys and girls in the Eastern

Mediterranean region (WHO, 2010).

2.2.2 Season

According to the study by Nyombi in 2000, child deaths have a seasonal pattern occurring more

frequently during certain months of the year. There may exist seasonality in death level among

children, that is there are more deaths occurring in a particular time of the year or day due to

specific diseases being rampant in certain months of the year e.g. cases of death due to anemia,

are predominant in dry seasons when there is little vegetables, and also when malaria cases are

rampant causing break down of red blood cells. Cases due to malaria are most predominant in

months of April, June, July, September, and December, when there is stagnant water, which are

used by mosquitoes as breeding places.

2.3 Infectious diseases and under-five mortality

Preventable infectious diseases cause two-thirds of child deaths, according to a study published

by The Lancet in 2011. Experts from the World Health Organization (WHO) and UNICEF‟s

Child Health Epidemiology Reference Group (CHERG) assessed data from 193 countries to

produce estimates by country, region and the world. While the number of deaths has declined

globally over the last decade, the analysis reveals how millions of children under five die every

year from preventable causes. These causes include;

2.3.1 Malaria and under-five mortality

Malaria is a life-threatening disease caused by parasites that are transmitted to people through the

bites of infected mosquitoes. In 2010, malaria caused an estimated 655,000 deaths, mostly

among African children (WHO, 2011). According to the World Health Organization (WHO

2011) Malaria is responsible for 10 per cent of all under-five deaths in developing countries.

8

According to the world health report (2002), in 1970, there were 3.7 million deaths annually and

170 million cases, 88 percent of them in tropical Africa and the disease is endemic in 100

countries. The aim of the current global malaria strategy was to reduce mortality at least by 20

percent compared to 1995 in at least 75 percent of the countries that would have been affected by

the year 2000 in WHO accelerated malaria control activities in 24 endemic countries in Africa.

Africa still remains the region that has the greatest burden of malaria cases and deaths in the

world. In 2000, malaria was the principal cause of around 18% that is 803 000 (uncertainty range

710,000 - 896,000) of deaths of children under 5 years of age in Africa south of the Sahara as by

Rowe AK et al (2005).

During the 1980s and the early 1990s, malaria mortality in rural Africa increased considerably,

probably as a result of increasing resistance to chloroquine as by Korenromp EL et al (2003).

According to Ter Kuile FO et al (2004) Malaria is also a significant indirect cause of death:

malaria-related maternal anemia in pregnancy, low birth weight and premature delivery are

estimated to cause 75 000–200 000 infant deaths per year in Africa south of the Sahara.

2.3.2 Tuberculosis and under-five mortality

There has been a perception, particularly in the industrialized world, that TB is a disease of the

old. Fifty years ago, however, hospital services for children today dedicate entire wards for

infants and children with TB. In developing countries where a large proportion of the

population is under the age of 15 years, as many as 40 per cent of tuberculosis notifications

may be children; tuberculosis may be responsible for 10 per cent or more of childhood hospital

admissions, and 10 per cent or more of hospital deaths.

According to the WHO (2008), complacency towards tuberculosis in the three decades led

control programs to be run down in many countries. The result has been a powerful resurgence of

the disease, now estimated to kill three million people a year, with 7.3 million new cases

annually. The WHO declared tuberculosis a global emergency in 1993. About 3 million cases a

year occur in south East Asia and nearly two million in sub Saharan Africa, with 340000 in

Europe. One third of the incidence in the last five years can be attributed to HIV infection which

9

weakens the immune system and makes the person infected with tubercle bacillus 30 times more

likely to become ill with tuberculosis strains of bacillus resistant to one or more drugs may have

infected up to 50 million people.

Tuberculosis may be responsible for more death worldwide than any other disease caused by any

pathogen, Sundre et al, 2000. The incidence of Tuberculosis among children will therefore

increase in the areas where HIV prevalence is high because HIV negative individuals could

increase in the areas where HIV prevalence is high because HIV negative individuals could

increase by 13-14 percent in African countries, depending on the prevalence of tuberculosis and

AIDS.

2.3.3 Tetanus and under-five mortality

Tetanus is a potentially deadly infection that can occur if a baby‟s umbilical cord is cut with an

unclean tool or if a harmful substance such as ash or cow dung is applied to the cord, as is

traditional practice in some African countries. When tetanus develops, child death rates are

extremely high, especially in countries where health systems are not strong and access to more

advanced medical treatment can be difficult.

Tetanus is a major cause of neo- natal death in African as well as among other age groups.

Tetanus mortality rates in Africa are probably among the highest in the world. The few available

studies in Uganda suggest that the rates of 10 to 20 neo-natal tetanus deaths per 1000 live birth

are not usual (Kawuma et al., MOH 2000). According to the world health report (2008), tetanus

of the newborn is the third killer of children after measles and pertusis among the six EPI

vaccine preventable disease and is concern in all WHO regions except Europe. Between 800,000

and 1 million newborn a year died from tetanus in the early 1980s. An estimated 730,000 such

deaths are now preventable every year, particularly by targeting the elimination efforts to high

risk areas. In 1997, there was an estimated 275000 deaths WHO Estimated than 1995, about 90

percent of neonatal tetanus cases occurred in only 25 countries of which Uganda was not part.

10

2.3.4 Measles and under-five mortality

Measles, an acute viral respiratory illness associated with high fever, rashes and vomiting, is

considered one of the most deadly vaccine-preventable diseases, accounting for an estimated

777,000 childhood deaths per year worldwide, with more than half occurring in Africa, according

to the United Nations Children's Fund (UNICEF, 2011).

Measles is caused by paramyxovirus called morbili. It is highly infectious and transmitted from

person to person via droplets spread (sneezes, coughs). Cough nasal congestion and

conjunctivitis follow the incubation period of approximately 10 to 12 hours. The characteristic

rash appears about 2 to 4 days after the onset of other symptoms. Measles is one of the major

causes of death among children in Africa. Its contributing factor is about 8 to 10% of deaths

among African children. (Ofosu- Amaah, 2003; Rodriguez).

Apart from death, children who are affected by measles may suffer from life-long disability

including brain damage, blindness and deafness. In Uganda, Measles deaths reduced from 6,000

to 300 between 1996 and 2006 and to none according to the New Vision Uganda (Oct 19, 2011).

Sabiiti and WHO officials attributed the achievement to aggressive immunisation of children

against killer diseases, measles inclusive. Babies are vaccinated against Measles at the age of

nine months.

2.3.5 Pneumonia and under-five mortality

Pneumonia is a form of acute respiratory infection that affects the lungs. The lungs are made up

of small sacs called alveoli, which fill with air when a healthy person breathes. When an

individual has pneumonia, the alveoli are filled with pus and fluid, which makes breathing

painful and limits oxygen intake.

Pneumonia is the single largest cause of death in children worldwide. Every year, it kills an

estimated 1.4 million children under the age of five years, accounting for 18% of all deaths of

children under five years old worldwide. Pneumonia affects children and families everywhere,

but is most prevalent in South Asia and sub-Saharan Africa (WHO, 2011).

11

In the early 1970s Cockburn & Assaad generated one of the earliest estimates of the worldwide

burden of communicable diseases. In a subsequent review, Bulla & Hitze described the

substantial burden of acute respiratory infections and, in the following decade, with data from 39

countries, Leowski estimated that acute respiratory infections caused 4 million child deaths each

year – 2.6 million in infants (0–1 years) and 1.4 million in children aged 1–4 years. In the 1990s,

also making use of available international data, Garenne et al. further refined these estimates by

modeling the association between all-cause mortality in children aged less than 5 years and the

proportion of deaths attributable to acute respiratory infection. Results revealed that between

one-fifth and one-third of deaths in preschool children was due to or associated with acute

respiratory infection. The 1993 World Development Report produced figures showing that acute

respiratory infection caused 30% of all childhood deaths.

2.3.6 HIV/ AIDS and under-five mortality

More than 1,000 children are newly infected with HIV every day, and of these more than half

will die as a result of AIDS because of a lack of access to HIV treatment (UNICEF, 2011). In

addition, over 7.4 million children every year are indirectly affected by the epidemic as a result

of the death and suffering caused in their families and communities.

Nine out of ten children infected with HIV were infected through their mother either during

pregnancy, labor and delivery or breastfeeding (UNAIDS, 2010). Without treatment, around 15-

30 percent of babies born to HIV positive women will become infected with HIV during

pregnancy and delivery and a further 5-20 percent will become infected through breastfeeding

(WHO, 2006). In high-income countries, preventive measures ensure that the transmission of

HIV from mother-to-child is relatively rare, and in those cases where it does occur a range of

treatment options mean that the child can survive - often into adulthood. This shows that with

funding, trained staff and resources, the infections and deaths of many thousands of children

could be avoided.

HIV has caused adult mortality rates to increase in many countries of sub-Saharan Africa

(Timaeus IM, 2000/2002), and there is some indication that child mortality rates are also rising

due to vertical transmission. Since HIV prevalence levels are high and still increasing in many

12

countries, the effect of AIDS on child mortality is likely to persist for several decades. However,

for a variety of reasons, direct evidence for the impact of HIV on child mortality is relatively

weak.

2.4 Forecasting model

a) The ARIMA procedure

The ARIMA procedure analyzes and forecasts equally spaced univariate time series data, transfer

function data, and intervention data using the autoregressive Integrated Moving Average

(ARIMA) or autoregressive moving-average (ARMA) model. An ARIMA model predicts a value

in a response time series as a linear combination of its own past values, past errors (also called

shocks or innovations), and current and past values of other time series. The ARIMA approach

was first popularized by Box and Jenkins, and ARIMA models are often referred to as Box-

Jenkins models. The general transfer function model employed by the ARIMA procedure was

discussed by Box and Tiao (1975). When an ARIMA model includes other time series as input

variables, the model is sometimes referred to as an ARIMAX model. Pankratz (2001) refers to

the ARIMAX model as dynamic regression. The ARIMA procedure provides a comprehensive

set of tools for univariate time series model identification, parameter estimation, and forecasting,

and it offers great flexibility in the kinds of ARIMA or ARIMAX models that can be analyzed.

The ARIMA procedure supports seasonal, subset, and factored ARIMA models; intervention or

interrupted time series models; multiple regression analysis with ARMA errors; and rational

transfer function models of any complexity.

Meyler (1998) states that the main advantage of ARIMA forecasting is that it require data on the

time series in question only. This feature is advantageous if one is forecasting a large set of time

series data. This also avoids a problem that occurs in multivariate models since timeliness can be

a problem. ARIMA models are unable to capture non linear relationships in time series and this

makes the process of forecasting limited.

b) Lee-carter forecasting model

The method proposed in Lee and Carter (1992) has become the “leading statistical model of

mortality forecasting in the demographic literature” (Deaton and Paxson, 2004). It was used as a

13

benchmark for recent Census Bureau population forecasts (Hollmann, Mulder and Kallan, 2000),

and two U.S. Social Security Technical Advisory Panels recommended its use, or the use of a

method consistent with it (Lee and Miller, 2001). Lee-Carter approach makes strong assumptions

about the functional form of the mortality surface. In the last decade, scholars have “rallied”

(White, 2002) to this and closely related approaches, and policy analysts forecasting all-cause

and cause-specific mortality in countries around the world have followed suit (Booth,

Maindonald and Smith, 2002; Deaton and Paxson, 2004; Haberland and Bergmann, 1995; Lee,

Carter and Tuljapurkar, 1995; Lee and Rofman, 2000; Lee and Skinner, 2002; Miller, 2001;

NIPSSR, 2002; Perls et al., 2002; Preston, 2004; Tuljapurkar and Boe, 2003; Tuljapurkar, Li and

Boe, 2000; Wilmoth, 1996, 2000). Lee-carter was able to capture non linear relationships in the

time series data whereas ARIMA models were not able to capture non linear relationships.

14

CHAPTER THREE

METHODOLOGY

3.1 Introduction

This chapter presents the data collection methods, sources of data, and methods of data analysis.

The selected variables used in this study are sex of the deceased, cause of death, and the period

of the occurrence of the death.

3.2 Sources and nature of data to be used

The data used is secondary data that was obtained from Mulago referral hospital‟s records

department office. The data was extracted from the mortuary register.

3.3 Techniques of data collection

The technique used was mainly by observation of the summaries made in the mortuary register

kept in the records department of the hospital.

3.4 Analysis software

Data entry was by use of the computer package, Microsoft Excel, and then exported to statistical

packages like SPSS, STATA, and E-Views for analysis.

3.5 Data processing and analysis

3.5.1 Time series analysis

A time series is a collection of observations of well-defined data items obtained through repeated

measurements over time. A basic assumption in any time series analysis is that some aspects of

the past pattern will continue to remain in the future.

Chatfield (1989) observed that time series methods are based on studying past behavior of the

series to make forecasts.

15

As an important step in analyzing time series data, the types of data patterns were considered so

that the models most appropriate to the patterns can be utilized. Four components of time series

can hence be distinguished.

i. Trend: This refers to the general direction, either upward or downward in which a series have

been moving.

ii. Cycle: This where the data exhibits a wave like pattern (rises and falls) that are not of fixed

periods.

iii. Seasonality: This is concerned with periodic fluctuations that recur on a regular periodic

basis.

iv. Irregular term: This is the movement left when Trend, Seasonality and Cyclic components

have been accounted for.

The analysis however concentrated on Trend and Seasonality.

Assuming a multiplicative model, then 𝑌𝑡=𝑇𝑡∗𝑆𝑡

Where 𝑌𝑡 is the mortality series, 𝑇𝑡 is Trend and 𝑆𝑡 is the seasons.

3.5.2 Data exploration techniques

a. Graphical presentation

This involved plotting the series 𝑌𝑡 against time t.

b. Statistical tests

Unit root test

The unit root test was used to establish if the mortality series is stationary. Stationarity

has to be established because;

16

The stationarity or otherwise of a series can strongly influence its behavior and

properties -e .g. persistence of shocks will be infinite for non stationary series

Spurious regressions. If two variables are trending over time, a regression of one

on the other could have a high R2 even if the two are totally unrelated.

If the variables in the regression model are not stationary, then it can be proved

that the standard assumptions for asymptotic analysis will not be valid. In other

words, the usual “t -ratios” will not follow a t-distribution, so we cannot validly

undertake hypothesis tests about the regression parameters.

The early and pioneering work on testing for a unit root in time series was done by

Dickey and Fuller (Dickey and Fuller 1979, Fuller 1976). The basic objective of the test

is to test the null hypothesis that φ =1 in:

Yt = φyt-1+ ut

Against the one-sided alternative φ <1. So in general we have;

Ho: the series is stationary

Ha: the series is trended or has seasonality

We usually use the regression:

∆ yt = ψyt-1+ ut

So that a test of φ=1 is equivalent to a test of ψ=0 (since φ-1= ψ).

Conclusions

Reject Ho: this means there is sufficient evidence at a given level of confidence that the

series is trended or has seasonality.

Fail to reject Ho: this means that there is no sufficient evidence at a given level of

significance that the series is trended or has seasonality.

c. Non parametric tests for trend

Run’s test: The runs test (Bradley, 1968) can be used to decide if a data set is from a

random process.

17

A run is defined as a series of increasing values or a series of decreasing values. The

number of increasing, or decreasing, values is the length of the run. In a random data set,

the probability that the (i+1)th

value is larger or smaller than the ith

value follows a

binomial distribution, which forms the basis of the runs test.

Testing procedure

Ho: the mortality series is stationary

Ha: the mortality series is non-stationary

Test statistic

𝑆𝑅=𝑚(𝑚−1)

2𝑚−1

Z=𝑅−µ𝑅

𝑆𝑅

Where m=number of pluses

Decision rule is at α=0.05

The researcher would reject Ho if Z>𝑍∝ 2 i.e. if the computed Z statistic is greater than

the notable value and then conclude with (1-α)*100% confidence, the series has trend.

d. Test for seasonality

Several scholars have come up with different ways of assessing seasonality in a series

such as graphical methods, non parametric methods, correlation analysis, analysis of

variance method, etc.

Despite the knowledge of seasonal effects on diseases for two millennia, the definition

and the measurement of seasonality has not been the center of attention until Edwards

(1961) developed a test based on a geometrical framework which was specially designed

for seasonality. It turned out to become the most cited and the benchmark against which

other tests are evaluated (Wallenstein et al, 2000, p. 817). In his article, Edwards

explicitly also mentions the possibility to estimate cyclic trends by considering the

ranking order of the events which are above or below the median number. This idea has

18

been taken up by Hewitt et al (2002). They did not use a binary indicator as suggested by

Edwards but all the ranking information. Rogerson (2000) made a first step to generalize

this test, relaxing the relatively strict assumption of Hewitt et al.(2000) that seasonality is

only present if a six-month peak period is followed by a six-month trough period.

Rogerson allowed that the peak period can also last three, four, or five months.

In this research, the researcher will use the Kruskal-Wallis test which is an alternative for

the parametric one-way analysis of variance test, if there are two or more independent

groups to compare (Siegel & Castellan 1988). Barker et al. (2006), for example, found

with the Kruskal-Wallis test that significant seasonal and monthly variations in mean

daily frequency of suicide attempts were observed in women, but not in men. In addition,

significant relationships (as assessed with the Mann-Whitney U-test) were found between

female parasuicides and „hot‟, „still‟, „still/hot‟ days as well as between male parasuicides

and „windy‟ days.

The test is described as below;

Ho: the series has no seasonality

Ha: the series has seasonality

Test statistics, H to compare with 𝑋∝2 (Chi square)

H=12

𝑁(𝑁+1)

𝑅𝑖2

𝑛𝑖− 3(𝑁 + 1)𝑘

𝑖=1

ni is the number of observations in the ith

season

N is the total number of specific seasons

Ri= 𝑟𝑎𝑛𝑘 (𝑦𝑖)

Yi is the specific season for time t.

Critical region

Reject Ho if H>𝑋∝(𝑖−1)2

19

3.5.3 Autoregressive Integrated Moving Average (ARIMA)

This is also known as the Box-Jenkins model. This methodology will be used to forecast the

under-five mortality rates. The model is based on the assumption that the time series involved are

stationary. Stationarity will first be checked and if not found, the series will be differenced d

times to make it stationary and then the Autoregressive Moving Average (ARMA) (p, q) will be

applied.

The ARIMA procedure provides a comprehensive set of tools for univariate time series model

identification, parameter estimation, and forecasting, and it offers great flexibility in the kinds of

ARIMA models that can be analyzed. The ARIMA procedure supports seasonal, subset, and

factored ARIMA models; intervention or interrupted time series models; multiple regression

analysis with ARMA errors; and rational transfer function models of any complexity.

The Box-Jenkins methodology has four steps that will be followed when forecasting the

mortality rates as stipulated below;

i. Identification. This involves finding out the values of p, d, and q where;

p is the number of autoregressive terms

d is the number of times the series is differenced

q is the number of moving average terms

The identification here will be done basing on the correlogram plot obtained. Where both

autocorrelation and partial correlation cuts of at a certain point, we conclude that the data follows

an autoregressive model. The order p, of the ARIMA model is obtained by identifying the

number of lags moving in the same direction. In case the series was non stationary, the number

of times we difference the series to obtain stationarity is the value of d.

ii. Estimation. This involves estimation of the parameters of the Autoregressive and Moving

average terms in the model. The non linear estimation will be used.

iii. Diagnostic checking. Having chosen a particular ARIMA model, and having estimated its

parameters, we now examine whether the chosen model fits the data reasonably well. The simple

20

test of the chosen model will be done to see if the residuals estimated from this model are white

noise. If they are, we can accept the particular fit and if not, the model will have to be started

over.

iv. Forecasting.

Exponential smoothing methods will be used for making forecasts. While exponential smoothing

methods do not make any assumptions about correlations between successive values of the time

series, in some cases you can make a better predictive model by taking correlations in the data

into account. Autoregressive Integrated Moving Average (ARIMA) models include an explicit

statistical model for the irregular component of a time series that allows for non-zero

autocorrelations in the irregular component.

3.6 Ethical considerations

Ethics in research refer to considerations taken to protect and respect the rights and well fare of

participants and other parties associated with the activity (Reynolds 2001). The rights of parties

involved at every stage of this study were treated with utmost care. The following considerations

were made to promote and protect the rights and interests of participants at the different stages of

the study.

During Data collection: steps taken to protect the rights of participants during actual data

collection included securing informed written consent by the head of department notifying the

management of Mulago hospital about my study and to grant me permission to collect data from

the hospital.

During analysis and reporting of findings: the investigation made sure to report modestly and

exactly what the findings were, without exaggerations that would create false impressions. In the

same respect, the database was created honestly using SPSS programme without any distortions.

21

CHAPTER FOUR

DATA PRESENTATION AND ANALYSIS OF RESULTS

4.1 Introduction

This chapter presents key findings on the trend of under-five mortality. It presents both

descriptive and inferential analysis of the relationship between variables. The choice of the

different test statistic used depended on the hypothesis to be tested. The data was obtained from

the records department of Mulago hospital and directly entered into Microsoft Excel from which

it was exported to SPSS, STATA and E-VIEWS for analysis

4.2. Graphical presentation of findings

Figure 4. 1: A Line Graph Showing the Trend of Under-Five Mortality by Gender from

1990-2010

From the Figure 4.1 above, the mortality series of both male and female showed a downward

trend between 1990 and 1995. However, the rate of decline for the female is greater than that of

the male. For the male under-five the rate of decline is about 30.6% whereas for the female

under-five, the rate is 38.1%. However between 1995 and 2010, the series exhibited stationarity

with a rate of decline of only 6.7% and 8.8% for the female and male under-five respectively.

0

200

400

600

800

1000

1200

1990

1991

1992

1993

1994

1995

1996

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2007

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2009

2010

tota

l nu

mb

er o

f d

eath

s

year

male

female

22

Variations in deaths by gender can also be observed in that the number of male deaths recorded

remained higher than that of female children except in 2003 where a total of 701 female deaths

were recorded against 633male deaths.

Figure 4. 2: A Bar Graph Showing Percentage Distribution of Under-Five Mortality by

Disease (1990-2010)

From Figure 4.2 above, Malaria accounted for most of the deaths (19.41%) for the period 1990

to 2010 followed by Pneumonia and Diarrhoea with 12.68% and 10.80% respectively. Genital

infection and oral disease accounted for the least number of deaths recorded with 0.68% and

0.77% respectively. The high number of deaths due to malaria can be attributed to the rainy

season that cause a lot of stagnant water which acts as breeding places for mosquitoes since most

deaths were recorded in June, February, December, July and august of which these months are

faced with heavy rains at times.

2.88%

1.10%

2.78%3.85%

10.80%

0.68%

19.41%

12.68%

2.06%

4.17%

1.25% 1.28%

3.32%4.81%

1.41%0.77%

2.32%3.68% 3.37% 2.98%

5.48%4.73%4.19%

0.00

5.00

10.00

15.00

20.00

25.00

Dys

entr

y

Mea

sles

Teta

nu

s

Aid

s

Dia

rrh

oea

Gen

ital

infe

ctio

n

Mal

aria

Pn

eum

on

ia

Res

oir

ato

ry in

fect

ion

Sep

tica

emia

Tub

ercu

losi

s

Men

ingi

tis

Deh

ydra

tio

n

An

aem

ia

Ast

hm

a

Ora

l Dis

ease

Dia

bet

es M

ellit

us

Car

dio

vasc

ula

r d

isea

se

Ner

vou

s sy

stem

dis

ord

er

Kw

ash

iork

or

Mar

asm

us

Inju

ries

DISEASES

percen

ata

ge o

fd

eath

s

23

Figure 4. 3: A Bar Graph Showing Percentage of Under-Five Mortality Recorded for Each

Month for the Period 1990-2010

Figure 4.3 above shows that most deaths were recorded in June, February, December, July and

August of which each accounted for 10.73%, 10.71%, 9.36%, 9.13% and 9.03% of the total

deaths respectively. The least number of deaths were recorded in the months of January and

October accounting for only 5.36% and 6.34% of the total number of deaths recorded for the

period 1990 to 2010.

5.36%

10.71%

7.70%

8.85%

7.50%

10.73%

9.13% 9.03%

7.76%

6.34%

7.48%

9.36%

0.00

2.00

4.00

6.00

8.00

10.00

12.00

Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

per

cen

tage

dea

ths

months

24

Figure 4. 4: General Trend in Under-five Mortality (1990-2010)

From Figure 4.4 above, Mulago Hospital recorded the highest number of child deaths in 1990

with 2062 deaths and the lowest in 2009 with 1172 deaths. The mortality series exhibited a

downward trend between 1990 and 1995 but after wards the series remained almost constant

over the remaining years. The down ward trend between 1990 and 1995 can be attributed to the

government constant effort to improve child health care over the years through provision of

better health facilities and increased number of health workers. The period of 1990-1995

according to (MFPED, 2002) was characterized with high economic growth, political stability

and poverty reduction under the NRM Government. Between 1996 and 2007, the series was

stationary and this can be attributed to the non improving health facility standards and inadequate

budget provisions for the health sector.

0

500

1000

1500

2000

2500

1990

1991

1992

1993

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1996

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2004

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2006

2007

2008

2009

2010to

taln

um

ber

of

d

eath

s

year

25

Figure 4. 5: Line Graph showing Variations in Mortality Series 2005-2010 by Quarters

From figure 4.5 above, the mortality series varied between different months of the year. The

series indicated consistently high number of deaths for the second quarter and first quarter of the

year as seen above. This can be attributed to the rainy season in the first and second quarter of

the year that cause a lot of stagnant water which acts as breeding places for mosquitoes which

cause malaria and lead to death of children with weak immune systems.

0

50

100

150

200

250

300

350

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450

500

Q1

2005

Q2

2005

Q3

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Q1

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Q2

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Q3

2006

Q4

2006

Q1

2007

Q2

2007

Q3

2007

Q4

2007

Q1

2008

Q2

2008

Q3

2008

Q4

2008

Q1

2009

Q2

2009

Q3

2009

Q4

2009

Q1

2010

Q2

2010

Q3

2010

Q4

2010

deaths

tota

l n

um

ber

of

dea

ths

year

26

Figure 4. 6: A Line Graph Showing Various Causes of Under-Five Mortality for the Period

1990-2010

From Figure 4.6 above, there has been a down ward trend in mortality by the selected causes;

malaria, tetanus, Aids, measles, pneumonia and anaemia for the period of 1990-2010. Tetanus

and measles according to WHO report 2011 was declared nonexistent in Uganda although

malaria still remains a big challenge. The general downward trend for the period of 1990-2010

can be attributed to improved health facilities and the government effort to achieve the MDG 4.

0

50

100

150

200

250

300

350

400

450

1990

1991

1992

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2004

2005

2006

2007

2008

2009

2010

dea

ths

year

MEASLES

TETANUS

AIDS

MALARIA

PNEUMONIA

ANAEMIA

27

4.3 Testing for stationarity in the mortality series

Before fitting a particular model to time series data, the series must be made stationary.

Stationary occurs in a series when statistical properties in the series tend to remain the same over

a given period of time. The test hypotheses are stated below;

Ho: mortality series is stationary

Ha: mortality series is not stationary

Table 4. 1: Unit Root Test for Under-five mortality

Null Hypothesis: TOTAL has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic based on SIC, MAXLAG=4) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -5.287243 0.0004

Test critical values: 1% level -3.808546

5% level -3.020686

10% level -2.650413 *MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(TOTAL)

Method: Least Squares

Date: 04/27/12 Time: 08:49

Sample (adjusted): 1991 2010

Included observations: 20 after adjustments Variable Coefficient Std. Error t-Statistic Prob. TOTAL(-1) -0.323773 0.061237 -5.287243 0.0001

C 412.2893 87.07992 4.734608 0.0002 R-squared 0.608312 Mean dependent var -43.00000

Adjusted R-squared 0.586551 S.D. dependent var 90.10111

S.E. of regression 57.93498 Akaike info criterion 11.05116

Sum squared resid 60416.32 Schwarz criterion 11.15073

Log likelihood -108.5116 Hannan-Quinn criter. 11.07060

F-statistic 27.95493 Durbin-Watson stat 2.407887

Prob (F-statistic) 0.000050

According to Table 4.1 above, the Dickey-Fuller Unit root test on the original series shows that

the series is stationary since the absolute value for the combined test statistics (5.287243) is

greater than the three test statistics at 1%, 5%, and 10% critical values 3.808546, 3.020686,

28

2.650413 respectively. Since the series is stationary, we now obtain the Autoregressive Moving

Average (p, q). Here the chief tools of model identification are the autocorrelation function

(ACF) and the Partial Autocorrelation Function (PAF) and there corresponding correlogram

plots.

Table 4. 2: Correlogram Plot for Mortality (1990-2010)

From the above captured correlogram in Table 4.2, we observe that Both AC and PAC cuts off

after a certain point hence we can say that the data follows an autoregressive model. The order of

the ARIMA model is now obtained by identifying the number of lags moving in the same

direction. By counting the lags moving in the same direction, we obtain 9 lags. Hence it‟s AR

(9).

29

4.4 Estimation of the model

Table 4. 3: Autoregressive Moving Average Model (9, 0, 0)

According to Table 4.3 above, the probability value of (0.000) is less than 0.05. This means that

the deaths in the previous quarter can significantly determine the deaths in the current quarter.

4.5 Diagnostic test

In order to check whether the model was good for the data, Bartlett‟s white noise test was carried

out. The residuals generated were plotted using a cumulative periodogram white Noise test.

30

Figure 4. 7: Bartlett’s Test for White Noise

As presented in Figure 4.7, using the periodogram white noise test for goodness of fit of the

model to the data, the researcher found that the model best fits the data since almost all values

appeared within the confidence bands thus the model is good for this data.

After carrying out the white noise test, the mortality series were predicted within the range of the

original series. This is done in order to find out whether the model is good for the data.

31

4.6: Forecasts of under-five mortality (2011Q1-2015Q4)

Table 4. 4: Model Description

Model Type

Model ID Mortality forecast

Model_1 ARIMA(9,0,0)

Table 4. 5: Forecasted Mortality Values

year quarter Forecast values

2011 Q1 303

2011 Q2 327

2011 Q3 281

2011 Q4 225

2012 Q1 294

2012 Q2 317

2012 Q3 272

2012 Q4 218

2013 Q1 284

2013 Q2 306

2013 Q3 263

2013 Q4 211

2014 Q1 274

2014 Q2 296

2014 Q3 254

2014 Q4 203

2015 Q1 265

2015 Q2 285

2015 Q3 245

2015 Q4 196

4.7 Test of hypotheses

4.7.1 Testing for death differentials by gender

A paired sample t-test was conducted to find out whether on average the male deaths and female

deaths are significantly different and the output is displayed below.

Ho: more male children die than female children

Ha: the number of deaths is the same between the sexes

32

Table 4. 6: Death Differential by Gender

From Table 4.6 above, it is revealed that the means of the male and female death figures have a

probability value of 0.000 which is less than 0.05. This implies that if 100 similar studies were

carried out under the same conditions, all of them would show that there is a significant

difference between the male mortality and female mortality. The null hypothesis is therefore

rejected and it‟s concluded that on average, more male children died than female children.

4.7.2 Testing for death differentials by year

Ho: mortality in the different years studied differ

Ha: mortality in the different years studied is the same

Table 4. 7: Death Differential by Year (Period)

Test Value = 0 (one sample t-test)

t df Sig. Mean Difference

95% Confidence Interval of the Difference

Lower Upper

total 29.601 20 0.000 1396.476 1298.07 1494.89

It is revealed from Table 4.7 above that the mean deaths of the years 1990-2010 are significantly

different with a probability value of 0.000 which is less than 0.05. This implies that if 100 similar

studies were carried out under the same conditions, all of them would show that there is a

significant difference in the deaths over the period of 1990-2010. This leads to the rejection of

the null hypothesis and a conclusion is made that on average the mean deaths in the years

considered are significantly different.

Paired Differences

t df Sig.

Mean Std. Deviation

Std. Error Mean

95% Confidence Interval of the Difference

Lower Upper

Pair 1 male - female 58.000 50.444 11.008 35.038 80.962 5.269 20 0.000

33

4.7.3 Testing for death differentials by disease

Ho: under-five deaths due to the different diseases differ

Ha: under-five deaths due to the different diseases is the same

Table 4. 8: Death Differential by Disease

One sample test

t df Sig. Mean Difference

95% Confidence Interval of the Difference

Lower Upper

deaths 4.758 22 0.000 1271.304 717.16 1825.45

From Table 4.8 above, it is established that means between figures of disease give a combined

significance value of 0.000 which is less than 0.05. This implies that if 100 similar studies were

carried out under the same conditions, all of them would show that there is a significant

difference in the number of deaths due to the different diseases. The null hypothesis is thus

rejected and a conclusion is made with 95% confidence that on average, deaths due to the

different diseases vary.

4.7.4 Trend analysis of mortality series

Run‟s test was used to establish whether there was a trend in the series of observations recorded.

Summary statistics generated are presented in the table below.

Ho: the mortality series is not trended

Ha: the mortality series is trended

Table 4. 9: Runs Test forUnder-five Mortality ( 1990-2010)

DEATHS

Test Valuea 334

Cases < Test Value 42

Cases >= Test Value 42

Total Cases 84

Number of Runs 34

Z -1.976

Asymp. Sig. 0.055

a. Median

34

From Table 4.9 above, the probability value (0.055) is greater than 0.05. This implies that if 100

similar studies were carried out under the same conditions, about 95 of them would show that the

series exhibited trend. Thus the null hypothesis is not rejected and it is concluded at 95% level of

confidence that the series did not significantly exhibited trend for the years observed (1990-

2010).

4.7.5 Trend analysis of mortality series by gender

Ho: the mortality series of male children is not trended

Ha: the mortality series of male children is trended

Ho: the mortality series of female children is not trended

Ha: the mortality series of female children is trended

Table 4. 10: Runs Test for Under-five Mortality by Gender

male female

Test Valuea 693 644

Cases < Test Value 10 10

Cases >= Test Value 11 11

Total Cases 21 21

Number of Runs 8 6

Z -1.336 -2.234

Asymp. Sig. (2-tailed) 0.82 0.026

a. Median

According to Table 4.10 above, the probability value of the male mortality series is 0.82 which is

greater than 0.05 and that of female is 0.026 which is less than 0.05. This implies that if 100

similar studies were carried out under the same conditions, only 18 of them would show that

trend significantly exists in the male mortality series and 97 of them would show that trend

significantly exists in the female mortality series.

4.7.6 Test for seasonality

The Kruskal-Wallis test also known as the H-test was used to investigate whether there was

seasonality in the recorded figures from 1990-2010.

35

Table 4. 11: Kruskal-Wallis Test for Seasonality

quarter Number of

observations

Rank sum

1 20 606.50

2 20 1182.50

3 21 1030.00

4 20 502.00

chi-squared = 27.580 with 3 d.f.

probability = 0.0001

It is established that the probability value =0.0001 which is less than 0.05 (5% level of

significance). The null hypothesis is thus rejected and it is concluded that the series exhibited

seasonality for the periods recorded. This also implies that if 100 similar studies were carried out

under the same conditions, all of them would show that seasonality significantly exists in the

mortality series. As observed in figure 4.3, most deaths were recorded in June, February,

December, July and August of which each accounted for 10.73%, 10.71%, 9.36%, 9.13% and

9.03% of the total deaths respectively. The least number of deaths were recorded in the months

of January and October accounting for only 5.36% and 6.34% of the total number of deaths

recorded for the period 1990 to 2010.

4.8 Discussion

The discussion of the key findings has been arranged in relation to the research hypotheses that

were investigated. The findings are also discussed in reference to the findings from some

relevant previous studies that were either similar or contrary to the findings in the present study.

4.8.1 Child sex and under-five mortality

The study findings revealed that mortality of under-five male children remained higher than

those of the female children. This study is in line with a study carried out by Lavy, 2000;

Ssewanyana and Younger, 2007 who also found out that boys are often found to be significantly

more likely to die than girls. Also according to (WHO, 2010), for the world as a whole, under-

five mortality rates are the same for boys and girls. However, the rate varies by income group

36

and region. In general, under-five mortality is higher for boys than it is for girls among low

income countries and upper middle and high income countries. The pattern seems reversed for

lower middle income countries. Similarly, under-five mortality is higher among boys for most

regions of the world except the South East Asia region where it is reversed, and there is little

difference among boys and girls in the Eastern Mediterranean region.

4.8.2 Seasonality and Under-five mortality

According to this study, the mortality series in Mulago varied between different months of the

year. The series indicated consistently high number of deaths for the second quarter and first

quarter of the year. According to the study by Nyombi in 2000, child deaths have a seasonal

pattern occurring more frequently during certain months of the year. There may exist seasonality

in death level among children, that is there are more deaths occurring in a particular time of the

year or day due to specific diseases being rampant in certain months of the year e.g. cases of

death due to anemia, are predominant in dry seasons when there is little vegetables, and also

when malaria cases are rampant causing break down of red blood cells.

4.8.3 Trend in under-five mortality

The study findings revealed that there is no trend in under-five mortality (0.055> 0.05). The

mortality series exhibited a downward trend between 1990 and 1995 but after wards the series

remained almost constant over the remaining years. The down ward trend between 1990 and

1995 can be attributed to the government constant effort to improve child health care over the

years through provision of better health facilities and increased number of health workers. Child

mortality fell significantly between 1948 and 1970 as a result of political stability, high economic

growth, and increased access to health care and scientific progress which, amongst others,

increased access to vaccines against immunizable diseases. Uganda‟s health sector was

considered to be one of the best in Africa during this period (Hutchinson, 2001). The recovery

period of 1986-1995 with high economic growth, political stability and poverty reduction under

the NRM Government, produced a reduction in child mortality (MFPED, 2002).

37

4.8.4 Infectious Diseases and Under-five mortality

According to this study,Malaria accounted for most of the deaths (19.41% ) for the period 1990

to 2010 followed by Pneumonia and Diarrhoea with 12.68% and 10.80% respectively. Genital

infection and oral disease accounted for the least number of deaths recorded with 0.68% and

0.77% respectively. The high number of deaths due to malaria can be attributed to the rainy

season that cause a lot of stagnant water which acts as breeding places for mosquitoes since most

deaths were recorded in June, February, December, July and august of which these months are

faced with heavy rains at times. This is in line with the study of (Nyombi 2000) who also found

out that cases due to malaria is predominant in the months of April, June, July, September and

December. According to the World Health Organization (WHO 2011) Malaria is responsible for

10 per cent of all under-five deaths in developing countries.

38

CHAPTER FIVE

SUMMARY OF FINDINGS, CONCLUSIONS AND RECOMMENDATIONS

5.1 Introduction

This chapter summarizes the findings, conclusions and recommendations in line with the

objectives of the study. The major objective of the study was to carry out a time series analysis of

under-five mortality in Mulago Hospital for the period 1990-2010.

5.2 Summary of the findings

This study focused on the behavior of the mortality series for under-five children obtained from

the records department of Mulago hospital. The study found out that mortality series in Mulago

hospital recorded the highest number of child deaths in 1990 and the lowest in 2009. The

mortality series exhibited a downward trend between 1990 and 1995 but after wards the series

remained almost constant over the remaining years. Malaria accounted for most of the deaths for

the period 1990 to 2010 followed by Pneumonia and Diarrhoea. Genital infection and oral

disease accounted for the least number of deaths recorded.

The study also revealed seasonality in Under-five mortality (0.0001<0.05). Most deaths were

recorded in June, February, December, July and August. The least number of deaths were

recorded in the months of January and October for the period 1990 to 2010. . It was also revealed

that under-five deaths varied by gender, year and disease (0.000<0.05) respectively.

The forecasted under-five mortality shows a decline in under-five mortality for the periods of

2011, 2012, 2013, 2014 and 2015. The study also revealed a general downward trend in under-

five mortality causes. Tetanus and measles accounted for the least deaths by 2010 and in 2011

Uganda was declared free of measles according to the ministry of health report 2011 and WHO

report 2011.

39

5.3 Conclusions

Basing on the findings of this study, it was possible to draw a number of conclusions. It was

inferred from the findings that the mortality series observed over the period 1990-2010 is

stationary since the Augmented Dickey-Fuller Test (Unit root) revealed that the series had a unit

root.

Run‟s test also revealed that the mortality series did not exhibit trend for the period studied.

However, the mortality series for the female exhibited trend whereas that of the male did not

exhibit any trend for the period 1990-2010.

It was also found out that under-five mortality significantly differ by gender. It was found out

that more male children die than the female children. Under-five mortality was also found to

vary significantly over the period of study.

The study also found out that under-five mortality varies significantly for the different causes

observed over the period 1990-2010. Malaria accounted for most of the deaths observed

followed by pneumonia and diarrhoea.

The study also established that the mortality series exhibited seasonality for the periods recorded.

As observed in figure 4.3, most deaths were recorded in June, February, December, July and

August. The least number of deaths were recorded in the months of January and October.

5.4 Recommendations

The study revealed that most under-five deaths are due to infectious diseases. By scaling up

effective health services, the government will be able to ensure that most of the under-five

mortality can be avoided with proven, low-cost preventive care and treatment. Preventive care

includes: continuous breast-feeding, vaccination, adequate nutrition and, the use of insecticide

treated bed nets. The major causes of under-five deaths need to be treated rapidly, for example,

with salt solutions for diarrhoea or simple antibiotics for pneumonia and other infections. To

reach the majority of children who today do not have access to this care, we need more and

40

better trained and equipped health workers. Families and communities need to know how best to

bring up their children healthily and deal with sickness when it occurs.

The study also revealed stionarity in the mortality series hence political awareness, commitment

and leadership are needed to ensure that child health receives the attention and resources needed

to accelerate progress towards MDG4. Better information on the number and causes of under-

five child deaths will help leaders to decide on the best course of action.

The results also revealed that Malaria accounted for the highest percentage of the deaths in the

period covered. Therefore, consistency in usage of treated mosquito nets must be encouraged and

presumptive malaria treatment of pregnant women must be done.

The researcher also found out that the doctor patient ratio in Mulago hospital is 1:40, this is a fair

ratio but more effort such as providing attractive wage for health workers has to be done in order

to increase the number of health workers in the health units.

Lastly health ministries should embark on enhancing workers‟ skills through workshops. This

would increase survival rates of children who visit health units.

5.5 Areas for further studies

The current study was done in Mulago hospital hence further studies should consider focusing on

mortality differences basing on regions and location. For example studies that will be able to

compare mortality in urban and rural areas.

Further studies on under-five mortality should consider finding out why more male children die

than female children as it was found in the present study and other studies carried out.

Further studies should also consider using other forecast models apart from ARIMA models for

the forecast of mortality figures in the future.

Further studies on mortality should also consider focusing on identification of several

determinants of under-five mortality in the country.

41

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44

APPENDICES

APPENDIX 1: UNDER FIVE DEATHS BY SEX

JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC TOTAL

1990 M 81 77 84 75 81 88 73 112 89 89 119 93 1061

F 71 73 76 79 88 86 79 102 86 71 102 88 1001

TOTAL 152 150 160 154 169 174 152 214 175 160 221 181 2062

1991 M 55 81 79 89 77 71 79 81 68 88 88 84 940

F 65 73 76 78 73 76 87 76 79 65 76 81 905

TOTAL 120 154 155 167 150 147 166 157 147 153 164 165 1845

1992 M 43 89 57 67 78 81 61 67 69 77 81 88 858

F 33 61 51 55 62 71 69 71 81 67 79 65 765

TOTAL 76 150 108 122 140 152 130 138 150 144 160 153 1623

1993 M 31 71 70 66 76 67 71 72 68 69 63 62 786

F 30 55 67 55 59 63 69 73 66 57 54 70 718

TOTAL 61 126 137 121 135 130 140 145 134 126 117 132 1504

1994 M 57 71 57 71 47 75 68 63 75 64 78 61 787

F 47 69 55 75 50 79 69 57 67 48 56 65 737

TOTAL 104 140 112 146 97 154 137 120 142 112 134 126 1524

1995 M 41 98 57 65 66 80 64 66 57 33 41 68 736

F 34 87 51 59 60 67 59 44 48 47 30 34 620

TOTAL 75 185 108 124 126 147 123 110 105 80 71 102 1356

1996 M 47 75 57 61 58 72 61 64 40 44 33 55 667

F 49 86 61 49 60 67 65 55 39 37 21 47 636

TOTAL 96 161 118 110 118 139 126 119 79 81 54 102 1303

1997 M 31 61 54 63 37 71 66 54 63 46 54 66 666

F 22 69 47 52 32 69 67 66 54 37 34 67 616

TOTAL 53 130 101 115 69 140 133 120 117 83 88 133 1282

45

1998 M 33 81 57 63 44 99 51 50 47 20 41 59 645

F 42 72 43 47 40 89 56 62 34 27 54 78 644

TOTAL 75 153 100 110 84 188 107 112 81 47 95 137 1289

1999 M 22 67 53 66 43 71 57 56 55 44 37 62 633

F 19 61 49 55 31 63 69 71 76 36 60 69 659

TOTAL 41 128 102 121 74 134 126 127 131 80 97 131 1292

2000 M 20 77 47 43 53 68 67 66 43 38 69 57 648

F 22 101 39 55 47 65 62 53 33 35 63 51 626

TOTAL 42 178 86 98 100 133 129 119 76 73 132 108 1274

2001 M 23 65 59 66 41 69 68 67 69 52 54 62 695

F 28 51 47 55 38 63 69 68 56 41 37 70 623

TOTAL 51 116 106 121 79 132 137 135 125 93 91 132 1318

2002 M 31 67 54 71 38 73 67 56 63 47 56 68 691

F 37 69 47 59 33 71 69 68 54 39 34 69 649

TOTAL 68 136 101 130 71 144 136 124 117 86 90 137 1340

2003 M 33 77 58 76 40 77 61 56 49 44 37 88 696

F 37 73 44 62 37 75 72 73 71 36 60 61 701

TOTAL 70 150 102 138 77 152 133 129 120 80 97 149 1397

2004 M 39 72 55 73 27 70 69 79 59 44 31 69 687

F 37 81 49 59 31 76 64 66 38 29 53 81 664

TOTAL 76 153 104 132 58 146 133 145 97 73 84 150 1351

2005 M 33 83 57 69 46 101 59 53 49 20 43 59 672

F 42 77 43 49 41 89 56 67 34 27 59 76 660

TOTAL 75 160 100 118 87 190 115 120 83 47 102 135 1332

2006 M 27 85 54 61 51 89 55 63 45 27 53 83 693

F 39 96 41 57 37 69 47 39 27 31 33 52 568

TOTAL 66 181 95 118 88 158 102 102 72 58 86 135 1261

2007 M 37 100 47 55 69 105 67 58 41 36 69 78 762

F 26 91 39 55 66 84 62 37 33 35 38 41 607

TOTAL 63 191 86 110 135 189 129 95 74 71 107 119 1369

2008 M 41 98 33 62 63 80 56 62 50 38 43 68 694

46

F 19 87 44 57 55 51 44 41 44 33 27 34 536

TOTAL 60 185 77 119 118 131 100 103 94 71 70 102 1230

2009 M 33 71 49 62 61 66 51 67 35 33 44 61 633

F 37 61 38 57 49 66 44 44 41 37 28 37 539

TOTAL 70 132 87 119 110 132 95 111 76 70 72 98 1172

2010 M 41 72 45 53 57 72 58 63 38 28 33 62 622

F 33 86 61 41 51 53 62 51 37 37 21 47 580

TOTAL 74 158 106 94 108 125 120 114 75 65 54 109 1202

Data source: Mulago Hospital Records Department

47

APPENDIX 2: UNDER FIVE MORTALITY BY CAUSE 1990-2010

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

DYSENTRY 55 71 65 60 76 68 70 48 45 38 47

MEASLES 55 42 31 27 23 12 6 7 9 12 10

TETANUS 77 57 55 42 57 47 41 39 32 38 49

AIDS 71 71 69 61 66 61 55 61 57 51 46

DIARRHOEA 206 212 201 197 183 173 172 159 143 132 121

GENITAL INFECTIONS 37 21 17 13 19 14 11 9 7 4 3

MALARIA 389 333 321 301 298 235 244 256 248 242 230

PNEUMONIA 237 227 211 172 183 153 143 164 172 162 157

RESPIRATORY INFECTIONS 41 37 45 37 58 61 52 41 37 31 21

SEPTICAEMIA 72 72 66 55 55 49 41 52 62 72 67

TUBERCULOSIS 41 36 27 31 27 17 4 9 11 15 22

MENINGITIS 16 7 11 13 17 13 17 11 15 21 27

DEHYDRATION 55 48 37 32 44 41 36 28 31 34 45

ANAEMIA 85 79 71 67 55 52 53 47 51 53 61

ASTHMA 61 57 48 39 27 26 22 18 22 24 18

ORAL DISEASE 47 31 22 24 16 11 7 5 3 6 3

DIABETES MELLITUS 88 47 40 31 29 27 22 21 27 30 34

ENDOCRINE AND METABOLIC

DISORDER 76 48 26 37 33 21 49 44 47 51 55

CARDIOVASCULAR DISEASES 65 72 66 58 52 47 51 55 51 47 37

NERVOUS SYSTEM DISORDER 41 55 41 36 37 31 29 27 29 33 32

KWASHIOKOR 91 82 53 71 66 74 61 74 65 71 61

MARASMUS 77 73 49 55 47 57 60 55 66 63 66

INJURIES 79 67 51 45 56 66 57 52 59 62 62

TOTAL 2062 1845 1623 1504 1524 1356 1303 1282 1289 1292 1274

48

UNDER- FIVE MORTALITY BY CAUSE 2001-2010 continued……

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

DYSENTRY 30 27 22 26 22 18 17 13 10 14

MEASLES 8 7 15 15 13 11 9 3 5 2

TETANUS 51 38 44 31 28 21 20 16 18 11

AIDS 46 33 57 51 55 44 39 47 35 51

DIARRHOEA 139 137 121 135 147 135 137 110 105 111

GENITAL INFECTIONS 0 9 10 11 7 2 0 2 0 3

MALARIA 267 277 287 271 284 267 289 226 230 237

PNEUMONIA 155 171 169 163 141 163 193 201 197 183

RESPIRATORY INFECTIONS 17 13 17 11 9 12 19 11 11 22

SEPTICAEMIA 72 71 55 41 37 45 67 55 51 62

TUBERCULOSIS 21 25 13 15 11 13 8 4 7 9

MENINGITIS 23 15 22 27 21 14 24 21 17 21

DEHYDRATION 41 41 53 45 50 51 66 71 63 59

ANAEMIA 68 65 74 61 77 87 83 79 72 66

ASTHMA 13 9 11 7 5 1 3 0 1 0

ORAL DISEASE 9 5 7 11 7 3 4 1 1 3

DIABETES MELLITUS 38 47 40 37 33 23 21 17 15 11

ENDOCRINE AND METABOLIC

DISORDER 52 61 69 73 61 59 53 48 55 59

CARDIOVASCULAR DISEASES 33 37 47 44 39 31 45 36 38 33

NERVOUS SYSTEM DISORDER 31 39 43 43 51 66 63 54 41 49

KWASHIOKOR 60 61 73 99 91 89 93 98 82 88

MARASMUS 71 75 79 63 77 73 71 77 63 67

INJURIES 73 77 69 71 66 33 45 40 55 41

TOTAL 1318 1340 1397 1351 1332 1261 1369 1230 1172 1202

Data source: Mulago Hospital Records Department