ANALYSIS OF MALARIA PREVALENCE IN CHILDREN AGED BELOW 5 YEARS IN UGANDA FROM 2006 TO 2015

ACASE STUDY OF TORORO MAIN HOSPITAL

ABBREVIATIONS/ACRONYMS

ACTs               : Artemisnin Based Combination

IPTp                : Intermittent Preventive Treatment in pregnancy

LLITNs           : Long Lasting Insecticide Treated Nets

IRS                  : Insecticide Residual Spray

WHO               : World Health Organization

UNICEF          : United Nations International Children’s Fund

HIV                 : Human Immune Deficient Virus

AIDS               : Acquired immune deficient syndrome

WMR               : World Malaria Report

HMIS              : Health Management Information System

MoH                : Ministry of Health

PMI                 : Presidents Malaria Initiative

THMIS            : Tanzania HIV/AIDS Malaria Indicator

UMIS              : Uganda Malaria Indicator Survey

ABSTRACT

The topic of study is analysis of malaria prevalence in children aged below 5 years in Uganda from 2006 to 2015 and the case study a case study of Tororo main hospital.

The general objective of the study is to analyses the prevalence of malaria in children aged below 5 years in Uganda, while the study is guided by the following Specific Objectives of the Study; to determine the distribution of malaria among the children below 5 years, to compare and contrast malaria prevalence by residence and region and to forecast malaria prevalence for children aged below 5 years in Uganda

The study shall use 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.

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 was done in order to prepare data, analyze and compile a research report. The study used time series analysis and descriptive statistics was used to describe the information got from the field this will be inform of graphs and tables. Data Analysis involve applying statistical techniques on it for easy presentation. It include 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.

The study concludes that there is variation in malaria prevalence across the region some regions had higher malaria cases than others, 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, lastly the study also concludes that malaria is high in the rural areas than urban areas.

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

According to the latest WHO estimates, released in December 2016, there were 212 million cases of malaria in 2015 and 429 000 deaths, Between 2010 and 2015, malaria incidence among populations at risk fell by 21% globally; during the same period, malaria mortality rates among populations at risk decreased by 29%. An estimated 6.8 million malaria deaths have been averted globally since 2001, Sub-Saharan Africa continues to carry a disproportionately high share of the global malaria burden. In 2015, the region was home to 90% of malaria cases and 92% of malaria deaths. Some 13 countries mainly in sub-Saharan Africa account for 76% of malaria cases and 75% deaths globally, (WHO, 2016).

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. Between 2010 and 2015, the under-5 malaria death rate fell by 29% globally, However malaria remains a major killer of children under five years old, taking the life of a child every two minutes, (UNICEF, 2016).

Malaria remains one of the major killers of humans worldwide, threatening the lives of more than one-third of the world’s population. It thrives in the tropical areas of Asia, and African continents, Malaria has far-reaching medical, social and economic consequences for the  countries in which it is endemic due to its high and alarming morbidity and mortality rates. Each year approximately 2.5 million people die of malaria, many of whom are children. According to WHO estimates, 40% of the population of the world lives in areas where malaria is endemic with the direct and indirect costs of management being very high (WHO 2010).

Malaria is an important health problem and pregnant women recognize its serious  consequences (Mbonye et al., 2010). Effective malaria prevention and treatment interventions exist that have beneficial effects on the disease in pregnancy (Schultz et al, 2014). However, use of these interventions largely depends on local beliefs on malaria, access, costs, attitudes towards health care providers and the level of acceptability of the health system (Magnussen, 2007). These affect care seeking with regard to malaria prevention at health units and also reveal issues related to affordability and acceptance of these services in most parts of sub-Saharan Africa.

Between 2000 and 2008, the use of ITNs saved the lives of an estimated 250,000 infants in Sub-Saharan Africa from malaria; about 13% of households in Sub-Saharan countries owned ITNs in 2007 and 31% of African households were estimated to own at least one ITN in 2008. In 2000, 1.7 million (1.8%) African children living in areas of the world where malaria is common were protected by an ITN. That number increased to 20.3 million (18.5%) African children using ITNs in 2007, leaving 89.6 million children unprotected and to 68% African children using mosquito nets in 2015, most nets are impregnated with pyrethroids, a class of insecticides with low toxicity (WHO, 2013).

Malaria is the cause of outpatient, inpatient and admissions of children less than five years of age at health facilities in Tanzania (WHO, 2002).The high burden of malaria in Tanzania this is due to the fact that, every year 14-18 million new malaria cases are reported. The annual incidence rate is 400-500/1,000 people and this number doubles for children less than five years of age. There are 100,000-125,000 annual deaths due to malaria, (70-80,000 in under-fives) (Mercia et al, 2004).

Health indicators for children show very slow progress over the last 10 years. While Uganda has made some progress in reducing under-five mortality from 137 per 1,000 live births in 2005/06 to 90 per 1,000 live births in 2011/12, child and maternal health conditions continue to impose the highest total disease burden with perinatal and maternal conditions accounting for 20.4% (Ministry of Health [MoH], 2010). In 2011, Uganda ranked 26th amongst countries with highest under-five deaths globally (UNICEF, 2012). Progress in reducing maternal mortality, an underlying factor in child mortality, has been very slow coming from 438 per 100,000 live births to 320 in 2011 (MOFPED, 2013). Based on the rates of progress at the time of writing this report, Uganda was unlikely to achieve Millennium Development Goals (MDG) 4 and 5, which focused on reducing under-five mortality and improving maternal health respectively. It was also unlikely to achieve the goals set in the National Development Plan (NDP) II of reducing the Infant Mortality Rate per 1,000 live births from 54 to 44 and reducing the under-five mortality rate per 1,000 live births from 90 to 51 (MoH, 2013; GoU, 2015). The allocation to health as a percentage of the total government budget reduced from 9.6 percent in 2003 to 8.6% in 2014/15 contrary to the Abuja Declaration target of 15% (GoU, 2015)

Malaria remains the second killer disease among children under five, claiming 42 children daily and 1,095 annually, the Government of Uganda and partners called for more investments towards the reduction of malaria deaths especially among children under five years and women who are more vulnerable (Uganda Demographic Health Survey 2011).

During the week of 5-11 September 2016, a cumulative total of 195,424 cases of malaria with 43 deaths (CFR 0.02 percent) were reported. Over 40,062 children under the age of 5 years affected by the epidemic are under five years of age. Over the past month, there has been a reduction in the number of cases reported in the 10 Indoor Residual Spray (IRS) districts as well as in Arua, which could be due to the onset of the dry spell. Most malaria epidemic districts in Northern Uganda are still above the respective malaria threshold. The most at risk populations are in the districts of Gulu, Nwoya, Amuru, Kitgum, Lamwo, Agago, Pader, Oyam, Apac, Arua and Kole (MoH, 2016).

Malaria remains the second killer disease among children under five, claiming 42 children daily and 1,095 annually according to the Uganda Demographic Health Survey 2011, 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.

1.2 Statement of the problem.

The government of Uganda in conjunction with other non-government organizations have put a lot of effort to curb the malaria infection in children, adults and pregnant women in Uganda, 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 (Francis et al., 2006).  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). With that there is need to take analysis of the extent of this malaria prevalence to date since the perennially prevalent malaria, therefore, remains an ever existing danger for humanity, in every part of the globe.

1.3   Objectives of the Study

1.3.1General Objective:

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

1.3.2 Specific Objectives of the Study

1. To determine the distribution of malaria among the children below 5 years.
2. To compare and contrast malaria prevalence by residence and region.

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

1.4 Research hypotheses.

1. Ho₁: There is no trend for malaria prevalence among the children.

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

iii    Ho₄: There is no difference in malaria prevalence by region.

1.5 Scope of the study

The study scope covered the following aspects;

1.5.1 Study scope

This included; determining the distribution of malaria among the children below 5 years, to compare malaria prevalence by residence and region and to forecast malaria prevalence for children aged below 5 years in Uganda.

1.5.2 Geographical scope

The study was carried out in Ministry of health Kampala Uganda.

1.5.3 Time scope

The research was carried out from February to August 2017.

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

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 Distribution of malaria among the children under 5 years

Malaria is a major cause of morbidity and mortality worldwide, especially in young African children. It is a major parasitic disease that can be prevented and treated (US PMI, 2009). Several efforts based on protection of individuals, households at community level (Warrell et al, 2002) have been initiated to ensure morbidity and mortality due to malaria is reduced. Currently several proven and cost effective malaria control interventions have been largely initiated in malarias areas. These include prompt treatment with Artemisnin Based Combination (ACTs), high coverage with LLITNs, Intermittent Preventive Treatment in pregnancy (IPTp) and Insecticide Residual Spray (IRS). These measures have significantly proven to reduce clinical and risks of malaria infection particularly in pregnancy and children under five years who are vulnerable groups to malaria.

Prevalence of malaria in young children has been reported in many studies from both developing and underdeveloped countries. In Democratic Republic of Congo, surveys which were conducted in 1980s and 2000 reported two fold increase of blood smears positivity from 17% in 1980s to 34% in 2000. In Tanzania the first national, population-based 2007-08 Tanzania HIV/AIDS Malaria Indicator survey (2007-08 THMIS) showed that 18% of children under five years of age had tested positive for malaria on the Mainland, whereby in rural areas higher prevalence of 20% compared to the urban areas of 8% was reported. There were marked regional variations that ranged from 0.4% in the highland areas around Arusha to 41.1% in the northwestern region of Kagera. The survey also showed an increasing prevalence by age from about 9% in infants (6-11 months) to 22% in children aged 2-4 years. Malaria prevalence showed a direct relationship with the socio-economic status and education of the mother of children under-five years of age. Households with lowest wealth quintile were more likely to test positive for malaria than those from households in the highest quintile.

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.

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.

Knowledge on malaria A number of studies have investigated differences in knowledge and reported health seeking behavior between men and women. Most found either no difference or those women had more limited decision-making and financial power to act. This was associated with failures and delays in seeking treatment, with differential understanding of malaria between men and women, and differential health-seeking behaviour. Women delayed seeking care until men were available, while men were less willing to spend on child health. (Al-Taiar et al 2009 & Oberlander and Elverdan 2000).These differences are critical when considering the main child-caring role of women and children‘s increased vulnerability to malaria.

2.2 Malaria prevalence by residence and region for children under 5 years

According to (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. Households can be forced to sell their foods crops in order to cover the cost of treatment (Wandiga et al, 2006.) Depleting household resources and leading to increased food shortages, debts, and poverty for the poorest households. The costs of malaria are highly regressive, with the poorer households spending a significantly higher proportion of their income on the on the treatment of malaria than their least poor counterparts.

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.

Socioeconomic conditions of the community have direct bearing on the problem of malaria. Ignorance and impoverished conditions of people contribute in creating source and spread of malaria and hinder disease control strategy (Collins et al, 1997& Yadav et al., 1999). 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.

Health education communication is one of the key components in malaria control and prevention. Serious obstacles in most disease control strategies include lack of effective health information, education, and communication programs. Community and health providers need to understand the problem in all its relevant aspects, as well as be aware of the options available for improvement (Mboera et al, 2007).

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 Malaria forecast

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 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 will be done in order to prepare data, analyze and compile a research report.

The study used time series analysis and descriptive statistics was used to describe the information got from the field this will be 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.

a. Graphical presentation

This involved plotting the series 𝑌𝑡 against time t.

b. 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 was done basing on the correlogram plot obtained. Where both autocorrelation and partial correlation cuts of at a certain point, we conclude that the data follows an autoregressive model. The order p, of the ARIMA model is obtained by identifying the number of lags moving in the same direction. In case the series was non stationary, the number of times we difference the series to obtain stationarity is the value of d.

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 was 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 was 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 2018 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_R =1 if Rural , 0 other wise

D_C is a dummy for central, D_c=1 if central, 0 otherwise

D_N is a dummy for north , D_N= 1 if North , 0 otherwise

D_E is a dummy for East, D_E=1 if East, 0 otherwise

3.8 Ethical considerations

The researcher began 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 were informed that the purpose of the information shall be strictly for academic purposes only and the information provided was treated with highest level of confidentiality.

CHAPTER FOUR

PRESENTATION OF RESULTS

4.0 Introduction

         4.1 Comparison of Malaria Cases Residence

Table 1: Comparison of Malaria Cases Residence

The above table shows the statistics obtained from places of residences on the malaria prevalence from the year 2006 to 2015 making it a total of 10observations.

The study shows that rural residence had mean total malaria cases of 3109.70 with a standard deviation of 665.553 while urban residence had mean of 428.90 with a standard deviation of 207.819. This implies 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.

This indicates that there is more malaria cases in towns than in rural areas , it further proves the point that perhaps urban people are better informed on the different ways of preventing malaria that is why there are fewer malaria cases in towns than villages.

4.1.2 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 Ten years.

Table 2: Comparison of Malaria Cases by Region

From the above table the study revealed that Northern region had the highest registered number of malaria cases with highest mean of 1011.00 and standard deviation of 247.958 followed by Eastern with a mean of 1000.90 and the standard deviation 249.880.  Western and central regions had mean malaria cases of 833.60 and 693.60 with the standard deviation of 261.615 and 693.60 respectively. Despite eastern region registering the highest number of malaria there was little variation in the data.

This also shows that majority of people in northern Uganda have malaria than any other region in Uganda showing that there is need for the government to increase omn the camp[aighns of fighting malaria in the area.

Table 3: correlations

The table above shows the relationship between the numbers of registered malaria cases by region for the children below 5 years from 2006 to 2015. The correlations between north and east, north and west, north and central are -0.061, .790^** and .547 respectively and correlations are only significant between north and west.

That between east and west, east and central, west and central, is -0.061, .790^** and .547, respectively and correlation is significant between central and west. For significance it implies that those regions almost registered the similar number of cases during the same period or the prevalence rate was the same across those regions.

4.1.4 Correlations of Malaria Cases by Residence

Table 4: Correlations of Malaria Cases by Residence

The above table shows that relationship of malaria prevalence by residence in Uganda for the study that was conducted. There is a high significant and positive relationship of 0.698^* between rural and urban areas and the results are significant since sig 0.000<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.

4.1.5 Determination of the distribution of malaria across Regions

The results above were obtained to determine the distribution of malaria across Regions by using Kruskal-Wallis Test since region is categorized into four categories i.e. north, east, west and central. From the above test the significant value obtained is 0.647 which is greater than 0.05 therefore we fail reject the null hypothesis and conclude that the distribution of total malaria cases is the same across categories of region.

4.1.6 Regression of total malaria cases on rural and urban

Table 5: Regression of total malaria cases on rural and urban

The R-value tells us about correlation coefficient (1.000) 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 100% of the variations in the total malaria cases are explained by the changes in the malaria cases of both rural and urban areas.

Table 6: ANOVA

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.

Table 7: Coefficients

The table is used to explain the effect of places of residence on malaria cases. The model is estimated as Y=B+B₁X₁+B₂X₂ where B is constant, B₁ is coefficient for rural and B₂ is the coefficient for urban.

From the model 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.

4.1.8 Forecasting model for the year 2018

Table 8: Forecasting model for the year 2018

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

4.1.9 Autoregressive Integrated Moving Average forecasting

Table 9: Autoregressive Integrated Moving Average forecasting

The forecasting of the model required to first test the principle of stationary in order to find out if the time series was stationary 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 nit root hence non stationary of the time series data.

4.1.10 correlation between rural and urban

Table 10: correlation between rural and urban

The study indicates that there is a strong correlation between rural and urban in malaria prevalence, this is shown by the mean value of 0.698.

This finding shows that when there is a strong relationship between rural and urban in malaria prevalence.

4.1.11 Correlation between the regions

Table 11: 4.1.11 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.

Table 12:ANOVA

From the results the significance level is 0.004 implying that the study rejects the null hypothesis, Since sig 0.004 <0.05 and the study concludes that there is a difference in malaria prevalence among the 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.

Forecast

In the year 2007 the total malaria cases were lower compared to 2016, 2017 and 2018. It was evidenced that the year 2007 registered the least number of malaria cases. However malaria cases again shoot up between 2008 and 2010 as seen on the graph, the graph further indicates that malaria cases are on the rise.

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.

Findings revealed that Northern region had the highest registered number of malaria cases with highest mean of 1011.00 and standard deviation of 247.958 followed by Eastern with a mean of 1000.90 and the standard deviation 249.880.  Western and central regions had mean malaria cases of 833.60 and 693.60 with the standard deviation of 261.615 and 693.60 respectively. Despite eastern region registering the highest number of malaria there was little variation in the data.

The table above shows the relationship between the numbers of registered malaria cases by region for the children below 5 years from 2006 to 2015. The correlations between north and east, north and west, north and central are -0.061, .790^** and .547 respectively and correlations are only significant between north and west.

That between east and west, east and central, west and central, is -0.061, .790^** and .547, respectively and correlation is significant between central and west. For significance it implies that those regions almost registered the similar number of cases during the same period or the prevalence rate was the same across those regions.

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.

In the year 2007 the total malaria cases were lower compared to 2016, 2017 and 2018. It was evidenced that the year 2007 registered the least number of malaria cases. However malaria cases again shoot up between 2008 and 2010 as seen on the graph, the graph further indicates that malaria cases are on the rise, the total malaria cases for 2018 will be 5329.

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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