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    Time series analysis of under five mortality in mulago hospital... by me Time series analysis of under five mortality in mulago hospital... by me Document Transcript

    • TIME SERIES ANALYSIS OF UNDER-FIVE MORTALITY IN MULAGO HOSPITAL (1990-2010) BY OKUDA BONIFACE 09/U/3224/PS 209004160A 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
    • DECLARATIONI Okuda Boniface affirm that this proposal is entirely my original work and has not beenpresented for any award of a degree in any institution of higher learning unless otherwise cited.………………………………… ………………………………..,.Signature DateThis proposal has been submitted with my approval as a University Supervisor.………………………………………… ……………………………………….Mr. Odur Benard DateLecturerSSAE, Makerere University Kampala
    • DEDICATIONThis 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 andmy friends for the support. ii
    • ACKNOWLEDGEMENTSpecial thanks to the almighty God for the special help and guidance. I am deeply indebted tosome individuals whose contributions made it possible to reach a successful completion of thisdissertation.My utmost gratitude goes to my supervisor, Mr. Odur Bernard for his tireless effort in readingand providing relevant comments and corrections that have enabled me produce this researchproject.Finaly special thanks goes to my father, mother and friends for all invaluable contributions bothfinancially and morally especially during this time. iii
    • TABLEOFCONTENTSDECLARATION ............................................................................................................................ iDEDICATION ............................................................................................................................... iiACKNOWLEDGEMENT ........................................................................................................... iiiLIST OF TABLES ....................................................................................................................... viiLIST OF FIGURES ................................................................................................................... viiiACCRONYMS ............................................................................................................................. ixDEFINITIONS AND CONCEPTS .............................................................................................. xABSTRACT ................................................................................................................................. xiiCHAPTER ONE: BACKGROUND ............................................................................................ 11.1 Introduction ............................................................................................................................... 11.2 Previous trends of child mortality ............................................................................................. 21.3 Problem statement ..................................................................................................................... 31.4 Objectives ................................................................................................................................. 41.5 Hypotheses ................................................................................................................................ 41.6 Significance of the study ........................................................................................................... 51.7 Scope of the Study .................................................................................................................... 51.8 Limitation of the study .............................................................................................................. 5CHAPTER TWO: LITERATURE REVIEWS .......................................................................... 62.1 Introduction ............................................................................................................................... 62.2 demographic factors .................................................................................................................. 62.2.1 Sex of the child ...................................................................................................................... 62.2.2 Season .................................................................................................................................... 72.3 Infectious diseases and under-five mortality ............................................................................ 7 iv
    • 2.3.1 Malaria and under-five mortality ........................................................................................... 72.3.2 Tuberculosis and under-five mortality ................................................................................... 82.3.3 Tetanus and under-five mortality ........................................................................................... 92.3.4 Measles and under-five mortality ........................................................................................ 102.3.5 Pneumonia and under-five mortality.................................................................................... 102.3.6 HIV/ AIDS and under-five mortality ....................................................................................112.4 Forecasting model ................................................................................................................... 12CHAPTER THREE: METHODOLOGY ................................................................................. 143.1 Introduction ............................................................................................................................. 143.2 Sources and nature of data to be used ..................................................................................... 143.3 Techniques of data collection .................................................................................................. 143.4 Analysis software .................................................................................................................... 143.5 Data processing and analysis .................................................................................................. 143.5.1 Time series analysis ............................................................................................................. 143.5.2 Data exploration techniques................................................................................................. 153.5.3 Autoregressive Integrated Moving Average (ARIMA) ........................................................ 193.6 Ethical considerations ............................................................................................................. 20CHAPTER FOUR: DATA PRESENTATION AND ANALYSIS OF RESULTS ................... 214.1 Introduction ............................................................................................................................. 214.2. Graphical presentation of findings ......................................................................................... 214.3 Testing for stationarity in the mortality series ........................................................................ 274.4 Estimation of the model .......................................................................................................... 294.5 Diagnostic test ......................................................................................................................... 294.6: Forecasts of under-five mortality (2011Q1-2015Q4) ............................................................ 31 v
    • 4.7 Test of hypotheses ................................................................................................................... 314.7.1 Testing for death differentials by gender ............................................................................. 314.7.2 Testing for death differentials by year ................................................................................. 324.7.3 Testing for death differentials by disease ............................................................................. 334.7.4 Trend analysis of mortality series ........................................................................................ 334.7.5 Trend analysis of mortality series by gender ....................................................................... 344.7.6 Test for seasonality............................................................................................................... 344.8 Discussion ............................................................................................................................... 354.8.1 Child sex and under-five mortality ...................................................................................... 354.8.2 Seasonality and Under-five mortality .................................................................................. 364.8.3 Trend in under-five mortality ............................................................................................... 364.8.4 Infectious Diseases and Under-five mortality...................................................................... 37CHAPTER FIVE: SUMMARY OF FINDINGS, CONCLUSIONS AND RECOMMENDATIONS ........................................................................... 385.1 Introduction ............................................................................................................................. 385.2 Summary of the findings ......................................................................................................... 385.3 Conclusions ............................................................................................................................. 395.4 Recommendations ................................................................................................................... 395.5 Areas for further studies .......................................................................................................... 40REFFERENCES ......................................................................................................................... 41APPENDICES ............................................................................................................................. 44 vi
    • LIST OF TABLESTable 4. 1: Unit Root Test ............................................................................................................. 27Table 4. 2: Correlogram of Mortality Series ................................................................................. 28Table 4. 3: Autoregressive Moving Average Model (9, 0, 0) ........................................................ 29Table 4. 4: Model Description ...................................................................................................... 31Table 4. 5: Forecasted Mortality Values ........................................................................................ 31Table 4. 6: Death Differential by Gender ...................................................................................... 32Table 4. 7: Death Differential by Year (Period) ............................................................................ 32Table 4. 8: Death Differential by Disease ..................................................................................... 33Table 4. 9: Runs Test on Mortality Series ..................................................................................... 33Table 4. 10: Runs Test on Mortality Series by Gender ................................................................. 34Table 4. 11: Kruskal-Wallis Test for Seasonality .......................................................................... 35 vii
    • LIST OF FIGURESFigure 4. 1: Trend in Under-Five Mortality by Gender from 1990-2010 ..................................... 21Figure 4. 2: Percentage Distribution of Under-Five Mortality by Disease (1990-2010) .............. 22Figure 4. 3: Percentage Distribution of Under-Five Mortality for Each Month 1990-2010 ........ 23Figure 4. 4: General Trend in Mortality Series ............................................................................. 24Figure 4. 5: Variations in Mortality Series 2005-2010 by Quarters .............................................. 25Figure 4. 6: Causes of Under-Five Mortality for the Period 1990-2010 ...................................... 26Figure 4. 7: Bartlett‟s Test for White Noise for Under-five mortality .......................................... 30 viii
    • ACCRONYMSUDHS Uganda Demographic Health SurveyWHO World Health OrganisationUNICEF United Nations International Children‟s Emergency FundUN United NationsCHERG Child Health Epidemiology Reference GroupMDGs Millenium Development GoalsPEAP Poverty Eradication Action PlanHIV Human Immune VirusAIDS Acquired Immune Deficiency SyndromeNGOs Non Government OrganisationsMOH Ministry Of Health ix
    • DEFINITIONS AND CONCEPTSAdequate compilation and measurement of vital events requires that the concepts used be givenformal definitions even though the meaning of these concepts may appear as obvious to mostpeople.Hospital: This is a residential establishment which provides short and long term medical careconsisting of observational and rehabilitative service to persons suffering from diseases orsuspected to be suffering from an injury.Health: The World Health Organisation (WHO) defined health in 1948 as a „state of completephysical, 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 ofconception irrespective of the duration of the pregnancy, after which it shows evidence of lifesuch 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 probablyhas a specific cause and identifiable symptoms.Types of diseasesDiseases are classified according to the following, though a great deal of overlapping may befound 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 x
    • 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 disordersMortality: This is the risk of dying in a given year, measured by the death rate which is thenumber of deaths occurring per 100,000 people in a population.Neonatal mortality: the probability of dying within the first month of lifeInfant mortality: the probability of dying between birth and the first birthdayPost neonatal mortality: the arithmetic difference between infant and neonatal mortalityChild mortality: the probability of dying between exact age one and the fifth birthUnder-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. xi
    • ABSTRACTThe purpose of the current study was to carry out a time series analysis of under-five mortality inMulago hospital for the period of 1990-2010 with specific objectives of; establishing whetherthere is trend in the mortality series over the time period, investigating the occurrence ofseasonality in the mortality series, to analyse mortality differences in terms of sex, cause andperiod 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 byPneumonia and Diarrhoea with 12.68% and 10.80% respectively. Genital infection and oraldisease 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 therecorded period of 1990-2010. Under-five mortality was also found to vary by gender, period andsex, where the male deaths were higher than the female deaths. Run‟s test also revealed that themortality series did not exhibit any trend over the period of study. Whereas the mortality series ofthe 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 wererecorded in the month of June, February, December, July and August and the least in January andOctober. There was also a general reduction in mortality causes where causes due to measles andtetanus had the least deaths in 2010.The study therefore recommended political awareness, commitment and leadership that areneeded to ensure that child health receives the attention and resources needed to accelerateprogress towards MDG4, consistent use of treated mosquito nets for malaria prevention andenhancing workers‟ skills through workshops. This would increase survival rates of children whovisit health units. xii
    • CHAPTER ONE BACKGROUND1.1 IntroductionInfant and child mortality levels in Sub-Saharan Africa are the highest in the world. In themedian African country, more than 15 of 100 children die before their fifth birthday (Jameson etal., 2006). This compares to less than 25 out of 1,000 in the richer parts of the world. Not onlyare under-five mortality levels very high; in addition, progress in reducing child mortality is veryslow. 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 1990and in 2006, the average in developing countries was 79 (down from 103 in 1990), whereas theaverage in industrialized countries was 6 (down from 10 in 1990) (UNICEF press release, 2011).A child in Sierra Leone, which has the worlds highest child mortality rate 262 in 2007 (UNICEFpress release September, 12, 2010) is about 87 times more likely to die than one born in Swedenwith a rate of 3 (UNICEF Sweden statistics, 2010).According to the World Health Organization, 2008 questions and answer archives, the maincauses of child death are pneumonia, diarrhea, malaria, measles, and HIV. Malnutrition isestimated to contribute to more than one third of all child deaths in that 1 child dies every 5seconds as a result of hunger ,700 every hour, 16 000 each day, 6 million each year (2002-2008estimates Jacques Diouf). One in eight children in Sub-Saharan Africa dies before their fifthbirthday (UNICEF 2010). The biggest improvement between 1990 and 2006 was in LatinAmerica and the Caribbean, which cut their child mortality rates by 50% (UNICEF state of theworld‟s children report, 2008).Child mortality was an important indicator of the successful implementation of the PovertyEradication Action Plan (PEAP) in Uganda, and for good reasons, the level of child mortality is aconsequence 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 childmortality include household incomes, HIV/AIDS, gender disparities, cultural practices andnutrition, all of which can be influenced by Government. Child mortality is therefore an 1
    • important health issue, but it must be stressed from the beginning that the health sector is not theonly sector responsible for the child mortality outcome.Statistics from the Uganda Demographic and Health Survey (UDHS, 2006) reveal decliningtrends in the levels of infant, under-five and maternal mortality. Between 2000 and 2005 infantmortality decreased from 98 to 76 deaths per 1,000 births. This means that one in every 13newborn Ugandan die within the first year of life. During the same period, under-five mortalityincreased 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 per1000 in the more developed world, 50 deaths per 1000 in the developing world and 76 deaths per1000 in Uganda.The World Bank policy study 2010 indicates that the highest rates of child mortality continue tobe in the Sub-Saharan Africa, where 1 child in 8 dies before age five that is nearly 20 times theaverage of 1 in 167 for developed regions. Southern Asia has the second highest rates, with about1 child in 14 children dying before age five.1.2 Previous trends of child mortalityThe global under-five mortality rate has declined by a third, from 89 deaths per 1,000 live birthsin 1990 to 60 in 2009 (World Bank policy statement report, 2010). This report also highlightsthat 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) thatis nearly double the average in developing regions (66 deaths per 1,000 live births) and nearly 20times the average in developed regions (6 deaths per 1,000 live births). For sub-Saharan Africaas a whole there has been a decline in U5MR concentrated largely in the period between 1965and 1990, during which the median U5MR dropped from 232 t o 170 per 1000. Since 1990, thetrend seems to have stalled. The pattern of this overall trend also characterizes each region, 2
    • though at different levels and speeds. The countries of the West region had the highest U5MR in1960, with a median value around 290 per 1000 live births. This level fell Below 200 per 1000by 1985, a level similar to that of the Middle region, which had a median around 260 per 1000 in1960. The East region median oscillated around 200 per 1,000 prior to 1975 before declining to170 per 1000 in 1990. The Southern Region had the lowest median U5MR in 1960 (around 200per 1000) and experienced the sharpest decline to about 60 per 1000 by 1990. Declines appear tohave stalled in all regions in the 1990s. The West and Southern regions thus experienced thefastest declines from 1960 t o 1990, with the countries of t he Middle and East regions showingthe slowest improvement.In Uganda, Child mortality fell significantly between 1948 and 1970 as a result of politicalstability, high economic growth, and increased access to health care and scientific progresswhich, amongst others, increased access to vaccines against immunizable diseases. Uganda‟shealth 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 andconflict, severely limited access to health services, and a consequent stagnation in infantmortality was observed. The recovery period of 1986-1995 with high economic growth, politicalstability and poverty reduction under the NRM Government, produced a reduction in childmortality (MFPED, 2002).1.3 Problem statement7.6 million Children under age five died in 2010, representing an under-five mortality rate of57/1000 live births (WHO, 2011). Unlike in the developed countries where death rarely occursamong infants and children, in developing countries like Uganda, it is estimated that on average50% 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 thebiggest killers of young children today. Pneumonia, measles, diarrhea, malaria, HIV and AIDSand complications during pregnancy and after birth to mention but a few cause more than 90% ofdeaths in children under five (WHO, 2010). Children who are malnourished are at far greater riskof dying from these causes because they have low immunity. 3
    • The increasing focus on the reduction of child mortality arising from the Millennium Declarationand from the Millennium Development Goal (MDG) 4 of “reducing by two-thirds, between 1990and 2015, the under-five mortality rate”, has generated renewed interest in the development ofmore 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 becomecrucial if MDG 4 is to be achieved; thus a more accurate establishment of the causes of deaths inchildren aged less than 5 years becomes crucial.Although various studies have been conducted about under-five mortality in the country, notmuch has been done in Mulago concerning the documentation of trends, seasonality andmortality by sex and cause of death hence the research would like to find out the behavior ofmortality rates over time and the specific causes of these deaths.1.4 ObjectivesThe chief purpose of this study is to carry out a time series analysis of under-five mortality inMulago Hospital for the period 1990-2010.Other objectives may include the following;1. To establish if there is trend in under five mortality from 1990-20102. To investigate whether there is seasonality in the recorded figures from 1990-20103. To analyze death differentials by sex, year & diagnosis4. To make predictions for under five mortality5. To assess Cause reductions of under-five mortality overtime1.5 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 4
    • 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 StudyUnder-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 studyThere was a problem of extracting huge amount of data from the record files since Mulagohospital does not have a Hospital information management system. This took a lot of time for theresearcher. 5
    • CHAPTER TWO LITERATURE REVIEWS2.1 IntroductionIn Uganda, according to UNICEF (2009), the causes of childhood morbidity and mortality likeelsewhere in Sub-Saharan Africa were malaria, diarrhoea, measles and acute respiratoryinfections. In most recent years Acquired Immune Deficiency Syndrome (AIDS) has also joinedin as a major risk to women and children.Despite droughts, natural disasters and famine, mortality appears to have fallen in all parts ofAfrica 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 Ghanaover 30 years in the late 1930s and 1960s (from 37%-20%); in Congo over 20 years between the1940s and the 1960s(from 29%-15%) and in Kenya over the 25 years between late 1940s andearly 1970s from 26-15% (UNICEF statistics-Ghana, 2010).According to several studies conducted, age, sex and infectious disease have been found to bemajor factors affecting mortality. But also season of the year play a role in determining mortalitylevels (Kenneth Hill, 1988) hence mortality factors can be broken down into demographic factorsand infectious disease factors.2.2 demographic factors2.2.1 Sex of the childIn the reviewed micro-econometric studies, child characteristics typically show the expectedinfluence on mortality. Boys are often found to be significantly more likely to die than girls andthe 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 importantinfluence 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). 6
    • 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 higherfor boys than it is for girls among low income countries and upper middle and high incomecountries. The pattern seems reversed for lower middle income countries. Similarly, under-fivemortality is higher among boys for most regions of the world except the South East Asia regionwhere it is reversed, and there is little difference among boys and girls in the EasternMediterranean region (WHO, 2010).2.2.2 SeasonAccording to the study by Nyombi in 2000, child deaths have a seasonal pattern occurring morefrequently during certain months of the year. There may exist seasonality in death level amongchildren, that is there are more deaths occurring in a particular time of the year or day due tospecific 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 arerampant causing break down of red blood cells. Cases due to malaria are most predominant inmonths of April, June, July, September, and December, when there is stagnant water, which areused by mosquitoes as breeding places.2.3 Infectious diseases and under-five mortalityPreventable infectious diseases cause two-thirds of child deaths, according to a study publishedby The Lancet in 2011. Experts from the World Health Organization (WHO) and UNICEF‟sChild Health Epidemiology Reference Group (CHERG) assessed data from 193 countries toproduce estimates by country, region and the world. While the number of deaths has declinedglobally over the last decade, the analysis reveals how millions of children under five die everyyear from preventable causes. These causes include;2.3.1 Malaria and under-five mortalityMalaria is a life-threatening disease caused by parasites that are transmitted to people through thebites of infected mosquitoes. In 2010, malaria caused an estimated 655,000 deaths, mostlyamong African children (WHO, 2011). According to the World Health Organization (WHO2011) Malaria is responsible for 10 per cent of all under-five deaths in developing countries. 7
    • According to the world health report (2002), in 1970, there were 3.7 million deaths annually and170 million cases, 88 percent of them in tropical Africa and the disease is endemic in 100countries. The aim of the current global malaria strategy was to reduce mortality at least by 20percent compared to 1995 in at least 75 percent of the countries that would have been affected bythe 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 theworld. In 2000, malaria was the principal cause of around 18% that is 803 000 (uncertainty range710,000 - 896,000) of deaths of children under 5 years of age in Africa south of the Sahara as byRowe 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 areestimated to cause 75 000–200 000 infant deaths per year in Africa south of the Sahara.2.3.2 Tuberculosis and under-five mortalityThere has been a perception, particularly in the industrialized world, that TB is a disease of theold. Fifty years ago, however, hospital services for children today dedicate entire wards forinfants and children with TB. In developing countries where a large proportion of thepopulation is under the age of 15 years, as many as 40 per cent of tuberculosis notificationsmay be children; tuberculosis may be responsible for 10 per cent or more of childhood hospitaladmissions, and 10 per cent or more of hospital deaths.According to the WHO (2008), complacency towards tuberculosis in the three decades ledcontrol programs to be run down in many countries. The result has been a powerful resurgence ofthe disease, now estimated to kill three million people a year, with 7.3 million new casesannually. The WHO declared tuberculosis a global emergency in 1993. About 3 million cases ayear occur in south East Asia and nearly two million in sub Saharan Africa, with 340000 inEurope. One third of the incidence in the last five years can be attributed to HIV infection which 8
    • weakens the immune system and makes the person infected with tubercle bacillus 30 times morelikely to become ill with tuberculosis strains of bacillus resistant to one or more drugs may haveinfected up to 50 million people.Tuberculosis may be responsible for more death worldwide than any other disease caused by anypathogen, Sundre et al, 2000. The incidence of Tuberculosis among children will thereforeincrease in the areas where HIV prevalence is high because HIV negative individuals couldincrease in the areas where HIV prevalence is high because HIV negative individuals couldincrease by 13-14 percent in African countries, depending on the prevalence of tuberculosis andAIDS.2.3.3 Tetanus and under-five mortalityTetanus is a potentially deadly infection that can occur if a baby‟s umbilical cord is cut with anunclean tool or if a harmful substance such as ash or cow dung is applied to the cord, as istraditional practice in some African countries. When tetanus develops, child death rates areextremely high, especially in countries where health systems are not strong and access to moreadvanced 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 availablestudies in Uganda suggest that the rates of 10 to 20 neo-natal tetanus deaths per 1000 live birthare not usual (Kawuma et al., MOH 2000). According to the world health report (2008), tetanusof the newborn is the third killer of children after measles and pertusis among the six EPIvaccine preventable disease and is concern in all WHO regions except Europe. Between 800,000and 1 million newborn a year died from tetanus in the early 1980s. An estimated 730,000 suchdeaths are now preventable every year, particularly by targeting the elimination efforts to highrisk areas. In 1997, there was an estimated 275000 deaths WHO Estimated than 1995, about 90percent of neonatal tetanus cases occurred in only 25 countries of which Uganda was not part. 9
    • 2.3.4 Measles and under-five mortalityMeasles, an acute viral respiratory illness associated with high fever, rashes and vomiting, isconsidered one of the most deadly vaccine-preventable diseases, accounting for an estimated777,000 childhood deaths per year worldwide, with more than half occurring in Africa, accordingto the United Nations Childrens Fund (UNICEF, 2011).Measles is caused by paramyxovirus called morbili. It is highly infectious and transmitted fromperson to person via droplets spread (sneezes, coughs). Cough nasal congestion andconjunctivitis follow the incubation period of approximately 10 to 12 hours. The characteristicrash appears about 2 to 4 days after the onset of other symptoms. Measles is one of the majorcauses of death among children in Africa. Its contributing factor is about 8 to 10% of deathsamong African children. (Ofosu- Amaah, 2003; Rodriguez).Apart from death, children who are affected by measles may suffer from life-long disabilityincluding brain damage, blindness and deafness. In Uganda, Measles deaths reduced from 6,000to 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 childrenagainst killer diseases, measles inclusive. Babies are vaccinated against Measles at the age ofnine months.2.3.5 Pneumonia and under-five mortalityPneumonia is a form of acute respiratory infection that affects the lungs. The lungs are made upof small sacs called alveoli, which fill with air when a healthy person breathes. When anindividual has pneumonia, the alveoli are filled with pus and fluid, which makes breathingpainful and limits oxygen intake.Pneumonia is the single largest cause of death in children worldwide. Every year, it kills anestimated 1.4 million children under the age of five years, accounting for 18% of all deaths ofchildren under five years old worldwide. Pneumonia affects children and families everywhere,but is most prevalent in South Asia and sub-Saharan Africa (WHO, 2011). 10
    • In the early 1970s Cockburn & Assaad generated one of the earliest estimates of the worldwideburden of communicable diseases. In a subsequent review, Bulla & Hitze described thesubstantial burden of acute respiratory infections and, in the following decade, with data from 39countries, Leowski estimated that acute respiratory infections caused 4 million child deaths eachyear – 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 bymodeling the association between all-cause mortality in children aged less than 5 years and theproportion of deaths attributable to acute respiratory infection. Results revealed that betweenone-fifth and one-third of deaths in preschool children was due to or associated with acuterespiratory infection. The 1993 World Development Report produced figures showing that acuterespiratory infection caused 30% of all childhood deaths.2.3.6 HIV/ AIDS and under-five mortalityMore than 1,000 children are newly infected with HIV every day, and of these more than halfwill die as a result of AIDS because of a lack of access to HIV treatment (UNICEF, 2011). Inaddition, over 7.4 million children every year are indirectly affected by the epidemic as a resultof the death and suffering caused in their families and communities.Nine out of ten children infected with HIV were infected through their mother either duringpregnancy, 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 duringpregnancy 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 ofHIV from mother-to-child is relatively rare, and in those cases where it does occur a range oftreatment options mean that the child can survive - often into adulthood. This shows that withfunding, trained staff and resources, the infections and deaths of many thousands of childrencould 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 risingdue to vertical transmission. Since HIV prevalence levels are high and still increasing in many 11
    • 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 relativelyweak.2.4 Forecasting modela) The ARIMA procedureThe ARIMA procedure analyzes and forecasts equally spaced univariate time series data, transferfunction data, and intervention data using the autoregressive Integrated Moving Average(ARIMA) or autoregressive moving-average (ARMA) model. An ARIMA model predicts a valuein a response time series as a linear combination of its own past values, past errors (also calledshocks or innovations), and current and past values of other time series. The ARIMA approachwas 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 wasdiscussed by Box and Tiao (1975). When an ARIMA model includes other time series as inputvariables, the model is sometimes referred to as an ARIMAX model. Pankratz (2001) refers tothe ARIMAX model as dynamic regression. The ARIMA procedure provides a comprehensiveset 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 orinterrupted time series models; multiple regression analysis with ARMA errors; and rationaltransfer function models of any complexity.Meyler (1998) states that the main advantage of ARIMA forecasting is that it require data on thetime series in question only. This feature is advantageous if one is forecasting a large set of timeseries data. This also avoids a problem that occurs in multivariate models since timeliness can bea problem. ARIMA models are unable to capture non linear relationships in time series and thismakes the process of forecasting limited.b) Lee-carter forecasting modelThe method proposed in Lee and Carter (1992) has become the “leading statistical model ofmortality forecasting in the demographic literature” (Deaton and Paxson, 2004). It was used as a 12
    • 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 amethod consistent with it (Lee and Miller, 2001). Lee-Carter approach makes strong assumptionsabout 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-causeand 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 andBoe, 2000; Wilmoth, 1996, 2000). Lee-carter was able to capture non linear relationships in thetime series data whereas ARIMA models were not able to capture non linear relationships. 13
    • CHAPTER THREE METHODOLOGY3.1 IntroductionThis 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 periodof the occurrence of the death.3.2 Sources and nature of data to be usedThe data used is secondary data that was obtained from Mulago referral hospital‟s recordsdepartment office. The data was extracted from the mortuary register.3.3 Techniques of data collectionThe technique used was mainly by observation of the summaries made in the mortuary registerkept in the records department of the hospital.3.4 Analysis softwareData entry was by use of the computer package, Microsoft Excel, and then exported to statisticalpackages like SPSS, STATA, and E-Views for analysis.3.5 Data processing and analysis3.5.1 Time series analysisA time series is a collection of observations of well-defined data items obtained through repeatedmeasurements over time. A basic assumption in any time series analysis is that some aspects ofthe past pattern will continue to remain in the future.Chatfield (1989) observed that time series methods are based on studying past behavior of theseries to make forecasts. 14
    • As an important step in analyzing time series data, the types of data patterns were considered sothat the models most appropriate to the patterns can be utilized. Four components of time seriescan hence be distinguished.i. Trend: This refers to the general direction, either upward or downward in which a series havebeen moving.ii. Cycle: This where the data exhibits a wave like pattern (rises and falls) that are not of fixedperiods.iii. Seasonality: This is concerned with periodic fluctuations that recur on a regular periodicbasis.iv. Irregular term: This is the movement left when Trend, Seasonality and Cyclic componentshave been accounted for.The analysis however concentrated on Trend and Seasonality.Assuming a multiplicative model, then 𝑌𝑡=𝑇 𝑡 ∗𝑆 𝑡Where 𝑌𝑡 is the mortality series, 𝑇 𝑡 is Trend and 𝑆 𝑡 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; 15
    •  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. 16
    • 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 i th 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 𝑚 (𝑚 −1) 𝑆 𝑅= 2𝑚 −1 𝑅−µ 𝑅 Z= 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 17
    • been taken up by Hewitt et al (2002). They did not use a binary indicator as suggested byEdwards but all the ranking information. Rogerson (2000) made a first step to generalizethis test, relaxing the relatively strict assumption of Hewitt et al.(2000) that seasonality isonly 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 forthe parametric one-way analysis of variance test, if there are two or more independentgroups to compare (Siegel & Castellan 1988). Barker et al. (2006), for example, foundwith the Kruskal-Wallis test that significant seasonal and monthly variations in meandaily 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 betweenfemale parasuicides and „hot‟, „still‟, „still/hot‟ days as well as between male parasuicidesand „windy‟ days.The test is described as below;Ho: the series has no seasonalityHa: the series has seasonality 2Test statistics, H to compare with 𝑋∝ (Chi square) 12 𝑘 𝑅2𝑖H= 𝑁(𝑁+1) 𝑖=1 𝑛 − 3(𝑁 + 1) 𝑖ni is the number of observations in the ith seasonN is the total number of specific seasonsRi= 𝑟𝑎𝑛𝑘 (𝑦 𝑖 )Yi is the specific season for time t.Critical region 2Reject Ho if H>𝑋∝(𝑖−1) 18
    • 3.5.3 Autoregressive Integrated Moving Average (ARIMA)This is also known as the Box-Jenkins model. This methodology will be used to forecast theunder-five mortality rates. The model is based on the assumption that the time series involved arestationary. Stationarity will first be checked and if not found, the series will be differenced dtimes to make it stationary and then the Autoregressive Moving Average (ARMA) (p, q) will beapplied.The ARIMA procedure provides a comprehensive set of tools for univariate time series modelidentification, parameter estimation, and forecasting, and it offers great flexibility in the kinds ofARIMA models that can be analyzed. The ARIMA procedure supports seasonal, subset, andfactored ARIMA models; intervention or interrupted time series models; multiple regressionanalysis with ARMA errors; and rational transfer function models of any complexity.The Box-Jenkins methodology has four steps that will be followed when forecasting themortality rates as stipulated below;i. Identification. This involves finding out the values of p, d, and q where;p is the number of autoregressive termsd is the number of times the series is differencedq is the number of moving average termsThe identification here will be done basing on the correlogram plot obtained. Where bothautocorrelation and partial correlation cuts of at a certain point, we conclude that the data followsan autoregressive model. The order p, of the ARIMA model is obtained by identifying thenumber of lags moving in the same direction. In case the series was non stationary, the numberof 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 Movingaverage terms in the model. The non linear estimation will be used.iii. Diagnostic checking. Having chosen a particular ARIMA model, and having estimated itsparameters, we now examine whether the chosen model fits the data reasonably well. The simple 19
    • test of the chosen model will be done to see if the residuals estimated from this model are whitenoise. If they are, we can accept the particular fit and if not, the model will have to be startedover.iv. Forecasting.Exponential smoothing methods will be used for making forecasts. While exponential smoothingmethods do not make any assumptions about correlations between successive values of the timeseries, in some cases you can make a better predictive model by taking correlations in the datainto account. Autoregressive Integrated Moving Average (ARIMA) models include an explicitstatistical model for the irregular component of a time series that allows for non-zeroautocorrelations in the irregular component.3.6 Ethical considerationsEthics in research refer to considerations taken to protect and respect the rights and well fare ofparticipants and other parties associated with the activity (Reynolds 2001). The rights of partiesinvolved at every stage of this study were treated with utmost care. The following considerationswere made to promote and protect the rights and interests of participants at the different stages ofthe study.During Data collection: steps taken to protect the rights of participants during actual datacollection included securing informed written consent by the head of department notifying themanagement of Mulago hospital about my study and to grant me permission to collect data fromthe hospital.During analysis and reporting of findings: the investigation made sure to report modestly andexactly what the findings were, without exaggerations that would create false impressions. In thesame respect, the database was created honestly using SPSS programme without any distortions. 20
    • CHAPTER FOUR DATA PRESENTATION AND ANALYSIS OF RESULTS4.1 IntroductionThis chapter presents key findings on the trend of under-five mortality. It presents bothdescriptive and inferential analysis of the relationship between variables. The choice of thedifferent test statistic used depended on the hypothesis to be tested. The data was obtained fromthe records department of Mulago hospital and directly entered into Microsoft Excel from whichit was exported to SPSS, STATA and E-VIEWS for analysis4.2. Graphical presentation of findingsFigure 4. 1: A Line Graph Showing the Trend of Under-Five Mortality by Gender from 1990-2010 1200 1000 total number of deaths 800 600 male 400 female 200 0 1991 1999 1990 1992 1993 1994 1995 1996 1997 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 yearFrom the Figure 4.1 above, the mortality series of both male and female showed a downwardtrend between 1990 and 1995. However, the rate of decline for the female is greater than that ofthe male. For the male under-five the rate of decline is about 30.6% whereas for the femaleunder-five, the rate is 38.1%. However between 1995 and 2010, the series exhibited stationaritywith a rate of decline of only 6.7% and 8.8% for the female and male under-five respectively. 21
    • Variations in deaths by gender can also be observed in that the number of male deaths recordedremained higher than that of female children except in 2003 where a total of 701 female deathswere recorded against 633male deaths.Figure 4. 2: A Bar Graph Showing Percentage Distribution of Under-Five Mortality by Disease (1990-2010) 25.00 percenatage of deaths 19.41% 20.00 15.00 12.68% 10.80% 10.00 5.48% 4.81% 4.73%4.19% 3.85% 4.17% 5.00 2.88% 2.78% 3.32% 2.32% 3.68% 3.37% 2.98% 2.06% 1.10% 1.25% 1.28% 1.41% 0.68% 0.77% 0.00 Nervous system disorder Cardiovascular disease Resoiratory infection Genital infection Diabetes Mellitus Oral Disease Tuberculosis Dehydration Kwashiorkor Septicaemia Meningitis Pneumonia Diarrhoea Dysentry Marasmus Injuries Measles Tetanus Malaria Anaemia Asthma Aids DISEASESFrom Figure 4.2 above, Malaria accounted for most of the deaths (19.41%) for the period 1990to 2010 followed by Pneumonia and Diarrhoea with 12.68% and 10.80% respectively. Genitalinfection and oral disease accounted for the least number of deaths recorded with 0.68% and0.77% respectively. The high number of deaths due to malaria can be attributed to the rainyseason that cause a lot of stagnant water which acts as breeding places for mosquitoes since mostdeaths were recorded in June, February, December, July and august of which these months arefaced with heavy rains at times. 22
    • Figure 4. 3: A Bar Graph Showing Percentage of Under-Five Mortality Recorded for Each Month for the Period 1990-2010 12.00 10.71% 10.73% 10.00 9.36% 9.13% 9.03% 8.85% 8.00 7.70% 7.76% 7.50% 7.48% percentage deaths 6.34% 6.00 5.36% 4.00 2.00 0.00 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec monthsFigure 4.3 above shows that most deaths were recorded in June, February, December, July andAugust of which each accounted for 10.73%, 10.71%, 9.36%, 9.13% and 9.03% of the totaldeaths respectively. The least number of deaths were recorded in the months of January andOctober accounting for only 5.36% and 6.34% of the total number of deaths recorded for theperiod 1990 to 2010. 23
    • Figure 4. 4: General Trend in Under-five Mortality (1990-2010) 2500 2000 deaths 1500 1000 total number of 500 0 1996 1990 1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 yearFrom Figure 4.4 above, Mulago Hospital recorded the highest number of child deaths in 1990with 2062 deaths and the lowest in 2009 with 1172 deaths. The mortality series exhibited adownward trend between 1990 and 1995 but after wards the series remained almost constantover the remaining years. The down ward trend between 1990 and 1995 can be attributed to thegovernment constant effort to improve child health care over the years through provision ofbetter health facilities and increased number of health workers. The period of 1990-1995according to (MFPED, 2002) was characterized with high economic growth, political stabilityand poverty reduction under the NRM Government. Between 1996 and 2007, the series wasstationary and this can be attributed to the non improving health facility standards and inadequatebudget provisions for the health sector. 24
    • Figure 4. 5: Line Graph showing Variations in Mortality Series 2005-2010 by Quarters 500 450 400 350 300 250 total number of deaths 200 150 deaths 100 50 0 Q1 2006 Q3 2007 Q1 2009 Q1 2005 Q2 2005 Q3 2005 Q4 2005 Q2 2006 Q3 2006 Q4 2006 Q1 2007 Q2 2007 Q4 2007 Q1 2008 Q2 2008 Q3 2008 Q4 2008 Q2 2009 Q3 2009 Q4 2009 Q1 2010 Q2 2010 Q3 2010 Q4 2010 yearFrom figure 4.5 above, the mortality series varied between different months of the year. Theseries indicated consistently high number of deaths for the second quarter and first quarter of theyear as seen above. This can be attributed to the rainy season in the first and second quarter ofthe year that cause a lot of stagnant water which acts as breeding places for mosquitoes whichcause malaria and lead to death of children with weak immune systems. 25
    • Figure 4. 6: A Line Graph Showing Various Causes of Under-Five Mortality for the Period 1990-2010 450 400 350 300 250 MEASLES deaths TETANUS 200 AIDS MALARIA 150 PNEUMONIA ANAEMIA 100 50 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 yearFrom 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. Tetanusand measles according to WHO report 2011 was declared nonexistent in Uganda althoughmalaria still remains a big challenge. The general downward trend for the period of 1990-2010can be attributed to improved health facilities and the government effort to achieve the MDG 4. 26
    • 4.3 Testing for stationarity in the mortality seriesBefore 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 overa given period of time. The test hypotheses are stated below;Ho: mortality series is stationaryHa: mortality series is not stationaryTable 4. 1: Unit Root Test for Under-five mortalityNull Hypothesis: TOTAL has a unit rootExogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=4) t-Statistic Prob.*Augmented Dickey-Fuller test statistic -5.287243 0.0004Test critical values: 1% level -3.808546 5% level -3.020686 10% level -2.650413*MacKinnon (1996) one-sided p-values.Augmented Dickey-Fuller Test EquationDependent Variable: D(TOTAL)Method: Least SquaresDate: 04/27/12 Time: 08:49Sample (adjusted): 1991 2010Included observations: 20 after adjustmentsVariable Coefficient Std. Error t-Statistic Prob.TOTAL(-1) -0.323773 0.061237 -5.287243 0.0001C 412.2893 87.07992 4.734608 0.0002R-squared 0.608312 Mean dependent var -43.00000Adjusted R-squared 0.586551 S.D. dependent var 90.10111S.E. of regression 57.93498 Akaike info criterion 11.05116Sum squared resid 60416.32 Schwarz criterion 11.15073Log likelihood -108.5116 Hannan-Quinn criter. 11.07060F-statistic 27.95493 Durbin-Watson stat 2.407887Prob (F-statistic) 0.000050According to Table 4.1 above, the Dickey-Fuller Unit root test on the original series shows thatthe series is stationary since the absolute value for the combined test statistics (5.287243) isgreater than the three test statistics at 1%, 5%, and 10% critical values 3.808546, 3.020686, 27
    • 2.650413 respectively. Since the series is stationary, we now obtain the Autoregressive MovingAverage (p, q). Here the chief tools of model identification are the autocorrelation function(ACF) and the Partial Autocorrelation Function (PAF) and there corresponding correlogramplots.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 offafter a certain point hence we can say that the data follows an autoregressive model. The order ofthe ARIMA model is now obtained by identifying the number of lags moving in the samedirection. By counting the lags moving in the same direction, we obtain 9 lags. Hence it‟s AR(9). 28
    • 4.4 Estimation of the modelTable 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 thatthe deaths in the previous quarter can significantly determine the deaths in the current quarter.4.5 Diagnostic testIn order to check whether the model was good for the data, Bartlett‟s white noise test was carriedout. The residuals generated were plotted using a cumulative periodogram white Noise test. 29
    • Figure 4. 7: Bartlett’s Test for White NoiseAs presented in Figure 4.7, using the periodogram white noise test for goodness of fit of themodel to the data, the researcher found that the model best fits the data since almost all valuesappeared 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 theoriginal series. This is done in order to find out whether the model is good for the data. 30
    • 4.6: Forecasts of under-five mortality (2011Q1-2015Q4)Table 4. 4: Model Description Model TypeModel ID Mortality Model_1 forecast 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 1964.7 Test of hypotheses4.7.1 Testing for death differentials by genderA paired sample t-test was conducted to find out whether on average the male deaths and femaledeaths are significantly different and the output is displayed below.Ho: more male children die than female childrenHa: the number of deaths is the same between the sexes 31
    • Table 4. 6: Death Differential by Gender Paired Differences 95% Confidence Interval of the Difference Std. Std. Error Mean Deviation Mean Lower Upper t df Sig.Pair 1 male - female 58.000 50.444 11.008 35.038 80.962 5.269 20 0.000From Table 4.6 above, it is revealed that the means of the male and female death figures have aprobability value of 0.000 which is less than 0.05. This implies that if 100 similar studies werecarried out under the same conditions, all of them would show that there is a significantdifference between the male mortality and female mortality. The null hypothesis is thereforerejected and it‟s concluded that on average, more male children died than female children.4.7.2 Testing for death differentials by yearHo: mortality in the different years studied differHa: mortality in the different years studied is the sameTable 4. 7: Death Differential by Year (Period) Test Value = 0 (one sample t-test) 95% Confidence Interval of the Difference t df Sig. Mean Difference Lower Uppertotal 29.601 20 0.000 1396.476 1298.07 1494.89It is revealed from Table 4.7 above that the mean deaths of the years 1990-2010 are significantlydifferent with a probability value of 0.000 which is less than 0.05. This implies that if 100 similarstudies were carried out under the same conditions, all of them would show that there is asignificant difference in the deaths over the period of 1990-2010. This leads to the rejection ofthe null hypothesis and a conclusion is made that on average the mean deaths in the yearsconsidered are significantly different. 32
    • 4.7.3 Testing for death differentials by diseaseHo: under-five deaths due to the different diseases differHa: under-five deaths due to the different diseases is the sameTable 4. 8: Death Differential by Disease One sample test 95% Confidence Interval of the Mean Difference t df Sig. Difference Lower Upperdeaths 4.758 22 0.000 1271.304 717.16 1825.45From Table 4.8 above, it is established that means between figures of disease give a combinedsignificance value of 0.000 which is less than 0.05. This implies that if 100 similar studies werecarried out under the same conditions, all of them would show that there is a significantdifference in the number of deaths due to the different diseases. The null hypothesis is thusrejected and a conclusion is made with 95% confidence that on average, deaths due to thedifferent diseases vary.4.7.4 Trend analysis of mortality seriesRun‟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 trendedHa: the mortality series is trendedTable 4. 9: Runs Test forUnder-five Mortality ( 1990-2010) DEATHS aTest Value 334Cases < Test Value 42Cases >= Test Value 42Total Cases 84Number of Runs 34Z -1.976Asymp. Sig. 0.055a. Median 33
    • From Table 4.9 above, the probability value (0.055) is greater than 0.05. This implies that if 100similar studies were carried out under the same conditions, about 95 of them would show that theseries exhibited trend. Thus the null hypothesis is not rejected and it is concluded at 95% level ofconfidence that the series did not significantly exhibited trend for the years observed (1990-2010).4.7.5 Trend analysis of mortality series by genderHo: the mortality series of male children is not trendedHa: the mortality series of male children is trendedHo: the mortality series of female children is not trendedHa: the mortality series of female children is trendedTable 4. 10: Runs Test for Under-five Mortality by Gender male femaleTest Valuea 693 644Cases < Test Value 10 10Cases >= Test Value 11 11Total Cases 21 21Number of Runs 8 6Z -1.336 -2.234Asymp. Sig. (2-tailed) 0.82 0.026a. MedianAccording to Table 4.10 above, the probability value of the male mortality series is 0.82 which isgreater than 0.05 and that of female is 0.026 which is less than 0.05. This implies that if 100similar studies were carried out under the same conditions, only 18 of them would show thattrend significantly exists in the male mortality series and 97 of them would show that trendsignificantly exists in the female mortality series.4.7.6 Test for seasonalityThe Kruskal-Wallis test also known as the H-test was used to investigate whether there wasseasonality in the recorded figures from 1990-2010. 34
    • Table 4. 11: Kruskal-Wallis Test for Seasonality quarter Number of Rank sum observations 1 20 606.50 2 20 1182.50 3 21 1030.00 4 20 502.00chi-squared = 27.580 with 3 d.f.probability = 0.0001It is established that the probability value =0.0001 which is less than 0.05 (5% level ofsignificance). The null hypothesis is thus rejected and it is concluded that the series exhibitedseasonality for the periods recorded. This also implies that if 100 similar studies were carried outunder the same conditions, all of them would show that seasonality significantly exists in themortality 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% and9.03% of the total deaths respectively. The least number of deaths were recorded in the monthsof January and October accounting for only 5.36% and 6.34% of the total number of deathsrecorded for the period 1990 to 2010.4.8 DiscussionThe discussion of the key findings has been arranged in relation to the research hypotheses thatwere investigated. The findings are also discussed in reference to the findings from somerelevant previous studies that were either similar or contrary to the findings in the present study.4.8.1 Child sex and under-five mortalityThe study findings revealed that mortality of under-five male children remained higher thanthose 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 significantlymore 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 35
    • and region. In general, under-five mortality is higher for boys than it is for girls among lowincome countries and upper middle and high income countries. The pattern seems reversed forlower middle income countries. Similarly, under-five mortality is higher among boys for mostregions of the world except the South East Asia region where it is reversed, and there is littledifference among boys and girls in the Eastern Mediterranean region.4.8.2 Seasonality and Under-five mortalityAccording to this study, the mortality series in Mulago varied between different months of theyear. The series indicated consistently high number of deaths for the second quarter and firstquarter of the year. According to the study by Nyombi in 2000, child deaths have a seasonalpattern occurring more frequently during certain months of the year. There may exist seasonalityin death level among children, that is there are more deaths occurring in a particular time of theyear or day due to specific diseases being rampant in certain months of the year e.g. cases ofdeath due to anemia, are predominant in dry seasons when there is little vegetables, and alsowhen malaria cases are rampant causing break down of red blood cells.4.8.3 Trend in under-five mortalityThe study findings revealed that there is no trend in under-five mortality (0.055> 0.05). Themortality series exhibited a downward trend between 1990 and 1995 but after wards the seriesremained almost constant over the remaining years. The down ward trend between 1990 and1995 can be attributed to the government constant effort to improve child health care over theyears through provision of better health facilities and increased number of health workers. Childmortality fell significantly between 1948 and 1970 as a result of political stability, high economicgrowth, and increased access to health care and scientific progress which, amongst others,increased access to vaccines against immunizable diseases. Uganda‟s health sector wasconsidered to be one of the best in Africa during this period (Hutchinson, 2001). The recoveryperiod of 1986-1995 with high economic growth, political stability and poverty reduction underthe NRM Government, produced a reduction in child mortality (MFPED, 2002). 36
    • 4.8.4 Infectious Diseases and Under-five mortalityAccording to this study,Malaria accounted for most of the deaths (19.41% ) for the period 1990to 2010 followed by Pneumonia and Diarrhoea with 12.68% and 10.80% respectively. Genitalinfection and oral disease accounted for the least number of deaths recorded with 0.68% and0.77% respectively. The high number of deaths due to malaria can be attributed to the rainyseason that cause a lot of stagnant water which acts as breeding places for mosquitoes since mostdeaths were recorded in June, February, December, July and august of which these months arefaced with heavy rains at times. This is in line with the study of (Nyombi 2000) who also foundout that cases due to malaria is predominant in the months of April, June, July, September andDecember. According to the World Health Organization (WHO 2011) Malaria is responsible for10 per cent of all under-five deaths in developing countries. 37
    • CHAPTER FIVE SUMMARY OF FINDINGS, CONCLUSIONS AND RECOMMENDATIONS5.1 IntroductionThis chapter summarizes the findings, conclusions and recommendations in line with theobjectives of the study. The major objective of the study was to carry out a time series analysis ofunder-five mortality in Mulago Hospital for the period 1990-2010.5.2 Summary of the findingsThis study focused on the behavior of the mortality series for under-five children obtained fromthe records department of Mulago hospital. The study found out that mortality series in Mulagohospital recorded the highest number of child deaths in 1990 and the lowest in 2009. Themortality series exhibited a downward trend between 1990 and 1995 but after wards the seriesremained almost constant over the remaining years. Malaria accounted for most of the deaths forthe period 1990 to 2010 followed by Pneumonia and Diarrhoea. Genital infection and oraldisease accounted for the least number of deaths recorded.The study also revealed seasonality in Under-five mortality (0.0001<0.05). Most deaths wererecorded in June, February, December, July and August. The least number of deaths wererecorded in the months of January and October for the period 1990 to 2010. . It was also revealedthat 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 of2011, 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 2011Uganda was declared free of measles according to the ministry of health report 2011 and WHOreport 2011. 38
    • 5.3 ConclusionsBasing on the findings of this study, it was possible to draw a number of conclusions. It wasinferred from the findings that the mortality series observed over the period 1990-2010 isstationary since the Augmented Dickey-Fuller Test (Unit root) revealed that the series had a unitroot.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 notexhibit any trend for the period 1990-2010.It was also found out that under-five mortality significantly differ by gender. It was found outthat more male children die than the female children. Under-five mortality was also found tovary significantly over the period of study.The study also found out that under-five mortality varies significantly for the different causesobserved over the period 1990-2010. Malaria accounted for most of the deaths observedfollowed 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 andAugust. The least number of deaths were recorded in the months of January and October.5.4 RecommendationsThe study revealed that most under-five deaths are due to infectious diseases. By scaling upeffective health services, the government will be able to ensure that most of the under-fivemortality can be avoided with proven, low-cost preventive care and treatment. Preventive careincludes: continuous breast-feeding, vaccination, adequate nutrition and, the use of insecticidetreated 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. Toreach the majority of children who today do not have access to this care, we need more and 39
    • better trained and equipped health workers. Families and communities need to know how best tobring up their children healthily and deal with sickness when it occurs.The study also revealed stionarity in the mortality series hence political awareness, commitmentand leadership are needed to ensure that child health receives the attention and resources neededto 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 theperiod covered. Therefore, consistency in usage of treated mosquito nets must be encouraged andpresumptive 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 fairratio but more effort such as providing attractive wage for health workers has to be done in orderto increase the number of health workers in the health units.Lastly health ministries should embark on enhancing workers‟ skills through workshops. Thiswould increase survival rates of children who visit health units.5.5 Areas for further studiesThe current study was done in Mulago hospital hence further studies should consider focusing onmortality differences basing on regions and location. For example studies that will be able tocompare mortality in urban and rural areas.Further studies on under-five mortality should consider finding out why more male children diethan 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 forthe forecast of mortality figures in the future.Further studies on mortality should also consider focusing on identification of severaldeterminants of under-five mortality in the country. 40
    • REFFERENCESBulla. A, Hitze, KL (2000), Acute respiratory infections: a review. Bull World Health Organ;56:481-98 pmid: 308414.Cockburn. WC, Assaad. F (2010), Some observations on the communicable diseases as publichealth from http://www.who.int/whosis/whostat/2010/en/index.htmlGarenne. M, Ronsmans. C, Campbell H (2001), The magnitude of mortality from acuterespiratory infections in children under 5 years in developing countries. World Health Stat Q1992; 45: 180-91 pmid: 1462653.Health sector strategic & investment plan (2010/2011), http//www.who.intlfeatures/qa/en/indexhttp://www.un.org/millenniumgoals/.Jacques .d (2004), The state of food insecurity in the world: monitoring progress towards theworld food summit and millennium development goalsKorenromp, EL (2003), Measurement of trends in childhood malaria mortality in Africa: anassessment of progress toward targets based on verbal autopsy. Lancet Infectious Diseases,3(6):349–358.Leowski. J (2000), Mortality from acute respiratory infections in children under 5 years of age:global estimates. World Health Stat Q; 39: 138-44 pmid: 3751104MFPED (2002), The Complementarities of MDG Achievements: The Case of Child Mortality inSub-Saharan Africa problems. Bull World Health Organ 1973; 49: 1-12 pmid: 4545151.Rowe, AK (2005), The burden of malaria mortality among African children in the year 2000. 41
    • Ter Kuile, FO (2004), The burden of co-infection with HIV-1 and malaria in pregnant women insub-Saharan Africa. American Journal of Tropical Medicine and Hygiene, 2004, 71(Suppl.2):41–54.The world development report (1993), Investing in health Washington, DC: World Bank.Timaeus, I.M (2002), Adult Mortality in the era of AIDS. Third African Population Conference.Dakar, Senegal, Union fo r African Population Studies, 1999.Timaeus, I.M (2003), Impact of the HIV epidemic on mo rtality in sub-Saharan Africa: evidencefrom national surveys and censuses. AIDS 1998;12 Suppl 1:S15-27.Uganda annual health sector performance (2007-2008)Uganda Demographic and Health Survey (UDHS 2006)UNAIDS (2001), AIDS Epidemic Updat e - December 2001: UNAIDS/WHO.UNAIDS (2010), UNAIDS report on the global AIDS epidemicUNICEF (2008), state of the world‟s children reportUNICEF (2010), Sweden statisticsUNICEF (2011), press releaseUNICEF (2011), Statistics UgandaUNICEF 12, September (2010), press release SeptemberUNICEF September 17 (2010), press release.WHO (2006), „Antiretroviral drugs for treating pregnant women and preventing HIV infection ininfants in resource-limited settings: towards universal access‟ 42
    • WHO (2010) Statistics Data tables retrieved 2/9/2011WHO/UNAIDS/UNICEF (2011) „Global HIV/AIDS Response: Epidemic update and healthsector progress towards Universal Access 2011‟World Bank policy statement report (2010), Levels and trends in child mortality report 2010World Health Organization, (2010), World Health Statistics data tables, retrieved 2/9/11World Health Organoisation (2009), What are the key health issues for children?World population data sheet of population reference bureau Washington (2009),Worrall. E, Rietveld. A, Delacollette. C (2004), The burden of malaria epidemics and cost-effectiveness of interventions in epidemic situations in Africa. American Journal of TropicalMedicine and Hygiene, 2004, 71(Suppl. 2):136–140. 43
    • APPENDICES APPENDIX 1: UNDER FIVE DEATHS BY SEX JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC TOTAL1990 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 20621991 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 18451992 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 16231993 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 15041994 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 15241995 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 13561996 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 13031997 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 44
    • 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 12891999 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 12922000 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 12742001 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 13182002 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 13402003 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 13972004 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 13512005 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 13322006 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 12612007 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 13692008 M 41 98 33 62 63 80 56 62 50 38 43 68 694 45
    • 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 12302009 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 11722010 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 46
    • APPENDIX 2: UNDER FIVE MORTALITY BY CAUSE 1990-2010 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000DYSENTRY 55 71 65 60 76 68 70 48 45 38 47MEASLES 55 42 31 27 23 12 6 7 9 12 10TETANUS 77 57 55 42 57 47 41 39 32 38 49AIDS 71 71 69 61 66 61 55 61 57 51 46DIARRHOEA 206 212 201 197 183 173 172 159 143 132 121GENITAL INFECTIONS 37 21 17 13 19 14 11 9 7 4 3MALARIA 389 333 321 301 298 235 244 256 248 242 230PNEUMONIA 237 227 211 172 183 153 143 164 172 162 157RESPIRATORY INFECTIONS 41 37 45 37 58 61 52 41 37 31 21SEPTICAEMIA 72 72 66 55 55 49 41 52 62 72 67TUBERCULOSIS 41 36 27 31 27 17 4 9 11 15 22MENINGITIS 16 7 11 13 17 13 17 11 15 21 27DEHYDRATION 55 48 37 32 44 41 36 28 31 34 45ANAEMIA 85 79 71 67 55 52 53 47 51 53 61ASTHMA 61 57 48 39 27 26 22 18 22 24 18ORAL DISEASE 47 31 22 24 16 11 7 5 3 6 3DIABETES MELLITUS 88 47 40 31 29 27 22 21 27 30 34ENDOCRINE AND METABOLICDISORDER 76 48 26 37 33 21 49 44 47 51 55CARDIOVASCULAR DISEASES 65 72 66 58 52 47 51 55 51 47 37NERVOUS SYSTEM DISORDER 41 55 41 36 37 31 29 27 29 33 32KWASHIOKOR 91 82 53 71 66 74 61 74 65 71 61MARASMUS 77 73 49 55 47 57 60 55 66 63 66INJURIES 79 67 51 45 56 66 57 52 59 62 62TOTAL 2062 1845 1623 1504 1524 1356 1303 1282 1289 1292 1274 47
    • UNDER- FIVE MORTALITY BY CAUSE 2001-2010 continued…… 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010DYSENTRY 30 27 22 26 22 18 17 13 10 14MEASLES 8 7 15 15 13 11 9 3 5 2TETANUS 51 38 44 31 28 21 20 16 18 11AIDS 46 33 57 51 55 44 39 47 35 51DIARRHOEA 139 137 121 135 147 135 137 110 105 111GENITAL INFECTIONS 0 9 10 11 7 2 0 2 0 3MALARIA 267 277 287 271 284 267 289 226 230 237PNEUMONIA 155 171 169 163 141 163 193 201 197 183RESPIRATORY INFECTIONS 17 13 17 11 9 12 19 11 11 22SEPTICAEMIA 72 71 55 41 37 45 67 55 51 62TUBERCULOSIS 21 25 13 15 11 13 8 4 7 9MENINGITIS 23 15 22 27 21 14 24 21 17 21DEHYDRATION 41 41 53 45 50 51 66 71 63 59ANAEMIA 68 65 74 61 77 87 83 79 72 66ASTHMA 13 9 11 7 5 1 3 0 1 0ORAL DISEASE 9 5 7 11 7 3 4 1 1 3DIABETES MELLITUS 38 47 40 37 33 23 21 17 15 11ENDOCRINE AND METABOLICDISORDER 52 61 69 73 61 59 53 48 55 59CARDIOVASCULAR DISEASES 33 37 47 44 39 31 45 36 38 33NERVOUS SYSTEM DISORDER 31 39 43 43 51 66 63 54 41 49KWASHIOKOR 60 61 73 99 91 89 93 98 82 88MARASMUS 71 75 79 63 77 73 71 77 63 67INJURIES 73 77 69 71 66 33 45 40 55 41TOTAL 1318 1340 1397 1351 1332 1261 1369 1230 1172 1202Data source: Mulago Hospital Records Department 48