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ANALYSIS OF MALARIA PREVALENCE OF CHILDREN AGED BELOW 5 YEARS IN UGANDA

TABLE OF CONTENTS

Table of Contents

DECLARATION.. i

APPROVAL. ii

DEDICATION.. iii

ACKNOWLEDGMENT. iv

TABLE OF CONTENTS. v

LIST OF TABLES. vii

LIST OF ACRONYMS. viii

ABSTRACT. ix

CHAPTER ONE. 1

1.0 Introduction. 1

1.1Background. 1

1.2 Statement of the problem. 3

1.3   Objectives of the Study. 3

1.3.1General Objective: 3

1.3.2 Specific Objectives of the Study. 3

1.4 Research hypotheses. 3

1.5 Scope of the Study: 4

1.5.1 Subject scope. 4

1.6 Significance of the Study. 4

CHAPTER TWO: LITERATURE REVIEW… 6

2.0 Introduction. 6

2.1 Malaria Prevalence. 6

2.2 Malaria distribution. 7

2.3 Forecast malaria prevalence for children aged below 5 years. 8

CHAPTER THREE: RESEARCH METHODOLOGY. 11

3.0 Introduction: 11

3.2 Data Sources. 11

3.3 Data processing and Data analysis techniques. 11

3.3.1 Descriptive analysis. 11

3.3.2 Time series analysis. 11

Test for seasonality. 12

3.4.3 Autoregressive Integrated Moving Average (ARIMA). 13

3.4.4 Theoretical frame work. 15

3.8 Ethical considerations: 15

CHAPTER FOUR. 16

PRESENTATION OF RESULTS. 16

4.0 Introduction. 16

Unit Root Tests. 16

Histogram normality test. 17

Source: primary data. 17

Comparison of Malaria Cases Residence. 17

Comparison of Malaria Cases by Region. 18

Correlations of Malaria Cases by Residence. 21

Regression of total malaria cases on rural and urban. 22

4.1.6 Forecasting model for the year 2018. 25

Autoregressive Integrated Moving Average forecasting. 25

Correlation between the regions. 26

CHAPTER FIVE. 28

SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS. 28

5.0 Introduction. 28

5.1 Summary of findings. 28

5.3 Recommendation. 29

5.4 Areas of further study. 29

REFERENCES. 30

LIST OF TABLES

Table 1: Unit root tests of the series 1989-2018. 16

Table 2: Comparison of Malaria Cases Residence. 18

Table 3: Comparison of Malaria Cases by Region. 19

Table 4: coefficients for malaria basing on the regions. 20

Table 5: Correlations of Malaria Cases by Residence. 22

Table 6: Regression of total malaria cases on rural and urban. 23

Table 7: Forecasting model for the year 2018. 27

Table 8: Autoregressive Integrated Moving Average forecasting. 28

Table 9: Correlation between the regions. 28

LIST OF FIGURES

Figure 1 : Histogram normality test. 17

Figure 2: graphical analysis of malaria prevalence from 2015 to 2030. 21

Figure 3: Forecasting malaria prevalence from 2015 to 2030 for north, west and east. 26

LIST OF ACRONYMS

WHO:  world health organization

WMR:  world malaria report

MOH: ministry of health.

EIR: entomological inoculation rates

PMI: President’s Malaria Initiative

HMIS: Health Management Information System

UMIS: Uganda Malaria Indicator Survey

UBOS:  Uganda Bureau of Statistics

ARIMA: Autoregressive Integrated Moving Average

GDP: growth domestic product.

ABSTRACT

The topic of study was malaria prevalence of children aged below 5 years in Uganda and the study was guided by the following objectives; to compare malaria prevalence by residence and region in Uganda, to determine malaria distribution in Uganda and to forecast malaria prevalence for children aged below 5 years in Uganda up 2022.

The study shows that rural residence had mean total malaria cases of 2988.23 with a standard deviation of 457.718 while urban residence had mean of 314.32 with a standard deviation of 142.437, this indicates that majority of the people in rural areas have more malaria cases than urban areas. The results further indicates that the Eastern region had the highest registered number of malaria cases with highest mean of 1022.03 and standard deviation of 192.211 followed by Northern with a mean of 902.48 and the standard deviation 158.922.  Western and central regions had mean malaria cases of 578.06 and 787.23 with the standard deviation of 184.080 and 154.660 respectively.  According to the findings when malaria cases in the north, east and west equals to zero then malaria cases is 604.537, while a unit increase in malaria cases in central will on average lead to 0.366 decrease in the north, this is statistically insignificant since the P-value, (0.082) >0.05 thus the null hypothesis is accepted, therefore there is no difference in malaria prevalence across regions.

The study concludes that there is variation in malaria prevalence across the region some regions had higher malaria cases than others; the study also concludes that malaria cases are on the rise.

The study also states that the variations in the total malaria cases are explained by the changes in the malaria cases of both rural and urban areas. The study also concludes that malaria is high in the rural areas than urban areas.

The study recommends more infighting malaria so that the challenges brought by malaria are worked upon. The study also recommends that the government should increase the number of health workers in the government hospital. Regions with high malaria levels should increase findings for the rural health facilities especially in northern Uganda. The study recommends the following areas for further study; the influence of mosquito nets on malaria prevention, the influence of foreign aid on the influence of education of malaria prevalence in Africa and the influence of malaria medicine on malaria prevention

CHAPTER ONE

1.0 Introduction

This chapter shall deal with the background of the study, statement of the problem, the objectives of the study, research questions, study scope of the study and significance of the study.

1.1Background

Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. It is preventable and curable. In 2016, there were an estimated 216 million cases of malaria in 91 countries, an increase of 5 million cases over 2015. Malaria deaths reached 445 000 in 2016, a similar number (446 000) to 2015. The WHO African Region carries a disproportionately high share of the global malaria burden. In 2016, the region was home to 90% of malaria cases and 91% of malaria deaths. Total funding for malaria control and elimination reached an estimated US$ 2.7 billion in 2016. Contributions from governments of endemic countries amounted to US$ 800 million, representing 31% of funding (WHO, 2017).

According to the latest World Malaria Report, released in November 2017, there were 216 million cases of malaria in 2016, up from 211 million cases in 2015. The estimated number of malaria deaths stood at 445 000 in 2016, a similar number to the previous year (446 000). The WHO African Region continues to carry a disproportionately high share of the global malaria burden. In 2016, the region was home to 90% of malaria cases and 91% of malaria deaths. Some 15 countries all in sub-Saharan Africa, except India  accounted for 80% of the global malaria burden (World Malaria Report, 2017).

In areas with high transmission of malaria, children under 5 are particularly susceptible to infection, illness and death; more than two thirds (70%) of all malaria deaths occur in this age group. The number of under-5 malaria deaths has declined from 440 000 in 2010 to 285 000 in 2016. However, malaria remains a major killer of children under five years old, taking the life of a child every two minutes (Leder et al., 2017).

Antimalarial medicines can also be used to prevent malaria. For travellers, malaria can be prevented through chemoprophylaxis, which suppresses the blood stage of malaria infections, thereby preventing malaria disease. For pregnant women living in moderate-to-high transmission areas, WHO recommends intermittent preventive treatment with sulfadoxine-pyrimethamine, at each scheduled antenatal visit after the first trimester. Similarly, for infants Sliving in high-transmission areas of Africa, 3 doses of intermittent preventive treatment with sulfadoxine-pyrimethamine are recommended, delivered alongside routine vaccinations.

In 2012, WHO recommended Seasonal Malaria Chemoprevention as an additional malaria prevention strategy for areas of the Sahel sub-region of Africa. The strategy involves the administration of monthly courses of amodiaquine plus sulfadoxine-pyrimethamine to all children under 5 years of age during the high transmission season (Okello et al., 2016).

Globally 3.2 billion people remain at risk of malaria and nearly one million malaria deaths occur each year, mostly in children under five years of age in sub-Saharan Africa (WMR,2008). The greatest burden of malaria, by far, remains in the heartland of Africa, characterized by large contiguous areas of high transmission, low coverage of control interventions, and limited infrastructure to monitor disease trends. Besides neonatal-related causes, malaria is the second leading cause of morbidity and mortality in Africa, and accounts for 21-26% of all under-five mortality in Uganda. Many of these deaths occur at home due to poor access to health care, inappropriate or delayed care seeking and inadequate quality of health services hence need to take analysis the levels of malaria prevalence in the home with children below 5 years.

Although funding for malaria has remained relatively stable since 2010, the level of investment in 2016 is far from what is required to reach the first milestone of the GTS, which is a reduction of at least 40% in malaria case incidence and mortality rates globally when compared to 2015 levels. To reach this milestone, the GTS estimated that annual funding would need to increase to US$ 6.5 billion per year by 2020. The US$ 2.7 billion invested in malaria in 2016 represents less than half (41%) of that amount. Stepping up investments in malaria research and development is key to achieving the GTS targets. In 2015, US$ 572 million was spent in this area, representing 83% of the estimated annual need for research and development.

Malaria not only causes ill health and death but also hampers development due to the fact that lots of resources are spent combating the disease. For instance, expenditure on treatment and prevention is very high and there is loss of household incomes through absenteeism from work. According to a United Nations (UN) study, Malaria costs Uganda $347 million annually (The New Vision newspaper, 2004).

1.2 Statement of the problem.

In 2016, 15 million children in 12 countries in Africa’s Sahel sub region were protected through seasonal malaria chemoprevention (SMC) programs. However, about 13 million children who could have benefited from this intervention were not covered, mainly due to a lack of funding. Since 2012, SMC has been recommended by WHO for children aged 3-59 months living in areas of highly seasonal malaria transmission in this sub region.

According to Francis et al., (2006). there has an increased fight against malaria especially on the children below 5 years across Africa and Uganda in particularly, but malaria still claims a lot of morbidity with 40 percent comparison to other diseases. The prevalence of these polymorphisms has consistently measured well above 50% across Uganda. There were an estimated 438000 deaths, 90% from Africa, 7% from South eastern Asia region and 2% from Eastern Mediterranean region. Of these, 306000 deaths have occurred in children aged under 5 years.(WMR 2015) In comparison, 198 million infections and 584 000 deaths were estimated in 2013.(WMR 2014) More than 30000 cases of malaria are reported annually among travelers from developed world visiting malarious areas.(Leder et al., 2010).

Basing on this Background this study intends to investigate into malaria prevalence of children aged below 5 years in Uganda.

1.3   Objectives of the Study

1.3.1General Objective:

The general objective of the study is to malaria prevalence of children aged below 5 years in Uganda.

1.3.2 Specific Objectives of the Study

1. To compare malaria prevalence by residence and region in Uganda.
2. To determine malaria distribution in Uganda.

• To forecast malaria prevalence for children aged below 5 years in Uganda up 2022.

1.4 Research hypotheses.

1. Ho₁: There is no difference in malaria prevalence by residence and region.
2. Ho₂: All regions have the same malaria prevalence.

iii          Ho₃: Rural has the same malaria prevalence as urban.

IV    Ho₄: Malaria prevalence in Uganda will reduce by 2022.

1.5 Scope of the Study:

1.5.1 Subject scope

This research intended to analyses malaria prevalence of children aged below 5 years in Uganda.

1.6 Significance of the Study

1. The study will help the government and the health officers to come up with reasonable policies to overcome the high rates of malaria prevalence in Uganda among children below 5 years.
2. In the field of academics, the study will be helpful to the future researchers with enough data and a literature review for them to review their weak areas in their research.

• The study will act as a source for further research regarding the analysis of malaria prevalence in children aged below 5 years.

1. In regard to Uganda, the research is hoped to be generally of great benefit to people’s welfare. The research study is hoped to bring the public to the knowledge of how dangerous malaria is to society especially in children aged below 5 years.
2. The study is hoped to avail valuable information for consideration in making of important policies for example through sensitization of the public and government partnering with responsible organizations to reduce the malaria burden it may also beneficial to the researcher as it will equip him with skills into further research.

1.7 Conceptual frame work

MALARIA PREVALANCE                                              CHILDREN

CHAPTER TWO: LITERATURE REVIEW

2.0 Introduction

This section presents the literature related to the study based on the works of the scholars and is revealed on the basis of the objectives of the study.

2.1 Malaria Prevalence

Globally infant deaths are ranging from 4-5 to more than 100 per live births. It is estimated that 20.5 million LBW infants were born in 1995. Prevalence of infant mortality per 1000 live birth ranged from 6 to 77 in developed countries including United Kingdom, United States. LBW in the same counties ranged from 5.2% to 28.2% where as the pre term delivery ranged from 4.6% to 24% of all live births(1) A snapshot of progress since 2005 showed each year at least 4 million new born die worldwide which is unacceptably high number given that low cost solutions exist to save these lives. Neonatal mortality account for 40% of all under fives deaths.

Malaria is the leading cause of morbidity and mortality in Uganda especially in children under five years. Up to 70 per cent of outpatient cases and over 50 per cent of inpatient admissions in the under-fives are malaria cases. It is responsible for a specific death rate among this age group of 37/1000 and 18/1000 live births in high and low malaria endemic areas respectively or a total of 70,000–110,000 child health deaths annually. It is also the major killer of refugees and internally displaced people in Uganda.

The time of birth and first days of life are the riskiest period in human life span. Each year 3 million babies die in the first week of life and up to two third (2/3) of these die in the first 24 hours of life. In India alone, more than 1 million newborns die every year(7). The study which was carried out at King Fahad showed the perinatal mortality rate was 34.9% (65/186)(8). In another study from Guatemala found among 671 infants born in 4 rural ladino villages, 15.2% had birth weights < 2500 g. The prevalence of LBW of 41.3% among 415 live, singleton births.

Sub Saharan Africa remains the most dangerous region in the world for the baby to be born as 1.16 million babies die each year in the first 28 days of life. About 0.5 million Sub Saharan Africa babies die on the day they are born most at home and uncounted. Each year in Sub Saharan Africa, 30 million women become pregnant, and 18 million give birth at home without skilled care and therefore each day in Africa; 3,100 newborns die, and another 2,400 are stillborn, 9,600 children die after their first month of life and before their fifth birthday and1 in every 4 child deaths (under five years) in Africa is a newborn baby. Nigeria has the world highest new born mortality rate at 66 deaths per 1000 birth. Half of African’s 1.16 newborn deaths occur in just 5 countries, Nigeria, Democratic Republic of Congo, Ethiopia, United Republic of Tanzania and Uganda. The report found two third (2/3) up to 800000 of new born death in Sub Saharan Africa in a year could be saved if 90% of women and babies received feasible low cost health intervention like immunization, providing a skilled attendant at birth, etc which would have needed only 1.39USD per capital.

More than 30000 cases of malaria are reported annually among travelers from developed world visiting malarious areas.(Leder K et al) With the shrinking globe, perennially prevalent malaria, therefore, remains an ever existing danger for humanity, in every part of the globe. In most areas, malaria and poverty co-exist, with the average GDP and average growth of per capita GDP in malarious countries being about one fifth (1/5) of those in non-malarious countries.

According to the(WHO 2013) and the Global Malaria Action Plan 3.4 billion people (half the world’s population) live in areas at risk of malaria transmission in 106 countries and territories .In 2012, malaria caused an estimated 207 million clinical episodes, and 627,000 deaths. An estimated 91% of deaths in 2010 were in the African Region.

2.2 Malaria distribution.

According to the latest World Malaria Report 2015 (WMR, 2015), malaria transmission occurs in five of the six WHO regions, with Europe remaining free. Globally, an estimated 3.2 billion people continue to be at risk of being infected with malaria and developing disease, and 1.2 billion are at high risk .more than 1 in 1000 are at a high chance of getting malaria in a year. There were 214 million cases globally in 2015, of which 88% were from the African region, 10% from SE Asia region and 2% from Eastern Mediterranean region. There were an estimated 438000 deaths, 90% from Africa, 7% from SE Asia region and 2% from Eastern Mediterranean region.(WMR, 2015)in  comparison, 198 million infections and 584 000 deaths were estimated in 2013.

The climate in Uganda allows stable, year round malaria transmission with relatively little seasonal variability in most areas. Malaria is highly endemic in the country affecting approximately 90% of the 34 million population . Indeed, some of the highest recorded infective mosquito bites per person year) in the world have been seen in Uganda, including rates of 1586 in Apac District and 562 in Tororo District (Okello et al., 2006) measured in 2001–02. The Uganda MOH estimates that the entomological inoculation rates (EIR) is >100 in 70%, 10–100 in 20%, and <10 in 10% of the country (Uganda Bureau of Statistics, 2010). However, these estimates are based on little data, as few entomological surveys have been carried out in the country. Transmission is unstable and epidemic-prone in extreme southwestern areas and in the vicinity of the Rwenzori Mountains in the west and Mt. Elgon in the east, all areas extending above 1,800 meters in altitude.

The 2009 UMIS measured a prevalence of malarial parasitemia, assessed based on microscopy, approximately 30–50% exists in children 6–59 months of age( Uganda Bureau of Statistics, 2010). Anemia was also very common, with a hemoglobin lees than 11 g/dl seen in well over half of children .Prevalence was high (38–63% by blood smear) in all regions of Uganda except the major city, Kampala with 5%. and in the southwestern region, which includes highland areas (12%). As expected, prevalence was lower in urban areas, with increasing educational levels of mothers, and with increasing wealth. These prevalence measures are consistent with very high and stable transmission of malaria in most of Uganda.

2.3 Forecast malaria prevalence for children aged below 5 years

Because of the inadequacy of malaria case data from many sub-Saharan African countries, population infection prevalence can be used to enhance understanding of the level of malaria transmission and how it has changed over time. Nationally representative surveys of P. falciparum infection prevalence or parasite rate are increasingly being undertaken in sub-Saharan Africa. Modeling can help to estimate the proportion of the population at risk that are infected at any one time, and the total number of people infected.

During 2013, an estimated 128 million people were infected with P. falciparum in sub-Saharan Africa at any one time. In total, 18 countries account for 90% of infections in sub-Saharan Africa; 37 million infections (29%) arose in Nigeria and 14 million (11%) in the Democratic

Malaria cases increased from 1,444,352 in 1995 to 2,923,620 in 1999 (WMR, 2012). The malaria rate has consistently increased in 20015. There is considerable malaria morbidity due to repeated low level and mostly non-febrile infections with the parasites resulting into chronic anemia in children and pregnant women particularly primigravidae. Severe malarial anemia is responsible for a case fatality rate of 8–25 per cent among paediatric admissions. It is responsible for nearly 60 per cent abortions or miscarriages. High levels of resistance to classical malaria drugs have resulted in increased malaria morbidity (PMI, 2009)

As the worldwide focus on malaria is shifting toward planning for eradication, it is remarkable that evidence for a decrease in the malaria burden is lacking in Uganda. One exception may be Kampala, the only major city in Uganda, where decreasing malaria prevalence cases have been noted anecdotally, although definitive data are lacking. A cohort study conducted from 2004 to 2008 noted a remarkable decrease in malarial incidence, although this finding was influenced by other factors, including treatment of all malarial illnesses with highly effective agents, aging of the cohort population, and provision of insecticide-impregnated bed nets (Clark et al., 2010).

Regular reports from the Uganda HMIS are likely highly inaccurate, suffering both from underreporting of fevers (as only episodes captured by the national public health system are reported) and overstatement of malaria diagnoses in febrile children without diagnostic confirmation (Rowe et al., 2009). Nonetheless, the HMIS data provide the only available direct measure of disease numbers across the country. In recent years, HMIS reported cases increased since the 1990s, with over 10 million cases reported each year .Notably, 60–80% of fever cases are estimated to be treated in the informal and private sectors (not assessed by HMIS), and it has been estimated that the total number of fever cases in Uganda in 2005 was 60 million (President’s Malaria Initiative , 2010). Factors that may have influenced changes in malaria reporting over time include the abolition of user fees for public sector health care in 2001, which led to increased attendance at public facilities and the subsequent roll out of the Home-Based Management of Fever strategy (Uganda Ministry of Health, 2005), which shifted care to community centers without links to HMIS reporting. Another relevant factor is the rapid increase in population of the country, suggesting that, if the overal number of episodes of malaria has been stable, the incidence has decreased somewhat. Overall, it is difficult to ascertain from available data whether the incidence of malaria has decreased or increased over the last decade, but clearly the incidence of the disease in Uganda remains very high.

CHAPTER THREE: RESEARCH METHODOLOGY

3.0 Introduction:

This section presents a detailed description on how the study will be carried out and collecting the necessary data for the study. It therefore covers the research design, study area, data sources, data processing, data analysis techniques and anticipated limitations of the study.

3.1 Research Design:

The study used quantitative methods of research so as to obtain the viable data and this shall include structured secondary data in the records of the ministry of health.

3.2 Data Sources.

Secondary data was obtained from the data base, records, publications and journals in the ministry of health.

3.3 Data processing and Data analysis techniques.

The process of data processing involved editing in order to check for errors and omissions and coding to reduce the data to a meaningful pattern of responses. Model specification and soft wares employed in the tabulation and processing of the findings were done in order to prepare data, analyze and compile a research report.

The study used time series analysis and descriptive statistics used to describe the information got from the field this was inform of graphs and tables

Data Analysis involved applying statistical techniques on it for easy presentation. It included the interpretation of research findings in the light of the research questions, and objectives to determine if the results are consistent with those research questions.

3.3.1 Descriptive analysis.

3.3.2 Time series analysis

By the nature of data which is the time series

The analysis however will concentrate on trend and seasonality of malaria prevalence

Assuming a multiplicative model, then 𝑌𝑡=𝑇𝑡∗𝑆𝑡

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

This employs ARIMA modeling and it includes the following data exploration techniques.

1. Graphical presentation

This will involve plotting the series 𝑌𝑡 against time t.

1. 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.

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 malaria prevalence series is stationary

Ha: the malaria prevalence series is non-stationary.

Test statistic

Where m=number of pluses Decision rule is at α=0.05

The researcher will 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.

Test for seasonality

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).

The test is described as below; Ho: the series has no seasonality Ha: the series has seasonality

Test statistics, H to compare with  (Chi square)

n_i is the number of observations in the i^th 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

3.4.3 Autoregressive Integrated Moving Average (ARIMA)

This is also known as the Box-Jenkins model. This methodology will be used to forecast the malaria prevalence for children aged below 15 years. The model is based on the assumption that the time series involved are stationary. Stationary 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 malaria prevalence among children as below;

Identification.0 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 stationary is the value of d.

Estimation. This involves estimation of the parameters of the Autoregressive and Moving average terms in the model. The nonlinear estimation will be used.

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

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.

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.

The forecast for the year 2016 will be done by regressing malaria prevalence against time

The residence and region will be analyzed using the ANOVA test by regressing malaria prevalence (dependent) on the dummies for place of residence and dummies for region using SPSS since residence and region are both categorical independent variables.

   Y_t =β_o+β₁D_R+β₂D_C+β₃D_N+β₄D_E

Where Yt is the malaria prevalence at the time in a given region and residence

D_R is a dummy for rural

D_C is a dummy for central

D_N is a dummy for north

D_E is a dummy for East

While , β_o + β₁₊ β₂₊ β₃

3.4.4 Theoretical frame work

This study will adopt the Cobb-Douglas production function as a theoretical basis for estimation. The Cobb Douglas production function stipulates that output depends on capital and labor.

Where is output,  is capital and is labor

However in this study Y is prevalence of malaria, k is regions and

In regard to the malaria prevalence in Uganda under study, it is important to incorporate other control variables that characterize the unique features of the malaria prevalence. These include; different regions like East, West, North and South.

   Y_t =β_o+β₁D_R+β₂D_C+β₃D_N+β₄D_{E………………………………………………………………………………………(2)}

3.8 Ethical considerations:

The researcher will begin data collection by explaining the purpose of the research, which basically meant to help decision makers of ministry of health Uganda and the users of the information from other health organizations and hospitals. Respondents will be informed that the purpose of the information shall be strictly for academic purposes only and the information provided will be treated with highest level of confidentiality.

CHAPTER FOUR

PRESENTATION OF RESULTS

4.0 Introduction

This chapter discusses the data analyzed from secondary data source on analysis of malaria prevalence in children aged less than 5 years in Uganda from 1986 to 2017, The data where subject to transformation using SPSS, where the mean, standard deviation, correlations, Regression, Forecasting and ANOVA. Presentation and interpretation were based on specific objectives to address the problem.

4.1 Analysis of findings

4.1.1 Unit Root Tests

Unit root tests were carried out using the augmented Dickey-Fuller test statistic. This was carried out to check whether the series were stationary (integrated) or not. This is because standard inference procedures do not apply to regressions which contain an integrated dependent variable or integrated repressors. The test statistic tested the null hypothesis that the time series has a unit root against the alternative that there is no unit root. The test statistic values are compared to the critical values at five percent significant level. The test statistic values less than the critical values at five percent level of significance indicate that the series are non-stationary otherwise they are stationary.

Source: Authors computation

In table1 above, the data for malaria prevalence for urban and rural residents is stationary in the levels because the ADF statistics are greater than critical values while after the 1^st difference they are still stationary because after the first difference the  ADF statistic are greater than the critical values.

4.1.2 Histogram normality test

Figure 1 : Histogram normality test

Source: author’s computation

A regression was run and on clicking on the view-residual test-histogram-normality test, the histogram is bell-shaped, suggesting a normal curve shape, and the jarque-bera statistics has high p-value of 1.779 indicating that the errors in the regression are normal that is to say; the jarque-bera statistics probability of 0.410820 is greater than zero and it has a percentage of 41% greater than 10%(41%>10%) thus the errors in the regression are normal.

4.1.3 Comparison of Malaria Cases Residence

This analysis was done by the researcher to compare the malaria cases between rural areas and urban areas by determining the mean and standard deviation, this was to enable the researcher identify which region had the highest malaria prevalence against the other as shown in the table below;

This was done to answer the second hypothesis;

H_O           : Rural areas do not have the same malaria prevalence as urban areas.

Ha       : Rural areas have the same malaria prevalence as urban areas.

The comparison is done by comparing the mean and standard deviation of rural and urban places of residence as shown in the table below.

Table 2: Comparison of Malaria Cases by Residence

Source: author’s computation

The table above shows that rural areas on average had 2988.23 malaria cases with a standard deviation of 457.718 while urban areas had mean of 314.32 with a standard deviation of 142.437. This implies that there was much variation in the data in rural areas compared to urban areas for the last 31 years and the prevalence was high in rural areas compared to urban areas for last 31 years,  These results in the study indicates majority of the malaria cases occurs in the rural areas than in urban areas. This also implies that in Urban areas there is better health facilities than in rural areas therefore the malaria cases were lower this is also in line with Warrell et al,( 2002) who states that Malaria is a major cause of morbidity and mortality worldwide, especially in young African children under five years especially in rural areas because of lack the necessary facilities and limited number of hospitals especially in rural African communities and the prevalence of rural areas is high because of high illiteracy rates and lack of knowledge in rural areas.

4.1.4 Comparison of Malaria Cases by Region

The table below gives a comparison of the malaria cases (prevalence) among different regions across the Country-Uganda that was collected from time series data for last 31 years.

This was carried out to answer to answer the third objective.

Ho:      There is no difference in malaria prevalence by region

Ha:      There is difference in malaria prevalence by region

The comparison in this table was done using mean and standard deviation in which the area with the highest mean had the highest malaria cases while low mean value indicated low prevalence of malaria cases.

Table 3: Comparison of Malaria Cases by Region

Source: author’s computation

From the above table the study revealed that Eastern region had the highest registered number of malaria cases with highest mean of 1022.03 and standard deviation of 192.211 followed by Northern with a mean of 902.48 and the standard deviation 158.922.  Western and central regions had mean malaria cases of 578.06 and 787.23 with the standard deviation of 184.080 and 154.660 respectively. Despite eastern region registering the highest number of malaria there was little variation in the data and therefore there is difference in malaria cases by region.

From the above results majority of malaria cases were in eastern Uganda this is because eastern Uganda has not been able to engage into the preventive malaria programmes this explains the reason for the high malaria prevalence for children under the age of 5 years, this is also in line with (US PMI, 2009) which states that in an area were there is little malaria prevention like encouraging people to sleep under treated mosquito nets has escalated malaria cases in such regions.

4.1.5 Coefficients of malaria cases basing on the regions

Ho     Malaria cases are the same across the region

Ha     Malaria cases are not the same across the regions

Table 4: coefficients for malaria basing on the regions

Source: author’s computation

According to the table above it indicates that when malaria cases in the north, east and west  equals to zero then malaria cases is  604.537, while  a unit increase in malaria cases in central will on average lead to 0.366 decrease in the north, this is statistically insignificant since the P-value, (0.082) >0.05 thus the null hypothesis is accepted, therefore there is no  difference in malaria prevalence across regions.

A unit increase in central will on average lead to (0.062) decrease of malaria in the East, this is statistically insignificant since the P-value (0.585)>0.05, the null hypothesis is accepted, hence there is no difference in malaria cases across regions.

A unit increase in central will on average lead to (0. 996) increase of malaria cases in the west, this is statistically significant since the P-value (0.000)<0.05, the null hypothesis is rejected, hence there is a difference in malaria cases across regions.

This implied that the central has lower cases of malaria compared to north, east and west, then reject null hypothesis and conclude that there is a difference in malaria prevalence by region, this is also in line with (Okello et al., 2006) who states that the climate in Uganda allows stable, year round malaria transmission with relatively little seasonal variability in most areas. Malaria is highly endemic in Uganda affecting approximately 90% of the 34 million population , most of the regions of Uganda especially eastern and north are face too much malaria prevalence as compared to the other parts of the country though the whole country faces significvant amounts of malaria cases.

4.1.6 Graphical analysis of malaria prevalence from 1989 to 2030

Figure 2: graphical analysis of malaria prevalence from 1989 to 2030

Source: author’s computation

The results from the graph above indicates that malaria cases significantly dropped in 2018 but it will decrease in 2017 up to 2030 however the biggest increase in malaria cases will be in 2030. This findings indicates that the government of Uganda will need to increase on the more malaria prevent cases so that there shouldn’t be an increase in malaria prevalence in Uganda. There a result also indicates that the international development partners need to work hard and reduce malaria prevalence in the country.

4.1.7 Correlations of Malaria Cases by Residence

The relationship of malaria prevalence by residence in Uganda for the study that was conducted, this was carried out in order to ensure that the researcher is able to determine the relationship between malaria prevalence in urban and rural areas.

Ho: There is no difference in malaria prevalence by residence.

Ha: There is difference in malaria prevalence by residence.

The correlation table below indicates the malaria prevalence on the places of residence either rural or urban however the null hypothesis was rejected incase the P-Value was greater than 0.05, therefore the null is rejected, otherwise it is accepted.

Table 5: Correlations of Malaria Cases by Residence

Source: author’s computation

There is a high significant and positive relationship of malaria cases between rural and urban parts of Uganda. The results are significant since sig 0.001<0.05. This results indicates that malaria cases in both regions are high and there is a high positive relationship between the regions in terms of malaria prevalence, these results are also in line with  WHO (2013) and the Global Malaria Action Plan 3.4 billion people (half the world’s population) live in areas at risk of malaria transmission in 106 countries and territories .In 2012, malaria caused an estimated 207 million clinical episodes, and 627,000 deaths. An estimated 91% of deaths in 2010 were in the African Region and both people in urban and rural areas are at risk of getting malaria unless the government in there respective governments.

4.1.8 Regression of total malaria cases on rural and urban

The table below shows Regression of total malaria cases on rural and urban in Uganda for the study that was conducted, this was carried out in order to enable the researcher determine percentage contribution of the independent variables on the dependent variable and  also determine variations in the total malaria cases in rural and urban.

This was in order to determine the hypothesis.

Ho : Rural does not have the same malaria cases like urban.

Ha  : Rural has the same malaria cases like urban.

The regression analysis was carried out to determine the relationship between total malaria cases and malaria cases by region.

The higher the R-square value the more the relationship and the lower the R-square Value the lower the relationship.

Table 6: Regression of total malaria cases on rural and urban

Source: author’s computation

The R-value tells us about correlation coefficient (0. 992) means that there is a very high positive relationship between malaria cases by region and total malaria cases. R Square value explains the percentage contribution of the independent variables on the dependent variable.

Therefore approximately 99.2% of the variations in the total malaria cases are explained by the changes in the malaria cases of both rural and urban areas. This view is also in line with (Moh, 2014) which states that there is very high malaria prevalence in both rural; and urban areas and therefore the government ought to increase more investments in the fight against malaria and ensure the citizens are aware of the preventive malaria programmes to eliminate the increase in the malaria volumes in the country.

4.1.9 ANOVA

The regression analysis was carried out to calculate if places of residence have an effect on the total malaria cases, this was carried out to answer the two hypotheses.

Ho       : Places of residence do not have an effect on total malaria cases.

Ha       : Place of residence have an influence on total malaria cases.

The Anova indicates if places of residence have an effect on the total malaria cases this was indicated determined by P-value, when the Pvalue is less than 0.05 the we reject the null hypothesis and otherwise we accepted.

The above ANOVA table explains the overall significant of the model.

Source: author’s computation

The above ANOVA table explains the overall significant of the model. Basing on the hypothesis that the independent variable shave no effect on malaria case, since the sig value 0.00 <0.05 we reject the hypothesis and conclude that the independent variables (places of residence) have an effect on the total malaria cases this is also in line with (Makundi et al, 2007) it was reported that the burden of malaria is greatest among poor people, imposing significant direct and indirect costs on individuals and households and pushing households into in a vicious circle of disease and poverty. Furthermore vulnerable households with little coping and adaptive capacities are particularly affected by malaria.

4.1.10 Coefficients

The table is used to explain the effect of places of residence on malaria cases, this was done by using coefficient tests, the model is estimated as Y=B+B₁X₁+B₂X_2.  This was specifically to enable the researcher determine malaria prevalence in both rural and urban areas of the country.

Where B is constant, B₁ is coefficient for rural , x₁ is rural areas  and B₂ is the coefficient for urban ,x₂ is urban areas and Y is the malaria prevalence at the time in a given region and residence.

Source: author’s computation

From the table, the coefficients are -1.849, 1.001, and .995 for B, B₁, and B₂ respectively. All the effects are significant since the sig-values for both rural and urban are less than 0.05.  In summary residences from urban areas have higher chances of contracting malaria compared to their village counterparts this is also in line with (WMR, 2015), which indicates that, malaria transmission occurs in five of the six WHO regions, with Europe remaining free. Globally, an estimated 3.2 billion people continue to be at risk of being infected with malaria and developing disease, and 1.2 billion are at high risk .more than 1 in 1000 are at a high chance of getting malaria in a year. There were 214 million cases globally in 2015, of which 88% were from the African region, 10% from SE Asia region and 2% from Eastern Mediterranean region. There were an estimated 438000 deaths, 90% from Africa, 7% from SE Asia region and 2% from Eastern Mediterranean region.(WMR, 2015)in  comparison, 198 million infections and 584 000 deaths were estimated in 2013.

4.1.11 Forecasting malaria prevalence from 2015 to 2030 fin rural

Figure 3: Forecasting malaria prevalence from1989 to 2030 for rural

Source: author’s computation

The graph above indicates that malaria prevalence in 2012 was the lowest in rural areas while in 2022 malaria prevalence was highest. This results indicates that the government has increase in investment in malaria campaighn to enable the rural people be able to fight malaria increase in the country.

4.1.12 Forecasting model for the year 2018

The researcher forecasted total malaria cases for 2018, this was done to determine the future malaria prevalence for the year 2018.

Table 7: Forecasting model for the year 2018

Source: author’s computation

The model from the above table was obtained by regressing total malaria cases against time to be able to forecast malaria cases for the 2018. The model is written as Y=B+B₁X₁ where B is constant B₁ is the coefficient of years. Hence the model is fitted as Y=-40825.148+22.065X₁

4.1.13 Autoregressive Integrated Moving Average forecasting

The table below shows the unit root tests for malaria prevalence by residence in Uganda for the study that was conducted, this was carried out in order to ensure that the researcher is able to determine if the time series was stationary or non stationary hence use the unit root test.

This was to answer the hypothesis;

Ho       : The data is not stationary

Ha       : The data is stationary

Table 8: Autoregressive Integrated Moving Average forecasting

Source: author’s computation

The forecasting of the model required to first test the principle of stationary in order to find out if the time series was stationary or non stationary hence use the unit root test. From the above results the Dickey Fuller value (18)>stationary R-squared (0.749) therefore we accept the hypothesis and conclude that there is a unit root hence non stationary of the time series data.

4.1.14 Correlation between the regions

The table below shows correlation between the different regions in Uganda for the study that was conducted, this was carried out in order to enable the researcher determine relationship between the four regions across the country namely; north, east, west and central as shown in the modle summary table, Anova table and table for the Coefficients as shown below.

This was in order to determine the hypothesis.

Ho : There is no difference in malaria prevalence by region

Ha  : There is difference in malaria prevalence by region.

The researcher used the correlation coefficient to determine malaria prevalence across different regions, this was specifically to determine malaria prevalence in central east and west using the northern region as a constant, the researcher used the P-value, when the P-Value is less than 0.05 we reject the null hypothesis and other wise accept the hypothesis.

Table 9: Correlation between the regions

From the figure above the results indicates that the R Square 0.877 implying that 87.7 of malaria prevalence is due the difference in the regions, this is also indicated by significance level of 0. .004 <0.05, therefore the results rejects the null hypothesis and the conclusion that there is a difference in malaria prevalence among the regions. Tshis is also in line with (Collins et al, 1997& Yadav et al., 1999) who states that socio-economic conditions of the community have direct bearing on the problem of malaria in Uganda some very poor parts of the country experience more cases of malaria cases. Ignorance and impoverished conditions of people contribute in creating source and spread of malaria and hinder disease control strategy. This was also evidenced by Filmer (2002) that high costs of malaria treatment may lead to delays in Treatment seeking behavior, whereby he found that the poorest groups in a society did not seek care as much as the non-poor, and did so at lower level public facilities.

4.1.15 Forecasting of malaria prevalence between in Urban areas in 2019

Ss s

Source: author’s computation

The results in the study indicates that malaria prevalence in 2016 was the lowest then it began to rise up to 2018 in urban setting there was an increase in malaria prevalence.

CHAPTER FIVE

SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS

5.0 Introduction

This chapter discusses what various scholars have written about the research findings;

5.1 Summary of findings

The results shows that there was much variation in the data in urban centers compared to rural areas for the last 10 years and the prevalence was high in towns compared to villages for last 10 years.

The study shows that rural residence had mean total malaria cases of 2988.23 with a standard deviation of 457.718 while urban residence had mean of 314.32 with a standard deviation of 142.437, this indicates that majority of the people in rural areas have more malaria cases than urban areas.

The results further show that most of the government should invest more programmes in fighting against malaria cases in rural areas.

The results further indicates that the Eastern region had the highest registered number of malaria cases with highest mean of 1022.03 and standard deviation of 192.211 followed by Northern with a mean of 902.48 and the standard deviation 158.922.  Western and central regions had mean malaria cases of 578.06 and 787.23 with the standard deviation of 184.080 and 154.660 respectively.

According to the findings when malaria cases in the north, east and west  equals to zero then malaria cases is  604.537, while  a unit increase in malaria cases in central will on average lead to 0.366 decrease in the north, this is statistically insignificant since the P-value, (0.082) >0.05 thus the null hypothesis is accepted, therefore there is no  difference in malaria prevalence across regions.

5.2 Conclusion

The study concludes that there is variation in malaria prevalence across the region some regions had higher malaria cases than others.

The study also concludes that malaria cases are on the rise.

The study also states that the variations in the total malaria cases are explained by the changes in the malaria cases of both rural and urban areas.

The study also concludes that malaria is high in the rural areas than urban areas.

5.3 Recommendation

The study recommends more infighting malaria so that the challenges brought by malaria are worked upon.

The study also recommends that the government should increase the number of health workers in the government hospital

Regions with high malaria levels should increase findings for the rural health facilities especially in northern Uganda.

5.4 Areas of further study

The study recommends the following areas for further study;

• The influence of mosquito nets on malaria prevention
• The influence of foreign aid on the influence of education of malaria prevalence in Africa.
• The influence of malaria medicine on malaria prevention

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APPENDIX

URBAN