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*Corresponding author: E-mail: Ikeokwu.anderson@gmail.com; 
 
 
 

Asian Journal of Immunology 
 
4(1): 1-14, 2021; Article no.AJI.65262 
 

 
 

 

 

Burden of Type 1 Diabetes Mellitus (TID) in Saint 
Vincent and the Grenadines from 2014-2018 

 
Adedeji Okikiade1, Ikeokwu Anderson1,2*, Olayinka Afolayan-Oloye1,   

Olanrewaju Adeola O.1 and Mane Paulpillai1 

 
1
All Saints University College of Medicine, Saint Vincent and the Grenadines. 

2
Preventive and Social Medicine University of Port-Harcourt Teaching Hospital, Nigeria. 

. 
Authors’ contributions 

 
This work was carried out in collaboration among all authors. Authors AO and IA designed the study, 

performed the statistical analysis, wrote the protocol, and wrote the first draft of the manuscript. 
Authors AO and IA managed the analyses of the study. Author OAO managed the literature searches. 

All authors read and approved the final manuscript. 
 

Article Information 
 

Editor(s): 
(1) Dr. Cynthia Aracely Alvizo Báez, Autonomous University of Nuevo Leon, Mexico. 

Reviewers: 
(1) Claudio Mascheroni, National University of Rosario, Argentina. 

(2) Augustine Ikhueoya Airaodion, Federal University of Technology Owerri, Nigeria. 
Complete Peer review History: http://www.sdiarticle4.com/review-history/65262 

 
 
 
 

Received 28 November 2020  
Accepted 02 February 2021 
Published 23 February 2021 

 
 

ABSTRACT 
 

Background: Type 1 Diabetes Mellitus (TID), is a disease that has long been connoted to as 
insulin-dependent, childhood-onset, young adult-onset or juvenile onset diabetes with essential 
insulin deficiency that requires daily insulin administration with peak onset during puberty (10-15) 
years of age.  
Aim: This study was aimed at assessing the Burden of TID among diagnosed Autoimmune disease 
in Saint Vincent and the Grenadines. 
Methods: From 2014 to 2018, individuals with Autoimmune, Immunological and Rare disease were 
identified from the hospital records of Milton Cato Memorial Hospital, which records information on 
all patient coming in for healthcare services. A structured data extraction tool was employed to 
extract the data from the hospital record using the open data kit (ODK). Data was analysed using 
Statistical Package for Social Sciences (SPSS) version 23 and R Studio statistical software for 
analysis. The Chi-square test was used to test for association. All statistical tests were two-tailed 
and Level of Confidence was set at 95%, and P values < 0.05 was considered to be statistically 
significant. 

Original Research Article 



 
 
 
 

Adedeji et al.; AJI, 4(1): 1-14, 2021; Article no.AJI.65262 
 

 

 
2 
 

Results: The mean age of patient with Type 1 Diabetes was 26.58 ± 11.73 yrs. old and the median 
Age was 31 years. old, almost two-third 81(64.8%) were females. Yearly, women showed a 
significantly higher incidence of T1D than men, there was an annual decrease in the incidence from 
2014 to 2018, with a peak incidence in 2014 (0.49/1000 person-years). There was an annual 
decrease in the incidence from 2014 to 2018, with a peak incidence in 2017 for male (0.40/1000 
person-years) and in 2014 for females (0.69/1000 person-years). The lowest incidence was noted 
in 2018 (0.00/1000 person-years) and (0.08/1000 person-years) for both male and female 
respectively. There was increased mortality in people with T1D with a baseline of 3% in 2014 to 
33% in 2018. 
Conclusions: Sequel to the findings of this study, the incidence of autoimmune disease, Type 1 
diabetes Mellitus have decreased in the last decade, whereas the mortality rates have increased. 
This finding of increased mortality of T1D suggests that this disease is no longer rare and will have 
implications for future healthcare planning. 
 

 

Keywords: Type 1 diabetes mellitus; incidence; Saint Vincent and the Grenadines. 
 

1. INTRODUCTION 
 
Type 1 Diabetes Mellitus (TID), is a disease that 
has long been connoted to as insulin-dependent, 
childhood-onset, young adult-onset or juvenile 
onset diabetes with essential insulin deficiency 
that requires daily insulin administration with 
peak onset during puberty (10-15) years of age 
[1]. The pathophysiology of Type 1 diabetes is an 
insulin-requiring chronic disorder which stems 
from an autoimmune demolition of the pancreatic 
β cells through cell mediated immunity as well as 
a humoral immune response, culminating in 
elevated concentrations of blood sugar and 
gradual functional excoriation and degeneration 
of various organs and tissues. Elevated blood 
glucose concentrations activate oxidative stress 
with concomitant degeneration of DNA, lipid and 
protein macromolecules by free radicals with 
accelerated diabetes-linked non enzymatic 
glycosylation or glycation of proteins and tissues 
damage in non-healthy persons, but not so in 
healthy persons.  
 

The etiology of type 1 diabetes is unknown; T1D 
often starts suddenly and may include symptoms 
such as the most important polydipsia, polyuria, 
polyphagia, lack of energy, excessive fatigue, 
sudden weight loss, slow healing of wounds, 
frequent infections and blurred vision and 
diabetic ketoacidosis following severe 
dehydration especially in children and 
adolescents. The symptoms are more severe in 
children than adults. Economically 
disadvantaged families, access to self-care tools 
including self-management education as well as 
access to insulin is limited. This pitfall leads to 
severe disability and reducing the Quality-of-life 
years of the individual. Even though, type 1 
diabetes permeates all age groups, numerous 

epidemiological investigations emphasize 
disease features with clinical disease in 
childhood and adolescence, and sometimes 
present difficulties of differentiation from certain 
forms of type 2 diabetes or Latent Autoimmune 
Diabetes in Adults, LADA [2]. 
 
Type 1 diabetes is an important and excruciating 
chronic disease of children globally, with 
resultant 8- to 10-fold excess risk of death in 
developed regions, whereas most cases die 
within a short period in developing countries, or 
where not adequately followed-up or registered 
may elude healthcare personnel in developed 
countries [3]. A 60-fold internal gradient in the 
incidence of type 1 diabetes and epidemic 
periods have ostensibly been identified and 
reported. In 1995, the global prevalence of 
diabetes mellitus (DM) in adults was estimated to 
be 4.0% and projected to rise to 5.4% by the 
year 2025 [4]. However, by 2011, the 
International Diabetes Federation (IDF) 
estimated the global prevalence of diabetes 
mellitus to be 8.3% and projected a rise to 9.9% 
by 2030. In absolute numbers, this          
translates to 366 million persons with diabetes 
mellitus in 2011 which will rise to 552 million 
people by 2030. Eighty percent of those with 
diabetes live in low- and middle-income   
countries [5]. In the Caribbean, the overall 
prevalence of diabetes mellitus is estimated to be 
approximately 9%   and is responsible for 13.8% 
of all deaths among adults in the region [5, 6]. 
Diabetes mellitus is therefore one of the major 
public health challenges for the Caribbean in    
the twenty-first century. 
 
Living with type 1 diabetes remains a   challenge 
for the child and the whole family even in 
countries with access to multiple daily injections 



 
 
 
 

Adedeji et al.; AJI, 4(1): 1-14, 2021; Article no.AJI.65262 
 

 

 
3 
 

or an insulin pump, glucose monitoring, diabetes 
education and expert medical care. Poor 
metabolic control may result in the acute 
complications of hypoglycemia and ketoacidosis, 
chronic microvascular and macro vascular 
complications and death [7- 9]. Children are 
more sensitive to a lack of insulin than adults and 
are at higher risk of a rapid and dramatic 
development of diabetic ketoacidosis. Episodes 
of severe hypoglycemia or ketoacidosis, 
especially in young children, are risk factors for 
structural brain abnormalities and impaired 
cognitive function, which may cause schooling 
difficulties and limit future career choices [10, 
11]. Many children and adolescents find it difficult 
to cope emotionally with their condition. Diabetes 
causes them embarrassment, results in 
discrimination and limits social relationships. It 
may impact on school performance and family 
functioning. Many schools and nurseries are 
reluctant to receive children with diabetes [12]. 
 

Like many developing nations, Caribbean 
countries are undergoing significant demographic 
changes. As such, these countries have a double 
burden of infectious/communicable diseases 
(e.g., HIV/AIDS) and chronic, non-communicable 
diseases (especially diabetes), and these 
diseases are assuming epidemic proportions. 
Few reviews of diabetes in this population have 
been conducted; however, this article 
summarizes the available information on the 
epidemiology of diabetes, the types of diabetes, 
the etiologic factors and complications of 
diabetes, and the public health burden 
associated with diabetes in the Caribbean. 
 

Due to the paucity of information on the 
incidence and mortality of diabetes mellitus Type 
1 within the population of Saint Vincent and the 
Grenadines. This study was aimed at assessing 
the Burden of TID among diagnosed 
Autoimmune disease in Saint Vincent and the 
Grenadines. This would provide empirical 
information in characterizing the incidence and 
mortality of type 1 diabetes in order to effectively 
and efficiently collate and evaluate healthcare 
and economics of diabetes, promote and 
establish domestic programs in the epidemiology 
of diabetes to prevent and curb the debilitating 
disorder and its concomitant complications.  
 

2. METHODOLOGY 
 

2.1 Study Area  
 

This study was carried out in Kingstown, Saint 
Vincent and the Grenadines. St. Vincent and The 

Grenadines comprises of 32 islands and                     
cays, of which 9 are inhabited. The largest is St. 
Vincent, where the nation’s capital, Kingstown, is 
located. St Vincent and the Grenadines                             
has a population of 111,000, which has                  
remained fairly flat since 1990. In 2019, St 
Vincent and the Grenadines has an estimated 
population of 110,589. The country is densely 
populated with 307 people per square kilometer 
(792/sq. mi). The capital and largest city is 
Kingstown, with a population estimated at 
35,000. 
 
Most Vincentians are the descendants of African 
slaves brought to the region to work plantations, 
as well as Portuguese and East Indians, who 
were brought to the island after slavery was 
abolished by the British living in the region. The 
largest ethnic group was African (66%), followed 
by those of mixed descent (19%), East Indian 
(6%), Europeans (mostly Portuguese (4%), and 
Carib Amerindian (2%). There is a growing 
community of Chinese people in the country. In 
2012, the male population (55,551) outnumbered 
the female (53,637). The population is young, 
with almost 25 per cent under the age of 15 and 
41.7 per cent under the age of 35. Although this 
under-35 age group has decreased since the 
2001 census by 6.1 per cent, it remains the 
largest proportion of the total population. The 
2012 census determined the population                       
aged under 5 years to be 8,645. A little over 9.1 
per cent of the population is over the age of   
65.5. 
 
St Vincent has both public hospitals and private 
clinics in the area around Kingstown. Being a 
small developing nation, the level of care is well 
below that of the US or Europe and the public 
facilities are often stretched way beyond 
capacity. 
 
There are public hospitals and clinics throughout 
the islands, with each place (with the exception 
of Mayreau) having some form of medical center. 
Any serious problems, however, will require a trip 
to out of St Vincent. Public spending on health in 
St Vincent and the Grenadines was four per cent 
of GDP in 2011, equivalent to US$310 per 
capita. In the most recent survey, conducted 
between 1997 and 2010, there were 75 doctors, 
and 379 nurses and midwives per 100,000 
people. Additionally, in the period 2007-12 
virtually all births (99 per cent) were attended by 
qualified health staff and in 2012, 94 per cent of 
one-year-olds were immunized with one dose of 
measles. 



 
 
 
 

Adedeji et al.; AJI, 4(1): 1-14, 2021; Article no.AJI.65262 
 

 

 
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The Milton Cato Memorial Hospital (MCMH) is a 
215-bed hospital serving the 110,000 inhabitants 
of St. Vincent and the Grenadines. The hospital 
was originally called the Colonial Hospital, built in 
the early 19

th
 century by the British government 

under the colonial system, and later renamed 
Kingstown General Hospital. In the late 1800s, 
the Imperial Parliament granted permission for 
construction of a new wing, which was opened in 
1889, and contained a total of seven beds. In 
1914, the Princess Mary Louise wing was 
completed, and used mainly as nurses’ quarters. 
As a public hospital, all of Milton Cato                  
Memorial Hospital’s services operate under the 
auspices of the Ministry of Health, Wellness                
and the Environment. Patients are required to 
pay user fees for medical services, which                  
don’t often recover the services’ true costs.                  
That being said, patients who cannot afford to 
pay are not prevented from accessing 
healthcare. 
 

2.2 Study Design 
 
The study utilized a retrospective population-
based study which consists of secondary data 
derived from Milton Cato Memorial Hospital of 
patients with Autoimmune disease. 
 

2.3 Inclusion Criteria 
 

 Data on Immunological disease and rare 
disease 

 

2.4 Exclusion  
 

 Incomplete data on Immunological disease 
and rare disease 

 
2.5 Data Collection Procedure 
 
A semi-structured interviewer-administered 
questionnaire with close and open-                                
ended questions will be used to collect                      
relevant information with the aid of an                   
android mobile device using the open data kit 
(ODK). 

 
The interview schedule consisted of 15               
sections:  

 
A structured data extraction tool was employed 
to extract the data from the hospital record with 
the aid of an android mobile device using the 
open data kit (ODK). The data extraction tool 
was developed and modified with reference to 
existing tools used in similar studies. The data 

extraction tool comprises of information on 
sociodemographic (age, sex), year of diagnosis 
and diagnosis.  
 

2.6 Outcome Measures and Data        
Analysis  

 
For annual incidence, the year-specific 
numerator included subjects with incident cases 
of Autoimmune disease in the specific calendar 
year, and the denominator included the mid-year 
population from the Population and Demographic 
Health Survey (DHS) from 2013-2019 which are 
cross-sectional surveys conducted every year, 
compiled by the Statistical Office Ministry of 
Finance, Economic Planning, Sustainable 
development and Information Technology of the 
Government of Saint Vincent and the Grenadines 
Population. This nationally representative survey 
involved a multi-stage sampling design up to the 
household level with enumeration areas 
distributed by region and type of residence using 
the most recent national census as its sampling 
frame. Crude rates, sex- and age-specific rates, 
standardized rates adjusted for sex and age 
using the 2014-2018 mid-year population, and 
their 95% confidence intervals (CIs) were 
calculated. 
 
Incident cases of T1D disease were defined as 
those with disease in a particular year (e.g., 
2014) and the preceding year (e.g., 2013 to 
2014) that met the algorithm in that year (e.g., 
2015) and the following year (e.g., 2016). 
Subgroup analyses were performed according to 
age and sex, the mortality rate in cases of 
Diabetes Mellitus Type 1 was estimated by 
dividing the number of incident Diabetes                          
Mellitus Type 1 who died during the study                     
period by the number of person-years for                
incident of Diabetes Mellitus Type 1. Mortality 
rates were also stratified by time since           
diagnosis. 
 
Data was being edited, collated and entered into 
the 2019 Microsoft Excel Data Sheet, after which 
it was exported into the International Business 
Machine (IBM) Statistical Package for Social 
Sciences (SPSS) version 23.0 and R Studio 
statistical software for analysis. The analysis 
involved the calculation of descriptive statistics 
(such as frequency distributions, percentages 
and means) and inferential statistics. Continuous 
variables were expressed as means ± standard 
deviation while categorical variables were 
expressed as absolute frequencies. Parametric 
analysis was used after tests for normality 



 
 
 
 

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5 
 

confirmed that continuous variables were 
normally distributed. The Chi-square test was 
used to test for association. All statistical tests 
were two-tailed and Level of Confidence was set 
at 95%, and P values < 0.05 was considered to 
be statistically significant.  
 

Test of normality was done to check for normal 
distribution of data using the Shapiro-Wilk test 
and Kolmogorov-Smirnov Test with significance 
level set at 0.05. Assumptions were set that If 
the Sig. value of both Test (p>0.05), the data is 
normal. If it is below 0.05, the data significantly 
deviate from a normal distribution and non-
parametric testing will employed such as the 
median will be used instead of the mean to 
represent summative statistics due to the median 
is no affected by outliers or extreme values. 
 

The information provided by the probability value 
(p-value) does not provide an estimate for the 
magnitude of the effect of interest and the 
precision of this magnitude. As a result of this, 
most of the inferential statistics reported in this 
report, did not only provide information on the p-
value but also on the magnitude of the effect 
(effect size statistics) in the form of correlation 
coefficient, regression coefficient and also their 
confidence intervals (CIs). Confidence intervals 
(CIs) were interpreted as the value that 
encompasses the population or ‘true’ value. This 
style of reporting both the effect sizes and their 
CIs gave a clear understanding of the 
relationships between the variables. 

3. RESULTS 
 
3.1 Socio-Demographics Characteristics 

of Patients with Diabetes Mellitus 
Type 1 

 
Table 1 shows the socio-demographics 
distribution of patient with Diabetes Mellitus Type 
1 in respect to age, sex. From 2014 to 2018, the 
total number of cases of Diabetes Mellitus Type 
1 in Milton Cato General Hospital was 125, with 
almost one-third 38(30.4%) occurring in the year 
2014. Among the cases of Diabetes Mellitus 
Type 1 the mean age was 26.58 ± 11.73yrs old 
and the median age= 31 years old, almost two-
third 81(64.8%) were females. 

 
Fig. 2 shows the trend in incidence by year. 
Every year, women showed a significantly higher 
incidence of Diabetes Mellitus Type 1 than men, 
there was an annual decrease in the incidence 
from 2014 to 2018, with a peak incidence in 2014 
(0.49/1000 person-years). The lowest incidence 
was noted in 2018 (0.04/1000 person-years). 
Among sex, there was an annual decrease in the 
incidence from 2014 to 2018, with a peak 
incidence in 2017 for male (0.40/1000 person-
years) and in 2014 for females (0.69/1000 
person-years). The lowest incidence was noted 
in 2018 (0.00/1000 person-years) and (0.08/1000 
person-years) for both male and female 
respectively. 
 

 

 
 

Fig. 1. Saint Vincent and the Grenadines map 



 
 
 
 

Adedeji et al.; AJI, 4(1): 1-14, 2021; Article no.AJI.65262 
 

 

 
6 
 

Fig. 3 shows that the overall peak age of 
incidence was 31 to 35 years in 2014. In 2014, 
the peak age incidence among men was different 
36-40 years. However, the peak age of 
prevalence among women was similar to the 
overall incidence graph 31 to 35 years of age. 
 

Fig. 4 shows that the overall peak age of 
incidence was 36 to 40 years in 2015. In 2015, 
the peak age incidence among men and women 
similar to the overall incidence graph 36 to 40 
years of age. 
 

Fig. 5 shows that the overall peak age of 
incidence was 36 to 40 years in 2016.  In 2016, 
the peak age incidence among men was different 
26-30 years. However, the peak age of 
prevalence among women was similar to the 
overall incidence graph 36-40 years of age. 
 
Fig. 6 shows that the overall peak age of 
incidence was 36 to 40 years in 2017.  In 2017, 
the peak age incidence among men and women 
was similar to the overall incidence graph 36-40 
years of age. 
 

Fig. 7 shows that the overall peak age of 
incidence was 6-10 years in 2018.  In 2018, the 
peak age incidence among men and women was 
similar to the overall incidence graph 6-10 years 
of age. 

3.2 Association between Social 
Demographic Characteristics 

 
In the table 2, among sex, the females had 
higher proportions across the years (2014-2018) 
compared to that of the male we hereby fail to 
reject the null hypothesis which postulates that, 
there is no significant higher proportion of 
females to males who have Diabetes Mellitus 
Type 1 from 2014 -2018 in Saint Vincent and the 
Grenadines due to there was no statistically 
significant association observed (p>0.05). 
Among the age group, those within the age 
group of 31-40 years had significantly higher 
proportions across the years (2014-2018) 
compared to that of other age group, this 
difference was not statistically significant 
(p>0.05). We hereby fail to reject the null 
hypothesis which postulates that there is no 
significant higher proportion of individuals ≤ 20 
years of age compared to other age groups who 
have Diabetes Mellitus Type 1 from 2014 -2018 
in Saint Vincent and the Grenadines. 

 
In the table 3, those within the age group of 36-
40 years had higher proportions of both male and 
female compared to that of other age group to 
having diabetics mellitus type 1, however, there 
was no statistically significant association 
observed between age and sex (p>0.05). 

 
Table 1. Socio-demographics characteristics of patients with diabetes mellitus type 1 

 

Variable Frequency (n=125) Percentage (%) 

Age   

≤5 9 7.2 
6-10 7 5.6 
11-15 12 9.6 
16-20 10 8.0 
21-25 14 11.2 
26-30 9 7.2 
31-35 28 22.4 
36-40 34 27.2 
>40 2 1.6 

 Mean ± S.D (26.58 ± 11.73) yrs. old, 95% C.I for Mean (24.50-28.65), Median Age= 31 yrs. old 

Sex    

Male 44 35.2 
Female 81 64.8 

Year   

2014 38 30.4 
2015 25 20.0 
2016 23 18.4 
2017 36 28.8 
2018 3 2.4 

S.D = Standard deviation, C.I = Confidence Interval 



 
 
 
 

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7 
 

 
 

Fig. 2. Incidence of Diabetes Mellitus Type 1 from 2014 -2018 
 

 
 

Fig. 3. Peak age of Incidence of Diabetes Mellitus Type 1 in 2014 
 

Fig. 8 shows the case fatality from Diabetics 
Mellitus Type 1 of the total, 2018 had the highest 
case fatality of 33% compared to the other years 
with 2016 having no case fatality at all. 

 

4. DISCUSSION 
 
In this study we observed a decreasing trend in 
the incidence of Type 1 Diabetes Mellitus (T1D) 
with a baseline of 0.49 per1000 person-years 
2014 to 0.04 per 1000 person-years in 2018. The 
decrease in trend reported in the present study 
might be explained due to the use of the hospital 
database could stem from the lack of accurate 
diagnosis. Another explanation of the findings is 
that data analyzed in this study were obtained 
when subjects visited healthcare institutions. 
Therefore, no information was available for T1D 
patients who did not visit a healthcare institution, 
which could underestimate the T1D burden. 
 
The incidence rate of T1D reported was lower 
compared to findings from a similar study who 
found an age-adjusted type 1 diabetes incidence 
difference from 0.1/100,000 per year in China 

and Venezuela to 36.8/100,000 per year in 
Sardinia and 36.5/100,000 in Finland. Findings 
from the study also showed that lowest incidence 
(<1/100, 000 per year) was realized from China 
and South America populations. Similar study 
carried out by the DIAMOND Project Group 
(2006), also showed that with age-adjusted 
incidence of type 1 diabetes varied from 0.1 per 
100,000/year in China and Venezuela to 40.9 per 
100,000/year in Finland [13].  
 

This difference between the incidence rate of 
both studies can be overestimated or 
underestimated due to various population size of 
both countries and the difference in multiplier 
rate (1000 vs 100000) by both studies which 
impairs the basis of comparison.  The population 
size in the Caribbean was approximately 120,000 
people as at the time of the study. 

 
4.1 Social Demographic Characteristics 

of Individuals Who Have T1D 
 
The incidence of T1D increased continuously 
with age until it reached a peak, after which, it 

2014 2015 2016 2017 2018 

Total Population 0.49 0.32 0.30 0.47 0.04 

Male 0.30 0.25 0.15 0.40 0.00 

Female 0.69 0.40 0.45 0.53 0.08 

0.00 
0.20 
0.40 
0.60 
0.80 

IN
C

ID
EN

C
E 

R
A

TE
 (

P
ER

 1
0

0
0

 
P

ER
SO

N
S)

 

INCIDENCE OF DIABETES MELLITUS TYPE 1 
FROM 2014 -2018 



 
 
 
 

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8 
 

 
 

Fig. 4. Peak age of incidence of diabetes mellitus type 1 in 2015 
 

 
 

Fig. 5. Peak age of incidence of diabetes mellitus type 1 in 2016

<5 6_10 11_20 16_20 21-25 26_30 31_35 36_40 41-45 

Total Population 0.34 0.12 0.30 0.20 0.35 0.00 0.88 0.79 0.00 

Male  0.00 0.23 0.00 0.19 0.23 0.00 1.01 0.77 0.00 

Female 0.69 0.00 0.64 0.21 0.47 0.00 0.75 0.81 0.00 

0.00 

0.20 

0.40 

0.60 

0.80 

1.00 

1.20 

IN
C

ID
EN

C
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R
A

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 (

P
ER

 1
0

0
0

 
P

ER
SO

N
S)

 

PEAK AGE OF INCIDENCE OF DIABETES MELLITUS TYPE 1  IN 2015  

<5 6_10 11_20 16_20 21-25 26_30 31_35 36_40 41-45 

Total  Population 0.00 0.35 0.10 0.20 0.23 0.48 0.38 1.05 0.00 

Male 0.00 0.23 0.00 0.19 0.00 0.46 0.25 0.26 0.00 

Female 0.00 0.47 0.21 0.21 0.00 0.49 0.50 1.88 0.00 

0.00 

0.50 

1.00 

1.50 

2.00 

IN
C

ID
EN

C
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R
A

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  (

P
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 1
0

0
0

 
P

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PEAK AGE OF INCIDENCE OF DIABETES MELLITUS TYPE 1  IN 2016  



 
 
 
 

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9 
 

 
 

Fig. 6. Peak age of incidence of diabetes mellitus type 1 in 2017 
 

 
 

Fig. 7. Peak age of incidence of diabetes mellitus type 1 in 2018 
 

declined slowly. The incidence of T1D among 
female in this study peaks occurring at 31 to 
35years. This pattern might be related to a type 
of diabetes termed late onset Type 1 Diabetes 
Mellitus which dissimilar with other studies which 
showed that T1D is the major type of diabetes in 
youth, accounting for ≥85% of all diabetes cases 
in youth < 20 years of age worldwide [14, 15].  
 
In general, the incidence rate increases from 
birth and peaks between the ages of 10–14 
years during puberty [16] which is in contrast with 
this study. The increasing incidence of T1D 
throughout the world is especially marked in 
young children. Registries in Europe suggest that 

recent incident rates of T1D were highest in the 
youngest age-group (0– 4 years) [16].  
 
Incidence rates decline after puberty and appear 
to stabilize in young adulthood (15–29 years). 
However, in a similar study which reported that 
the incidence of T1D in adults is lower than in 
children, although approximately one fourth of 
persons with T1D are diagnosed as adults. 
Clinical presentation occurs at all ages and as 
late as the 9th decade of life. Up to 10% of adults 
initially thought to have type 2 diabetes are found 
to have antibodies associated with T1D and beta 
cell destruction in adults appears to occur at a 
much slower rate than in young T1D cases, often  

<5 6_10 11_20 16_20 21-25 26_30 31_35 36_40 41-45 

Total Population 0.23 0.00 0.30 0.30 0.57 0.48 0.38 2.09 0.00 

Male 0.23 0.00 0.19 0.39 0.68 0.00 0.25 2.04 0.00 

Female 0.23 0.00 0.42 0.21 0.47 0.98 0.50 2.15 0.00 

0.00 

0.50 

1.00 

1.50 

2.00 

2.50 

IN
C

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P
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0

0
0

 P
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S   
PEAK AGE OF INCIDENCE OF DIABETES MELLITUS TYPE 

1  IN 2017  

<5 6_10 11_20 16_20 21-25 26_30 31_35 36_40 41-45 

Total Population 0.11 0.23 0.10 0.00 0.00 0.00 0.00 0.00 0.00 

Male 0.00 0.23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Female 0.23 0.24 0.21 0.00 0.00 0.00 0.00 0.00 0.00 

0.00 

0.05 

0.10 

0.15 

0.20 

0.25 

IN
C

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(P

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0
0

0
 P

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N
S)

 

P EA K  A G E O F  I N C I D EN C E  O F  D I A B ET ES  M EL L I T U S  T Y P E  1  I N  2 0 1 8  



 
 
 
 

Adedeji et al.; AJI, 4(1): 1-14, 2021; Article no.AJI.65262 
 

 

 
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Table 2. Trend analysis by age group and sex 
 

Variable      df        χ2 
(p-value) 

95% Confidence interval (p-
value) 

Sex 2014 2015 2016 2017 2018      

 Freq (%) Freq (%) Freq (%) Freq (%) Freq (%) Total (%)   Lower limit Upper limit 

Male 12(31.6) 10(22.7) 6(26.1) 16(44.4) 0(0.) 44(35.2)     
Female 26(68.4) 15(60.0) 17(73.9) 20(55.6) 3(100.0) 81(64.8) 4 3.783 

(0.392) 
F
 

0.308 0.476 

Total 38(100) 25(100) 23(100) 36(100) 3(2.4) 125(100)    

Age           

≤5 3(7.9) 3(12.0) 0(0.0) 2(5.6) 1(33.3) 9(7.2) 32 0.088 
(0.708) 

F
 

0.308 0.476 

6-10 2(5.3) 1(4.0) 3(13.0) 0(0.0) 1(33.3) 7(5.6)     
11-15 4(10.5) 3(12.0) 1(4.3) 3(8.3) 1(33.3) 12(9.6)     
16-20 3(7.9) 2(8.0) 2(8.7) 3(8.3) 0(0.0) 10(8.0)     
21-25 4(10.5) 3(12.0) 2(8.7) 5(13.9) 0(0.0) 14(11.2)    
26-30 1(2.6) 0(0.0) 4(17.4) 4(11.1) 0(0.0) 9(7.2)     
31-35 15(39.5) 7(28.0) 3(13.0) 3(8.3) 0(0.0) 28(22.4)     
36-40 4(11.8) 6(24.0) 8(34.8) 16(44.4) 0(0.0) 24(27.2)    
>40 2(5.3) 0(0.0) 0(0.0) 0(0.0) 0(0.0) 2(1.6)     
Total 38(100) 25(100) 23(100) 36(100) 3(2.4) 125(100)     

*Statistically significant (p<0.05). F (Fisher’s Exact test) CI = Confidence Interval) χ2= chi-square test statistics, df= degree of freedom 



 
 
 
 

Adedeji et al.; AJI, 4(1): 1-14, 2021; Article no.AJI.65262 
 

 

 
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Table 3. Association between social demographic characteristics 
 

Variable Sex  df        χ2 
(p-value) 

95% Confidence interval 
(p-value) 

Sex Male Female      

 Freq (%) Freq (%) Total (%)   Lower limit Upper limit 

Age        

≤5 1(2.3) 8(9.9) 9(7.2) 8 10.632 
(0.231) 

F*
 

0.158 0.303 

6-10 2(4.5) 5(6.2) 7(5.6)     
11-15 2(4.5) 10(12.3) 12(9.6)     
16-20 6(13.6) 4(4.9) 10(8.0)     
21-25 7(15.9) 7(8.6) 14(11.2)    
26-30 2(4.5) 7(8.6) 9(7.2)     
31-35 8(18.2) 20(24.7) 28(22.4)     
36-40 15(34.1) 19(23.5) 34(27.2)    
>40 1(2.3) 1(1.2) 9(1.6)     

Total 44(100) 81(100) 125(100)     
*Statistically significant (p<0.05). F (Fisher’s Exact test) CI = Confidence Interval) χ2= chi-square test statistics, 

df= degree of freedom 

 

 
 

Fig. 8. Case Fatality from Type 1 Diabetes Mellitus 
 
delaying the need for insulin therapy after 
diagnosis. Individuals diagnosed with 
autoimmune diabetes when they are adults have 
been referred to as having latent autoimmune 
diabetes of adults [17-19]. 
 
Also, findings from the study confirmed female 
predominance in the incidence rate of T1D, with 
ap-proximately 2-fold higher incidence in women 
than in men. This finding is similar to some 
studies who highlighted a distinctive pattern with 
an observation that regions with a high incidence 
of T1D (populations of European origin) have a 
male excess, whereas regions with a low 

incidence (populations of non- European origin) 
report a female excess [20, 13, 21, 22] which is 
resonate with this study with a baseline of 0.49 
per1000 person-years 2014 to 0.04 per 1000 
person-years in 2018.  
 

4.2 Case-Mortality and Morbidity from 
T1D 

 

The study findings found increased mortality in 
people with T1D compared with the general 
population with a baseline of 3% in 2014 to 33% 
in 2018. This which that one-third of person that 
have T1D dies from it. This finding has serious 

3% 

8% 

0% 

28% 

33% 

0% 

5% 

10% 

15% 

20% 

25% 

30% 

35% 

2014 2015 2016 2017 2018 

C
as

e 
Fa

rt
al

it
y 

R
at

e 
%

 

Diabetes Mellitus Type 1 Case Fatality Rate % 



 
 
 
 

Adedeji et al.; AJI, 4(1): 1-14, 2021; Article no.AJI.65262 
 

 

 
12 

 

implication on the healthcare of the country, this 
finding reveals the gap in the management and 
treatment of TID. This rate was higher than that 
found by a Norwegian cohort of 1,906 T1D 
patients diagnosed at <15 years of age                   
between 1973–1982 (46,147 person-years) 
reported an SMR for all-cause mortality of 4.0 
with an SMR of 20 for ischemic heart disease. 
Acute metabolic complications of T1D were the 
most common cause of death <30 years of age 
[23].   

 
However, this difference could be attributed to 
both rates were unadjusted. The SMR is more 
informative than the crude mortality rate because 
this compares mortality rates to people without 
SLE of the same age and gender and therefore 
assesses the excess mortality due to SLE. 
Alternatively, it may be due to our cases having 
milder disease or to the different study methods 
used. However, our study didn’t compute age 
specific mortality rate and sex-specific mortality 
rate due lack of availability of data and poor 
management health information system in the 
country as at the time of the study. 
 

5. CONCLUSION 
 
Sequel to the findings of this study, this study 
showed that the incidence of autoimmune 
disease, Type 1 diabetes Mellitus have 
decreased in the last decade, whereas the 
mortality rates of Type 1 Diabetes Mellitus have 
increased. This finding of increased mortality of 
T1D suggests that this disease is no longer rare 
and will have implications for future healthcare 
planning. 
 

6. RECOMMENDATIONS 
 
The study highlights the increase in the mortality 
due to TID in Saint Vincent and the Grenadines. 
Primary prevention of diabetes by lifestyle 
modification, screening for individuals at high risk 
and prevention of disease complications in 
established disease through non-
pharmacological and therapeutic interventions 
are all needed to reduce the high burden of 
diabetes and associated mortality in Saint 
Vincent and the Grenadines and similar 
population.  
 
Type 1 diabetes treatment is a challenging issue 
in developing regions, but not so in developed 
countries where those living with the disease 
have easier access insulin, glucometer strips and 
other materials from government subvention or 

personal savings. Non-industrialized countries 
are faced with inadequate resources, limitation in 
diagnosis, insulin initiation and storage, family, 
marital and emotional issues and challenges. 
Since type 1 diabetes affects a few people 
compared to the general population, it is palpably 
ignored by governments and policy maker. The 
socio-economic status in developing regions 
does not provide the latitude for the required 
insulin therapy and inextricably-linked monitoring 
of blood glucose. It is pertinent to spread 
awareness regarding the metabolic disorder and 
its sequelae and to undergird government health 
and healthcare ambient regarding the 
consequences, pros and cons, in the 
administration of medicinal drugs for the 
treatment of diabetes. 

 
From key findings from study, it reinforces the 
need for improved access to insulin and blood 
glucose meters and test strips in lower income 
countries and the training of healthcare workers 
in such countries to recognize and treat this 
condition. Three tiers of care (minimal, 
intermediate and comprehensive) have been 
defined by availability of insulin and blood 
glucose monitoring regimens, requirements for 
HbA1c testing, complications screening, diabetes 
education, and multidisciplinary care, and it is to 
be hoped that policy-makers will aspire to attain 
the highest levels of care possible given the 
resources available. Provide increased training 
opportunities for health care professionals by 
establishing collaborative training programs 
between professional and non-profit health 
organizations and clinical programs for research 
in T1D. 

 
Thus, it is imperative to focus on expansive 
clinical trials which compare the appropriateness 
of diverse diabetes medications to provide the 
guidelines for healthcare providers on which 
patients to prescribe certain drugs. There tends 
to be decrease in diabetes complications in 
certain parts of the world, and the survival and 
quality of life have improved tremendously, but 
financial constraints and awareness have 
restricted ample access to type 1 diabetes 
prevention, control and treatment, as well as 
meeting the informed inventiveness and 
creativity of gadgets, such as the closed-loop 
systems. The essential management and access 
to medicinal drugs are more imperative than 
high-tech systems in developing countries or 
elsewhere. There is extant optimism with 
opportunities for the future in unravelling the 
metabolic and cellular processes in convergence 



 
 
 
 

Adedeji et al.; AJI, 4(1): 1-14, 2021; Article no.AJI.65262 
 

 

 
13 

 

for researchers, clinicians, healthcare providers 
and policy makers to undertake intensive 
measures regarding the issues, challenges and 
presenting opportunities underlying type 1 
diabetes and its sequelae which are solvable. 
 

CONSENT 
 
It is not applicable 
 

ETHICAL APPROVAL 
 

Approval was gotten to access medical 
information of patients from Ministry of Health 
and Wellness and Hospital Administrator at 
Milton Cato Memorial Hospital in Saint Vincent 
and the Grenadines. 
 

COMPETING INTERESTS 
 

Authors have declared that no competing 
interests exist. 
 

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provided the original work is properly cited. 
 
 
 

 

 

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