Research proposal writer

ANALYSIS OF MALARIA PREVALENCE IN CHILDREN AGED BELOW 15 YEARS IN UGANDA

LIST OF ACRONYMES

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.

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.

1.1 Background

Malaria is a name derived from the Italian word, “mal aria,” or bad air. The French scientist discovered the real cause of malaria as the single-celled Plasmodium parasite Alphonse Laveran (1880). The parasites are spread to people through the bites of infected female Anopheles mosquitoes, called “malaria vectors.” There are 5 parasite species that cause malaria in humans, and 2 of these species pose the greatest threat. The parasites live part of its life in humans and part in mosquitoes. 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, Africa, and Central and South America, where it strikes millions of people. Each year 350 to 500 million cases of malaria occur worldwide. Sadly, more than 1 million of its victims, mostly young children, die yearly.

According to WHO, (2015) estimates, on the other hand released in December 2015, there were 214 million cases of malaria in 2015 and 438 000 deaths. But Between 2000 and 2015, malaria incidence among populations at risk fell by 37% globally; during the same period, malaria mortality rates among populations at risk decreased by 60%. An estimated 6.2 million malaria deaths have been averted globally since 2001.

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 15 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

1.3.1   General Objective:

The general objective of the study is to analyses the prevalence of malaria in children aged below 15 years in Uganda.

1.3.2 Specific Objectives of the Study

1. To determine the distribution of malaria among the children below 15 years.
2. To compare malaria prevalence by residence and region.

• To forecast malaria prevalence for children aged below 15 years in Uganda

1.4 Research hypotheses.

1. Ho₁: There is no trend for malaria prevalence among the children.
2. Ho₂: There is no seasonality for malaria prevalence among the children

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

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

1.5 Scope of the Study:

1.5.1 Subject scope

This research intends to analyses the prevalence of malaria in the children aged below 15 years in Uganda and my data source will be ministry of health 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 15 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 15 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 15 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 .

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 The distribution of malaria among the children in Uganda.

The vector most commonly is transmitted by an infected female Anopheles mosquito. The mosquito bite introduces the parasites from the mosquito’s saliva into a person’s blood. The parasites then travel to the liver where they mature and reproduce. Five species of Plasmodium can infect and be spread by humans (Shah, 2010). Most deaths are caused by P. falciparum because P. vivax, P. ovale, and P. malariae generally cause a milder form of malaria. The species P. knowlesi rarely causes disease in humans. Malaria is typically diagnosed by the microscopic examination of blood using blood films, or with antigen-based rapid diagnostic tests (White and Dondorp, 2012). Malaria.

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.

2.2 The compare malaria prevalence by residence and region.

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 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 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 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.

3.2 Data Sources.

Secondary data will be 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 will involve 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 will use time series analysis and descriptive statistics will be used to describe the information got from the field this will be inform of graphs and tables

Data Analysis will involve applying statistical techniques on it for easy presentation. It will 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.

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. This involves finding out the values of p, d, and q

where;

p is the number of autoregressive terms

d is the number of times the series is differenced

q is the number of moving average terms

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

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

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.

REFERENCES

1. . World Malaria Report.; Geneva: 2008
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6. Uganda Bureau of Statistics. Uganda Demographic and Health Survey 2006. 2007.
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9. World Health Organization’s World Malaria Report 2013 and the Global Malaria Action Plan
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11. President’s Malaria Initiative.President’s Malaria Initiative Uganda Malaria Operational Plan for FY 2010. 2010
12. Uganda Ministry of Health.Uganda Malaria Control Strategic Plan 2005/6–2009/10. 2005