Final work

i
A TIME SERIES ANALYSIS OF DIABETES CASES IN RWANDA FOR THE
PERIOD 2006-2015: A CASE STUDY OF KIREHE HOSPITAL
BY
KAYITARE ERICK
13/U/6395/EVE
SUPERVISOR: ASS.PROFESSOR L.K. ATUHAIRE
A DISSERTATION SUBMITTED TO THE SCHOOL OF STATISTICS AND
PLANNING IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
AWARD OF DEGREE OF BACHELOR OF SCIENCE IN QUANTITATIVE
ECONOMICS OF MAKERERE UNIVERSITY KAMPALA
JUNE 2016

i
DECLARATION
I, KAYITARE ERICK, declare that this work is original and has never been presented to any
institution of higher learning or organization by any person for the award of any qualification.
Signature ……………………… Date …. /…./…….
KAYITARE ERICK
Student
Signature ………………………. Date …. /…../…….
ASS.PROF. L.K. ATUHAIRE
Supervisor

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DEDICATION
I dedicate this piece of work to my mother Kayitesi and my grandmother Mukangira who are
both victims of Type II diabetes mellitus.

iii
ACKNOWLEDGEMENTS
My Lord and God! You are worthy to receive glory, honour, and power. For you created all
things. Had it not been for your Grace, this report would not be a success.
Boundless and ecstatic thanks to many who in one way or the other assisted in the preparation
of this report. First and foremost, to Bushayija’s family, Safi’s family and lesavant family for
their unstinting encouragement, support and cheer-leading. This journey has been an
interesting one, many mountains and valleys, which in turn have made me a better and strong
person.
Many thanks to my supervisor Assoc.Prof. L.K. Atuhaire, a senior lecturer at the SSP for his
outstanding tireless dedication of time since the beginning of this research to its completion.
Every time I went to his office, my whole brain could function at its best because I was
always challenged by his questions. Honestly, if it were not for his constructive criticisms,
comments and corrections, this research would have been impossible. I take this wonderful
opportunity to thank the SSP teaching staff for the knowledge they have been giving me way
back in 2013 to date. Heartfelt thanks go to the dean of SSP-Assoc.Prof.Robert Wamala for
igniting my love for data analysis, today am a proud teacher of the same subject.
In a special way, I am really grateful to Kirehe hospital especially Mr.Cyprien who helped
me to get all the data I needed for this research project. I highly believe that this research will
be of much help to the same hospital.
A vote of thanks to my dearest friends, fellow gallant makererians, crème de la crème, the
entire RAW discussion group for the cooperation and teamwork we had for three years.
Whenever I was around Wadada Robert, Okello Milton, Sanoni Isabirye, Brigitte, Amanda
and others, my fear of the hard course units like stochastic modelling would vanish
immediately, because these great people always had the right information.
Finally, I want to thank everyone who has been with me for this undergraduate course,
indirectly or directly. I must say, am humbled for having met you, in one way or the other, we
helped each other, supported each other, strangers became brothers and sisters while others
followed in the steps of Adam and Eve, all this made life at MUK interesting.
About learning, this is what my role model Jim Rohn said: “Learning is the beginning of
wealth. Learning is the beginning of health. Learning is the beginning of spirituality.
Searching and learning is where the miracle process all begins.”

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ABSTRACT
This study of Time series analysis of diabetes mellitus was carried out in Kirehe hospital
which is located in Eastern Rwanda, Kirehe district. The main objective of the study was to
carry out a time series of diabetes mellitus in Rwanda.
In this study, secondary data was collected from the Hospital’s data records on diabetes
mellitus with respect to age, sex, age groups and type of diabetes. Data collected was
thereafter entered, analysed using Ms-Excel and Stata and tests of hypotheses by the use of
Daniels test test at 95% confidence level were done to determine whether there was a trend in
both types of diabetes, also trend testing was done on both male patients and female patients.
Finally trends were fitted to forecast diabetes mellitus cases for the next 10 years.
The results from the study revealed that the year 2013 had the highest number of diabetes
cases, 33 patients which accounted for 18.33%. Results also revealed that Type II diabetes
mellitus was the most common type of diabetes with 97.22% prevalence levels for the period
2006-2015. The most affected age group was that of 35 years of age and above which
accounted for 83.89% of the overall diabetes mellitus incidence.
This research revealed that diabetes mellitus cases for females or the series was trended with
95% confidence level. The researcher also failed to reject the null hypothesis that diabetes
mellitus occurrence for males is not trended, he therefore concluded with 95% confidence
that the data was trended, in the same vein, type II diabetes mellitus cases were found to be
trended. Surprisingly, type I diabetes mellitus cases showed signs of stationarity with 95%
confidence level.
The researcher found out that both type I and type II diabetes mellitus were estimated to be
increasing over the next 10 years. By 2025, the former was estimated to be 2049 cases while
the latter was estimated to be 2011 cases.
The researcher made four recommendations namely; priority should be preventive care,
where physical exercises and diet should be among top priorities in order to avoid this deadly
non-communicable disease. Second, the researcher went ahead to call for attention on type I
diabetes mellitus which was found to be increasing at an increasing rate especially among the
young people.
Further still, much attention and support is needed to people aged 35 and above. This is
because, results revealed that 80% diabetes cases were among those aged 35 years and above.
Lastly, the researcher recommended Ministry of health and the government of Rwanda in
general to put up measures against diabetes mellitus by targeting females first since they are
the most affected gender.

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LIST OF ABBREVIATIONS
DM: Diabetes Mellitus
MoH: Ministry of Health
MUK: Makerere University Kampala
NCD: Non-communicable disease
SSP: School of Statistics and Planning
T1D: Type I diabetes
T2D: Type II diabetes
WHO: World Health Organization

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TABLE OF CONTENTS
DECLARATION........................................................................................................................i
DEDICATION...........................................................................................................................ii
ACKNOWLEDGEMENTS..................................................................................................... iii
ABSTRACT..............................................................................................................................iv
LIST OF ACRONYMS .............................................................................................................v
TABLE OF CONTENTS..........................................................................................................vi
LIST OF TABLES.....................................................................................................................x
LIST OF FIGURES ..................................................................................................................xi
CHAPTER ONE: INTRODUCTION....................................................................................1
1.1BACKGROUND TO THE STUDY.....................................................................................1
1.2. PROBLEM STATEMENT.................................................................................................2
1.3. OBJECTIVES OF THE STUDY........................................................................................3
1.3.1. Major objective...................................................................................................................3
1.3.2. Specific objectives………………………………………………………………………3
1.4. HYPOTHESES...................................................................................................................3
1.5. SCOPE AND COVERAGE OF THE STUDY ..................................................................4
1.6. SIGNIFICANCE OF THE STUDY....................................................................................4
CHAPTER TWO: LITERATURE REVIEW.......................................................................5
2.0. Introduction.........................................................................................................................5
2.1. Definition and facts about diabetes mellitus.......................................................................5
2.2. Epidemiology of diabetes mellitus......................................................................................5
2.3. Prevalence of diabetes mellitus...........................................................................................6
CHAPTER THREE: METHODOLOGY .............................................................................8
3.0. Introduction.........................................................................................................................8
3.1. Study population.................................................................................................................8
3.2.Nature and source of data ....................................................................................................8
3.3. Data collection procedure ...................................................................................................8
3.4. Data analysis procedure ......................................................................................................8
3.4.1. Descriptive statistics ......................................................................................................9
3.4.2. Bivariate analysis...........................................................................................................9

vii
3.5. Analysis of Diabetes mellitus cases...................................................................................9
3.5.1. Analysis of diabetes mellitus cases for females............................................................9
3.5.2. Analysis of diabetes mellitus cases for males.............................................................10
3.5.3. Analysis of type II diabetes cases ...............................................................................10
3.5.3.1. Trend fitting for Type II Diabetes and forecast ......................................................11
3.5.3.2. Estimation of the Type II Diabetes trend model.....................................................12
3.5.4. Analysis of type I diabetes cases .................................................................................10
3.5.4.1. Trend fitting for Type I Diabetes and forecast........................................................11
3.5.4.2. Estimation of the Type I Diabetes trend model ......................................................11
3.6. Challenges faced ...............................................................................................................12
CHAPTER FOUR..................................................................................................................13
PRESENTATION, ANALYSIS AND INTERPRETATION OF FINDINGS .................13
4.0. Introduction.......................................................................................................................13
4.1. Descriptive statistics of diabetes mellitus.........................................................................13
4.1.1. Diabetes mellitus by Type .........................................................................................14
4.1.2. Diabetes cases with respect to Age............................................................................14
4.2. Bivariate Analysis of diabetes mellitus.............................................................................15
4.2.1. Diabetes type in relation to sex..................................................................................15
4.2.2. Diabetes type in relation to age groups......................................................................15
4.3. Hypothesis testing.............................................................................................................16
4.3.1. Research hypothesis one............................................................................................16
4.3.2. Research hypothesis two............................................................................................17
4.3.3. Research hypothesis three..........................................................................................18
4.3.3. Research hypothesis four...........................................................................................19
4.4. Fitting Trends and forecasting for diabetes mellitus.........................................................19
4.4.1.1. Trend fitting for Type I diabetes.............................................................................19
4.4.1.2. Forecast for Type I diabetes mellitus......................................................................21
4.4.2.1. Trend fitting for Type II diabetes............................................................................21
4.4.2.2. Forecast for Type II diabetes ..................................................................................23
CHAPTER FIVE ...................................................................................................................24
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS .......................................24
5.0. Introduction.......................................................................................................................24
5.1. SUMMARY AND CONCLUSIONS ...............................................................................24
5.1.1. Type I diabetes mellitus.............................................................................................24
5.1.2. Type II diabetes mellitus............................................................................................25

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5.2. RECOMMENDATIONS..................................................................................................25
APPENDIX: DATA COLLECTED FROM KIREHE HOSPITAL........................................26
REFERENCES ........................................................................................................................31

x
LIST OF TABLES
Table 4. 1: Diabetes mellitus distribution by type ...................................................................14
Table 4. 2: Percentage distribution of diabetes mellitus by age ..............................................14
Table 4.3: Cross tabulation of sex by type of diabetes: Chi-square.........................................15
Table 4. 4: Cross tabulation of age groups by type of diabetes: Chi-square ...........................15
Table 4 .5: Female Diabetes mellitus cases (yt) in Kirehe hospital .........................................16
Table 4 .6: Male Diabetes mellitus cases (yt) in Kirehe hospital.............................................17
Table 4. 7: Type II Diabetes mellitus cases (yt) in Kirehe hospital.........................................18
Table 4. 8: Type I Diabetes mellitus cases (yt) in Kirehe hospital ..........................................19
Table 4 .9: Table used to fit the trend for Type I diabetes.......................................................20
Table 4 .10: Forecasting for Type I diabetes mellitus for the years 2016-2025 ......................21
Table 4. 11: Table used to fit the trend for Type II diabetes ...................................................22
Table 4 .12: Forecasting for Type I diabetes mellitus for the years 2016-2025 ......................23

xi
LIST OF FIGURES
Figure 4 .1: Diabetes mellitus cases for the period 2006-2015................................................13
Figure 4. 2: Trend of Type I diabetes cases in Kirehe hospital (2006-2015) ..........................20
Figure 4. 3: Trend of type II diabetes cases for the period 2006-2015....................................22

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CHAPTER ONE: INTRODUCTION
1.1. BACKGROUND TO THE STUDY
Around 1990’s, many people were suffering from diabetes without knowing, it was worse
when doctors told someone that they had failed to diagnose the problem. Such innocent
people resorted to herbal medicine while others went as far as consulting witch doctors.
However, around early 2000’s, the truth came to surface and it was none other than the
deadly diabetes mellitus-as scientists call it. Measures were put across in order to avoid
getting this disease. People started to be diet-conscious; less sugar was taken and for the first
time, edible salt and cooking oil could be measured carefully before cooking. In general, it
was an awakening moment.
Diabetes mellitus is one of the most common non-communicable diseases globally. It is said
to be the fourth or fifth leading cause of death in most developed countries and is epidemic in
many developing and newly industrialized nations. Many complications from diabetes
include stroke, amputations, peripheral neuropathy, foot ulcer, cataracts and albuminuria.
Diabetes is regarded as one of the most challenging health problems in the 21st century
(International Diabetes Federation, 2003).
Diabetes mellitus is basically of two types: Type I diabetes or Insulin Dependent Diabetes
Mellitus (IDDM) or juvenile diabetes and Type II diabetes or Non-Insulin-Dependent
Diabetes Mellitus (NIDDM) or adult onset diabetes. Type I diabetes, is primarily a disease of
the pancreas. It results when the pancreas produces insufficient amounts of insulin to meet
the body’s needs. A Type I diabetic patient needs daily injections of insulin to live, hence the
name IDDM. It develops most often in children and young adults, but the disorder can appear
at any age. Type II diabetes, is a disease of insulin function and it is the most common form.
Subjects with this type present with measurable quantities of insulin in the blood, and
sometimes, as in obesity, the insulin levels are relatively high. This insulin is however
ineffective because of the insensitivity of target tissues to its action. About 90 to 95 percent
of people with diabetes have Type II diabetes. This form of diabetes usually develops in
adults over the age of 40 and is most common among adults over age55. About 80 percent of
people with Type II diabetes are overweight. Type II diabetes is often part of a metabolic
syndrome that includes obesity, elevated blood pressure, and high levels of blood lipids.
Unfortunately, as more children and adolescents become overweight, Type II diabetes is
becoming more common in young people (International Diabetes Federation [IDF], 2001).

2
Diabetes mellitus is widespread throughout the world. In 1985, the World Health
Organization noted that diabetes mellitus affects more than 30 million people worldwide
(WHO, 1985). In many countries, it is now the leading cause of death, disability and high
health care costs. It is noted that approximately 177 million people are now diagnosed with
diabetes worldwide and around 4 million deaths every year are attributable to its
complications (WHO, 2000). Rwanda is one of the 32 countries of the International Diabetes
Federation Africa (IDF AFR) region. 387 million people have diabetes in the world and more
than 22 million people in the AFR region; by 2035 this figure will almost double. There were
299,100 cases of Diabetes in Rwanda in 2014 (Gishoma, 2014).
This non-communicable disease did not spare my family. Currently, many family members
have this disease—six in total, including my lovely mother. I believe that all problems have
solutions; it may take many years or centuries, but if we keep searching for the solution, we
will eventually find it. As a quantitative economics student, I want to be part of the team
looking for the solution to this problem-by providing the necessary quantitative data in order
to make informed decisions.
1.2. PROBLEM STATEMENT
Diabetes mellitus is a growing problem worldwide with rising prevalence rates, including in
sub-Saharan Africa. The global rise in type 2 diabetes is occurring fastest in developing
countries. This is all compounded by the fact that as many as 50% of people with diabetes are
yet to be diagnosed (International Diabetes Federation, 2003).
Alemu (2009) points out that the burden of non-communicable diseases is increasing rapidly.
He argues that such diseases have a high cost of management and yet they can be prevented
and controlled using simple and cost effective means by targeting their risk factors.
In Rwanda, Non-communicable diseases (NCDs)—probably accounting for about 25% of the
national burden of disease—have yet to be addressed in a strategic and systematic way. These
diseases include cardiovascular disease, cancer, epilepsy, pulmonary disease, and diabetes,
among others. These are global diseases and yet, more often than not, NCDs are thought to be
problems of middle- and high-income countries. In such countries, risk factors for NCDs
include obesity, tobacco use, and other factors termed poor lifestyle choices. However, in
Rwanda and other developing countries, this is not the case. NCDs are instead linked to

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malnutrition, infection, congenital abnormalities, and toxic environments. All of these factors
are ultimately exacerbated by poverty (World Health Organization, 2008).
A number of interventions are available to control the complications of diabetes mellitus,
these include insulin therapy, dietary restrictions and physical exercises. However, no study
has been conducted to address the problem of time series model which can help the
Government and other concerned parties to make the necessary planning as far as diabetes
mellitus is concerned. This study addresses this problem.
1.3. OBJECTIVES
1.3.1. Major objective
The main objective of this study is to carry out a time series analysis of diabetes cases in
Rwanda.
1.3.2. Specific objectives
i. To estimate a time series model on the number of diabetes cases for type I
diabetes.
ii. To estimate a time series model on the number of diabetes cases for type II
diabetes.
iii. To fit trends and carry out forecasts for diabetes cases in the next 10 years.
iv. To make a trend analysis of diabetic cases for males.
v. To make a trend analysis of diabetic cases for females.
1.4. HYPOTHESES
i. Diabetes mellitus occurrence for females is trended.
ii. Diabetes mellitus occurrence for males is trended.
iii. The type two diabetes time series is trended.
iv. The type one diabetes time series is trended.

4
1.5. SCOPE AND COVERAGE OF THE STUDY
The study will cover Type 1 and type 2 diabetes cases that were registered at Kirehe hospital
for the period January 2006 to December 2015. The study will go beyond descriptive analysis
to a time series analysis of Annual registration of Diabetes cases by various variables like
gender, age, and type of diabetes.
1.6. SIGNIFICANCE OF THE STUDY
Diabetes mellitus is a multi-system disorder. Its complications can involve any organ in the
body. Some of these complications include visual impairment, cardiovascular disease, limb
and brain damage, impotence, kidney failure, urinary tract and stroke. Because of its chronic
nature, the severity of its complications and the means required to control them, diabetes is a
costly disease, not only for the affected individual and his/her family, but also for the health
authorities.
The study will be of much relevance to the Government and Medical society at large who
will use the model predictions to know how they can plan for the treatment, control and
prevention of the disease and how much money the Government and its development partners
will spend in future. The findings will help academicians, especially those interested in non-
communicable diseases. It will furthermore be of use to the Ministry of Health of Rwanda
which will be able to identify the most affected categories.

5
CHAPTER TWO: LITERATURE REVIEW
2.0. Introduction
This chapter presents a review of the literature related to the study. Most of the literature
presented here, concentrates on the epidemiology of diabetes mellitus, its control measures
and various prevalence rates across the world.
2.1. Definition and facts about diabetes mellitus
Diabetes mellitus is a growing public health affecting people worldwide both in developing
and developed countries, and poses a major socio-economic challenge. A chronic metabolic
disorder of multiple aetiologies is assuming epidemic proportions worldwide. It is also a
complex disorder with profound consequences both acute and chronic. Genetic and
environmental factors play a role in the development of the disease. The cells of the body
cannot metabolise sugar properly due to a total or relative lack of insulin. The body then
breaks down its own fat, protein, and glycogen to produce sugar resulting in high sugar levels
in the blood with excess by products called ketones being produced by the liver. Diabetes
causes disease in many organ systems, the severity of which may be related to how long the
disease has been present and how well it has been controlled. The term diabetes mellitus
describes a metabolic disorder of multiple aetiology characterised by chronic hyperglycaemia
with disturbances of carbohydrate, fat, and protein metabolism resulting from defects in
insulin secretion, insulin action or both (World Health Organization, 1999).
2.2. Epidemiology of diabetes mellitus
It is estimated that 366 million people had DM in 2011; by 2030 this would have risen to 552
million. The number of people with type 2 DM is increasing in every country with 80% of
people with DM living in low- and middle-income countries. DM caused 4.6 million deaths
in 2011.It is estimated that 439 million people would have type 2 DM by the year 2030.The

6
incidence of type 2 DM varies substantially from one geographical region to the other as a
result of environmental and lifestyle risk factors (Diabetic Atlas, 2011).
Literature search has shown that there are few data available on the prevalence of type 2 DM
in Africa as a whole. Studies examining data trends within Africa point to evidence of a
dramatic increase in prevalence in both rural and urban setting, and affecting both gender
equally (www.medscape.com).
According to information from Diabetes Care, the majority of the DM burden in Africa
appears to be type 2 DM, with less than 10% of DM cases being type 1 DM.
A 2011 Centre for Disease Control and Prevention (CDC) report estimates that DM affects
about 25.8 million people in the US (7.8% of the population) in 2010 with 90% to 95% of
them being type 2 DM (Diabetes fact sheet, 2011).
It is predicted that the prevalence of DM in adults of which type 2 DM is becoming
prominent will increase in the next two decades and much of the increase will occur in
developing countries where the majority of patients are aged between 45 and 64 years.It is
projected that the latter will equal or even exceed the former in developing nations, thus
culminating in a double burden as a result of the current trend of transition from
communicable to non-communicable diseases (Global burden of chronic diseases, 2004).
2.3. Prevalence of diabetes mellitus
The prevalence of diabetes has increased worldwide, including in Africa, where the greatest
proportional increase (90%) in the number of diabetes cases is projected to occur by 2030.
Type 2 diabetes (T2D) represents more than 90% of diabetes cases in Africa. The surge in
T2D in Africa has been primarily attributed to environmental factors due to nutrition
transition and urbanization that bring about lifestyle changes; however, current understanding
of the pathophysiology of T2D in populations within Africa remains elusive. From
observations among other populations around the world, it is obvious that T2D has a strong
genetic component, as evidenced by a high concordance rate (70%) in monozygotic twins and
the over 40% risk of having T2D for an offspring of a T2D patient. Unlike simple monogenic
diseases, the pathophysiology of T2D involves most of the time an interaction of several
common genetic risk factors of low penetrance with environmental factors. Hitherto, the vast
majority of T2D-related risk alleles have been identified through genome-wide association
studies, mostly conducted in European Descendants and Asian populations (Diabetes
Research and Clinical Practice, 2014).

7
The excess global mortality attributable to diabetes in the year 2000 was estimated to be 2.9
million deaths, equivalent to 5.2% of all deaths. Excess mortality attributable to diabetes
accounted for 2-3 % of deaths in poorest countries (Diabetes care, 2005).
Diabetes is a serious illness with multiple complications and premature mortality accounting
for at least 10 % of total health care expenditure in many countries. Diabetes is often
perceived as a disease of affluent countries. A serious chronic disease which leads to a
substantial reduction in life expectancy, decreased quality of life and increased costs of care
(O’Connor, 2009).
Most African studies are hospital based and give data on patients that visit the hospital only.
Type 1 diabetes has been estimated to affect approximately 19000 people in the world’s
poorest countries but there is lack of good data on the disease prevalence in developing
countries and in particular in sub-Saharan Africa. The present increase in the rate of both type
2 diabetes mellitus and type 1 diabetes mellitus indicate the great and urgent need for more
epidemiological surveys in sub-Saharan Africa. Such a need is dictated by the prevalence of
undiagnosed diabetes mellitus. (Diabetes Care, 2005).

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CHAPTER THREE: METHODOLOGY
3.0. Introduction
This section shows in detail the road map of this research project, it includes the study
population, the nature and source of data, the way data was recorded, analysed and presented,
finally it ends with anticipated challenges.
3.1. Study population
The population studied was Kirehe district where Kirehe Hospital is located. The community
is majorly rural where the main occupation is agriculture.
3.2. Nature and source of data
The data collected was purely secondary in nature. It was collected from Kirehe Hospital’s
data records for the period 2006-2015. The Hospital is situated in Kirehe district, Eastern
Rwanda. The data collected was on annual basis.
3.3. Data collection procedure
The data on diabetes mellitus for the period 2006-2015, was collected on the following
variables;
1. Sex
2. Age
3. Type of diabetes
4. Year
3.4. Data analysis procedure
Data will be analysed starting with descriptive statistics, then bivariate analysis followed by
model estimation for type I diabetes, type II diabetes then for gender.

9
3.4.1. Descriptive statistics
Descriptive statistics will be used to describe basic features of the data under study, in this
case data on diabetes mellitus cases at Kirehe hospital. Variables such as age, sex and type of
diabetes will be analysed.
3.4.2. Bivariate analysis
Bivariate analysis is the simultaneous analysis of two variables. It explores the concept of
relationship between two variables, whether there exists an association and the strength of
this association, or whether there are differences between two variables and the significance
of these differences.
3.4.3. Analysis of Diabetes mellitus cases
3.4.3.1. Analysis of diabetes mellitus cases for females
To analyse diabetes mellitus cases for females, the test of hypotheses was done using Daniels
test. This non-parametric test is based upon the spearman’s coefficient.
Hypotheses
Ho: Diabetes mellitus occurrence for females is not trended
Ha: Diabetes mellitus occurrence for females is trended
Level of significance α = 0.05
Test statistic
 1
6
1 2
2



nn
dt
sr
Where dt = t – rank (yt)
t: order number of years
yt: Diabetes cases for females
rs: rank correlation coefficient
n: number of observations

10
3.4.3.2. Analysis of diabetes mellitus cases for males
To analyse diabetes mellitus cases for males, the test of hypotheses was done using Daniels
test. This non-parametric test is based upon the spearman’s coefficient.
Hypotheses
Ho: Diabetes mellitus occurrence for males is not trended
Ha: Diabetes mellitus occurrence for males is trended
Test statistic
 1
6
1 2
2



nn
dt
sr
Where yt : Diabetes cases for males.
3.5.1.1. Analysis of type II diabetes cases
To analyse type II diabetes mellitus cases, the test of hypotheses was done using Daniels test.
This non-parametric test is based upon the spearman’s coefficient.
Hypotheses
Ho: Type II Diabetes mellitus occurrence is not trended
Ha: Type II Diabetes mellitus occurrence is trended
Test statistic
 1
6
1 2
2



nn
dt
sr
Where yt: Type II diabetes cases
3.5.1.2. Analysis of type I diabetes cases
To analyse type I diabetes mellitus cases, the test of hypotheses was done using Daniels test.
This non-parametric test is based upon the spearman’s coefficient.
Hypotheses
Ho: Type I Diabetes mellitus occurrence is not trended
Ha: Type I Diabetes mellitus occurrence is trended

11
Test statistic
 1
6
1 2
2



nn
dt
sr
Where yt: Type II diabetes cases
3.5.1.3. Trend fitting for Type I Diabetes and forecast
A trend will be fitted for T1D cases which will be used to make forecast for the next 10 years.
A regression least square method will be used to fit the trend.
Үb = α + βTt + et
Where α: a constant independent of time changes
β: effect of changes in time t
et: error term
Yb: T1D cases which is the dependent variable
Tt: Time in years which is the independent variable
3.5.1.4. Trend fitting for Type II Diabetes and forecast
A trend will be fitted for Type I Diabetes cases which will be used to make forecast for the
next 10 years. A regression least square method will be used to fit the trend.
Үa = α + βTt + et
Where α: a constant independent of time changes
β: effect of changes in time t
et: error term
Ya: T2D cases which is the dependent variable
Tt: Time in years which is the independent variable
3.5.1.5. Estimation of the Type I Diabetes trend model
Estimation of the trend model will be done as follows:
Db = a + bTt

12
Where

























2
TT
YYTT
t
Bt
b
TY ba


For t= 1, 2,…, n
Db: estimated trend for T1D cases.
After fitting the above trend, forecast of T1D for the next 10 years will be done.
3.5.1.6. Estimation of the Type II Diabetes trend model
Estimation of the trend model will be done as follows:
Da = a + bTt
Where Da: estimated trend for T2D cases.
After fitting the above trend, forecast of T2D for the next 10 years will be done.
3.6. Challenges faced
The following challenges were encountered by the researcher while carrying out this research
project:
I. Writing such a project required data, however this data was confidential. Therefore
getting this secondary data about diabetes cases was not easy.
II. Time was another constraint as far as carrying out this research is concerned. Writing
at the same time studying several course units was not an easy one.
III. Limited resources needed to carry out this research, for instance transport fee to
Kirehe hospital was a tug of war.

13
CHAPTER FOUR
PRESENTATION, ANALYSIS AND INTERPRETATION OF FINDINGS
4.0. Introduction
In this chapter, data is presented, analysed and interpreted. Results are presented in various
tables and graphs for visual analysis and descriptive statistics. Poisson heterogeneity statistic
was used to carry out hypothesis testing for the study hypotheses stated in chapter one, from
which interpretations are made.
4.1. Descriptive statistics of diabetes mellitus
Figure 4 1: Diabetes mellitus cases for the period 2006-2015
From the above figure, the year 2013 indicates the highest number of diabetes mellitus cases
that were reported at Kirehe hospital, with a total number of 33 diabetes cases. This is
followed by 2012 with a total number of 29 diabetes cases reported. The least number of
diabetes cases reported at Kirehe hospital was in 2006 where 1 case was reported. The graph
further indicates that, diabetes mellitus cases increase over time, though the increase is not
steady. This is because from 2006 to 2013, diabetes reported cases are seen to be increasing,
however beyond 2013, there is a decrease in diabetes mellitus cases.
1
4
11
14
19 19
29
33
26
24
0
10203040
2006 2008 2010 2012 2014
Years
Source: Compiled by the Researcher
DIABETES MELLITUS CASES FOR THE PERIOD 2006-2015

14
4.1.1. Diabetes mellitus by Type
Table 4 1: Diabetes mellitus distribution by type
Type Frequency Percentage
Type I 5 2.78
Type II 175 97.22
Total 180 100
Table 4.0 above shows the distribution of diabetes by type as reported in Kirehe hospital.
According to the results, the majority of the patients (97.22%) had Type II diabetes mellitus
while the remaining patients (2.78%) had Type I diabetes.
4.1.2. Diabetes cases with respect to Age
Table 4 2: Percentage distribution of diabetes mellitus by age
Age groups Frequency Percentage
Below 14 2 1.1
14-35 27 15.00
Above 35 151 83.89
Total 180 100
From the table 4.1 above, majority of diabetes mellitus cases for the period 2006-2015 in
Kirehe hospital (83.89%) were above 35 years of age, followed by the youth (15%) then
lastly by those below 14 years of age (1.1%).

15
4.2. Bivariate Analysis of diabetes mellitus
4.2.1. Diabetes type in relation to sex
Table 4 3: Cross tabulation of sex by type of diabetes: Chi-square
Sex Type
Type I Type II
Total
Female 4 91
4.21 95.79
80.00 52.00
95
100.00
52.78
Male 1 84
1.18 98.82
20.00 48.00
85
100.00
47.22
Total 5 175
2.78 97.22
100.00 100.00
180
100.00
100.00
Pearson chi2(1) = 1.53
Pr = 0.216
Fisher’s exact = 0.372
From the table 4.2 above, there is a significant evidence of relationship between sex and type
of diabetes (p < 0.05).
4.2.2. Diabetes type in relation to age groups
Table 4 4: Cross tabulation of age groups by type of diabetes: Chi-square
Age groups Type
Type I Type II
Total
Below 14 2 0
100.00 0.00
40.00 0.00
2
100.00
1.11
14-35 3 24
11.11 88.89
60.00 13.71
27
100.00
15.00
Above 35 0 151
0.00 100.00
0.00 86.29
151
100.00
83.89
Total 5 175
2.78 97.22
100.00 100.00
180
100.00
100.00
Fisher’s exact = 0.372
From table 4.3 above, there is a significant evidence of relationship between age groups and
type of diabetes (p < 0.05).

16
4.3. Hypothesis testing
In testing hypotheses, the Daniels test statistic was used. The rs of Daniels test was calculated
and then compared with 0.05 as the p-value. If the computed r was greater than the tabulated
rα/2, the researcher rejected the null hypothesis and if the value of the computed r was found
to be less or equal to the tabulated rα/2, the researcher failed to reject the null hypothesis.
4.3.1. Research hypothesis one
HO: Diabetes mellitus for females is not trended
Ha: Diabetes mellitus for females is trended
Calculation;
Table 4 5: Female Diabetes mellitus cases (yt) in Kirehe hospital
T yt Rank of yt dt dt
2
1 1 1 0 0
2 2 2 0 0
3 6 3.5 -0.5 0.25
4 6 3.5 1.5 2.25
5 10 6 -1 1
6 10 6 0 0
7 19 10 -3 9
8 13 8 0 0
9 18 9 0 0
10 10 6 4 16
∑dt
2
28.5
 1
6
1 2
2



nn
dt
sr =  
8273.0
110010
5.28*6
1 


r0.025 = 0.648
Since the computed rcalculated = 0.8273>0.648, the null hypothesis was rejected. The researcher
therefore, concluded that diabetes for females is trended.

17
4.3.2. Research hypothesis two
HO: Diabetes mellitus for males is trended
Ha: Diabetes mellitus for males is not trended
Calculation;
Table 4 6: Male Diabetes mellitus cases (yt) in Kirehe hospital
2
1 0 1 0 0
2 2 2 0 0
3 5 3 0 0
4 8 4.5 -0.5 0.25
5 9 6.5 -1.5 2.25
6 9 6.5 -0.5 0.25
7 10 8 -1 1
8 20 10 -2 4
9 8 4.5 4.5 20.25
10 14 9 1 1
∑dt
2
29
 1
6
1 2
2



nn
dt
sr =
  82424.0
11010
29*6
1 2



r0.025 = 0.648
From the above calculations, rcalculated = 0.82424 > 0.648, the null hypothesis was rejected,
thus male diabetes mellitus cases are trended.

18
4.3.3. Research hypothesis three
HO: Type II diabetes time series is not trended
Ha: Type II diabetes time series is trended
Calculation;
Table 4 7: Type II Diabetes mellitus cases (yt) in Kirehe hospital
2
1 1 1 0 0
2 4 2 0 0
3 11 3 0 0
4 14 4 0 0
5 18 5 0 0
6 19 6 0 0
7 28 9 -2 4
8 33 10 -2 4
9 26 8 1 1
10 21 7 3 9
∑dt
2
18
 1
6
1 2
2



nn
dt
sr =
  89091.0
11010
18*6
1 2



r0.025 = 0.648
From the above calculations, rcalculated = 0.89091>0.648, the null hypothesis was rejected, thus
type II diabetes mellitus cases are trended.

19
4.3.3. Research hypothesis four
HO: Type I diabetes time series is not trended
Ha: Type I diabetes time series is trended
Calculation;
Table 4 8: Type I Diabetes mellitus cases (yt) in Kirehe hospital
2
1 0 4 -3 9
2 0 4 -2 4
3 0 4 -1 1
4 0 4 0 0
5 1 8.5 -3.5 12.25
6 0 4 2 4
7 1 8.5 -1.5 2.25
8 0 4 4 16
9 0 4 5 25
10 3 10 0 0
∑dt
2
73.5
 1
6
1 2
2



nn
dt
sr =
  5545.0
11010
5.73*6
1 2



r0.025 = 0.648
From the above calculations, rtabulated = 0.5545 <0.648, the null hypothesis was accepted, thus
type I diabetes mellitus cases are not trended therefore showing support for stationarity.
4.4. Fitting Trends and forecasting for diabetes mellitus
Least square regression method was used in fitting the required trends.
4.4.1. Trend fitting for Type I diabetes
Before fitting the trend for Type I diabetes, a plot of Type I diabetes cases was done to
determine the nature of the trend as shown in figure 4.3. The figure predicts that there is an
upward trend of type I diabetes mellitus from the year 2012 to 2015.

20
Figure 4 2: Trend of Type I diabetes cases in Kirehe hospital (2006-2015)
Estimation of the trend
Db = a + bTt
Table 4.9: Table used to fit the trend for Type I diabetes
Db Coefficient T P>|t|
T 2 2.31 0.260
Constant 2009 1211.47 1987.929
Source: Developed from STATA by the researcher
R2
= 0.8421 (obtained from STATA)
The model results showed that the coefficient of determination R2
= 0.8421, this indicates a
very good fit of the model.
The trend below was then fitted using results from table 4.4
Db = 2009 + 2Tt
1
1.5
2
2.5
3
2006 2008 2010 2012 2014 2016
year
Trend of Type I diabetes mellitus cases for the period 2006-2015

21
From the above fitted trend, it is evident that there is a positive relationship between Type I
diabetes mellitus and time since the coefficient b = 2 is positive. This means that as the time
T increases, Type I diabetes cases also increase by the factor 2. The constant a = 2009 means
that at time T = 0, Type I diabetes cases were about 2009.
4.4.1.1. Forecast for Type I diabetes mellitus
Forecast for the next 10 years was done using the fitted trend. The results are as follows.
Table 4.10: Forecasting for Type I diabetes mellitus for the years 2016-2025
Year (Time) No. of type I cases
2016 2031
2017 2033
2018 2035
2019 2037
2020 2039
2021 2041
2022 2043
2023 2045
2024 2047
2025 2049
From the above table, type I diabetes cases are estimated to be increasing over the next 10
years. By 2025, Type I diabetes cases are estimated to be approximately 2,049.
4.4.2. Trend fitting for Type II diabetes
Before fitting the trend for Type II diabetes, a plot of Type II diabetes cases was done to
determine the nature of the trend as shown in figure 4.4. The figure predicts that there is an
upward trend of type I diabetes mellitus from the year 2006 to 2013.

22
Figure 4. 3: Trend of type II diabetes cases for the period 2006-2015
Estimation of the trend
Da = a + bTt
Table 4. 11: Table used to fit the trend for Type II diabetes
Da Coefficient T P>|t|
T .2572636 5.02 0.001
Constant 2005.998 1956.45 0.000
Source: Developed from STATA by the researcher
0
10203040
2006 2008 2010 2012 2014 2016
year
Trend of Type II diabetes mellitus cases for the period 2006-2015

23
R2
= 0.7593 (From STATA)
From the results, the coefficient of determination R2
= 0.7593, shows a good fit the sample
observation.
The results also show that the p-values of the constant (0.001) and the independent variable
(0.001) are significant at 0.05.
The estimated trend for Type II diabetes is therefore stated below;
Da = 2005.998 + 0.2573Tt
From the above estimated Type II diabetes trend, it means that there is a positive relationship
between Type II diabetes and time. The increase in time T increases Type II diabetes cases by
.2573 and the constant means that at T=0, Type II diabetes cases were about 2006 cases.
4.4.2.1. Forecast for Type II diabetes
Forecast for the next 10 years was done using the fitted trend. The results are as follows.
Table 4.12: Forecasting for Type I diabetes mellitus for the years 2016-2025
Year (Time) No. of type I cases
2016 2008.828
2017 2009.086
2018 2009.343
2019 2009.6
2020 2009.858
2021 2010.115
2022 2010.372
2023 2010.629
2024 2010.887
2025 2011.144
From Table 4.11, the number of type II diabetes cases is estimated to be about 2011.144 by
the year 2025.

24
CHAPTER FIVE
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
5.0. Introduction
This chapter covers the summary, it further includes the conclusions based on the findings
from the study and presents the appropriate recommendations.
5.1. SUMMARY AND CONCLUSIONS
Findings from this research have highlighted a number of issues concerning diabetes mellitus
particularly type I and type II.
5.1.1. Type I diabetes mellitus
It was found out that Type I diabetes mellitus has been increasing over the past 10 years.
Results show that in 2005, Type I diabetes cases reported at Kirehe hospital were 1 as
compared to 2015 where 3 cases were reported.
The researcher found out that type I diabetes is prevalent in females as compared to males.
Results from the study showed that type I diabetes cases reported to Kirehe hospital were
80% for females as compared to 20% for males. This means that females are highly affected
by this non-communicable disease.
The researcher also found out that there is a statistical evidence of relationship between age
groups and type of diabetes. Those above 35 years of age accounted for 83.89% of all
diabetes cases which is more than the rest of the age groups.
The researcher found out that Type I diabetes mellitus occurrence is mainly among teenagers
and type I diabetes cases were 2.78% of the 185 cases of diabetes in Kirehe hospital for the
period 2006-2015.
When forecast for type I diabetes was done, results showed that by 2025, type I diabetes
cases in Kirehe hospital will be 2049.

25
5.1.2. Type II diabetes mellitus
Findings from the study showed that Type II diabetes was the main type of diabetes which
patients who went to Kirehe hospital suffered, that is 97.22%.
From 2006 to 2013, type II diabetes was increasing at an increasing rate, where in 2006; there
was 1 case while in 2013, there were 33 cases.
The researcher found out that type II diabetes was affecting females more than males, that is
52% of the total cases were female patients
Forecast for the next 10 years was done and results showed that by 2025, type II diabetes
cases in Kirehe hospital will be 2011.
5.2. RECOMMENDATIONS
The government of Rwanda should be given credit for finally giving non-communicable
diseases the attention they deserve-particularly the 4th
leading cause of death in the world,
which is the deadly diabetes mellitus.
However a lot has to be done as regards this disease. The following suggestions depending on
the findings have been recommended:
i. Many government policies have put much emphasis on treatment of diabetes
by providing the test strips, insulin to mention but a few. Nevertheless,
preventive care should be number one priority. People should be sensitised on
the importance of diet, the benefits of physical fitness and the side effects of
too much sugar in the body.
ii. Type II diabetes has been given much attention, results show that Type I
diabetes deserve attention too, since it attacks the young who are a big asset to
the country in terms of their productivity.
iii. Above 80% of diabetes patients are above 35 years of age. This is a threat
because majority are the bread winners of their families. Therefore the
government needs to do whatever it takes to curb down the increasing trend of
diabetes. One suggestion is to have a national weekly 2 hours’ time as the
sports time, this will make obese and fat people lose weight.
iv. Results of this research revealed that females are the most affected compared
to the males. Ministry of health therefore is advised to put up measures which
target females first. For example, providing free diabetes testing to all females.

26
APPENDIX: DATA COLLECTED FROM KIREHE HOSPITAL
sex age Year weight Type
female 54 2013 94 Type II
male 46 2010 63 Type II
female 5 2010 Type I
female 25 2010 68 Type I

27
male 22 2012 54 Type I
male 46 2012 Type II

28

29
female 35 2014 Type II

30

31
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Otim, et al (1998): Guidelines for Non Communicable diseases at district level. Kampala:
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