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One Health Approach: The key to addressing pandemics and
other complex challenges of the 21st
Century
1
Senior Resident, Department of Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India. 2
Assistant
Professor, Department of Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India. 3
Professor and HOD,
Department of Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India.
The corona virus infectious disease or Covid 19 pandemic has been
causing unprecedented loss of lives and livelihoods across the globe.
This is the third time a Beta coronavirus has crossed the animal-
human species barrier in the last 20 years resulting in a major
zoonotic outbreak [1]. The first was in 2002, when the SARS CoV-1
virus caused an outbreak in China and second was in 2012 with the
MERS CoV causing an outbreak in the Middle East. The SARS CoV-
1 originated from bats and the MERS CoV originated from camels.
Covid 19 disease is a zoonotic infection caused by SARS CoV-2
virus, which originated in Wuhan city in China in December 2019,
which quickly spread across the world. The zoonotic source of SARS
CoV-2 is not known but is closely related to a group of SARS CoV
viruses found in bats a, humans and civets [2].
The complex challenges of the 21st
century like climate change and
the recent disease outbreaks are evidence of increased human –
animal interactions and human influence which will continue to
increase, given the increasing human demand for space, food and
unbridled consumerism. They also are an indicator of the
interconnectedness of human and animal and environmental health.
Hippocrates, the great Greek physician in his book ‘On air, waters
and places’ had dwelled on the importance of relationship between
human health and the environment [3]. The ‘One Health’ approach
recognizes this important relationship between human, animal and
environmental health. In 2004, the wildlife conservative society with
a group of partner organizations launched the ‘the one world, one
health ‘initiative which was the primary step in the evolution of the
modern concept of One Health [3]. One health is defined by the One
Health High Level Expert Panel (OHLLEP) as “an integrated,
unifying approach that aims to sustainably balance and optimize the
health of people, animals and ecosystems” [4].
The one health approach calls upon human medicine, veterinary
This is an open access journal, and articles are distributed under the terms of the
Creative Commons Attribution-Non-Commercial-ShareAlike 4.0 International
License, which allows others to remix, tweak, and build upon the work
non-commercially, as long as appropriate credit is given and the new creations are
licensed under the identical terms.
How to cite this article: Priyanka Raj C K. One Health Approach: The
key to addressing pandemics and other complex challenges of the 21st
Century. Int J Med Sci and Nurs Res 2021;1(2):1–2.
Article Summary: Submitted: 22-October-2021 Revised: 06-November-2021 Accepted: 03-December-2021 Published: 31-December-2021
International Journal of Medical Sciences and Nursing Research 2021;1(2):1-2 Page No: 1
medicine, public health, environmental sciences, and a host of other
disciplines to work together to improve the health of humans,
animals and the environment. The scope of one health includes areas
such as climate change, biodiversity loss, food, and water security,
emerging and reemerging diseases, antimicrobial resistance etc.…
Image source: https://www.who.int [4]
The key strategies of One health for the prevention and control of
zoonotic diseases are as follows [3, 5].
1. Surveillance of disease or infections in wildlife, livestock and
human populations including environmental surveillance.
2. Minimizing human -animal interactions and spread of infections
from animals to humans – for example safe handling of livestock,
pets and wildlife, livestock vaccinations etc...
3. Reducing antimicrobial resistance through rational antibiotic use
in animals and livestock.
5.Addressing climate change at local, national, and international
levels.
6. Promoting collaborative research
These strategies have been effective in controlling SARS CoV
outbreak in 2002 by banning of trade of civet cats [6]. One health
strategies also helped in reducing the MERS CoV case fatalities [7].
In Chad, simultaneous human and animal vaccinations have proven
effective against brucellosis [8].
We are facing complex challenges with regard to climate change,
emerging and reemerging diseases, food and water security. Isolated

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4. Integrating and coordinating disease prevention, surveillance and
response across all sectors (animal husbandry, education, health,
communications, agriculture etc.…)
5. Addressing climate change at local, national, and international levels.
6. Promoting collaborative research
These strategies have been effective in controlling SARS CoV outbreak in
2002 by banning of trade of civet cats [6]. One health strategies also helped
in reducing the MERS CoV case fatalities [7]. In Chad, simultaneous human
and animal vaccinations have proven effective against brucellosis [8].
We are facing complex challenges with regard to climate change, emerging
and reemerging diseases, food and water security and individual responses
are incapable of addressing these issues and the only way to deal with these
complex issues is to collaborate and coordinate our efforts across disciplines,
sectors and nations. Barriers to implementing One health do exist, but One
Health approach is the key to ensure sustainability and survivability of all
life on planet earth.
References:
1. Schmiegea D, Arredondo AMP, Ntajal J, Paris JMG, Savi MK, Patel
K, et al. One Health in the context of coronavirus outbreaks: A
systematic literature review. One Health. 2020;10:1-9. DOI:
https://doi.org/10.1016/j.onehlt.2020.100170
2. World Health Organization: WHO Coronavirus (COVID-19)
Dashboard. Available from: https://covid19.who.int/ [Accessed on:
10 August 2021]
3. Katz DL, Elmore JG, Wild D, Lucan SC. Jekel’s Epidemiology,
biostatistics, preventive medicine and public health. 4th
edition
2014:364-377. Elsevier Saunders. ISBN-13: 978-1455706587
Priyanka Raj C K. One Health Approach: Key to addressing the pandemics and other complex challenges of the 21st
Century
4. WHO: Tripartite and UNEP support OHHLEP’s definition
of “One Health”. Available from:
https://www.who.int/news/item/01-12-2021-tripartite-and-
unep-support-ohhlep-s-definition-of-one-health [Accessed
on: 25th
August 2021]
5. Hamida MG, Ba Abdullah MM. The SARS-CoV-2
outbreak from a one health perspective. One Health.
2020;10:100127. DOI:
https://doi.org/10.1016/j.onehlt.2020.100127
6. Parry J. WHO queries culling of civet cats. BMJ
2004;328(7432):128.
7. Hemida MG, Alnaeem A. Someone health-based control
strategies for the middle east respiratory syndrome
coronavirus, One Health 2019;8:100102. PMID: 31485476
8. Roth J, Zinsstag J, Orkhon D, Chimed-Ochir G, Hutton G,
Cosivi O, et al. Human health benefits from livestock
vaccination for brucellosis; case study. Bulletin WHO
2003;81:867-876.
Dr. C. K. Priyanka Raj,
Deputy Editor-In-Chief, IJMSNR,
Associate Professor.
Department of Public Health and Epidemiology,
College of Medicine and Health Sciences,
Sohar, National University of Science and Technology,
Sultanate of Oman.
Email ID: priyankaraj@nu.edu.om and
DeputyEditor-in-chief@ijmsnr.com

Web Site
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Challenges met by Healthcare Professionals (Nurses) at
the time of Covid-19 Pandemic
1
Senior Resident, Department of Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India.
2
Assistant Professor, Department of Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India.
3
Professor and HOD, Department of Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India.
Introduction:
The fast-growing Covid-19 pandemic has a great health problem to
people in worldwide and has a major challenge for nurses and other
health care professionals as well as nursing students. Here, few
major challenges are listed that the health care workers (HCW)
especially Nurses are faced and facing many problems in their day-
to-day life.
Major Challenges are facing by the nurses:
Increased risk of infection among nurses: The reports
from across the world shows that the healthcare workers were
affected by Covid-19 outbreak in the early period. [1] It was
particularly nurses who took care of Covid-19 unit are getting
infected or dying due to Covid-19 most of the hospitals and isolation
centres were overloaded by Covid cases which leads to nurses are
susceptible to infection. But now this situation is changed in few
hospitals because of awareness of Covid outbreaks.
Lack of awareness of Covid-19 among Healthcare
Workers: As this disease has spread suddenly the nurses were not
aware about this type of disease will go worst. But now nurses are
prepared in somewhat extent for future Covid out breaks. Now, a
day’s most of the hospitals also prepared with adequate ICU and
emergency rooms. Few countries like Hong Kong, Taiwan,
Singapore are already learned the lessons well from SARS and H1N1
out breaks. The health care workers in those countries already aware
about these pandemic outbreaks. [2]
Shortage of experienced nurses in Covid-19 unites:
In most of the hospitals the nurse’s patient ratio need to be well
non-commercially, as long as appropriate credit is given and the new creations are
licensed under the identical terms.
How to cite this article: Senthilvel S. Challenges met by healthcare
professionals (Nurses) at the time of Covid-19 Pandemic. Int J Med Sci
and Nurs Res 2021;1(2):3–4.
Article Summary: Submitted:26-October-2021 Revised:10-November-2021 Accepted:02-December-2021 Published:31-December-2021
maintained as it highly affects the healthcare delivery system.
Professional training includes the hazards of disease, and its routes,
routes of transmission, personal protection, prevention and control
measures will extend the knowledge and skill of nurses and nursing
students, who might be brought to the pandemic to support their
colleagues when there are sufficient trained nurses can have more
advocate with patient and their relatives about patient care. [3]
Shortage of personal protection equipment:
There is a shortage of PPE in most hospitals and health centers in
India including face mask, gowns and respirators. Local product of
face mask and other kits are reported to be of low quality which is
not protective against infection. [3]
Long working hours:
Shortage of staff pattern results in long working hours and sometimes
double shift also some nurses care needs to do. [3]
Inadequate quarantine facilities:
In earlier period of this outbreaks the nurses are quarantined between
14 – 15 days after they completed one rotation of duty. But, later as
the cases increases the rules of quarantine period was reduced to 2 to
3 days which is happened particularly the Urban Centre of Delhi and
Mumbai. The rules of testing the health workers also changed which
leads to increased incidence of infection among nurses. [4]
Mental Violence:
It will lead to inefficient care nurses facing mental violence can be in
the form of threats, verbal abuse, hostility and possible source of
violence includes patient, visitors and co-workers. [3, 5]

International Journal of Medical Sciences and Medical Research
Lack of teamwork:
One of the highly sought-after tools in the field of human resource
management in team work. Since there is lack of team work in Covid-19
management working as a team will get and share innovative ideas to
tackle this Covid-19 pandemic.
Importance of nursing administration:
The nursing service and administration is very important and essential in
the COVID-19 care unit. In Saudi Arabia, the MOH has collaborated
with the private sector and planned to sector wise and nursing
administration to strengthen in all the levels. [6]
Conclusion:
Nurses are playing important role in the battle against COVID unit.
Nurses are facing challenges while working in COVID care unites as
mentioned like risk of infection, more working hours, lack of awareness
and etc. These challenges immediately need to meet which will be
improving efficient nursing care in COVID-19 pandemic. More training
programs and researchers are needed to the healthcare workers (Staff
Nurses) to prepare them for future pandemics.
References:
1. Lai X, Wang M, Qin D, Tan L, Ran L, Chen D, et. al. Coronavirus
Disease 2019 (Covid-2019) Infection Among Health Care Workers
and Implications for Prevention Measures in a Tertiary Hospital in
Wuhan, China. JAMA Netw Open 2020;3(5):e209666. DOI:
10.1001/jamanetworkopen.2020.9666.
2. Tripathi R, Alqahtani SS, Albarraq AA, Meraya AM, Tripathi P,
Banji D, et al. Awareness and preparedness of COVID-19
Outbreak Among Healthcare Workers and Other Residents of
South-West Saudi Arabia: A Cross-Sectional Survey. Front Public
Helath 2020;8:482. DOI: 10.3389/fpubh.2020.00482.
3. Challenges in Nursing: What Do Nursing Face on a Daily Basis
Available on: https://online.arbor.edu/news/challenges-in-nursing
[Last Accessed on 18 July 2021]
4. Nurses concerned of COVID-19 exposure, lack of quarantine
quarters. Available on:
https://www.breakingbelizenews.com/2020/08/08/nurses-
concerned-of-covid-19-exposure-lack-of-quarantine-quarters/
Last Accessed on 19 July 2021]
5. Chhugani M, James MM. Challenges faced by nurses in India-the
major workforce of the healthcare system. Nursing & Care Open
Access Journal 2017;2(4):112-114. DOI:
10.15406/ncoaj.2017.02.00045.
Senthilvel S. Challenges met by healthcare professionals (Nurses) at the time of Covid-19 Pandemic
6. Alluhidan M, Tashkandi N, Alblowi F, Omer T, Alghaith T,
Alghodaier H, et al. Challenges and policy opportunities in
nursing in Saudi Arabia. Human Resources for Health
2020;18:98. DOI: 10.1186/s12960-020-00535-2.
Mrs. Sumathi Senthilvel,
M.Sc., (Nursing), RN., RM.,
Associate Editor, IJMSNR,
Formerly Assistant Professor in Nursing,
Department of Fundamental Nursing,
Amrita College of Nursing. Ponekkara, Kochi, Kerala.
Email ID: AssociateEditor@ijmsnr.com

Web Site
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A multivariate analysis approach on identifying of influencing factors
and the chance of development of diabetic eye disease among diabetes in
a diabetic Centre of Southwestern Malabar region of India
Amitha Prasad1
, Senthilvel Vasudevan2
1
Biostatistician Technician, IQVIA, World Trade Center Kochi (Brigarde), 7th
floor, Tower A, Info Park SEZ, Info Park Phase-1 Campus,
Kakkanad, Kochi, Kerala, India. 2
Assistant Professor of Statistics (Biostatistics and Epidemiology), Department of Pharmacy Practice, College
of Pharmacy, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Background: Diabetic Retinopathy is a non-communicable disease and metabolic disorder. It is a public health problem in Worldwide. In
this paper, finding influencing factors and how much probability to development of DR among known T2DM patients.
Materials and Methods: This was a hospital-based cross-sectional and observational study among T2DM patients, with and without DR
in the diabetes clinic with sample of one hundred and fifty patients. Statistical analysis used chi-square and binary logistic regression analysis
was used to identify correlates of DR after controlling of confounders.
Results: In this present study, one hundred and fifty DM patients were included and in that, 39 (26%) patients had DR. Smoking habit was
strongly associated with development of DR (AOR=15.39, p=0.002), patients had history of hypertension was associated with DR
(AOR=1.10, p=0.016), medication, in that insulin users were strongly associated with DR (AOR=5.72, p=0.002), duration of diabetes mellitus
with >10 years was associated with DR (AOR=1.18, p=0.001), total cholesterol with abnormal was 5-fold more increase in risk with the
development of DR (AOR=5.86, p=0.065) but not significant, high hba1c with >6.5% was associated with the progression of DR (AOR=1.34,
p=0.035), and fasting blood sugar with abnormal was associated with the progression of DR (AOR=1.01, p=0.027) except age but, showed
positive association in bivariate with DR. The probability of developing DR in a known T2DM patient was 98%.
Conclusion: From this study, we revealed that influencing variables were hba1c, smoking habit, intake of tablet/insulin, duration of DM,
history of hypertension and fasting blood sugar. The chance/probability of developing retinopathy was very high among known diabetes
patients those who had longer duration of DM. Hence, we have recommended a periodic eye screening is mandatory in T2DM patients.
Keywords: diabetes mellitus, diabetic retinopathy, influencing factors, probability, multivariate analysis
Keywords:
Introduction
Diabetes Mellitus (DM) is called otherwise by the word “Diabetes”. DM is a non-communicable disease [1]. DM is the public health problem
in Worldwide. It is classified into two major types namely Type I DM, Type II DM [2]. Diabetic Retinopathy (DR) is a non-communicable
and metabolic disorder. It is the complication of DM. DR is also called as “eye threatening disease”. DR affects the minor blood vessels in
the retina. It is a public health problem in both developing and developing countries. Overall, in India there are 65 million people with DM,
and it would be projected to increase to 134 million in coming year 2045. [3] If the body glucose level is not maintaining correctly for a long
period, then it leads to last stage vision loss [4]. The prevalence of DR was 27% in between 2015 – 2019 based on Worldwide and in that
Proliferative DR (PDR) was 1.4% [5].
The prevalence of DR is more in male gender, urban area had more prevalence and 22.18% patients had DR. [6] Even though the literacy
rate is high in Kerala, but the prevalence of DM is 16.3% also very high and vision threatening was seen in 39.5% population. So many
studies were done with small sample size, and some studies were done with larger sample size. [7] DR progression was associated with older
age, male sex, hyperglycaemia (higher HbA1C) and with not smoking. [8] There was no separate paper related to find probability of
developing or progressing DR in DM patients. That’s why, we did this study with a reasonable sample size. The main aims of this study
was to identify the influencing factors of DR among T2DM patients and to estimate the probability of developing of DR among known
T2DM patients.
How to cite this article: Prasad A, Vasudevan S. A multivariate analysis approach on identifying of influencing factors and the
chance of development of diabetic eye disease among diabetes in a diabetic Centre of Southwestern Malabar region of India.
Int J Med Sci and Nurs Res 2021;1(2):5-9.
non-commercially, as long as appropriate credit is given and the new creations
are licensed under the identical terms.
Corresponding Author: Dr. Senthilvel Vasudevan,
Assistant Professor of Statistics, Department of Pharmacy Practice,
College of Pharmacy, King Saud Bin Abdulaziz University for Health
Sciences, Riyadh, Saudi Arabia. Email ID: vasudevans@ksau-hs.edu.sa
Abstract

Materials and Methods:
A hospital-based cross-sectional and observational study was conducted
with one hundred and fifty known DM patients by simple random
sampling method were recruited and included in this study. Data were
collected from the Diabetic Centre patients in Amrita Institute of
Medical Sciences, Kochi, Kerala. This study was done in between
February and March 2018.
Selection of variables and allocation for the data analysis: In our
present study, we have considered the variables as binary variables for
the purpose of data analysis.
Gender (X1): Male = 0, Female = 1,
Age (X2): ≤50 years = 0, >50 years = 1,
Educational status(X3): School = 0, College = 1,
Family history of Diabetes Mellitus (X4): No = 0, Yes = 1,
Alcohol consumption (X5): No = 0, Yes = 1.
Smoking habit (X6): No = 0, Yes = 1,
History of hypertension (X7): No = 0, Yes = 1,
Medication (X8): Tablet Users = 0, Insulin Users = 1,
Duration of Diabetes Mellitus (X9): <10 years = 0, ≥ 10 years = 1,
Body Mass Index classification (X10): Normal = 0, Over Weight = 1,
Total cholesterol (X11): Normal = 0, Abnormal = 1,
HbA1C (X12): ≤ 6.5% = 0, > 6.5% = 1, and
Fasting blood sugar (X13): Normal = 0, Abnormal = 1 as shown in Table
– 1.
For the analysis, I have taken the variables were converted as binary
variables. We have found the association between dichotomous variables
(gender, educational status, family history of DM, smoking habit, history
of hypertension, medication, BMI classification, total cholesterol, and
fasting blood sugar) and found mean comparison between continuous
variables (age, duration of diabetes mellitus, and hba1c), with and
without variables by using Chi-Square test.
To find out the odds ratio (Probability of developing DR in a DM patient)
as follows:
Y = β0 + β1X1 + β2X2 + β3X3 + … + βiXi + … + βnXn … … … (1)
Find the value of Y and substitute in eY
, and then
P
------------ = eY
… … … (2)
1 – P
and find the value of P.
This P – value is the probability of developing DR in a DM patient.
Inclusion Criteria: T2DM patients with aged ≥30 years those who have
been lived permanently in area in and around Kochi area.
Exclusion Criteria: Patients those who had other chronic diseases and
other communicable and non-communicable diseases.
Statistical analysis: All data were entered and managed by using
Microsoft Excel 2010 [Microsoft Office 360, Microsoft Ltd., USA]
and data were analyzed by using SPSS 20.0 version for windows
[IBM SPSS Ltd., Chicago IL, USA].
Descriptive Statistics: Quantitative variables were expressed as
mean and standard deviation, and qualitative variables were
expressed as frequency, and proportions. Bivariate analysis: Chi-
Square test was used to compare dichotomous variables.
Multivariate Logistic Regression (MLR) Analysis: Binary Logistic
Regression equation (Y = β0 + β1X1 + β2X2 + β3X3 + … … … + βnXn)
with backward conditional analysis was used to find the influencing
factors in the development of DR among known T2DM patients. [9]
The statistically significant (p<0.05) variables were identified from
bivariate analysis and variables had p-value <0.20 were identified and
included in the final Binary Logistic Regression analysis. [10] The
level of significant was fixed as p<0.05.
Ethical Consideration: This study was done with prior permissions
were obtained from both the institutions before conducted. Patients’
data were obtained from the medical records and some information
from the patients directly. Patients’ data were confidential and
preserved by the AIMS institutions, Kochi, Kerala. Ethical approval
from the Institutional Review Board/Ethics Committee had been
obtained and informed all the details about the study and had got the
oral consents were taken from all participants at the time of study
period.
Results:
In our present study, two hundred T2DM patients as per inclusion and
exclusion criteria with aged thirty years and above were recruited and
included. In that, 39 (26%) patients had DR and 111 (74%) patients
were not having DR. The average age of the participants was 58.2 ±
10.5 (31–87) years. The other variables were presented in Table – 1.
In bivariate analysis, the variables duration of diabetes mellitus,
medication, duration of hypertension, smoking habit, HbA1C, and
FBS were showed statistically significant with and without DR with
p<0.05. So, these variables were influencing with the development
of DR among known T2DM patients.
In this study, we have used Binary Logistic Regression (BLR)
Analysis with backward conditional analysis to predict the
influencing factor to develop the diabetic retinopathy among known
T2DM patients. From the multivariate logistic regression analysis,
the results were obtained and in that, Hosmer-Lemeshow test was
showed a goodness of fit with Chi-Square value of 2.891 and p-value
was 0.941 (p>0.05). Hence, we have concluded that the selection of
prediction variables was very much suitable to the final model binary
logistic regression model was a good fit and the substitute variables.
The history of hypertension wasn’t significant in the bivariate
analysis but included in the final BLR analysis. The history of
hypertension wasn’t significant in the bivariate analysis but included
in the final BLR analysis.
Prasad A et al. A multivariate analysis approach on influencing factors and the chance of development of diabetic eye disease

Table: 1 Distribution of basic and clinical characteristics of
with and without Diabetic Retinopathy among Type 2 Diabetes
Mellitus patients
Variables
No. of Patients
n (%)
Diabetic Retinopathy
With DR Without DR
Gender (X1) Male 85 (56.7) 20 (23.5) 65 (76.5)
Female 65 (43.3) 19 (29.2) 46 (70.8)
Age groups
(in years) (X2)
≤ 50 34 (22.7) 60.38 9.06
> 50 116 (77.3) 57.37 10.84
Educational Status
(X3)
School 91 (60.7) 23 (25.3) 68 (74.7)
College 59 (39.3) 16 (27.1) 43 (72.9)
Family History of
DM (X4)
Yes 47 (31.3) 9 (19.1) 38 (80.9)
No 103 (68.7) 30 (29.1) 73 (70.9)
Alcohol
Consumption (X5)
Yes 127 (84.7) 32 (25.2) 95 (74.8)
No 23 (15.3) 7 (30.4) 16 (69.6)
Smoking Habit
(X6)
Yes 136 (90.7) 33 (24.3) 103 (75.7)
No 14 (9.3) 6 (42.9) 8 (57.1)
History of
hypertension (X7)
Yes 55 (36.7) 8 (14.5) 47 (85.5)
No 95 (63.3) 31 (32.6) 64 (67.4)
Medication (X8) Tablet Users 93 (62.0) 11 (11.8) 82 (88.2)
Insulin Users 57 (16.0) 28 (49.1) 29 (50.9)
Duration of DM
Mean (SD) (X9)
< 10 years 64 (42.7) 16.62 7.57
≥ 10 years 86 (57.3) 10.21 6.65
BMI
Classifications
(X10)
18.5 – 24.9
(Normal)
68 (45.3) 17 (24.6) 52 (75.4)
25.0 – 29.9
(Over Weight)
82 (54.7) 22 (27.2) 59 (72.8)
Total Cholesterol
(X11)
Normal 123 (82.0) 36 (29.3) 87 (70.7)
Abnormal 27 (18.0) 3 (11.1) 24 (88.9)
HbA1C (in %)
Mean (SD) (X12)
≤ 6.5 30 (20.0) 8.94 2.12
> 6.5 120 (80.0) 7.97 1.83
Fasting Blood
Sugar~
(X13)
Normal 14 (10.4) 2 (14.3) 12 (85.7)
Abnormal 121 (89.6) 33 (27.3) 88 (72.7)
In the third step of backward elimination only, the variables smoking
habit, β-regression value=0.002, Adjusted Odds Ratio, [AOR:15.39;
95%CI:(2.66–89.18); p=0.002], (p<0.05), was 15-times more risk than
non-smokers. History of hypertension, β-regression value=0.013,
[AOR:1.10; 95%CI:(1.02–1.18); p=0.016], (p<0.05) with hypertension
10% increase in risk in the development of DR. Medication, β-regression
value=0.009, [AOR = 5.72; 95%CI:(1.93–16.91); p=0.002], (p<0.05). The
risk was five times more in insulin users than tablet users.
Duration of diabetes mellitus, β-regression value=0.085, [AOR:1.18;
95%CI:(1.07–1.31); p=0.001], The risk was 18% more those who had DM
≥10 years (p<0.05). Total cholesterol, β-regression value=0.001,
[AOR:5.86; 95%CI: (0.89–38.41); p=0.065], (p>0.05). The risk was 5-
times more in abnormal than normal but not significant. According to
HbA1C, β-regression value = 0.218, [AOR:1.34; 95%CI: (1.02–
1.75); p=0.035], (p<0.05). 34% risk increase as shown in Table–2.
Table – 2 List of predictor variables in the multivariate
logistic regression equation, β-Values, its significance,
odds ratios and 95% Confidence Interval
Variables in the
Multivariate Logistic
Regression Equation
β
Value
OR Significance
95% CI
Lower
Limit
Upper
Limit
Age (X2) 0.458 0.97 >0.05, NS 0.92 1.03
Smoking habit (X6) 0.002 15.39 <0.01, HS 2.66 89.18
History of HTN (X7) 0.013 1.10 <0.05, S 1.02 1.18
Medication (X8) 0.009 5.72 <0.01, HS 1.93 16.91
Duration of DM (X9) 0.085 1.18 <0.01, HS 1.07 1.31
Total Cholesterol (X11) 0.001 5.86 >0.05, NS 0.90 38.41
HbA1C (X12) 0.218 1.34 <0.05, S 1.02 1.75
FBS (X13) 0.002 1.01 <0.05, S 1.00 1.02
Constant 1.486 0.72 <0.05, S
HTN - Hypertension; DM - Diabetes Mellitus; β - Regression Values; OR -
Odds Ratio; CI - Confidence Interval, HS- Highly Significant; S -
Significant; NS - Not Significant
In bivariate analysis, the association between groups (with and
without DR) and duration of DM was showed a highly statistically
significant with p-value<0.01 as shown in Figure–1.
Figure:1 Relationship between with and without diabetes
and classifications of duration of diabetes mellitus
The other variables like medication, duration of hypertension,
smoking habit, HbA1C, and FBS were also showed statistically
significant with and without DR with p<0.05. HbA1C in the
progression of DR. Next, to find the probability of the development
of DR in a DM patient. Here, we have taken clinical data of a DM
patient with DR and in high and substitute in the equations (1) and
(2), the variables were as follows: smoking habit (X6) = yes = 1;
history of hypertension (X7) = yes = 1; medication (X8) = yes = 1;
duration of diabetes mellitus (X9) = 20 years; hba1c (X12) = 7.2%;
34.90%
14.30%
65.10%
85.90%
0% 20% 40% 60% 80% 100% 120%
≥ 10 years
<10 years
With DR Without DR

fasting blood sugar (X13) = 190 mg/dL. Substitute in equation – 1,
Hence, the binary logistic regression equation (1) became,
Y = β0 + β1X1 + β2X2 + β3X3 + … … … + β13X13 ---------- (1)
According to final multivariate logistic regression analysis, the above
equation was rewritten as follows, ie., modified (1) equation was,
Y = β0 + β6X6 + β7X7 + β8X8 + β9X9 + β12X12 + β13X13
Y = 1.486 + (0.002) (1) + (0.013) (1) + (0.009) (1) + (0.085) (20)
+ (0.218) (7.2) + (0.002) (190)
Y = 4.160
Therefore, eY
= 64.072 and Substitute, the value of eY
= 64.072 in
the equation (2), We have got following,
P
------------ = eY
------------------ (2)
1 – P
P
------------ = 64.072
1 – P
P = 0.984 ~ 98%
Hence, the probability of developing DR was P = 0.984 (Odds Ratio).
So, the probability of developing DR in a known T2DM patient was
estimated as 98%.
Discussion:
This is the study in Kerala related to find the influencing factors and
probability to the progression of DR in diabetic patients. DR is one of
the public health problems in Worldwide. [3] DM patients have not
controlled their blood glucose level over a period of time then, they
will have to effect by retinopathy. If not screened in time and not
properly controlled the risk factors then, it will affect the retina and it
will cause to vision loss. In bi-variate analysis, duration of DM,
medication, total cholesterol, HbA1C, fasting blood sugar were showed
a significant with development of DR. But body mass index wasn’t
showed any significance with the progression of DR.
In the final statistical model in the BLR analysis the variables HbA1C,
FBS, smoking habit, intake of tablet/insulin, duration of DM and
history of hypertension were only showed a significant with the
development of DR. In our present study, the newly diagnosed with
Type 2 DM patients, 26% had DR. After the multivariate analysis the
related factors, smoking was a prominent risk factor in the development
of DR. ie, smoking habit was very highly significantly associated with
DR (AOR = 15.39, p=0.002). Similar type of result was mentioned by
Kumari et al. [11] In some other studies that the history of smoking
was found as a factor of DR development. [12, 13] Medication ie.,
insulin use [AOR = 5.72, 95%CI:(1.93–16.91)]; p<0.05. Similar
results were found by Kumari et al. [11, 14] History of hypertension
was a risk factor in the progression of DR. Similar type results were
determined by Hong et al., Pradeepa et. al. [15, 16] But, in our study
also the history of hypertension was showed a significant association
in the progression of DR.
Duration of diabetes mellitus 10 years or longer was showed a
significant factor in the development of DR in diabetes. Similar type
result was found by Roberts et. al., Kawasaki et. al. [17, 18] HbA1C was
a risk factor and association with the development/progression of DR.
The same type of results was found by Song et al. [19] In this study, we
have got total cholesterol was a prominent risk factor with 5-fold with
DR and it was an influencing with the development/progression of DR
but not showed any significant with DR in the multivariate analysis.
In a study by Abougalambou and Abougalambou. [20] have obtained
fasting blood sugar was a risk factor in the progression of retinopathy.
Brambilla et al. has also arrived similar result in the study. [21] There
was a positive correlation between DR and age with 60 years and above
but, not showed any significant with DR development. But in a study
by Stratton et al. has determined the older age was associated with the
progression of DR. [22]
Conclusion: From this study revealed that the influencing
variables were HbA1C, smoking habit, intake of tablet/insulin, duration
of DM (longer years), history of hypertension and fasting blood sugar
in a known T2DM patient. The chance/probability of developing
retinopathy was very high among diabetes patients those who have had
longer duration of diabetes mellitus. Hence, we have to recommend to
the diabetic/retinopathy patients to get health education and eye care
from their family physician/endocrinologist/authorized diabetic/retina
Centre public health professionals. Moreover, the diabetic patients have
to go for a periodic eye screening once in six months to prevent from
the development of DR, or to avoid, or to retain in the same severity
stage or to rescue themselves from loss of eye sight.
Acknowledgement: The authors are thankful to the Medical-Director,
Medical Superintend, Head of Retina Centre, and Head of the
Department of Biostatistics of Amrita Institute of Medical Sciences,
Kochi, Kerala for their support and guidance to proceed the study.
Authors’ contributions: AP, SV: Conception and Study design; AP:
Acquisition of Data; AP, SV: Data processing, Analysis and
Interpretation of Data; Both the authors – AP and SV were drafting the
article, revising it for intellectual content; Both authors were checked
and approved of the final version of the manuscript.
Here, AP – Amitha Prasad; SV – Senthilvel Vasudevan
Source of funding: None
Conflict of interest: None
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Web Site
http://ijmsnr.com/
Clinical Profile and Risk Assessment of Infections Among Diabetics
in a Community Health Hospital in Chennai: A Hospital Based
Descriptive and Cross-Sectional Study
Shalini Kaliaperumal1
, Ezhilan Naganathan2
, Betty Chacko3
1, 2, 3
Department of Medicine, CSI Kalyani Multi-Speciality Hospital, Chennai, Tamil Nadu, India.
Background: Incidence of diabetes mellitus continues to rise, common focus areas for diabetes control are blood glucose levels, diet, and
exercise. Controlling these factors are essential for a better quality of life in diabetes patients. Patients with diabetes have an increased risk
of asymptomatic bacteriuria and pyuria, cystitis, and, more important, serious upper urinary tract infection.
Materials and Methods: This was a hospital based descriptive and cross-sectional study which included 250 Study subjects who were
admitted in CSI Kalyani General hospital during the period from July 2017 to July 2018 and who has Diabetic as a comorbidity were
interviewed using structured protocol based proforma. Patient underwent routine clinical, pathological and biochemical investigations.
Results: In this study, 250 in-patients were included and analyzed. The prevalence of Infection in Diabetes mellitus was 65.6%. There is
no significant association between age, education, occupation, hba1c, duration and type of treatment and biochemical values. The commonest
organism in Urine sample among the study group was E.coli followed by Klebsiella. UTI is more common in females, respiratory infection
is more common in males and it was statistically significant (p<0.009) and (p<0.007) respectively.
Conclusion: From this study, we have concluded that patient with diabetes mellitus is at increased risk for common infections due to poor
glycemic control and obesity. Poor glycemic control suppresses the immunity and more prone for infection. Therefore, the challenges will
be to attain good glycemic control, change in lifestyle to maintain normal BMI. This will prevent the morbimortality, reduce the long-term
complication and maintenance to prolong the life without any sequele. More prospective case control studies on the management of infections
in DM patients are needed.
Keywords: type 2 diabetes mellitus, infections, clinical profile, hba1c, glycemic control
Introduction
Diabetes is fast gaining the status of a potential epidemic in India with more than 62 million diabetic individuals currently diagnosed with
the disease. In 2000, India (31.7 million) topped the world with the highest number of people with diabetes mellitus followed by China (20.8
million) with the United States (17.7 million) in second and third place respectively. The prevalence of diabetes is predicted to double globally
from 171 million in 2000 to 366 million in 2030 with a maximum increase in India. It is predicted that by 2030 diabetes mellitus may afflict
up to 79.4 million individuals in India, while China (42.3 million) and the United States (30.3 million) will also see significant increases in
those affected by the disease. Indians are genetically predisposed to the development of coronary artery disease due to dyslipidemia and low
levels of high-density lipoproteins; these determinants make Indians more prone to development of the complications of diabetes at an early
age (20-40 years) compared with Caucasians (>50 years) and indicate that diabetes must be carefully screened and monitored regardless of
patient age within India. [1]
Diabetes mellitus (DM) is a common non communicable disease in India. The prevalence of type 2 DM is 11% in urban areas in comparison
to 3-9% in rural areas. Infections play a significant role in morbidity and mortality of diabetic patients. Studies revealed that defect in the
function of neutrophils, lymphocytes, and monocytes were the reason for increased infections in diabetics. Other reasons are low levels of
leukotriene B4, thromboxane B2, and prostaglandin E. Some studies showed decreased lymphocyte function in diabetics, and decreased
How to cite this article: Kaliaperumal S, Ezhilan N, Chacko B. Clinical Profile and Risk Assessment of Infections Among Diabetics in a
Community Health Hospital in Chennai: A Hospital Based Descriptive and Cross-Sectional Study. Int J Med Sci and Nurs Res 2021;1(2):10–
18.
Corresponding Author: Dr. Shalini Kaliaperumal,
No.5, First Floor, Main Road, Manakula
Vinayagar Nagar, Pondicherry, India.
Email ID: shalinikaliaperumal@gmail.com Cell No: +91 96296 04933
Abstract

levels of phagocytosis in monocyte. There is also evidence that
improving glycemic status in diabetics, improves cellular immunity. [2]
Diabetes and related complications are associated with long-term
damage and failure of various organ systems. Diabetes induces changes
in the microvasculature, causing extracellular matrix protein synthesis,
and capillary basement membrane thickening which are the pathognomic
features of diabetic microangiopathy. These changes in conjunction with
advanced glycation end products, oxidative stress, low grade
inflammation, and neovascularization of vasa vasorum can lead to macro
vascular complications. [3] A positive association between diabetes and
infection was previously the subject of debate in the literature , but recent
evidence suggests that bacterial infections are a relatively frequent
occurrence in diabetic patients and that there may be an associated
increase in morbidity and mortality .The weight of evidence suggests that
patients with type 2 diabetes have an increased incidence of common
community acquired infections, including lower respiratory tract
infection, urinary tract infection (UTI), and skin and mucous membrane
infections . There is also a substantially increased susceptibility to rare
but potentially fatal infections including necrotizing fasciitis and
emphysematous pyelonephritis. [4] In patients with Diabetes mellitus,
soft tissue and bone infection of the lower limbs is the most common
cause for hospital admission. The rate of lower extremity amputation
among diabetics is more than 40 times that of non-diabetics. [5] The risk
of infection-related mortality is notably increased for diabetic adults
compared with those without diabetes, but only among people with
concurrent cardiovascular disease. [6]
Hepatitis C virus (HCV) infection may contribute to the development of
diabetes mellitus. This relationship has not been investigated at the
population level, and its biological mechanism remains unknown. [7]
Infections are widely considered to be a source of significant health care
costs and to reduce quality of life among people with diabetes mellitus
(DM). A recent review of higher-quality population-based
epidemiological studies found clinically important (∼1.5–3.5 times
higher) infection risks associated with poorer DM control in some
studies (usually defined as a glycated hemoglobin [HbA1C] level >7–8%
[53 – 64 mmol/mol]).
Preventing the development of diabetic complications such as infections,
kidney failure, and amputations involves proper glycemic control.
Addressing different aspects of diabetes control aid in the reduction of
infection susceptibility. [8] Literature suggests maintaining causal blood
glucose levels below 200 mg/dL. Glucose levels above 200 mg/dL are
expected to pose an increased risk of infections. To assist in the
maintenance of proper perfusion through blood vessels, adherence to
standard of care is vital. The risk and burden of infection is more in case
of diabetics than in case of non-diabetic individuals. There is also
evidence of altered glycemic control in diabetic patients with infection
and Obesity as a risk of infection; the main of complications related with
diabetes mellitus is due to impaired glucose tolerance and improper
glucose control, and it has also revealed that with good glycemic control
the number of complications has reduced, and also with good control of
infection the glycemic control is also good. Maintaining a normal BMI
is also essential to reduce the risk of disease burden among Diabetes
mellitus. Although DM is very common in south India, studies on type
of infections in patients with DM from rural South Indian areas are
lacking. Therefore, the aim of this study was to explore this problem
in our own setup. The main objectives are to study the epidemiology
of infections among diabetics; to assess the risk of infections among
diabetic patients; to study the clinical profile of infection among
diabetic patients; and to study the common organisms isolated in
Urine, Sputum and Pus sample.
We have done this hospital based descriptive cross-sectional study in
CSI Kalyani Multi-specialty hospital, Chennai with a sample of 250
patients in the study period of July 2017 – July 2018.
Sample Size Calculation: The prevalence of Infections in diabetes
mellitus is 30% [2, 10] We required 250 samples to estimate 30%
prevalence of Infections in diabetic patients with the precision of 6%
and 95% confidence interval.
N =
𝑍(1−𝛼/2)
2
∗ p(1 − p)
𝑑2
p - Expected proportion; d – Precision; Z1-α/2 – Two-sided Z value
for corresponding α; N – required sample size.
The inclusion criteria were both male and female patients willing to
participate, in-patients in all wards, CSI Kalyani Multi-speciality
Hospital with aged >12years and diabetes mellitus (both Type 1 & 2)
as comorbidity and with some exclusion criteria of aged less ≤12
years, patient not willing for admission, non diabetic and patient not
willing to participate, GDM and OPD Patients with DM. [9]
250 Study subjects, who are diabetic were included after obtaining
their written consent. Patients who were admitted in CSI Kalyani
General hospital during the period of July 2017 to July 2018 and who
has Diabetic as a comorbidity were interviewed using structured
protocol based proforma. Complete clinical examination was done.
Patient underwent routine clinical, pathological and biochemical
investigations such as Total count, differential, count, HbA1C, FBS,
PPBS, S. Urea, S. Creatinine, SGOT, SGPT were done. Appropriate
microbiological investigations such as Urine c/s, Sputum c/s, Blood
c/s, Pus c/s were done according to the clinical profile of the patients.
Other imaging methods were done such as Chest X ray, CT Chest,
CT Abdomen and CT Brain as required. Established diagnosis were
documented and results were tabulated. The data collected were
entered and analysed by using SPSS for Windows Version 20. Mean
and Standard deviation was used for normally distributed continuous
data. The dichotomous data were expressed as number and
percentages. The association was found using Chi-Square test
/Fisher’s Exact test wherever applicable. p-value was considered as
statistically significant
Ethical Consideration: This study was done with prior permission
and approval from the institutional research and ethical committee
and with patients’ written consents and data were confidential.
Results:
This study was done among the Diabetic patients of age >12years
who are all treated as In-Patient during July 2017 to July 2018 in CSI
Kalyani Hospital, Chennai. A total of 250 patients were analyzed and
their data were given in Table – 1.
Kaliaperumal S et al. Clinical Profile and Risk Assessment of Infections Among Diabetics

Table – 1 Distribution of socio-demographic and clinical variables
Variables
Number of
Patients Percentage
Gender
Male 128 51.2
Female 122 48.8
Age (in years)
30 – 39 12 4.8
40 – 49 47 18.8
50 – 59 70 28.0
60 – 69 59 23.6
70 – 79 43 17.2
>80 19 7.6
Educational Status
Illiterate 44 17.6
Primary 11 4.4
Middle school 54 21.6
High school 103 41.2
Diploma 28 11.2
Graduate 10 4.0
Postgraduate 0 0
Employment Status
Unemployed 119 47.6
Unskilled worker 12 4.8
Semi-skilled worker 33 13.2
Skilled worker 43 17.2
Clerical/shop/farm 29 11.6
Semi professional 14 5.6
Professional 0 0
Duration of Diabetes Mellitus (in years)
≤ 0.5 36 14.4
0.6 – 5.0 65 26.0
5.1 – 10.0 79 31.6
10.1 – 15.0 27 10.8
15.1 – 20.0 28 11.2
>20 15 6.0
Types of treatment with diabetes mellitus
OHA 137 54.8
Insulin 23 9.2
Diet only 51 20.4
Insulin & OHA 39 15.6
General symptoms in diabetes mellitus
Fever 106 42.4
Swelling of legs 24 9.6
Fatigue 14 5.6
Loss of appetitie 10 4.0
(Contd…)
In this study group, the prevalence of diabetes mellitus is more in
the age group of 50 – 59 years (28%) followed by the age group 60
– 69 years (23.6%), the youngest case recorded in the study is 30
years of age. In our study, both male and female nearly equal in
this study. It was observed that predominant group in this study
were in high school (41.2%) followed by middle school (21.6%).
Among this study group 17.6% of the people were illiterates.
Majority of them in this study group were unemployed (48%).
Majority of study group were with the duration of 5.1 – 10 years
(31.6%) followed by 0.6 – 5.0 years (26%). In our present study,
54.8 % of diabetics were taking only OHA‘s predominantly
followed by 20.4 % of Diabetics were on Diet only. Among the
general symptoms majority of them had fever (42.4%) followed by
swelling of legs (9.6%). In the predominant group in this study had
systemic hypertension (45.6%) followed by CAD (25.6%) as a
comorbidity.
It was observed that majority of Diabetics in this group had history
of UTI in the past (10.8%) followed by Respiratory infection in the
past (8.0%). In this study, predominant group were with the BMI
of 25-29.9 (36%), pre-obese group followed by 18.5 – 22.9 (25%)
Normal group according to Asian criteria of BMI. In this study
group, 33.4 % of them had Leukocytosis. In this, FBS>126 in 77.2
% of study group, PPBS >140 in 88.4 % of study group, S. Urea
elevated in 26.4 % of study group, S. Creatinine elevated in 17.6%
of study group, SGOT >40 in 15.2 % of study group and SGPT >40
in 14% of study group. It is observed that, 58.8 % of the study group
had HbA1C >8 followed by 19.6 % of the study group had HbA1C
6.1 to 7%. Predominant culture positivity was in Urine sample
(24%) followed by Sputum sample (14.4%). Among the urine
sample which had growth the commonest organism which was
found as E.Coli (31.1%) followed by Klebsiella (6.6%). Among the
sputum sample the commonest organism was Klebsiella (32%).
Second commonest was Mycobacterium Tuberculosis (14%)
detected by Gene Xpert method. Among the pus sample which had
growth, the commonest organism was found to be Staphylococcus
aureus (33.3%) and Pseudomonas (33.3%). Major microvascular
complication in this study was found to be diabetic nephropathy
(17.2%) followed by Diabetic Retinopathy (5.6%). Among the 250
study subjects it was observed that 65.6% of the Diabetics had
Infection and 34.4 % of the Diabetics had no infection. Among the
study subjects the commonest infection found was Urinary
infection (37.2%) followed by Respiratory infection (21.6%). 78.5
% of this study group had UTI, followed by Pyelonephritis (15.1
%). It was significant that 61.6 % of them had Asymptomatic UTI
and respiratory infections LRTI (13.6%) is more common.
The commonest foot infections in this study group were found to be
Cellulitis (52.9%) followed by Diabetic foot ulcer (29.4%). Among
the soft tissue infections, the commonest was found to be
Candidiasis (25%). In our study the commonest TB manifestation
was found to be Pulmonary Tuberculosis (77.8%). Moreover,
Hepatitis B and Acute Gastroenteritis were distributed equal in
number (36.3%) as shown in Table–1. Infection was more common
in females (53.7%) and it was statistically significant (p=value
0.03). It was observed that infection is predominant among
semiprofessional group (71.6%)

Table – 1 Distribution of socio-demographic and clinical variables
(Contd… Table-1)
Comorbidities in Diabetes Mellitus
HTN 114 45.6
CAD 64 25.6
Anemia 30 12.0
Dyslipidemia 25 10.0
CKD 23 9.2
CVA 20 8.0
Hypothyroid 14 5.6
Others 52 20.8
Past infection history in diabetes mellitus
UTI 27 10.8
Respiratory infection 20 8.0
DM foot ulcer 18 7.2
Body Mass Index in DM
<18.5 17 6.8
18.5 – 22.9 62 24.8
23 – 24.9 41 16.4
25 – 29.9 90 36.0
≥30 40 16.0
Total count in DM
Leukocytosis (>11000) 84 33.6
Normal count (4000-11000) 149 59.6
Leukopenia (<4000) 16 6.4
Urine Pus cells in DM
<5 83 33.2
5 to 10 36 14.4
10 to 20 26 10.4
20 to 30 21 8.4
Numerous 22 8.8
Occasional 34 13.6
None 28 11.2
Biochemical values in DM
FBS >126 193 77.2
PPBS >140 221 88.4
S. Urea > 40 66 26.4
S. Creat >1.3 44 17.6
SGOT > 40 38 15.2
SGPT > 40 35 14.0
HbA1C
4 to 6% 13 5.2
6.1 to 7% 49 19.6
7.1 to 8% 41 16.4
>8% 147 58.8
Positive Culture Sensitivity (Contd… Table-1)
Urine 60 24.0
Sputum 36 14.4
Pus 9 3.6
Blood 1 0.4
Organisms in Urine Sample
E.Coli 38 31.1
Klebsiella 8 6.6
Pseudomonas 4 3.3
Staph Epidermidis 3 2.5
Candida albicans 2 1.6
Enterococcus 2 1.6
Staph.aureus 2 1.6
Non albican candida 1 0.8
No growth 62 50.8
Organisms in Sputum Sample
Klebsiella 16 32.0
Mycobacterium Tuberculosis 7 14.0
Pseudomonas 6 12.0
Proteus Vulgaris 4 8.0
Staph aureus 3 6.0
Streptococcus 2 4.0
E.coli 2 4.0
Citrobacter 1 2.0
Acinetobacter 1 2.0
No growth 8 16.0
Organisms in Pus sample
Staph Aureus 3 33.3
Pseudomonas 3 33.3
E.coli 1 11.1
MRSA 1 11.1
No growth 1 11.1
Micro Vascular Complications
Nephropathy 43 17.2
Retinopathy 14 5.6
Neuropathy 9 3.6
Infection in Diabetes Mellitus
Yes 164 65.6
No 86 34.4
Type of infections in Diabetes Mellitus
Urinary 93 37.2
Respiratory 54 21.6
Foot infections 20 8.0
Skin and soft tissue 15 6.0
Tuberculosis 9 3.6
Cholecystitis 2 0.8
Others 19 7.6

It is observed that infection is more common in underweight group
(BMI<18.5) followed by obese group (BMI>30) and the test was
showed statistically highly significant (p-value<0.01) as shown in
Figure–1.
Figure: 1 Comparison of Body Mass Index and with Infection
In our present study, 68.2% of the Diabetics with Urinary symptoms
had positive urine culture and this was statistically significant (p-
value<0.001) as shown in Figure–2.
Figure: 2 Comparison of urinary symptoms with urine c/s
Discussion:
Diabetes Mellitus [12] is a non-communicable disease and is one of
the major disease burdens worldwide and also a leading cause for
non-traumatic lower limb amputations, the association of the
Infection and diabetes mellitus is not a new entity it’s been known
for quite some time for now, the recent studies also suggest the
increased
Table – 2 Association between with and without infection among
diabetes patients
and unemployed (70.6%) and it not statistically significant with p-
value=0.418 (>0.05). In our study, infection is more common when the
duration of diabetes is 0.6 – 5 years (76.9%) followed by 15.1 – 20 years
(71.4%) and this was not statistically significant with p-value=0.070
(>0.05).
Infection is more common in diabetics who are only on diet and only on
OHA. Among the Diabetics who are only on diet, 68.6 % of them had
infection and Diabetics who are only on OHA, nearly 67.2 % of them
had infections. It was not statistically significant with p>0.05.
It was not statistically significant with p>0.05. It is observed that
infection is more common in diabetics who had systemic hypertension
as a comorbidity but this was not statistically significant (p>0.05).
However, Infection was less common in Dyslipidemia and CVA group
and it was highly statistically significant (p<0.01).

prevalence of infections among diabetics with, many research has also
proved that glycemic control within appropriate normal limits will also help
to reduce the morbimortality and long-term complications [14] of Diabetes
mellitus. [11, 12] Physicians should be aware of risk factors and type of
infections present in patients with diabetes in order to provide proper care.
Prospective studies on the management of infections in patients with
diabetes mellitus are needed. [13] Diabetic retinopathy is a major
complication of DM. [15, 16] Diabetic neuropathy is also a complication of
DM and insulin complications in the long-term. [17, 18, 19, 20]
Other type of infections is also happening to DM patients. [21] Complete
clinical examination was done. Patient underwent routine clinical,
pathological and biochemical investigations such as Total count,
differential, count, HbA1C, FBS, PPBS, S. Urea, S. Creatinine, SGOT,
SGPT were done. Appropriate microbiological investigations [21, 22, 23]
such as Sputum c/s [24], Urine c/s [25, 26], Blood c/s, Pus c/s [26] were
done according to the clinical profile of the patients. Other imaging methods
were done such as Chest X ray, CT Chest, CT Abdomen and CT Brain as
required. Established diagnosis were documented and results were tabulated
as per results. [24, 26]
In our study the number of male and female were equal. Mean age of study
subject was 60 years. In my study, the maximum number of Diabetics with
infection were seen in 50 – 59 years’ age group (78.3%). This increase in
incidence of infection with age was observed in a study by Gillani et al. [27]
However there was no statistical significance with age and infection in my
study. In my study the infection rate was higher among females (53.7%).
However, this was not statistically significant. UTI is more common in
females (36.9%) and this was statistically significant (p=0.009). Similarly,
in Al-Rubeaan et al study, the prevalence of UTI was more common in
diabetic females. [28] In my study Age, duration of diabetes and HbA1C did
not influence the incidence of infection and there is no statistical
significance, while BMI above 30 kg/m2
increased the risk of infection and
it is statistically significant (p<0.01). Similar statistical significance
observed in Al-Rubeaan et al study. [28] In my study respiratory infection
is more common in males (23.4%) and it was statistically significant
(p=0.007). Similarly, in Dutt and Dabhi study, male patients and
uncontrolled DM had higher prevalence on pneumonia associated with
diabetes. [29] In this study it was also revealed that there was no significant
statistical association between Education, Occupation, Type of treatment,
biochemical values and HbA1C with infections among diabetics. However,
58.8% of them had HbA1C >8%, and infection is less common with HbA1C,
4 to 6% but it wasn’t statistically significant. In Critchley et al study, it was
observed that long-term infection risk rose with increasing HbA1C for most
outcomes. Poor glycemic control was powerfully associated with serious
infections and should be a high priority. [30] In our study there was a
positive correlation that the risk of infection is high in diabetics who are on
diet only (68.6%) and only on Oral hypoglycemic agents (67.2%). There
was a positive correlation observed that Diabetics who are on Insulin has
good control of blood sugars and less prone to infection. But this was not
statistically significant with p>0.05. However, in a study by Ooi et al, it was
statistically significant that Intensive insulin therapy and tight glycemic
control were associated with a lower risk of infection. [31] Out of 250 study
subjects, 164 diabetics had infections and 86 diabetic patients without
infections. In our study, the prevalence of infections among Diabetics was
65.6%. The predominant infections encountered were Urinary infection
(37.2%), Respiratory infection (21.6%), Foot infections (8.0%), Skin and
Conclusion:
Acknowledgement: The authors thank the participants, members of the
soft tissue infections (6.0%), Tuberculosis (3.6%) and
Cholecystitis (0.8%). Escherichia coli (31.1%) and Klebsiella (6.6%)
were the commonest organisms isolated from urine sample.
Klebsiella (32%) and Mycobacterium tuberculosis (14%) were the
commonest organism isolated from the sputum sample. In a
retrospective study was done by Bettegowde et al. from a rural
Tertiary care hospital of South Karnataka, out of 842 diabetics, 254
(30.1%) had infections. The commonest comorbidity was
Hypertension (62.99%). Common infections encountered were upper
respiratory tract infection (29.13%), urinary tract infection (26.77%),
Lower respiratory tract infection (15.74%), Tuberculosis (11.81%),
Skin and soft tissue infections (11.02%) and Foot infections (8.66%).
Escherichia coli and Candida albicans were the common causative
organisms of urinary tract infection. Staphylococcus aureus and
Mycobacterium tuberculosis were the most common microorganisms
causing respiratory tract infections. [2]
In my study urinary infection (37.2%), Respiratory infection (21.6%),
foot infection (8.0%), Skin and soft tissue infection (6.0%),
Tuberculosis (3.6%) and Cholecystitis (0.8%). In Sow et al. study the
mean infections were the skin and soft tissues (54.91%), urogenital
infections (16.18%), respiratory infections (14.45%), malaria
(3.46%), infections of the skin and soft tissues were dominated by the
diabetic foot (41.90%). [32] In our study positive correlation found
between Asymptomatic UTI and Diabetic patients. Out of 77.4% of
Urinary tract infection, 66% of the Diabetics had an Asymptomatic
UTI. Similarly, in Bissong et. al. study, it was observed that there
was a high prevalence of ASB in diabetics than in non-diabetics. [33]
In my study the common organism isolated from urine sample was
found to be E.coli (31.1%) followed by Klebsiella (6.6%). Similarly,
in Aswani et al study, a total of 181 diabetics (83 males and 98
females) and 124 non-diabetic subjects (52 males and 72 females)
with culture positive UTI were studied. The isolation rate of
Escherichia coli (E. coli) from urine culture was higher (64.6 per cent)
among diabetic patients followed by Klebsiella (12.1 per cent) and
Enterococcus (9.9 per cent). [34] The present study revealed that
Klebsiella were the commonest organism isolated from Sputum
sample. Similarly, in Saibal et al [35] study totally 47 diabetics and
43 non-diabetic adult hospitalized patients with CAP were enrolled.
Klebsiella pneumoniae was the most frequent causative pathogen for
CAP in diabetic patients, whereas Streptococcus pneumoniae was the
most frequent causative agent for non-diabetic patients. [36] In the
present study the common organism isolated in Pus sample was
Staphylococcus aureus (33.3%) and Pseudomonas (33.3%), which is
similar to a study done by Banu et al. [37], prospective study done at
a tertiary care hospital, one hundred patients over the age of 18,
having chronic diabetic foot ulcer, and attending the surgery
outpatient department were included Staphylococcus aureus was the
predominant organism, followed by Pseudomonas aeruginosa. In
my study there is a positive correlation that oral candidiasis is
common in diabetics. Similarly, in a study done by Radmila R. et al
it was concluded that oral candidiasis is significantly more frequent
in diabetic patients compared to the non-diabetic subjects. [32, 38,
39] In our study the predominant comorbidity was systemic
hypertension (45.6%) followed by CAD (25.6%), Dyslipidemia
(10%), CKD (9.2%), CVA (8%), NAFLD (7.6%) and PVD (0.8%).

The predominant microvascular complication among the study group was
Diabetic Nephropathy (17.2%) followed by Diabetic Retinopathy (5.6%)
and Diabetic Neuropathy (3.6%). However in Behera et al study, there
was high prevalence of vascular complications and infections in T2DM
patients. Of the total patients, 56% had nephropathy, 20% neuropathy,
17.3% retinopathy, 31.3% CVD, 11.3% CAD, 4.6% acute metabolic
complications, 44% infections and 16.6% had NAFLD respectively.
Macrovascular events occurred earlier than microvascular complications.
[11] In our study, the prevalence of Herpes zoster was 6.3% and there
was a positive correlation that Diabetes increases the risk of Herpes
zoster. Similarly, in a retrospective study was done by Guignard et al.
[40], revealed that type II diabetes was associated with an increased risk
of developing HZ, which was particularly high in adults 65 years and
older and moderately increased in adults under 65 years of age.
Conclusion:
This study revealed that infection is more common in females rather than
males. The risk of infection increases with the duration of diabetes.
Infection is predominant in Diabetics who are only on diet and only on
Oral hypoglycemic agents. Majority of them in this study group had
HbA1C >8% which highlights that the risk of increases with poor
glycemic control. Majority of the Diabetics had past history of Urinary
tract and Respiratory tract infection. It is highlighted that infection rate
increases in Underweight (BMI<18.5) and Obese group BMI (>30).
Majority of them in this study had Systemic Hypertension and Coronary
artery disease as a comorbidity.
The commonest microvascular complication in this study was Diabetic
Nephropathy followed by Diabetic Retinopathy. The commonest
infection found was Urinary tract infection, Respiratory infection, Foot
infection, Skin and soft tissue infection, Tuberculosis and Cholecystitis.
Urinary Tract Infection (UTI) is common in age group 60–69 years and
Respiratory infection is common in age group >80 years. UTI is more
common in females and Respiratory infection more common in males.
The commonest organism isolated in urine sample was E.coli followed
by Klebsiella.
The commonest organism in sputum sample was Klebsiella followed by
Mycobacterium tuberculosis. Hence good glycemic control, proper
maintenance and maintaining an appropriate BMI especially in long
duration of diabetics is essential to reduce long term complications and
infections. It is essential that appropriate screening measures should be
initiated at an early stage.
Recommendations:
This study is based on local small population and therefore has
limitations, it is recommended that wider areas must be covered to find
out the incidence and prevalence of infections in diabetes mellitus. More
prolonged duration of study is needed to identify the wide spectrum of
diseases among the Diabetics. Infection, which has been demonstrated
to be significantly associated with diabetics must therefore be identified
and treated at an early stage to reduce the consequence of both
uncontrolled Diabetes and infections and to reduce the morbimortality.
Diabetic screening for all adult patients who are all coming with
infection is mandatory to reduce the mortality and morbidity
associated with it. Diabetic screening tests should be mandatory at
their first visit to the hospital above 30 years of age and then every 3
years to reduce long term complication of Diabetes mellitus. Further
studies are required to find out the morbimortality of infections
among diabetic patients.
Limitations:
As it is a hospital-based study, this cannot be extrapolated to the
general population. Patient who was not willing to participate in the
study could not be included, thereby the exact prevalence of infection
in diabetics could not find out. As this study done only in inpatients
with diabetics, OP patients with diabetics and infection could not be
assessed. As it was a cross sectional study, the outcome after treating
infection could not be measured. The morbimortality of infection in
diabetics could not be assessed as there is no follow up in this study.
Authors Contributions: SK, EN, BC: Conception and
design.: Acquisition of Data. EN, BC: Analysis and Interpretation of
data. All authors SK, EN, BC: Drafting the article, revising it for
Intellectual content. All authors were checked and approved of the
final version of the manuscript.
Here, SK: Shalini Kaliaperumal; EN: Ezhilan Naganathan; and BC:
Betty Chacko
Source of funding: We didn’t get any types of financial
support from our parent institution and any other financial
organization.
Conflict of Interest: The authors declared no conflict of interest
Abbreviations:
FBS - Fasting blood sugar
PPBS - Post prandial blood sugar
BMI - Body mass index
OHA - Oral Hypoglycemic agent
UTI - Urinary tract infection
LRTI - Lower respiratory tract infection
URTI - Upper respiratory tract infection
TB - Tuberculosis
CAP - Community acquired pneumonia
CAD - Coronary Artery disease
CKD - Chronic Kidney Disease
SHTN - Systemic hypertension
PVD - Peripheral vascular disease

CVA - Cerebrovascular accident
NAFLD - Non-alcoholic fatty liver disease
AGE - Acute gastroenteritis
CSOM - Chronic suppurative otitis media
MRSA - Methicillin Resistant staphylococcus aureus
USG - Ultrasonography
CT - Computed Tomography
ATT-Antitubercular drugs
BP - Blood pressure
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Hidden Markov Model of Evaluation of Break-Even Point of HIV
patients: A Simulation Study
Mahalakshmi Rajendran1
, Senthamarai Kannan Kaliyaperumal2
, Balasubramaniam Ramakrishnan3
1, 3
Research Scholar, Department of Statistics, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, India.
2
Professor of Statistics, Department of Statistics, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, India.
.
Background: The HIV virus carries projection of significant global population with specific estimations of the mathematical results of
evolutionary methods which was presented in Tree Hidden Markov model (HMM).
Materials and Methods: Hidden Markov models used to model the progression of the disease among HIV infected people. The author
predicts a Baum Welch Algorithm method through HMM that can assess an unknown state of transition.
Results: The Tree HMM model predicts the break down point starts once patient is infected with the HIV virus as it affects the immune
system. The immune system drops more quickly in the initial inter arrival time when compared with the later time interval. The HIV virus
length in the nth
state within regrouping is uncertain to occur in each state of the given model. A simulation study was done to assess the
goodness of fit for the model.
Conclusion: The HIV virus length in the nth
state within regrouping is uncertain to occur in each state of the given model. The inter arrival
censoring between each state is essential in each infected HIV patients. The outcome of this works states that health care expert can use this
model for effective patient cares.
Keywords: expectation, hidden markov model, human immunodeficiency virus, immune system, transition
Introduction
Twenty-Six million people in 2020 June, were assessing the human immunodeficiency virus (HIV) antiretroviral therapy when compared to
2019 end an estimation of 25.4 million, an estimated 2.4% of increase was observed. Awareness among pregnant and breastfeeding women
have been increased around 85% who have received ART living with HIV, this avoids HIV transmission to their newborns and also ensures
their protective health. The 69th
World Wellbeing Gathering proposed a "Worldwide wellbeing area technique on HIV for 2016-2021”. [1]
The arrangement offered five vital headings, which are as per the following: data on designated activity of once pestilence and reaction,
counteraction, treatment, and care, and exploration. The impact of mediations on the administrations required, guaranteeing uniformity for
the populaces needing administrations, getting long haul subsidizing to pay the expenses of administrations, and speeding up the change to
a manageable future are immensely significant contemplations. [2] UNAIDS has set a 2030 cutoff time for the destruction of the HIV
pandemic, which will match with World Guides Day in 2014. As indicated by gauges, about 2.39 million individuals in India are tainted
with HIV, making it the third most crowded country on the planet. South India was the main region to be hit by the HIV pandemic since it
had the most noteworthy populace thickness at that point. [3]
Hidden Markov Model (HMM) is an extension of Markov model. Markov Model was named after Andrei Andreyevich Markov who lived
in the year (1856-1922). Markov Chain is a statistical model where the data describes in sequence form. HMM is an especially embedded
How to cite this article: Rajendran M, Kaliyaperumal SK, Ramakrishnan B. Hidden Markov Model of Evaluation of Break-Even Point of
HIV patients: A Simulation Study. Int J Med Sci and Nurs Res 2021;1(2):19-22
Corresponding Author: Ms. Mahalakshmi Rajendran,
Research Scholar, Department of Statistics, Manonmaniam
Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, India.
Email ID: mahalakshmirajendran@gmail.com
International Journal of Medical Sciences and Nursing Research 2021;1(2):19-22 Page No:19
Abstract

under the umbrella of stochastic process where each state holds the
Markov property. [4] The three main information to be observed in the
HIV affected immune system is the parameter space, state space and
state transition probability. [5]
Mathematical and Statistical models for infectious diseases commonly
in the process of looking forward in estimating the epidemic which helps
different public health sectors to plan optimally. Recent literature shows
large number of literatures on Mathematical Models for communicable
diseases. [6] A validated goodness of fit model (HMM) been used as an
investigative to expect the diseases progression outcomes in infected
cows. [7] Mathematical Modelling has been identified at the early stage
of HIV epidemiological research, also concluded that theoretical
research focuses on quantitative data on sequential changes in the
mathematical distribution of sexual partner change along with other
factors like variations in epidemiologic abundance in serum and
emissions. [8] Mathematical Modelling suggests the cost effectiveness
and time of HIV pandemic interventions, when given the right
information to experimental trials. As the HIV pandemic is being a silent
global threat since last four decades. [9] The HMM topology inference
model denotes its graphical figures including the number of states with
the association of symbols in each different state and state transitions
with non-zero probabilities. Assuming the HMM model always specify
the states prior to the information received. [10]
The Baum Welch Algorithm was published by Baum LE and along with
coauthors who worked through his articles, even the name “Welch”
appears as the coauthor that have been worked in developing this Baum
Welch Algorithm. This algorithm was an example of Expectation
Maximization (EM) algorithm. Mathematical methods associate to the
algorithm along with an explanation as how the Baum Welch Algorithm
fits the EM were also seen. [11 – 15]
We assume that the human immune system gets affected with HIV in a
future state when the present state is already affected with HIV. The
non-observable damage causing the immune system which leads to the
HMM is the one to observe in this article. When the human system gets
affected with HIV, it is represented by time t=1, which is the initial state
of the process. At every time interval the human system moves from the
current position to another position, i.e., t = (1, 2, 3, … …), the transition
probabilities are independent of the time t.
Hidden Markov Model: [10] A continuous process to develop
model parameters in the transition state to explain the respective time
point in the infected patients. A Hidden Markov Model (HMM) is
usually represented by 𝐻𝑀𝑀: 𝜇 = (𝐴, 𝐵, 𝜋). This model tells us; the
state transition probability, observational probability, probability of
starting in a particular state. The Baum-Welch algorithm also known as
EM-algorithm to emphasis on parameter estimation built on direct
numerical maximum likelihood estimation. To maximize and find the
posterior estimation of the hidden variables of HIV infected patients. The
estimation depends on the assumption of the independent observations
Tree HMM as seen in Figure-1. Transition variables defined as;
𝑝𝑡(𝑖, 𝑗), 1 ≤ 𝑡 ≤ 𝑇, 1 ≤ 𝑖, 𝑗 ≤ 𝑁
Figure–1 Hidden random variable shown with Tree HMM
𝑎𝑖𝑗
,
=
𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑖𝑡𝑖𝑜𝑛𝑠 𝑓𝑟𝑜𝑚 𝑠𝑡𝑎𝑡𝑒 𝑖 𝑡𝑜 𝑗
𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑖𝑡𝑖𝑜𝑛𝑠 𝑓𝑟𝑜𝑚 𝑠𝑡𝑎𝑡𝑒 𝑖
=
∑ 𝑝𝑡 (𝑖, 𝑗)
𝑇
𝑡=1
∑ 𝛾𝑖(𝑡)
𝑇
𝑡=1
𝑎𝑖𝑗
,
=
∑ 𝛼𝑖(𝑡)𝑎𝑖𝑗𝑏𝑗(𝑂𝑡+1)
𝑇
𝑡=1 𝛽𝑗(𝑡 + 1)
∑ 𝛼𝑖(𝑡)𝛽𝑖(𝑡)
𝑇
𝑡=1
… … … (1)
𝑃𝑟(𝑖𝑗) = 𝑃𝑟(𝑆𝑡=𝑖,𝑆𝑡+1=𝑗/𝑂, 𝜇)
=
𝑃𝑟(𝑆𝑡=𝑖,𝑆𝑡+1=𝑗/𝑂, 𝜇)
𝑃(𝑂/𝜇)
… … … (2)
=
𝛼𝑖(𝑡)𝑎𝑖𝑗𝑏𝑖𝑗𝑜𝑡𝛽𝑗(𝑡 + 1)
∑ ∑ 𝛼𝑚(𝑡)𝑎𝑚𝑛𝑏𝑚𝑛𝑜𝑡
𝑁
𝑛=1
𝑁
𝑚=1 𝛽𝑛(𝑡 + 1)
Equation (2) observes the probability of being at state 𝑖 at time 𝑡, and
at state 𝑗 at time 𝑡 + 1, given the model 𝜇 and the observation 𝑂.
Then, define 𝛾𝑖(𝑡) this is the probability of being at state 𝑖 at time 𝑡,
given the observation 𝑂 and the model 𝜇, as seen in equation (3),
𝛾𝑖(𝑡) = 𝑃𝑟 (
𝑆𝑡=𝑖
𝑂
, 𝜇) = ∑ 𝑃𝑟(𝑆𝑡=𝑖,𝑆𝑡+1=𝑗/𝑂, 𝜇)
𝑁
𝑗=1
… … … (3)
= ∑ 𝑃𝑟(𝑖, 𝑗)
𝑁
𝑗=1
The above equation (3) holds because 𝛾𝑖(𝑡) is the expected number of
transitions from state 𝑖 and 𝑝𝑡(𝑖, 𝑗) is the expected number of transitions
from 𝑖to 𝑗. Given the above definitions we begin with an initial model
𝜇 and simply it for different states.
Rajendran M et al. Hidden Markov Model of Evaluation of Break-Even Point of HIV patients

Rajendran M et al. Hidden Markov Model of Evaluation of Break-Even Point of HIV patients
𝜋𝑖
,
= 𝑃𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑏𝑒𝑖𝑛𝑔 𝑎𝑡 𝑠𝑡𝑎𝑡𝑒 𝑖 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡 = 1; = 𝛾𝑖(𝑡)
𝑎𝑖𝑗
,
=
𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑖𝑡𝑖𝑜𝑛𝑠 𝑓𝑟𝑜𝑚 𝑠𝑡𝑎𝑡𝑒 𝑖 𝑡𝑜 𝑗
𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑖𝑡𝑖𝑜𝑛𝑠 𝑓𝑟𝑜𝑚 𝑠𝑡𝑎𝑡𝑒 𝑖
=
∑ 𝑃𝑟(𝑖, 𝑗)
𝑇
𝑡=1
∑ 𝛾𝑖(𝑡)
𝑇
𝑡=1
… … … (4)
𝑏𝑖𝑗𝑛
,
=
𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑖𝑡𝑖𝑜𝑛𝑠 𝑓𝑟𝑜𝑚 𝑖 𝑡𝑜 𝑗 𝑤𝑖𝑡ℎ 𝑛 𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑
𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑖𝑡𝑖𝑜𝑛𝑠 𝑓𝑟𝑜𝑚 𝑖 𝑡𝑜 𝑗
=
𝑡:𝑂𝑡=𝑛,1≤𝑡≤𝑇
𝑇
𝑡=1
… … … (5)
Results and Discussion
The three states are defined as; First state the initial state of HIV infection
identified and under treatment (i.e., the person identified as HIV positive
starting from the initial time period); Second State identified as the person
infected under HIV after some period of initial time period; Third state
observes the later time period of the infected person (i.e., the HIV infected
persons are not aware of the diseases in them and identified it very lately).
A simulation study was done to assess the goodness of fit for the model.
The simulation was carried out using Mathcad Software and the graphical
representation was figured through Minitab software.
Table–1 HIV infected patients risk observed in the three states
as time increases
Time Per
Week
First State Second
State
Third
State
1 2 3 4
2 1.5 1.5 2
3 1.33 1 1.333
4 1.25 0.75 1
5 1.2 0.6 0.8
6 1.16 0.5 0.667
7 1.14 0.429 0.571
8 1.12 0.375 0.5
9 1.11 0.333 0.444
10 1.1 0.3 0.4
20 1.05 0.15 0.2
30 1.03 0.1 0.133
40 1.02 0.075 0.1
50 1.02 0.06 0.08
The Tree HMM model predicts the break down point starts once
patient is infected with the HIV virus as it affects the immune
system. As the infected patient passes from one state to another the
likelihood of high risk is more in the HIV patient as observed in
Table-1 and Figure-2. The hidden nature of the virus is clearly
observed in Table-1, stating the infected patient has a very less
chance of survival as and when the time increases. The immune
system drops more quickly in the initial inter arrival time when
compared with the later time interval. The model finally concludes
that, assessing the HIV patients at the initial time and state the
likelihood of risk is less. As the time and state increases the
likelihood of risk increases compared to the previous state.
Figure–2 Three states of HIV infected patient’s risk
This simulation study attempts to make predictions of HIV patients
and assess the performance of the model. For this, the dataset had
taken from the World Health Organization Website. [2] The dataset
had categorized into three subparts and renamed by states. The
states are: S1 also known as the first state, is the initial state of HIV
infection identified and under treatment. In this way S2, second
state is the person infected under HIV; S3 is the state observes the
later time period of the infected person.
Using the three states, the risk for the patients in the above states in
every week was estimated and tabulated as shown in Table-1. The
same estimated values were visualized using a three-dimensional
graph as shown in Figure-2. Thus, the Hidden Markov Model was
trained and the prediction was made using the Baum Welch
Algorithm. [13, 14] The performance of the trained model was
assessed. The risk of the patients in the three states also discussed.
Conclusion
The HIV virus carries projection of significant global population
with specific estimations of the mathematical results of
evolutionary methods which was presented in Tree HMM model.
Our model assumes that the HIV infected patients are possibly of
high risk in after state one. This HIV infected patients are of a single
controlling strain in each state of the Tree HMM model. The HIV
virus length in the nth
state within regrouping is uncertain to occur

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