




































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

Asian Journal of Immunology 
 
4(1): 59-76, 2021; Article no.AJI.66498 
 

 
 

 

 

Trends and Socio-demographic Distribution of 
Immunological Diseases in Saint Vincent and the 

Grenadines 
 

Adedeji Okikiade1, Ikeokwu Anderson1*, Twanna Browne-Caesar1,  
Kevin Brown1, Rebecca Lawrence1, Daniel E. Osieme1  

and O. Funmilayo Janet 1 
 

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

 

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. AO 

and IA managed the analyses of the study. All authors managed the literature searches. All authors 
read and approved the final manuscript. 

 

Article Information 
 

Editor(s): 
(1) Dr. Tania Mara Pinto Dabés Guimarães, Federal University of Minas Gerais, Brazil. 

(2) Prof. Darko Nozic, University of Belgrade, Serbia. 
(3) Prof. Cynthia Aracely Alvizo Báez, Autonomous University of Nuevo Leon, Mexico. 

Reviewers: 
(1) Irina A. Pashnina, Regional Clinical Children’s Hospital, Russia. 

(2) Leonid P. Churilov, Saint Petersburg State University, Russia. 
Complete Peer review History: http://www.sdiarticle4.com/review-history/66498 

 
 
 
 

Received 14 April 2021  
Accepted 19 June 2021 
Published 24 June 2021 

 
 
ABSTRACT 
 

Background: More than 100 human diseases are due at least in part to an inappropriate immune 
system response that results in damage to an individual’s organs, tissues, or cells. Immunological 
diseases can affect any part of the body, and have myriad clinical manifestations that can be difficult 
to diagnose. At the same time, immunological diseases share many features related to their onset 
and progression. In addition, overlapping genetic traits enhance susceptibility to many of the 
diseases, so that a patient may suffer from more than one immunological disorder, or multiple 
immunological diseases may occur in the same family.  
Aim: The study aims is to explore the trends and Socio-demographic distribution of Immunological 
diseases in Saint Vincent and the Grenadines from 2014-2018 in Milton Cato Memorial Hospital 
Saint Vincent and the Grenadines, including temporal trends and variations in age and sex from 

Original Research Article 



 
 
 
 

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60 

 

2014 to 2018 by using routinely collected administrative health data/patient records. 
Methods: From 2014 to 2018, individuals with immunological diseases 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. 
Results: The mean age of patient with SLE was 35.65 ± 21.16 yrs. old and the median Age was 34 
years old, almost two-third 218(62.6%) were females. Yearly, women showed a significantly higher 
incidence of immunological disease than men except in 2017 where the incidence for males were 
slightly higher than that of the females, there was an annual decrease in the incidence from 2014 to 
2018, with a peak incidence in 2016 (0.94/1000 person-years). The lowest incidence was noted in 
2018 (0.17/1000 person-years). Among sex, there was an annual decrease in the incidence from 
2014 to 2018, with a peak incidence in 2014 for male (0.71/1000 person-years) and in 2016 for 
females (1.34/1000 person-years). The lowest incidence was noted in 2018 (0.14/1000 person-
years) and (0.20/1000 person-years) for both male and female respectively. 
Conclusions: The study showed that the incidence of immunological disease, Type 1 diabetes 
Mellitus Myopathy/Myositis and SLE in Saint Vincent have decreased in the last decade, whereas 
the mortality rates of both SLE and Type 1 Diabetes Mellitus have increased. This finding of 
increased mortality of SLE and T1D suggests that this disease is no longer rare and will have 
implications for future healthcare planning. Age and sex were found to be risk factors for SLE. Our 
data confirmed the known predilection of SLE in women. The peak age of diagnosis is middle age, 
contrary to the generally held belief that lupus mainly targets young people.  
 

 

Keywords: Immunological disease; incidence; Saint Vincent and the Grenadines. 
 

1. INTRODUCTION 
 
More than 100 human diseases are due at least 
in part to an inappropriate immune system 
response that results in damage to an 
individual’s organs, tissues, or cells. 
Immunological diseases are a spectrum of 
diseases affecting multiple organs and tissues 
[1]. Immunological disease could be classified as 
either autoinflammatory or autoimmunity. 
Autoinflammatory is defined as self-regulated 
inflammation which is when local mediators at 
site of cell injury leads to activation of innate 
immunity cell, and this includes; macrophages 
and neutrophils, in which its final outcome is to 
target tissue damage [1]. Autoimmunity is self-
regulated inflammation, where there is an 
anomaly in dendritic cell, B and T cell, response 
responses in primary and secondary lymphoid 
organs lead to breaking of tolerance, with 
development of immune reactivity towards native 
antigens. The adaptive immune response plays 
the predominant role in the eventual clinical 
expression of disease. Organ-specific 
autoantibodies may predate clinical disease 
expression by years and manifest before target 
organ damage is discernible [1]. 
 
Immunological diseases can affect any part of 
the body, and have myriad clinical manifestations 

that can be difficult to diagnose. At the same 
time, Immunological diseases share many 
features related to their onset and progression. In 
addition, overlapping genetic traits enhance 
susceptibility to many of the diseases, so that a 
patient may suffer from more than one 
immunological disorder, or multiple 
immunological diseases may occur in the same 
family [2].  
 
Treatments are available for many immunological 
diseases, cures have yet to be discovered. For 
these and other reasons, the immunological 
diseases are best recognized as a family of 
related disorders that must be studied collectively 
as well as individually. While many of these 
diseases are rare, collectively they affect 14.7 to 
23.5 million people in a country like the USA and 
for reasons unknown their prevalence is rising 
[2]. 
 
Since cures are not yet available for most 
immunological diseases, patients face a lifetime 
of illness and treatment. This therefore affects 
QALY and DALY, QALYs (Quality-Adjusted Life 
Year) and DALYs (Disability-Adjusted Life Year) 
are common terms used to evaluate and 
compare health interventions using cost-
effectiveness analysis to measure the impact on 
both the length and the quality of life. QALYs are 



 
 
 
 

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a measure of years lived in perfect health gained 
whereas DALYs are a measure of years in 
perfect health lost [3]. They often endure 
debilitating symptoms, loss of organ function, 
reduced productivity at work, high medical 
expenses. Most of these diseases 
disproportionately affect both sexes, and are 
among the leading causes of death for young 
and middle-aged individuals on which they 
impose a heavy burden on patients’ families and 
on society [3].  
 

There are gaps in evident literature for the 
Incidence rates of Immunological in the 
Caribbean and Saint Vincent and the Grenadines 
in specific. Hence, this gap gave a recognition 
that more needs to be done to highlight evidence 
needed to close the gaps in our knowledge and 
achieve our overall goal of reducing the rising toll 
of immunological disease. For example, we need 
to gain a better understanding of the distribution 
of these diseases through epidemiologic studies, 
and of the environmental triggers that contribute 
to their onset. This research would give more 
insight about the genetic and environmental 
factors contributing to these diseases, and also 
set a platform to develop effective prevention 
strategies that arrest the immunological process 
before it can irreversibly damage the body. 
 

This research sets forth an ambitious and 
comprehensive research agenda aimed at 
generating more accurate epidemiologic profiles 
of immunological diseases; developing a greater 
understanding of the fundamental biologic 

principles underlying disease onset and 
progression; devising improved diagnostic tools; 
creating more effective interventions; and 
producing public and professional education and 
training programs. The study aims are to explore 
the trends and Socio-demographic distribution of 
Immunological diseases in Saint Vincent and the 
Grenadines from 2014-2018. 
 

2. RESEARCH METHODOLOGY 
 
2.1 Study Area  
 
Saint Vincent and the Grenadines (SVG) is an 
upper-middle-income multi-island state in the 
eastern Caribbean, located in the Windward 
Island chain of the Lesser Antilles. It consists of 
32 islands, inlets, and cays, but only 7 of these 
beyond the main island of Saint Vincent are 
inhabited (Bequia, Canouan, Mayreau, Union, 
Mustique, Palm Island, and Petit Saint Vincent) 
[4]. The main island of Saint Vincent is the 
largest island in size at 340 square kilometers 
and in population with over 90 percent of the 
population. The islands are connected by sea 
ferries and air charters through four Grenadine 
airports. The country is divided into six 
administrative units, or parishes: Charlotte, 
Grenadines, Saint Andrew, Saint David, Saint 
George, and Saint Patrick. Five of these parishes 
are located on the island of Saint Vincent. 
Kingstown, located in the Saint George Parish on 
Saint Vincent Island, is the capital of the country 
and largest urban centre [5]. 

 

 
 

Fig. 1. Map and Parishes of Saint Vincent and the Grenadines 



 
 
 
 

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In 2020, SVG had an estimated population of 
110172, the male population (56,052) out-
numbered the female (54,120). 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 
2020 census determined the population aged 
under 5 years to be 8,723. A little over 9 per cent 
of the population is over the age of 65.5. 
According to the Saint Vincent 2001 Housing and 
Population Census, 24.2 percent of the 
population lived in and around the capital, 
Kingstown, in 2011 [6]. Since then, the urban 
population has increased by 2 percentage points 
[7]. 
 

Saint Vincent, like the rest of the countries of the 
Organization of Eastern Caribbean States 
(OECS), is experiencing an increase in elderly 
population and a decline in the fertility rate. This 
shift is largely the result of long-term successes 
in increasing access to care and treatment for 
infectious diseases [8]. The aging population 
contributes to the increased burden of chronic 
diseases. Life expectancy for Vincentians 
averages 72 years of age overall, 74 for females 
and 70 for males. Chronic noncommunicable 
diseases (NCDs) account for 70 percent of visits 
to outpatient services and are among the top five 
causes of death [9]. In 2004 the top five cause of 
death, in rank order, were diabetes, malignant 
neoplasms, cerebrovascular disease, heart 
disease, and hypertension [8].  
 

As with many of its neighbouring countries, 
primary care service coverage indicators are 
extremely strong, with universal coverage of 
vaccines for key childhood illnesses and skilled 
attendance at delivery. The country is 
experiencing epidemiological transitions, as seen 
in the increasing burden of non-communicable 
diseases (NCDs), which accounts for the top five 
causes of death, and in the increasing average 
age of the population [5]. The estimated 
prevalence of HIV in Saint Vincent is 1%, but 
stigma against individuals with HIV and AIDS 
continues to persist across the islands [5].  
 

Health care service delivery in Saint Vincent is 
largely provided by the public sector, but the 
private sector has grown in recent years to 
complement the limited specialty services and 
alleviate some of the burden on the public sector 
[5]. The private commercial sector is not well 
documented but is known to be concentrated in 
Kingstown. Data on the division of health 

services between the public and private sectors 
are not available. Specialized health services are 
also concentrated in Kingstown. NGOs provide 
limited care, mostly through service delivery [5]. 
At the primary care level, the public sector is 
divided into nine Health Districts with 39 health 
clinics spread throughout the country. On 
average, each health clinic is equipped to cater 
to a population of 2,900 with no patient required 
to travel more than three miles to access care. At 
the secondary level, Milton Cato Memorial 
Hospital (MCMH) which is a 215-bed hospital, is 
the country’s only governmental acute care 
referral hospital providing specialist care. The 
private sector is active at the primary care level 
with private providers offering generalist and/or 
obstetric services [4]. Tertiary care is limited on 
the island in both sectors. The private sector 
offers more long-term care facilities for the 
elderly with five facilities, while the one public 
sector facility primarily serves the impoverished 
populations. The private sector also offers 
advanced diagnostics, which are limited in the 
public sector to the lab at MCMH [5].  
 
Like many other Caribbean countries, many 
citizens of Saint Vincent travel abroad for tertiary 
care. Though the majority of health service 
providers are in the public sector, the private 
sector also plays a prominent and growing role; 
physicians in Saint Vincent commonly practice in 
both the public and private sectors [5]. 
 
Financing for the health sector is provided 
through the Ministry of Health’s (MOHE) portion 
of the Consolidated Fund, the National Insurance 
Service (NIS), and private expenditures. 
Available data on private expenditures are 
limited. Public health services are primarily 
covered most through the MOHE budget. 
Primary care services are free of charge and all 
other services are highly subsidized. NIS covers 
the costs of hospital services for its members [5].  
 

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 Immunological disease from 2014-
2018. 
 
2.2.1 Inclusion criteria 
 
Data on Immunological disease from 2014-2018 
(For the purpose of this study, Immunological 
consisted of both autoinflammatory disease 



 
 
 
 

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cases and autoimmune disease cases. Cases of 
autoinflammatory disease was identified as 
cases in which: (1) there is an idiopathic 
recurrent fever with systemic inflammation, (2) an 
inflammatory finding for which the trigger is not 
clear is present, (3) there is no association with a 
high titre of autoantibodies or self-responsive T 
cells and (4) an abnormality of innate immunity is 
observed [1]. Cases of autoinflammatory disease 
was identified cases in which; [1] t an 
abnormality of adaptive immunity is observed, [2] 
there is an association with a high titre of 
autoantibodies or self-responsive T cells [1]. 
 

2.2.2 Exclusion criteria 
 

Incomplete data on Immunological disease from 
2014-2018. 
 

2.3 Data Collection Procedure 
 

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.  
 

2.4 Outcome Measures and Data Analysis  
 

For annual incidence, the year-specific 
numerator included subjects with incident cases 
of immunological 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 immunological disease were 
defined as those without immunological disease, 
disease in a particular year (e.g., 2014) and the 
preceding year (e.g., 2012 to 2013) that met the 

algorithm in that year (e.g., 2014) and the 
following year (e.g., 2015). Subgroup analyses 
were performed according to age and sex. 
 
Data was 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 
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 deviates from a 
normal distribution and non-parametric testing 
was employed such as the median will be used 
instead of the mean to represent summative 
statistics due to the median is not 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 [10]. 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 [10]. 
 

3. RESULTS 
 

Table 1, shows the socio-demographics 
distribution of patient with immunological 
diseases in respect to age and sex. From 2014 
to 2018, the total number of immunological 
cases seen in Milton Cato General Hospital was 



 
 
 
 

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347, with almost one-third 104(30%) occurring in 
the year 2016. Among the cases of 
immunological diseases, the mean age was 
35.65 ± 21.16 yrs old and the median Age= 34 
yrs old, almost two-third 218(62.6%) were 
females. 
 

Table 2 shows that a greater percentage 
125(36.0%) had diabetes Mellitus Type 1, 
111(32.0%) had Unspecified myopathies, 
26(7.5%) had SLE, only a few rare diseases 
were identified, this includes Charge disease 
1(0.3%), Iridocyclitis 17(4.9%) and ARPKD 
2(0.6%) 
 

Table 3 shows that among patients with Diabetes 
Mellitus, more than two-thirds 81(64.8%) were 
females. In patient with SLE, only few 2(7.7%) 
were males with almost all 24(92.3%) females. 
Females with Myositis more than half 62(55.9%) 
compared to the males 49(44.1%) with myositis.  
 

Table 4 shows that among patients with Diabetes 
Mellitus, all of the patients 125(100%) are within 
21-40 years of age.  

Fig. 2 shows the trend in incidence by year. 
Yearly, women showed a significantly higher 
incidence of immunological disease than men 
except in 2017 where the incidence for males 
were slightly higher than that of the females, 
there was an annual decrease in the incidence 
from 2014 to 2018, with a peak incidence in 2016 
(0.94/1000 person-years). The lowest incidence 
was noted in 2018 (0.17/1000 person-years). 
Among sex, there was an annual decrease in the 
incidence from 2014 to 2018, with a peak 
incidence in 2014 for males (0.71/1000 person-
years) and in 2016 for females (1.34/1000 
person-years). The lowest incidence was noted 
in 2018 (0.14/1000 person-years) and (0.20/1000 
person-years) for both male and female 
respectively. 
 
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 
> 70 years. However, the peak age of prevalence 
among women was similar to the overall 
incidence graph 31 to 35 years of age. 
 

Table 1. Socio-demographics Characteristics of Patients 
 

Variable Frequency (n=347) Percentage (%) 

Age   

≤5 23 6.6 

6-10 18 5.2 

11-15 22 6.3 

16-20 32 9.2 

21-25 28 8.1 

26-30 18 5.2 

31-35 45 13.0 

36-40 46 13.3 

41-45 20 5.8 

46-50 13 3.7 

51-55 12 3.5 

56-60 20 5.8 

61-65 11 3.2 

66-70 16 4.6 

>70 23 6.6 

Sex    

Male 129 37.2 

Female 218 62.8 

Year   

2014 103 29.7 

2015 62 17.9 

2016 104 30.0 

2017 59 17.0 

2018 19 5.4 

Mean ± S.D (35.65 ± 21.16 yrs. old), 95% C.I for Mean (33.41-37.88), Median Age= 34 yrs. old  

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



 
 
 
 

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Table 2. Type of Immunological Diseases/Rare Disease 

 
Variable Frequency (n=347) Percentage (%) 

Disease Type   

ADPKD 9 2.6 
ARPKD 2 0.6 
Autoimmune Hemolytic Anemia 2 0.6 
Calciphylaxis 1 0.3 
Charge Syndrome 1 0.3 
Crohn Disease 1 0.3 
Crohn Disease/Diabetes Type 2 1 0.3 
Cryoglobulinemia 1 0.3 
Diabetes Type 1 125 36.0 
Diabetes Type 2/Pseudogout 1 0.3 
Gout 11 3.2 
Gout /Diabetes Type 2 1 0.3 
Gout/Iridocyclitis 1 0.3 
Iridocyclitis 16 4.6 
Iridocyclitis/Diabetes Type 2 2 0.6 
Juvenile Arthritis 1 0.3 
Kawasaki 5 1.4 
Leukemia 2 0.6 
Myasthenia Gravis 1 0.3 
Unspecified Myopathy/Myositis 111 32.0 
Unspecified Myopathy/Myositis/Diabetes Type 2 7 2.0 
Pernicious Anemia 1 0.3 
Idopathic hypophysitis 2 0.6 
Rheumatoid Arthritis 5 1.4 
Sarcoidosis 1 0.3 
Sjogren Syndrome 3 0.9 
Systemic Lupus Erythematous (SLE) 26 7.5 
SLE/Diabetes Mellitus/Type 2 1 0.3 
Systemic Sclerosis 3 0.9 
Ulcerative Colitis 3 0.9 

ADPKD= Autosomal Dominant Polycystic Kidney Disease, ARPKD= Autosomal Recessive Polycystic 

 

Fig. 4 shows that the overall peak age of 
incidence was 31 to 35 years in 2015. In 2015, 
the peak age incidence among men was similar 
to the overall incidence graph 31 to 35 years of 
age. However, the peak age of prevalence 
among women was different (56-70) years of age 
to the overall incidence graph. 

 
Fig. 5 shows that the overall peak age of 
incidence was 66-70 years in 2016. In 2016, the 
peak age incidence among men and women was 
similar to the overall incidence graph 66-70years 
of age.  

 

Fig. 6 shows that the overall peak age of 
incidence was 36-40 to 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 66-70 years in 2018. In 2014, the 
peak age incidence among men was different > 
70 years. However, the peak age of prevalence 
among women was similar to the overall 
incidence graph 66-70 years of age. 
 

In the Table 5, 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 a immunological 
disease from 2014 -2018 in Saint Vincent and 
the Grenadines due to there was no statistically 
significant association observed (p>0.05). 
Among the age groups, those within the age 
group of 31-40 years had significantly higher 
proportions across the years (2014-2018) 
compared to that of other age groups, this 
difference was statistically significant (p<0.05). 



 
 
 
 

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Table 3. Distribution of Immunological Diseases by Sex 
 

Variable                                      Sex  

 Male Female  

Rare Disease Freq (%) Freq (%) Total (%) 

ADPKD 5(55.6) 4(44.4) 9(100) 
ARPKD 2(100) 0(0) 2(100) 
Charge Syndrome 0(0) 1(100) 1(100) 
Calciphylaxis 1(100) 0(0) 1(100) 
Pituitary Adenoma 0(0) 1(100) 1(100) 

Immunological Disease    

Autoimmune Haemolytic Anaemia 0(0) 2(100) 2(100) 
Crohn Disease 0(0) 1(100) 2(100) 
Cryoglobulinemia 0(0) 1(100) 1(100) 
Diabetes Type 1 44(35.2) 81(64.8) 125(100) 
Gout 7(63.6) 4(36.4) 11(100) 
Iridocyclitis 7(43.8) 9(56.2) 16(100) 
Juvenile Arthritis 0(0) 1(100) 1(100) 
Kawasaki 1(20) 4(80) 5(100) 
Leukaemia 0(0) 2(100) 1(100) 
Myasthenia Gravis 0(0) 1(100) 1(100) 
Unspecified Myopathies/Myositis 49(44.1) 62(55.9) 111(100) 
Pernicious Anaemia 0(0) 1(100) 1(100) 
Rheumatoid Arthritis 2(40) 3(60) 5(100) 
Sarcoidosis 1(100) 0(0) 1(100) 
Sjogren Syndrome 0(0) 3(100) 3(100) 
Systemic Lupus Erythematous (SLE) 2(7.7) 24(92.3) 26(100) 
Systemic Sclerosis 1(33.3) 2(66.7) 3(100) 
Ulcerative Colitis 0(0) 3(100) 3(100) 

Immunological Disease with Comorbidity 
(Diabetes Type 2) 

   

Crohn Disease & Diabetes Type 2 0(0) 1(100) 1(100) 
Unspecified Myopathies/Myositis & Diabetes Type 2 5(60) 2(40) 7(100) 
Pseudogout & Diabetes Type 2 0(0) 1(100) 1(100) 
Gout & Diabetes Type 2 1(100) 0(0) 1(100) 



 
 
 
 

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Variable                                      Sex  

 Male Female  

Rare Disease Freq (%) Freq (%) Total (%) 

Gout & Iridocyclitis 1(100) 0(0) 1(100) 
Iridocyclitis& Diabetes Type 2 0(0) 2(100) 2(100) 
SLE & Diabetes Type 2 0(0) 1(100) 1(100) 

ADPKD= Autosomal Dominant Polycystic Kidney Disease, ARPKD= Autosomal Recessive Polycystic 
N.B: Unspecified myopathy/myositis are collection of inflammatory and non-inflammatory muscle disorders with clinical and /or laboratory evidence 

 
Table 4. Distribution of Immunological Disease by Age 

 

Variable                                                        Age   

 ≤10 11-20 21-30 31-40 41-50 51-60 61-70 >70  

Rare Disease Freq (%) Freq (%) Freq (%) Freq (%) Freq (%) Freq (%) Freq (%) Freq (%) Total (%) 

ADPKD 0(0) 0(0) 1(11.) 1(11.1) 2(22.) 1(11.1) 3(33.3) 1(11.1) 9(100) 
ARPKD 1(50) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1(50) 2(100) 
Charge Syndrome 1(100) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1(100) 
Calciphylaxis 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1(100) 0(0) 1(100) 
Pituitary Adenoma 0(0) 0(0) 0(0) 0(0) 1(50) 1(50) 0(0) 0(0) 2(100) 

Immunological Disease          

Autoimmune Haemolytic 
Anaemia 

0(0) 2(100) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 2(100) 

Crohn Disease 0(0) 0(0) 0(0) 0(0) 1(0) 0(0) 0(0) 0(0) 1(100) 
Cryoglobulinemia 0(0) 0(0) 0(0) 0(0) 0(0) 1(100) 0(0) 0(0) 1(100) 
Diabetes Type 1 0(0) 0(0) 0(0) 125(100) 0(0) 0(0) 0(0) 0(0) 125(100) 
Gout 0(0) 2(18.2) 0(0) 0(0) 0(0) 3(273) 2(18.2) 1(9.1) 11(100) 
Iridocyclitis 0(0) 1(6.2) 2(12.5) 4(25.0) 5(31.2) 3(18.8) 1(6.2) 0(0) 16(100) 
Juvenile Arthritis 1(100) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1(100) 
Kawasaki 5(100) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 5(100) 
Leukaemia 1(50) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1(50) 2(100) 
Myasthenia Gravis 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1(100) 1(100) 
Unspecified 
Myopathies/Myositis 

13(11) 20(18.0) 11(9.9) 14(12.6) 11(9.9) 17(15.3) 11(12.6) 14(12.6) 111(100) 

Pernicious Anaemia 1(100) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1(100) 
Rheumatoid Arthritis 0(0) 0(0) 0(0) 0(0) 1(20) 3(60) 0(0) 1(20) 5(100) 



 
 
 
 

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Variable                                                        Age   

 ≤10 11-20 21-30 31-40 41-50 51-60 61-70 >70  

Rare Disease Freq (%) Freq (%) Freq (%) Freq (%) Freq (%) Freq (%) Freq (%) Freq (%) Total (%) 

Sarcoidosis 0(0) 0(0) 1(100) 0(0) 0(0) 0(0) 0(0) 0(0) 1(100) 
Sjogren Syndrome 0(0) 0(0) 1(33.3) 2(66.7) 0(0) 0(0) 0(0) 0(0) 3(100) 
Systemic Lupus 
Erythematous (SLE) 

1(3.8) 7(26.9) 6(23.1) 8(30.8) 3(11.5) 1(3.8) 0(0) 0(0) 26(100) 

Systemic Sclerosis 0(0) 0(0) 0(0) 0(0) 2(66.7) 0(0) 0(0) 1(33.3) 3(100) 
Ulcerative Colitis 0(0) 0(0) 1(33.3) 0(0) 0(0) 0(0) 2(66.7) 0(0) 3(100) 

Immunological Disease 
with Comorbidity 
(Diabetes Type 2) 

         

Crohn Disease & Diabetes 
Type 2 

0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1(100) 0(0) 1(100) 

Unspecified 
Myopathies/Myositis & 
Diabetes Type 2 

1(14.3) 0(0) 0(0) 0(0) 0(0) 1(14.3) 3(42.8) 2(28.6) 7(100) 

Pseudogout & Diabetes 
Type 2 

0(0) 0(0) 0(0) 0(0) 0(0) 1(100) 0(0) 0(0) 1(100) 

Gout & Diabetes Type 2 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1(100) 1(100) 
Gout & Iridocyclitis 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1(100) 0(0) 1(100) 
Iridocyclitis& Diabetes Type 
2 

0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 2(100) 0(0) 2(100) 

SLE & Diabetes Type 2 0(0) 0(0) 0(0) 0(0) 1(100) 0(0) 0(0) 0(0) 1(100) 
ADPKD= Autosomal Dominant Polycystic Kidney Disease, ARPKD= Autosomal Recessive Polycystic 

N.B: Unspecified myopathy/myositis are collection of inflammatory and non-inflammatory muscle disorders with clinical and /or laboratory evidence 
 

 
 
 
 
 
 
 
 
 



 
 
 
 

Okikiade et al.; AJI, 4(1): 59-76, 2021; Article no.AJI.66498 
 

 

 
69 

 

Table 5. 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 40(38.8) 28(45.2) 32(30.8) 21(35.6) 8(42.1) 129(37.2)     
Female 63(61.2) 34(54.8) 72(69.2) 38(64.4) 11(57.9) 218(62.8) 4 3.971 

(0.423) 
F
 

0.371 0.475 

Total 103(100) 62(100) 104(100) 59(100) 19(100) 347(100    

Age           

≤5 8(7.8) 5(8.1) 5(4.8) 4(6.8) 1(5.3) 23(6.6) 4 80.026 
(0.000) 

F*
 

0.00 0.09 

6-10 2(1.9) 5(8.1) 9(8.7) 0(0.0) 2(10.5) 18(5.2)     
11-15 9(8.7) 3(4.8) 2(1.9) 5(8.5) 3(15.8) 22(6.3)     
16-20 10(9.7) 4(6.5) 13(12.5) 4(6.8) 1(5.3) 32(9.2)     
21-25 7(25.0) 4(6.5) 7(6.7) 9(15.3) 1(5.3) 28(8.1)    
26-30 4(22.2) 2(3.2) 8(7.7) 4(6.8) 0(0) 18(5.2)     
31-35 19(42.2) 11(17.7) 9(8.7) 4(6.8) 2(10.5) 45(13.0)     
36-40 7(6.8) 6(9.7) 15(14.4) 18(30.5) 0(0.0) 46(13.3)    
41-45 9(8.7) 5(38.5) 3(2.9) 2(3.4) 1(5.3) 20(5.8)     
46-50 3(2.9) 2(3.2) 5(4.8) 2(3.4) 1(5.3) 13(3.7)     
51-55 2(1.9) 4(33.3) 2(1.9) 2(3.4) 2(10.5) 12(3.5)    
56-60 7(6.8) 5(25.0) 6(5.8) 1(1.7) 1(5.3) 20(5.8)     
61-65 2(1.9) 3(27.3) 3(2.9) 2(3.4) 1(5.3) 11(3.2)     
66-70 4(3.9) 0(0.0) 10(9.6) 1(1.7) 1(5.3) 16(4.6)    
>70 10(9.7) 3(4.8) 7(6.7) 1(1.7) 2(10.5) 23(6.6)     
Total 103(100) 62(100) 104(100) 59(100) 19(100) 347(100     

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

 
 
 
 



 
 
 
 

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70 

 

Table 6. 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 4(3.1) 19(8.7) 23(6.6) 14 24.861 
(0.017) 

F*
 

0.04 0.31 

6-10 5(3.9) 13(6.0) 18(5.2)     
11-15 2(1.6) 20(9.2) 22(6.3)     
16-20 14(10.9) 18(8.3) 32(9.2)     
21-25 13(10.1) 15(6.9) 28(8.1)    
26-30 5(3.9) 13(6.0) 18(5.2)     
31-35 15(11.6) 30(13.8) 45(13.0)     
36-40 20(15.5) 26(11.9) 46(13.3)    
41-45 6(4.7) 14(6.4) 20(5.8)     
46-50 4(3.1) 9(4.1) 13(3.7)     
51-55 4(3.1) 8(3.7) 12(3.5)    
56-60 11(8.5) 9(4.1) 20(5.8)     
61-65 6(4.7) 5(2.3) 11(3.2)     
66-70 7(5.4) 9(4.1) 16(4.6)    
>70 13(10.1) 10(4.6) 23(6.6)     
Total 129(100) 218(100) 347(100)     

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



 
 
 
 

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71 

 

 
 

Fig. 2. Incidence of immunological diseases from 2014 -2018 
 

 
 

Fig. 3. Peak Age of Incidence of immunological diseases in 2014 
 

In the Table 6, a statistically significant 
association was observed between Age and Sex, 
those within the age group of 36-40 years had 
significantly higher proportions of both male and 
female compared to that of other age group, this 
difference was statistically significant (p<0.05). 
 

4. DISCUSSION  
 
Findings from the study showed that Type I 
diabetes was the leading immunological disease 

in the country, which was followed by Myopathies 
and Myositis. In contrast to the study findings, a 
systematic review conducted reported that celiac 
disease increased the most and the highest 
increase in incidence, comparing old to new 
surveys is allocated to myasthenia gravis [11]. 
However, the study also indicated that between 
the countries, celiac disease, type 1 diabetes and 
myasthenia gravis frequencies increased the 
most in Canada, Israel and Denmark, 
respectively.

2014 2015 2016 2017 2018 

Total Population 0.93 0.56 0.94 0.53 0.17 

Male 0.71 0.50 0.57 0.67 0.14 

Female 1.17 0.63 1.34 0.39 0.20 

0.00 

0.20 

0.40 

0.60 

0.80 

1.00 

1.20 

1.40 

1.60 

IN
C

ID
EN

C
E 

R
A

TE
  (

P
ER

 1
0

0
0

 P
ER

SO
N

) 

INCIDENCE OF IMMUNOLOGICAL  DISEASES FROM 2014  -2018  

 

<5 
6_1

0 
11_
15 

16_
20 

21-
25 

26_
30 

31_
35 

36_
40 

41-
45 

46-
50 

51-
55 

56-
60 

61-
65 

66-
70 

>70 

Male 0.00 0.00 0.19 0.97 0.90 0.47 1.01 1.02 1.30 0.25 0.29 1.12 1.00 2.02 1.45 

Female 1.85 0.47 1.69 1.03 0.71 0.49 3.77 0.81 1.18 0.55 0.32 1.67 0.00 0.72 1.33 

Total Population 0.92 0.23 0.91 1.00 0.81 0.48 2.40 0.92 1.25 0.40 0.30 1.38 0.52 1.40 1.39 

0.00 

0.50 

1.00 

1.50 

2.00 

2.50 

3.00 

3.50 

4.00 

IN
C

ID
EN

C
E 

R
A

TE
 (

P
ER

 1
0

0
0

 P
ER

SO
N

S)
 

 

PEAK AGE OF INCIDENCE OF 
IMMUNOLOGICAL DISEASES IN 2014 



 
 
 
 

Okikiade et al.; AJI, 4(1): 59-76, 2021; Article no.AJI.66498 
 

 

 
72 

 

 
 

Fig. 4. Peak Age of Incidence of immunological diseases in 2015 
 

 
 

Fig. 5. Peak Age of Incidence of immunological diseases in 2016 
 
In this study we observed a decreasing trend in 
the incidence of immunological diseases, also 
observed was an annual decrease in the 
incidence from 2014 to 2018, with a peak 
incidence in 2016 (0.94/1000 person-years). The 
lowest incidence was noted in 2018 (0.17/1000 

person-years). 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 which could lead to 
missed or undiagnosed cases. Another 
explanation of the findings is that data analysed 

<5 
6_1

0 
11_
15 

16_
20 

21-
25 

26_
30 

31_
35 

36_
40 

41-
45 

46-
50 

51-
55 

56-
60 

61-
65 

66-
70 

>70 

Total Population 0.57 0.58 0.30 0.40 0.46 0.24 1.39 0.79 0.69 0.27 0.60 0.98 0.78 0.00 0.42 

Male 0.00 0.68 0.00 0.39 0.45 0.00 1.77 0.77 0.26 0.25 0.57 0.74 1.00 0.00 0.87 

Female 1.15 0.47 0.64 0.41 0.47 0.49 1.00 0.81 1.18 0.28 0.64 1.25 0.54 0.00 0.00 

0.00 
0.20 
0.40 
0.60 
0.80 
1.00 
1.20 
1.40 
1.60 
1.80 
2.00 

IN
C

ID
EN

C
E 

R
A

TE
 (

P
ER

 1
0

0
0

 P
ER

SO
N

S 
PEAK AGE OF INCIDENCE OF 

IMMUNOLOGICAL DISEASES IN 2015 

<5 
6_1

0 
11_
15 

16_
20 

21-
25 

26_
30 

31_
35 

36_
40 

41-
45 

46-
50 

51-
55 

56-
60 

61-
65 

66-
70 

>70 

Total Population 0.57 1.05 0.20 1.30 0.80 0.95 1.13 1.97 0.41 0.66 0.30 1.18 0.78 3.48 0.97 

Male 0.68 0.23 0.00 0.97 0.23 0.70 0.76 1.02 0.00 0.25 0.00 1.49 0.00 2.69 0.87 

Female 0.46 1.90 0.42 1.64 1.41 1.22 1.51 2.96 0.89 1.11 0.64 0.84 1.62 4.33 1.06 

0.00 
0.50 
1.00 
1.50 
2.00 
2.50 
3.00 
3.50 
4.00 
4.50 
5.00 

IN
C

ID
EN

C
E 

R
A

TE
 (

P
ER

 1
0

0
0

 P
ER

SO
N

S 

PEAK AGE OF INCIDENCE OF 
IMMUNOLOGICAL DISEASES IN 2016 



 
 
 
 

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73 

 

in this study were obtained when subjects visited 
healthcare institutions. Therefore, no information 
was available for immunological diseases 
patients who did not visit a healthcare institution, 
which could underestimate the immunological 
diseases burden. The incidence rate of 

immunological diseases reported was lower 
compared to the findings in a systematic review 
which reported a Mean ± S.D of the net % 
increased /year incidence of autoimmune 
diseases worldwide were 19.1±43.1 [12]. 
 

 

 
Fig. 6. Peak Age of Incidence of immunological diseases in 2017 

 

 
 

Fig. 7. Peak Age of Incidence of immunological diseases in 2018 
 

<5 6_10 
11_1

5 
16_2

0 
21-
25 

26_3
0 

31_3
5 

36_4
0 

41-
45 

46-
50 

51-
55 

56-
60 

61-
65 

66-
70 

>70 

Total Population 0.46 0.00 0.51 0.40 1.03 0.48 0.50 2.36 0.28 0.26 0.30 0.20 0.52 0.35 0.14 

Male 0.23 0.00 0.19 0.39 1.13 0.00 0.25 2.30 0.00 0.00 0.00 0.37 0.50 0.00 0.00 

Female 0.69 0.00 0.85 0.41 0.94 0.98 0.75 2.42 0.59 0.55 0.64 0.00 0.54 0.72 0.27 

0.00 

0.50 

1.00 

1.50 

2.00 

2.50 

3.00 

IN
C

ID
EN

C
E 

R
A

TE
 (

P
ER

 1
0

0
0

 P
ER

SO
N

S 

PEAK AGE OF INCIDENCE OF IMMUNOLOGICAL DISEASES IN 
2017 

<5 
6_1

0 
11_
15 

16_
20 

21-
25 

26_
30 

31_
35 

36_
40 

41-
45 

46-
50 

51-
55 

56-
60 

61-
65 

66-
70 

>70 

Total Population 0.11 0.23 0.30 0.10 0.11 0.00 0.25 0.00 0.14 0.13 0.30 0.20 0.26 0.35 0.28 

Male 0.00 0.23 0.00 0.00 0.23 0.00 0.00 0.00 0.00 0.25 0.29 0.37 0.50 0.00 0.58 

Female 0.23 0.24 0.63 0.21 0.00 0.00 0.50 0.00 0.29 0.00 0.32 0.00 0.00 0.72 0.00 

0.00 
0.10 
0.20 
0.30 
0.40 
0.50 
0.60 
0.70 
0.80 

IN
C

ID
EN

C
E 

R
A

TE
 (

P
ER

 1
0

0
0

 P
ER

SO
N

S 
 

PEAK AGE OF INCIDENCE OF 
IMMUNOLOGICAL DISEASES IN 2018 



 
 
 
 

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74 

 

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 Immuno- 
logical Disease  

 

The incidence of immunological disease peaked 
within the age group of 31-40 years, after which it 
declined slowly. The incidence of immunological 
disease among female was higher compared to 
that of males with a peak age occurring at 31-35 
years. Findings from the study was in conformity 
with that of a systematic review which reported 
most immunological diseases were more 
common in women and a disproportionate 
occurrence of these diseases among these 
women [12]. 
 

5. CONCLUSION 
 

Sequel to the findings of this study, this study 
showed that the incidence of immunological 
disease, Type 1 diabetes Mellitus 
Myopathy/Myositis and SLE in Saint Vincent 
have decreased in the last decade, whereas the 
mortality rates of both SLE and Type 1 Diabetes 
Mellitus have increased. This finding of increased 
mortality of SLE and T1D suggests that this 
disease is no longer rare and will have 
implications for future healthcare planning. Age 
and sex were found to be risk factors for SLE. 
Our data confirmed the known predilection of 
SLE in women. The peak age of diagnosis is 
middle age, contrary to the generally held belief 
that lupus mainly targets young people.  
 

6. RECOMMENDATIONS 
 

Disease Registries should be expanded to a 
population-based multidisciplinary immunological 
diseases registry to enhance collection and 
analysis of data over time on causation, natural 
history, morbidity and mortality of immunological 
diseases. Utilizing a multidisciplinary, integrated 
approach with collection of data on multiple 
diseases. Support research on the feasibility and 
optimal design of the registry to allow collation of 
data at the state and national levels. Provide 
epidemiology, statistical, clinical disease, and 
bioinformatics expertise, incorporate biomarker 
data in registries and provide infrastructure for 

long-term support of registries and epidemiology 
studies. 
 

Provide long-term support for existing genetic 
repositories; establish genetic repositories for 
additional immunological disorders; ensure 
adequate representation of disease phenotypes 
and races. Develop high throughput, 
standardized, specific, and sensitive laboratory 
assays for infectious and non-infectious 
environmental factors that can be used in large 
epidemiologic studies. 
 

Identify new opportunities and continue support 
for training and career development for new and 
established basic science and clinical 
investigators in immunological disease research. 
Include specialized training in epidemiology and 
bioinformatics. 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 immunological disease. Develop and promote 
the use of a wide range of educational programs 
and continuing medical education materials in 
immunological disease for health care 
professionals, incorporating the latest research 
advances on autoimmunity and autoimmune 
diseases.  
 

Establish a centralized, consolidated 
immunological disease information centre 
accessible to professionals and the public via the 
Internet where there is provision of information 
about clinical trials to evaluate prevention and 
treatment regimens that will enable patients and 
their physicians to make informed choices. 
 

The present literature survey is not aiming to 
investigate etiologies or environmental factors 
affecting immunological induction or progression. 
It is expected that an improved knowledge of the 
worldwide distribution of immunological disorders 
will help to understand the role of different 
genetic factors and different environmental 
influences involved in auto-immunogenesis.  
 

Support research on gene/environment 
interactions important in development and 
manifestation of immunological diseases. 
Provide resources for production, storage, and 
distribution of materials and probes for genetic 
research to the research community centrally.  
 

Support research to develop novel assays to 
identify prior exposures to environmental agents, 
including chemicals, toxins, and infectious 
agents. Support basic and clinical research on 



 
 
 
 

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75 

 

mechanisms by which infectious agents or other 
environmental factors may trigger or modulate 
immunological diseases.  
 

Support basic research on mechanisms and loss 
of self-tolerance, including mechanisms to 
control autoreactive cells. Support basic research 
on tissue specificity, target organ recognition, 
and immune injury and pathogenesis among 
different immunological diseases. Support core 
facilities for production and distribution of 
specialized reagents for research, including 
MHC-tetramers, antibodies, and microarrays. 
 

7. STRENGTH AND LIMITATION OF 
STUDY  

  
Despite our important findings, this study had a 
few strengths and limitations. The strengths to 
this study include; the data being population 
based, recall bias was not an issue to any 
misclassification errors on the side of providing 
conservative estimates, and it is the first of its 
kind to estimate the incidence of immunological 
diseases in Saint Vincent and the Grenadines. 
 

Some apparent limitations of using the patient 
records, which signifies our prevalence and 
incidence estimate were based on use of health 
services; stem from, implication from this 
signifies that data analysed in this study were 
obtained when subjects visited healthcare 
institutions. Therefore, no information was 
available for patients with immunological 
diseases who did not visit a healthcare 
institution, which could underestimate the 
autoimmune burden. However, this may not have 
had a substantial impact on our findings, as the 
Milton Cato General Hospital provides diverse 
healthcare delivery services that is accessible 
and affordable compared to other public and 
private healthcare providers. 
 

CONSENT 
 

It is not applicable 
 

ETHICAL APPROVAL 
 

Ethical approval was gotten to access medical 
information of patients from the 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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via%3Dihub 

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© 2021 Okikiade et al.; This is an Open Access article distributed under the terms of the Creative Commons Attribution License 
(http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, 
provided the original work is properly cited. 

 
 

 
 

Peer-review history: 
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http://www.sdiarticle4.com/review-history/66498 

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